Susan Weinschenk Notable QuotationsExpand to full screen
Your eyes and brain want to create patterns, even if there are no real patterns there.
Page: 7
Use patterns as much as possible, since people will automatically be looking for them. Use grouping and white space to create patterns.
Page: 8
Eye-tracking research shows that if a picture of a face looks away from us and toward a product on a Web page (see Figure 4.1), then we tend to also look at the product. But remember, just because people look at something doesn't mean they're paying attention. Page 9
- People recognize and react to faces on Web pages faster than anything else on the page (at least by those who are not autistic).
- Faces looking right at people will have the greatest emotional impact on a Web page, probably because the eyes are the most important part of the face.
- If a face on a Web page looks at another spot or product on the page, people will also tend to look at that product. This doesn't necessarily mean that they paid attention to it, just that they physically looked at
People have a mental model of where things tend to be on computer screens, and a mental model for particular applications or Web sites that they use. They tend to look at a screen based on these mental models.
Put the most important information (or things you want people to focus on) in the top third of the screen or in the middle. - Avoid putting anything important at the edges, since people tend not to look there.
- Design the screen or page so that people can move in their normal reading pattern. Avoid a pattern where people have to bounce back and forth to many parts of the screen to accomplish a task.
You've probably had the experience of encountering a door handle that doesn't work the way it should: the handle looks like you should pull, but in fact you need to push. These cues are called affordances. Page: 15
When you're designing an application or Web site, think about the affordances of objects on the screen. For example, have you ever wondered what makes people want to click on a button? Cues in the button's shadow tell people that it can be pushed in, the way a button on an actual device can be pushed in. Page 16
- Don't assume that people will see something on a computer screen just because it's there.
- If you want items (pictures, photos, headings, or text) to be seen as belonging together, then put them in close proximity.
- Put more space between items that don't go together and less space between items that do. This sounds like common sense, but many Web page layouts ignore this idea.
Avoid blue or green text on a red background, and red or green text on a blue background.
- Choose your colors carefully, taking into account the meaning that the colors may invoke.
- Pick a few major cultures or countries that you will be reaching with your design and check them on the cultural color chart from InformationIsBeautiful.net to be sure you're avoiding unintended color associations for that culture.
- Don't assume that people will remember specific information in what they read.
- Provide a meaningful title or headline. It's one of the most important things you can do.
- Tailor the reading level of your text to your audience. Use simple words and fewer syllables to make your material accessible to a wider audience.
Unusual or overly decorative fonts can interfere with pattern recognition and slow down reading. If people have trouble reading the font, they will transfer that feeling of difficulty to the meaning of the text itself and decide that the subject of the text is hard to do or understand. Page: 39
Provide ample contrast between foreground and background. Black text on a white background is the most readable.
Page: 42
Don't ask people to remember information from one place to another, such as reading letters or numbers on one page and then entering them on another page;
If you ask people to remember things in working memory, don't ask them to do anything else until they've completed that task. Working memory is sensitive to interference—too much sensory input will prevent them from focusing attention.
One of the interesting strategies people employ to help our fragile memories is "chunking" information together into groups. It's no accident that U.S. phone numbers look like this: 712-569-4532
Page: 48
People use schemata (plural for schema) to store information in long-term memory and to retrieve it. If people can connect new information to information that is already stored, then it's easier to make it stick, or stay in long-term memory, and easier to retrieve it. Schemata allow people to build up these associations in long-term memory.
Page: 51
The better people are at something, the more organized and powerful their schema about it will be.
Page: 52
Try not to require people to recall information. It's much easier for them to recognize information than recall it from memory. Page: 53
Information in the middle of a presentation will be the least likely to be remembered.
Page: 55
Don't rely on self-reports of past behavior. People will not remember accurately what they or others did or said.
Page: 57
Design with forgetting in mind. If some information is really important, don't rely on people to remember it. Provide it for them in your design, or have a way for them to easily look it up.
Page: 59
The Most Vivid Memories are Wrong
Page: 60
If you know that someone had a dramatic or traumatic experience, you need to understand two things: 1. They'll be convinced that what they remember is true and 2. It isn't exactly true!
Page: 60
Progressive disclosure means providing only the information people need at the moment. By giving them a little information at a time, you avoid overwhelming them, and also address the needs of different people—some may want a high-level overview, whereas others are looking for all the detail. Use progressive disclosure. Show people what they need when they need it. Build in links for them to get more information.
Page: 62
People will only focus on a task for a limited time. Assume that their minds are wandering often. If possible, use hyperlinks to grab onto this idea of quickly switching from topic to topic. People like Web surfing because it enables this type of wandering. Make sure you build in feedback about where people are so that if they wander, it's easier for them to get back to the original location or go to the next.
Page: 69
In 1956 Leon Festinger wrote a book called When Prophecy Fails. In it he describes the idea of cognitive dissonance. Cognitive dissonance is the uncomfortable feeling you get when you have two ideas that conflict with each other.
You don't like the feeling, so you'll try to get rid of the dissonance. There are two main ways you can do that: change your belief, or deny one of the ideas. Page: 70
Don't spend a lot of time trying to change someone's ingrained beliefs.
- The best way to change a belief is to get someone to commit to something very small.
- Don't just give people evidence that their belief is not logical, or tenable, or a good choice. This may backfire and make them dig in even harder.
Susan Carey's 1986 journal article "Cognitive Science and Science Education," which states: "A mental model represents a person's thought process for how something works (i.e., a person's understanding of the surrounding world). Mental models are based on incomplete facts, past experiences, and even intuitive perceptions. They help shape actions and behavior, influence what people pay attention to in complicated situations, and define how people approach and solve problems." Page: 73
In the field of design, a mental model refers to the representation of something—the real world, a device, software, and so on—that a person has in mind.
An important reason for doing user or customer research is so you can understand the mental models of your target audience. Page: 73
- A mental model is the representation that a person has in his mind about the object he is interacting with. A conceptual model is the actual model that is given to the person through the design and interface of the actual product. The actual interface is the conceptual model.
- If there is a mismatch between the person's mental model and the product's conceptual model, then the product or Web site will be hard to learn, hard to use, or not accepted.
- Sometimes you know that the mental model of the target audience will not fit the conceptual model, and instead of changing the design of the interface, you want to change people's mental model to match the conceptual model you've designed. The way to change a mental model is through training.
- The secret to designing an intuitive user experience is making sure that the conceptual model of your product matches, as much as possible, the mental models of your audience. If you get that right, you will have created a positive and useful experience.
- If you have a brand new product that you know will not match anyone's mental model, you'll need to provide training to prepare people to create a new mental model.
Stories are very powerful. They grab and hold attention. But they do more than that. They also help people process information and they imply causation. Page: 76
Stories aren't just for fun. No matter how dry you think your information is, using stories will make it understandable, interesting, and memorable. Page: 78
People learn best by example.
- Don't just tell people what to do. Show them.
- Use pictures and screen shots to show by example.
- Better yet, use short videos as examples Page: 81
If there is a lot of information and it is not in categories, people will feel overwhelmed and try to organize the information on their own.
- Always provide progress indicators so people know how much time something is going to take.
- To make a process seem shorter, break it up into steps and have people think less. It’s mental processing that makes something seem to take a long time. Page: 85
Assume that you have at most 7 to 10 minutes of a person’s attention.
- If you must hold attention longer than 7 to 10 minutes, introduce novel information or a break.
- Keep online demos or tutorials under 7 minutes in length.Page: 103
The shorter the distance to the goal, the more motivated people are to reach it. People are even more motivated when the end is in sight.
- You can get this extra motivation even with the illusion of progress. Page: 117
Look for ways to help people set goals and track them.
- Show people how they’re progressing toward goals.
Provide defaults if you know what most people will want to do most of the time, and if the result of choosing a default by mistake does not cause costly errors. Page: 136
Create a habit
- Give people a small, easy task to do, rather than a complex one.
- Give people a reason to come back and do the task every day or almost every day.
- Be patient. Creating a habit may take a long time. Page: 140
Don’t underestimate the power of watching someone else do something.
- If you want to influence someone’s behavior, then show someone else doing the same task. Page: 148
- Video at a Web site is especially compelling. Want people to get a flu shot? Then show a video of other people in line at a clinic getting a flu shot. Want kids to eat vegetables? Then show a video of other kids eating vegetables. Mirror neurons at work.
-
Look for opportunities to build synchronous activity into your product, using live video streaming, or a live video or audio connection.
Listening to someone talk creates a special brain syncing that helps people understand what is being said.
- Presenting information through audio and/or video where people can hear someone talking is an especially powerful way to help people understand the message.
- Don’t just rely on reading if you want people to understand information clearly. Page: 156
Because people mimic others’ expressions (see #64 on mirror neurons), showing a video of someone who is happy and smiling will tend to make the person watching smile, which will then make them feel happy, and that in turn may change the next action they take. Page: 167
Use anecdotes in addition to, or in place of, factual data.Page: 168
| | Gavin Lew, Robert M. Schumacher Jr.Notable QuotationsExpand to full screen
p. vii "To put it simply, we believe experiences matter. We want to make the world a little easier for people." [This should probably be the primary goal of UX, but making the world "easier" can be troubling when ease of use translates into addiction issues because the products are too easy to use and impossible to stop using.]
p. viii "Humans are impatient and fickle creatures; unless they are going to see the benefit very early on, they often will not invest the time or attention needed to appreciate the AI brilliance."
[When working with genAI, I think many people find themselves disappointed with the systems initial outputs, not realizing that if they are going to be disappointed with something, it should probably be the input they created. Once people understand how to re-iterarte and improve prompts and exercise some patience and perseverance, I think they can discover that "AI brilliance" the authors speak of.]
p. viii "If AI is to be successful, the design matters. The UX matters. How people would interact with AI matters. We believe UX can help; that's the main point of the book!"
p. 9 "developers need to think about what the experience of using their AI product will be like—even in the early stages, when that product is just a big idea."
p.11 "The next wave of AI needs to be designed with a UX framework in mind or risk the further limiting of acceptance. Good UX for AI applications will propel growth."
p. 16 "For any product, whether it has AI or not, the bare minimum should be that it be usable and useful. It needs to be easy to operate, perform the tasks that users ask of it accurately, and not perform tasks it isn't asked to do. That is setting the bar really low, but there are many products in the marketplace that are so poorly designed where this minimum bar is not met."
p. 17 "Our perception of a product is the sum total of the experiences that we have with that product. Does the product deliver the value we had hoped? Our willingness to "trust" the product hangs in that balance."
[I think some users of AI may have higher expectations as to what AI should be able to do and how easy it should be to use. When it does not meet their expectations, they are not likely to trust it and go back to it. For younger users, this may be a result of taking certain technologies completely for granted. It is hard to be impressed with advanced computer technology when one hasn't known a life without it.]
p. 40 (Licklider and Taylor, 1968) "In a few years, men will be able to communicate more effectively through a machine than face to face. That is a rather startling thing to say, but it is our conclusion."
p. 46 (From Stuart Card et al. 1983, The Psychology of Human-Computer Interaction) The user is not an operator. He does not operate the computer; he communicates with it to accomplish a task. Thus, we are creating a new arena of human action: communication with machines rather than operation of machines.
p. 47 . . . in today's world, most companies are hiring computer scientists to do natural language processing and eschewing linguists or psycholinguists. Language is more than a math problem.
p. 49 The continuous re-defining of AI: "Schank emphasized in 1991. . . that ‘intelligence entails learning,' implying that true AI needs to be able to learn in order to be intelligent."
[Interesting discussion of how the name of AI changed to expert systems after AI fell out of favor. Later terms like neural networks did the same thing to try restoke interest and funding in AI research. The lesson for UX is that once people have a negative experience with a product/technology, they are hard to win back and changing the name can help.]
The outgrowth of UX from HCI: p. 50 "Where HCI was originally focused heavily on the psychology of cognitive, motor, and perceptual functions, UX is defined at a higher level—the experiences that people have with things in their world, not just computers. HCI seemed too confining for a domain that now included toasters and door handles. Moreover, Norman, among others, championed the role of beauty and emotion and their impact on the user experience. Socio-technical factors also play a big part. So UX casts a broader net over people's interactions with stuff. That's not to say that HCI is/was irrelevant; it was just too limiting for the ways in which we experience our world."
[I was concerned that the 2022 release of ChatGPT (after this book was published) would render much of the author's points too dated to be helpful. However, they mention developments in technology that are significant and similar enough to stand in for the impact of todays' LLM systems.]
p.51 "We come into contact daily with things we have no mental model for, interfaces that present unique features, and experiences that are richer and deeper than they've ever been. These new products and services take advantage of new technology, but how do people learn to interact with things that are new to the world? These new interactions with new interfaces can be challenging for adoption."
p.51 "For AI to succeed, to avoid another winter, it needs good UX."
P. 64 "Look at older product designs to find features that deserve a second chance." [Because many products with poorly thought out UX design get shelved, it might be good to look back at those products to see what aspects can be salvaged and repurposed in a product with better UX design.]
Good lesson on what differentiated Alexa from Siri. Siri was a secondary feature of another product; Amazon's Echo (Alexa) was designed specifically to be a personal assistant.
Conversational Context seems to be a problem that today's LLMs have largely solved. They are now far better at remembering and drawing from earlier ideas in a conversation. They seem to offer appropriate responses to follow-up questions.
p. 67 Modern LLMs can follow three of Grice's four maxims of communication, but they struggle mightily with the truth maxim. Interestingly, I prompted ChatGPT to give me a summary of a book that was released in 2023 (and therefore should not be in its training data) and it wrote a plausible summary that was somewhat accurate. Its lie was pretty good. I asked it in a separate conversation to give me a summary of the same book, but I added "do not make one up if you do not have access to information about the book," and it responded that it could not give me the summary but instead gave me an idea of what a hypothetical summary of that book could look like—what it should have done all along.
p. 77 "Ultimately, engaging users with an AI service is the end goal, and recommendation engines achieve this. . . Recommendation engines exemplify the ways in which AI might fit into a user experience. While only a portion of Spotify's recommendation engine is in fact an AI system, that AI system blends seamlessly with other computing and human elements to build an engine that proves valuable to users."
p. 78 "In 2010, Northwestern University researchers released StatsMonkey, a program that could write automated stories about baseball games.39 By 2019, major news outlets including The Washington Post and the Associated Press were using AI to write articles." [This is three years before the release of ChatGPT.]
p. 81 "Humans are capable of constructing coherent narratives that make sense to other human beings, evoke emotions in their audiences, convey subtextual messages, and even contain aesthetic beauty. AI can't do any of those things. It's difficult to quantify aesthetics." [This is in the chapter of the book about film making and creativity. I think that AI has advanced drastically in this domain over the last two years, to the point that it can do the things the authors say it cannot.]
p. 82 "In fiction, one author has created an AI program that automatically auto-completes a writer's sentences while writing science fiction stories, based on a corpus of science fiction stories.54 He envisions the program as a kind of co-author, which generates ideas that might spark the writer's human creativity. Tellingly, none of these three projects are widely used. AI in the arts is not quite ready for prime time yet." [It is now.]
p. 85 ". . . what can we do to improve AI through the data itself? What are the elements where we can have an impact on AI?"
p. 93 "Under Moore's Law, the number of transistors in a CPU doubles every 2 years, but in AI's case, computing power for AI took advantage of the massively parallel processing of a GPU (graphics processing unit). These are the new graphics chips associated with making video games smoother and the incredible action movies we see today. Massively parallel processing required to present video games made AI much, much faster. AI systems often took months to learn the dataset. When graphics chips were applied to AI applications, training intervals dropped to single days, not weeks."
p. 95 "Capturing behavior is the prerogative of UX and requires research rigor and formal protocols. What we learned is that UX is uniquely positioned to collect and code these data elements through our tested research methodologies and expertise in understanding and codifying human behavior." [AI runs on data, and the authors discuss the collection and use of data extensively.]
P.98 ". . . talking about ethics in data. This is an area where AI has not developed fully. Companies are building AI not for foundational science, but for commercial advantage. The same sorts of issues that arise with bias in the culture also exist in the data. So the fear is that AI applications may have subtle—or even not-so-subtle— biases because the underlying data contain biases. . . There are no formal ethical standards or guidelines for AI. It is very much the proverbial "Wild West" where technology is being created without guardrails."
P. 109 "For many people, there's still a hesitance, a resistance, to adopt AI. Perhaps it is because of the influence of sci-fi movies that have planted images of Skynet and the Terminator in our minds, or simply fear of those things that we don't understand. AI has an image problem. Risks remain that people will get disillusioned with AI again."
p. 110 "Technology has become a commodity. What can set a product apart is good design. The same logic applies to AI-enabled products."
p. 112 "User-centered design (UCD) places user needs at the core. At each stage of the design process, design teams focus on the user and the user's needs. This involves a variety of research techniques to understand the user and is used to inform product design."
p. 115 "AI can be seen through the lens of how we look at the user experience of any product or application. AI is no different. To be successful, it must have the essential elements of utility, usability, and aesthetics."
|
Norman M. Bradburn, Seymour Sudman, Brian WansinkNotable QuotationsExpand to full screen
Asking Questions: The Definitive Guide to Questionnaire Design - Norman M. Bradburn, Seymour Sudman, Brian Wansink
Questions must be precisely worded if responses to a survey are to be accurate;
We must depend on pretesting to weed out ambiguities and to help reformulate questions as clearly as possible-to ask about what we want to know, not something else.
Loaded Words Produce Loaded Results
Frugging (fundraising under the guise) surveys, are often primarily intended to raise funds rather than to collect survey information.
Sometimes questions are simply complex and difficult to understand.
Yet even when there are no deliberate efforts to bias the question, it is often difficult to write good questions because the words to describe the phenomenon being studied may be politically charged.
Respondents ... must be persuaded to participate in the interview, and their interest (or at least patience) must be maintained throughout.
Their only reward is some measure of psychic gratification-such as the opportunity to state their opinions or relate their experiences to a sympathetic and nonjudgmental listener, the chance to contribute to public or scientific knowledge, or even the positive feeling that they have helped the interviewer.
(1) a survey is a transaction between two people who are bound by special norms; (2) the interviewer offers no judgment of the respondents' replies and must keep them in strict confidence; (3) respondents have an equivalent obligation to answer each question truthfully and thoughtfully; and (4) in the survey it is difficult to ignore an inconvenient question or give an irrelevant answer.
In general, although respondents are motivated to be "good respondents" and to provide the information that is asked for, they are also motivated to be "good people." That is, they will try to represent themselves to the interviewer in a way that reflects well on them.
Techniques for helping respondents resolve this dilemma on the side of being good respondents include interviewer training in methods of establishing rapport with the respondent, putting respondents at their ease, and appearing to be nonjudgmental.
Investigators should try to avoid asking respondents for information they do not have.
The term informed consent implies that potential respondents should be given sufficient information about what they are actually being asked and how their responses will be used. The intent is for them to be able to judge whether unpleasant consequences will follow as a result of their disclosure.
The fact that the researcher has promised confidentiality to the respondents will not protect the researcher from having to produce the individual records if required by legal action.
First you will need to identify the concepts involved in the research question. Then you will formulate specific questions that, when combined and analyzed, will measure these key concepts. For example, if you are interested in the attitudes of potential voters toward a particular candidate, you will have to decide which attitudes are important for the topic at hand: attitudes about the particular positions the candidate holds, attitudes about the candidate's personality, or attitudes about the candidate's likability.
As a general rule, when constructing a questionnaire, you must continuously ask "Why am I asking this question?" and must, in each instance, be able to explain how the question is closely related to the research question that underlies the survey.
Given our biases toward more information, a game of "Wouldn't it be nice to know?" can quickly ensue, and soon there are many more questions than the budget can afford or than respondents can endure.
Every time you write a question, ask yourself "Why do I want to know this?" Answer it in terms of the way it will help you to answer your research question.
Bias refers to an estimate that is either more or less than the true value. Variability is measured by the susceptibility of measurements to differences in question wording.
The incorrect placement of events in a particular time period is called telescoping.
Forward telescoping typically results in overreporting of events; backward telescoping typically results in underreporting.
Another source of error arises from the deliberate overstating or understating.
Another source of error stems from the respondent's failure to understand the question in the way the researcher intended.
Finally, respondents may simply be ignorant [and because people don't like to appear ignorant, they may just make something up or guess or say what they think is wanted.]
The most direct and probably the most common questions asked of respondents relate to their behavior.
When asking a closed-ended question about behavior, make sure that all reasonable alternative answers are included.
Make the question as specific as possible.
The time period of the question should be related to the saliency of the topic. Periods of a year (or sometimes even longer) can be used for highly salient topics, such as purchase of a new house,
Periods of a month or less should be used for items with low saliency, such as purchases of clothing and minor household appliances. Periods that are too short, however, should be avoided,
For regular, frequent behavior, respondents will estimate the number of events by using the basic rate they have stored in memory.
Where detailed information on frequent, low-salience behavior is required, providing diaries will result in more accurate results than memory.
Use words that virtually all respondents will understand.
Do not assume that the shorter questions are necessarily better.
Respondents may not know what is meant by the word "regularly." y specifying "on a daily basis," the question removes or reduces the uncertainty.
A general finding is that as the number of experiences of an event increases above five, respondents are more likely to estimate than to count.
Vary question formats where possible, to make the interview more engaging for the respondent and also to decrease the chances of respondent anticipation.
One simple reason for making each question as specific as possible is to make the task easier for the respondent, which, in turn, will result in more accurate reports of behavior.
The "when" question should specify the time period by using actual dates instead of terms such as "last week" or "last month."
If general or global questions are used, they should be tested to determine what respondents think they mean.
The more important the event, the easier it is for the respondent to remember.
there appear to be three dimensions that distinguish between events that are more and less salient: (1) the unusualness of the event, (2) the economic and social costs or benefits of the event, and (3) the continuing consequences of the event.
Diaries have been used for frequent, nonsalient events that are difficult to recall accurately.
The general principle is simple: use words that everyone in the sample understands and that have only the meaning you intend. Writing questions that satisfy this principle is a difficult art that requires experience and judgment.
Explain the word first and then provide the word itself.
Slang and colloquialisms should normally be avoided, not because such words violate good usage but because many respondents will not know what the words mean.
When surveying unfamiliar groups, an initial group interview with a small (nonrandom) sample of that group may be helpful in indicating the types of words to use or avoid.
even more troublesome than an unknown word is a word that has multiple meanings in the context of the question being asked.
For socially desirable behavior, the extent of overstatement depends not only on the level of desirability and the wording of the question, but also on the proportion of the population who have not behaved in the socially desirable manner.
There is, a general tendency for respondents to avoid extreme answers and to prefer an answer in the middle of a list
Setting out rules for formulating questions about attitudes is more difficult than for behavioral questions because questions about attitudes have no "true" answer.
Attitudes exist only in a person's mind.
Context in which questions are asked has a greater impact on attitude measurement than on behavior questions because the meaning of the questions may be strongly influenced by the context in which they appear.
The terms opinion and attitude are not clearly differentiated from one another. In general, opinion is most often used to refer to views about a particular object such as a person or a policy, and attitude is more often used to refer to a bundle of opinions that are more or less coherent and are about some complex object.
People are less likely to believe something derogatory about something they like and are in favor of, and they do not usually act in support of things they disapprove of.
Similar (if not synonymous) terms that indicate a positive orientation toward an attitude object may have somewhat different connotations and yield different responses.
Strongly held attitudes are generally more resistant to effects of question wording than are weakly held attitudes.
The fundamental idea behind Likert scales is that an attitude can be thought of as a set of propositions about beliefs, evaluations, and actions held by individuals.
Another commonly used scale type is the Guttman scale. Items in a Guttman scale are ordered such that some items should be agreed to only by those who are low on the attitude and others should be agreed to only by those who are high on the attitude scale.
Various methods for combining responses can be used. The simplest is to count the number of "yes" and "no" answers as appropriate.
Unipolar items, when rephrased into what appear to be their opposites, often produce surprising results. A famous study by Rugg () showed that even such apparently opposite words as "allow" and "forbid" can produce dissimilar results.
"Do you think the United States should allow public speeches against democracy?" and "Do you think the United States should forbid public speeches against democracy?" When the question was one of allowing public speeches, percent of the respondents supported free speech; when the question was phrased that the United States should forbid free speech,
The potential biasing effect of the positioning of questions in a questionnaire has long been recognized.
The order of questions provides a context within which questions are answered.
When a general question and a more specific-related question are asked together, the general question is affected by its position, whereas the more specific question is not.
If the specific question triggers positive associations, it appears to increase positive responses to the general question. If the thoughts aroused by the specific question are negative, the effect appears to be negative. The specific question may narrow the interpretation to the meaning of the general question and have a corresponding effect on answers to the general question.
Unintentionally Activating Norms and Values that Cause Biases
Questions involving the same underlying value (reciprocity) are asked about objects with differing degrees of popularity. When the more popular item comes first, it appears to have the effect of heightening the value, so that it applies in the second and less powerful instance.
Use open-ended questions sparingly; they are primarily useful for developmental work, to explore a topic in depth, and to obtain quotable material. Closed-ended questions are more difficult to construct, but they are easier to analyze and generate less unwanted interviewer and coder variance.
Although not so common as behavioral questions, knowledge-related questions have many uses in surveys. They can be used to help explain political behavior, which is strongly impacted by one's level of knowledge.
Before asking attitude questions about issues or persons, ask knowledge questions to screen out respondents who lack sufficient information or to classify respondents by level of knowledge.
If yes-no questions are appropriate, ask several on the same topic to reduce the likelihood of successful guessing.
The line between knowledge and attitude or opinion questions is often blurred.
The question that asks respondents to guess about the proportion of welfare chiselers is really an attitude question in the guise of a knowledge question.
Customer ratings are becoming more common as organizations become more customer-oriented. In addition to controlled, formal surveying of target populations, most large companies now have continuous
Feedback mechanisms involving comment cards, toll-free hot lines, and on-line Web surveys.
A mechanism used to assess service across a wide number of units is "mystery shoppers," people who are evaluating the company by simply behaving like a shopper (or diner) and whose identity is not known to those they are evaluating. The primary job of mystery shoppers is to frequent the franchised restaurants to ensure that food quality, timing, cleanliness, and other standards are being met.
In many cases, customers are often in a better position to evaluate the quality of products and services offered than are managers or fellow employees.
This third-party performance rating can be critical for accurate product or service ratings in an industry.
When the term psychographics was introduced by Emanuel Demby in the s, it was generally defined as "the use of psychological, sociological, and anthropological factors, self-concept, and lifestyle to determine how the market is segmented by the propensity of groups within the market-and their reasons-to
The most obvious use of psychographic research is to draw portraits or profiles of target groups.
Psychographic questions are typically used to segment people by the way they think or behave.
Using psychographic questions involves using one or two hypothesized characteristics (or personality traits) to explain differences in choice or behavior. For instance, one study hypothesized that a trait called "venturesome" was related to the probability that "venturesome" people would try new and different products. Researchers found that people with this trait were more likely to choose a new and different flavor of toothpaste
List of Values (LOV), which is gaining favor among academics because it is in the public domain and relates closely to consumer behavior and to trends
For instance, a study attempting to understand (and deter) binge drinking interviewed bartenders to try to develop personality profiles of those most predisposed to binge drinking. Similarly, a study of men who spent a lot of money on business shoes used shoe shiners in airports to get a better idea as to how a "Cole-Haan" man differed from an "Allen-Edmunds" man (Wansink, c).
Conduct in-depth interviews or focus groups.
Although correlations between psychographic variables and preferences seldom get higher than .3 or.4 the same is true of the relationships between demographics and product preference. [0 is no correlation and 1 is absolute correlation.]
Ultimately, every questionnaire must be tested and refined under real-world conditions. Testing takes the form of pretest interviews and of soliciting peer feedback of draft questionnaires
Ask respondents if the questions were straightforward and whether the format made logical sense.
It is always useful for questionnaire designers to play the roles of respondents and answer their own questions.
One common mistake is to include modifying adjectives and adverbs that are somewhat unclear, such as usually, often, sometimes, occasionally, seldom, and rarely. These words have highly variable meanings, as do such words as many, most, numerous, a minority of, a substantial majority, a considerable number of, a large proportion of, a significant number of, and several.
And. The word and can signal that you might be combining two questions and asking them as one question.
Or. Similar to the word and, the word or is often associated with a double question or with a false dilemma.
If. The word if is often associated with confusing directions or with skip patterns.
Not. Avoid using not in your questions if you're having respondents answer "yes" or "no" to a question. Using the word not can lead to double negatives and confusion.
A good question is one that yields a truthful, accurate answer.
A Good Question Asks for Only One Answer on Only One Dimension
Each question should be about one topic. Do not include questions that require a single response when two would be more appropriate.
Asking a multiple choice question that does not accommodate all possible responses can confuse and frustrate the respondent,
A good question leaves no ambiguity in the mind of the respondent.
A Good Question Produces Variability in Response
When a question produces no variability in responses, we learn very little.
Writing a questionnaire is similar to writing anything else. Transitions between questions should be smooth. Questions should be grouped so that they are similar and easier to complete.
People generally look at the first few questions before deciding whether or not to complete the questionnaire.
Make respondents want to continue by putting interesting questions first.
| Kate CrawfordNotable QuotationsExpand to full screen
Page 5 the concept of intelligence has done inordinate harm over centuries and has been used to justify relations of domination from slavery to eugenics. Page 7 this belief that the mind is like a computer, and vice versa, has "infected decades of thinking in the computer and cognitive sciences," creating a kind of original sin for the field. Page 8 In contrast, in this book I argue that AI is neither artificial nor intelligent. Rather, artificial intelligence is both embodied and material, made from natural resources, fuel, human labor, infrastructures, logistics, histories, and classifications. AI systems are ultimately designed to serve existing dominant interests. In this sense, artificial intelligence is a registry of power. AI systems both reflect and produce social relations and understandings of the world. Page 9 "Machine learning" is more commonly used in the technical literature. Yet the nomenclature of AI is often embraced during funding application season, For my purposes, I use AI to talk about the massive industrial formation that includes politics, labor, culture, and capital. what is being optimized, and for whom, and who gets to decide. Then we can trace the implications of those choices. Page 11 This colonizing impulse centralizes power in the AI field: it determines how the world is measured and defined while simultaneously denying that this is an inherently political activity. Page 13 the politics of technology, Page 15 Mining is where we see the extractive politics of AI at their most literal. building models for natural language processing and computer vision is enormously energy hungry, and the competition to produce faster and more efficient models has driven computationally greedy methods that expand AI's carbon footprint. Page 16 Systems are increasing surveillance and control for their bosses. When these collections of data are no longer seen as people's personal material but merely as infrastructure, the specific meaning or context of an image or a video is assumed to be irrelevant. Page 17 By looking at how classifications are made, we see how technical schemas enforce hierarchies and magnify inequity. affect recognition, the idea that facial expressions hold the key to revealing a person's inner emotional state. there is considerable scientific controversy around emotion detection, which is at best incomplete and at worst misleading. Despite the unstable premise, these tools are being rapidly implemented into hiring, education, and policing systems. The deep interconnections between the tech sector and the military are now being reined in to fit a strong nationalist agenda. Page 18 The concluding chapter assesses how artificial intelligence functions as a structure of power that combines infrastructure, capital, and labor. AI systems are built with the logics of capital, policing, and militarization— and this combination further widens the existing asymmetries of power. Artificial intelligence, then, is an idea, an infrastructure, an industry, a form of exercising power, and a way of seeing; it's also a manifestation of highly organized capital backed by vast systems of extraction and logistics, with supply chains that wrap around the entire planet. Page 20 This book argues that addressing the foundational problems of AI and planetary computation requires connecting issues of power and justice: from epistemology to labor rights, resource extraction to data protections, racial inequity to climate change. ONE. Earth Page 26 The history of mining, like the devastation it leaves in its wake, is commonly overlooked in the strategic amnesia that accompanies stories of technological progress. Page 28 The greatest benefits of extraction have been captured by the few. The effects of large-scale computation can be found in the atmosphere, the oceans, the earth's crust, the deep time of the planet, and the brutal impacts on disadvantaged populations around the world. Page 29 Tesla could more accurately be described as a battery business than a car company. 14 The imminent shortage of such critical minerals as nickel, copper, and lithium poses a risk for the company, making the lithium lake at Silver Peak highly desirable. Page 30 The term "artificial intelligence" may invoke ideas of algorithms, data, and cloud architectures, but none of that can function without the minerals and resources that build computing's core components. Rechargeable lithium-ion batteries are essential for mobile devices and laptops, in-home digital assistants, and data center backup power. Page 31 The cloud is the backbone of the artificial intelligence industry, and it's made of rocks and lithium brine and crude oil. From the perspective of deep time, we are extracting Earth's geological history to serve a split second of contemporary technological time, Page 32 The Bay Area is a central node in the mythos of AI, but we'll need to traverse far beyond the United States to see the many-layered legacies of human and environmental damage that have powered the tech industry. Page 33 There are seventeen rare earth elements: But extracting these minerals from the ground often comes with local and geopolitical violence. Mining is and always has been a brutal undertaking. Page 34 Mining profits have financed military operations in the decades-long Congo-area conflict, fueling the deaths of thousands and the displacement of millions. Page 35 While mining to finance war is one of the most extreme cases of harmful extraction, most minerals are not sourced from direct war zones. This doesn't mean, however, that they are free from human suffering and environmental destruction. Page 38 It is a common practice of life to focus on the world immediately before us, the one we see and smell and touch every day. It grounds us where we are, with our communities and our known corners and concerns. But to see the full supply chains of AI requires looking for patterns in a global sweep, a sensitivity to the ways in which the histories and specific harms are different from place to place and yet are deeply interconnected by the multiple forces of extraction. Page 41 algorithmic computing, computational statistics, and artificial intelligence were developed in the twentieth century to address social and environmental challenges but would later be used to intensify industrial extraction and exploitation and further deplete environmental resources. Advanced computation is rarely considered in terms of carbon footprints, fossil fuels, and pollution; metaphors like "the cloud" imply something floating and delicate within a natural, green industry. As Tung-Hui Hu writes in A Prehistory of the Cloud, "The cloud is a resource-intensive, extractive technology that converts water and electricity into computational power, leaving a sizable amount of environmental damage that it then displaces from sight." 52 Page 42 running only a single NLP model produced more than 660,000 pounds of carbon dioxide emissions, the equivalent of five gas-powered cars over their total lifetime (including their manufacturing) or 125 round-trip flights from New York to Beijing. 56 Page 43 Data centers are among the world's largest consumers of electricity. Page 45 Just as the dirty work of the mining sector was far removed from the companies and city dwellers who profited most, so the majority of data centers are far removed from major population hubs, whether in the desert or in semi-industrial exurbs. Page 48 We have seen how AI is much more than databases and algorithms, machine learning models and linear algebra. It is metamorphic: relying on manufacturing, transportation, and physical work; data centers and the undersea cables that trace lines between the continents; personal devices and their raw components; transmission signals passing through the air; datasets produced by scraping the internet; and continual computational cycles. These all come at a cost. TWO. Labor Page 54 Robotics has become a key part of Amazon's logistical armory, and while the machinery seems well tended, the corresponding human bodies seem like an afterthought. Humans are the necessary connective tissue to get ordered items into containers and trucks and delivered to consumers. But they aren't the most valuable or trusted component of Amazon's machine. Page 56 Many large corporations are heavily investing in automated systems in the attempt to extract ever-larger volumes of labor from fewer workers. Logics of efficiency, surveillance, and automation are all converging in the current turn to computational approaches to managing labor. Rather than debating whether humans will be replaced by robots, in this chapter I focus on how the experience of work is shifting in relation to increased surveillance, algorithmic assessment, and the modulation of time. Page 56 humans are increasingly treated like robots and what this means for the role of labor. Page 57 work. But large-scale computation is deeply rooted in and running on the exploitation of human bodies. Page 58 The common refrain for the expansion of AI systems and process automation is that we are living in a time of beneficial human-AI collaboration. engagement, where workers are expected to re-skill, keep up, and unquestioningly accept each new technical development. Page 60 During the eighteenth and nineteenth centuries, the propaganda about hard work came in the forms of pamphlets and essays on the importance of discipline and sermons on the virtues of early rising and working diligently for as long as possible. The use of time came to be seen in both moral and economic terms: understood as a currency, time could be well spent or squandered away. Page 63 Exploitative forms of work exist at all stages of the AI pipeline, from the mining sector, where resources are extracted and transported to create the core infrastructure of AI systems, to the software side, where distributed workforces are paid pennies per microtask. Page 64 The technical AI research community relies on cheap, crowd-sourced labor for many tasks that can't be done by machines. Between 2008 and 2016, the term "crowdsourcing" went from appearing in fewer than a thousand scientific articles to more than twenty thousand--which makes sense, given that Mechanical Turk launched in 2005. But during the same time frame, there was far too little debate about what ethical questions might be posed by relying on a workforce that is commonly paid far below the minimum wage. 21 Page 65 Sometimes workers are directly asked to pretend to be an AI system. Page 66 The writer Astra Taylor has described the kind of overselling of high-tech systems that aren't actually automated as "fauxtomation." 26 Automated systems appear to do work previously performed by humans, but in fact the system merely coordinates human work in the background. The true labor costs of AI are being consistently downplayed and glossed over, but the forces driving this performance run deeper than merely marketing trickery. It is part of a tradition of exploitation and deskilling, Page 67 Fauxtomation does not directly replace human labor; rather, it relocates and disperses it in space and time. In so doing it increases the disconnection between labor and value and thereby performs an ideological function. Some 250 years later, the hoax lives on. Amazon chose to name its micropayment-based crowdsourcing platform "Amazon Mechanical Turk," despite the association with racism and trickery. Page 68 On Amazon's platform, real workers remain out of sight in service of an illusion that AI systems are autonomous and magically intelligent. Now Mechanical Turk connects businesses with an unseen and anonymous mass of workers who bid against one another for the opportunity to work on a series of microtasks. In a paradox that many of us have experienced, and ostensibly in order to prove true human identity when reading a website, we are required to convince Google's reCAPTCHA of our humanity. So we dutifully select multiple boxes containing street numbers, or cars, or houses. We are training Google's image recognition algorithms for free. Page 69 Again, the myth of AI as affordable and efficient depends on layers of exploitation, including the extraction of mass unpaid labor to fine-tune the AI systems of the richest companies on earth. Contemporary forms of artificial intelligence are neither artificial nor intelligent. Page 71 work. As Astra Taylor argues, "The kind of efficiency to which techno-evangelists aspire emphasizes standardization, simplification, and speed, not diversity, complexity, and interdependence." 38 Page 75 A 2014 class action lawsuit against McDonald's restaurants in California noted that franchisees are led by software that gives algorithmic predictions regarding employee-to-sales ratios and instructs managers to reduce staff quickly when demand drops. 47 Employees reported being told to delay clocking in to their shifts and instead to hang around nearby, ready to return to work if the restaurant started getting busy again. Because employees are paid only for time clocked in, the suit alleged that this amounted to significant wage theft on the part of the company and its franchisees. 48 Page 76 There was an almost total removal of all conceptual work from execution of tasks." workers clock in to their shifts by swiping access badges or by presenting their fingerprints to readers attached to electronic time clocks. They work in front of timing devices that indicate the minutes or seconds left to perform the current task before a manager is notified. They sit at workstations fitted with sensors that continuously report on their body temperature, their physical distance from colleagues, the amount of time they spend browsing websites instead of performing assigned tasks, and so on. Page 77 Surveillance apparatuses are justified for producing inputs for algorithmic scheduling systems that further modulate work time, or to glean behavioral signals that may correlate with signs of high or low performance, or merely sold to data brokers as a form of insight. young, mostly male engineers, often unencumbered by time-consuming familial or community responsibilities, are building the tools that will police very different workplaces, quantifying the productivity and desirability of employees. The workaholism and round-the-clock hours often glorified by tech start-ups become an implicit benchmark against which other workers are measured, producing a vision of a standard worker that is masculinized, narrow, and reliant on the unpaid or underpaid care work of others. Page 81 Although there will always be ways to resist the imposed temporality of work, with forms of algorithmic and video monitoring, this becomes much harder--as the relation between work and time is observed at ever closer range. Page 82 defining time is an established strategy for centralizing power. Page 85 AI and algorithmic monitoring are simply the latest technologies in the long historical development of factories, timepieces, and surveillance architectures. Page 88 All kinds of workers are subject to the extractive technical infrastructures that seek to control and analyze time to its finest grain--many of whom have no identification with the technology sector or tech work at all. THREE. Data Page 93 I've looked at hundreds of datasets over years of research into how AI systems are built, but the NIST mug shot databases are particularly disturbing because they represent the model of what was to come. It's not just the overwhelming pathos of the images themselves. Nor is it solely the invasion of privacy they represent, since suspects and prisoners have no right to refuse being photographed. It's that the NIST databases foreshadow the emergence of a logic that has now thoroughly pervaded the tech sector: the unswerving belief that everything is data and is there for the taking. It doesn't matter where a photograph was taken or whether it reflects a moment of vulnerability or pain or if it represents a form of shaming the subject. It has become so normalized across the industry to take and use whatever is available that few stop to question the underlying politics. I argue this represents a shift from image to infrastructure, where the meaning or care that might be given to the image of an individual person, or the context behind a scene, is presumed to be erased at the moment it becomes part of an aggregate mass that will drive a broader system. It is all treated as data to be run through functions, material to be ingested to improve technical performance. This is a core premise in the ideology of data extraction. Page 94 A computer vision system can detect a face or a building but not why a person was inside a police station or any of the social and historical context surrounding that moment. The mug shot collections are used like any other practical resource of free, well-lit images of faces, a benchmark to make tools like facial recognition function. Page 95 The AI industry has fostered a kind of ruthless pragmatism, with minimal context, caution, or consent-driven data practices while promoting the idea that the mass harvesting of data is necessary and justified for creating systems of profitable computational "intelligence." This has resulted in a profound metamorphosis, where all forms of image, text, sound, and video are just raw data for AI systems and the ends are thought to justify the means. But we should ask: Who has benefited most from this transformation, and why have these dominant narratives of data persisted? Page 96 It's useful to consider why machine learning systems currently demand massive amounts of data. One example of the problem in action is computer vision, the subfield of artificial intelligence concerned with teaching machines to detect and interpret images. Page 96 These vast collections are called training datasets, and they constitute what AI developers often refer to as "ground truth." 13 The more examples of correctly labeled data there are, the better the algorithm will be at producing accurate predictions. Page 97 Training data also defines more than just the features of machine learning algorithms. It is used to assess how they perform over time. Like prized thoroughbreds, machine learning algorithms are constantly raced against one another in competitions all over the world to see which ones perform the best with a given dataset. Page 98 Once training sets have been established as useful benchmarks, they are commonly adapted, built upon, and expanded. Training data, then, is the foundation on which contemporary machine learning systems are built. 16 These datasets shape the epistemic boundaries governing how AI operates and, in that sense, create the limits of how AI can "see" the world. Page 99 In the 1970s, artificial intelligence researchers were mainly exploring what's called an expert systems approach: rules-based programming that aims to reduce the field of possible actions by articulating forms of logical reasoning. But it quickly became evident that this approach was fragile and impractical in real-world settings, where a rule set was rarely able to handle uncertainty and complexity. 19 By the mid-1980s, research labs were turning toward probabilistic or brute force approaches. In short, they were using lots of computing cycles to calculate as many options as possible to find the optimal result. Page 100 They started using statistical methods that focused more on how often words appeared in relation to one another, rather than trying to teach computers a rules-based approach using grammatical principles or linguistic features. the reduction from context to data, from meaning to statistical pattern recognition. Page 103 Text archives were seen as neutral collections of language, as though there was a general equivalence between the words in a technical manual and how people write to colleagues via email. Like images, text corpuses work on the assumption that all training data is interchangeable. But language isn't an inert substance that works the same way regardless of where it is found. The origins of the underlying data in a system can be incredibly significant, and yet there are still, thirty years later, no standardized practices to note where all this data came from or how it was acquired--let alone what biases or classificatory politics these datasets contain that will influence all the systems that come to rely on them. 31 Page 106 The internet, in so many ways, changed everything; it came to be seen in the AI research field as something akin to a natural resource, there for the taking. As more people began to upload their images to websites, to photo-sharing services, and ultimately to social media platforms, the pillaging began in earnest. The tech industry titans were now in a powerful position: they had a pipeline of endlessly refreshing images and text, and the more people shared their content, the more the tech industry's power grew. Page 108 ImageNet would become, for a time, the world's largest academic user of Amazon's Mechanical Turk, deploying an army of piecemeal workers to sort an average of fifty images a minute into thousands of categories. 40 Page 109 The approach of mass data extraction without consent and labeling by underpaid crowdworkers would become standard practice, and hundreds of new training datasets would follow ImageNet's lead. Page 110 Over and over, data extracted without permission or consent would be uploaded for machine learning researchers, who would then use it as an infrastructure for automated imaging systems. Even when datasets are scrubbed of personal information and released with great caution, people have been reidentified or highly sensitive details about them have been revealed. Page 111 For instance, the same New York City taxi dataset was used to suggest which taxi drivers were devout Muslims by observing when they stopped at prayer times. 50 Page 112 Contemporary organizations are both culturally impelled by the data imperative and powerfully equipped with new tools to enact it. 53 Behind the questionable belief that "more is better" is the idea that individuals can be completely knowable, once enough disparate pieces of data are collected. 54 Page 113 Terms like "data mining" and phrases like "data is the new oil" were part of a rhetorical move that shifted the notion of data away from something personal, intimate, or subject to individual ownership and control toward something more inert and nonhuman. Page 113 Ultimately, "data" has become a bloodless word; it disguises both its material origins and its ends. And if data is seen as abstract and immaterial, then it more easily falls outside of traditional understandings and responsibilities of care, consent, or risk. metaphors of data as a "natural resource" Page 114 High achievers in the mainstream economy tend to do well in a data-scoring economy, too, while those who are poorest become targets of the most harmful forms of data surveillance and extraction. data now operates as a form of capital. a shift away from ideas like "human subjects"--a concept that emerged from the ethics debates of the twentieth century--to the creation of "data subjects," agglomerations of data points without subjectivity or context or clearly defined rights. Page 115 Once AI moved out of the laboratory contexts of the 1980s and 1990s and into real-world situations--such as attempting to predict which criminals will reoffend or who should receive welfare benefits--the potential harms expanded. Further, those harms affect entire communities as well as individuals. But there is still a strong presumption that publicly available datasets pose minimal risks and therefore should be exempt from ethics review. 64 Page 116 The risk profile of AI is rapidly changing as its tools become more invasive and as researchers are increasingly able to access data without interacting with their subjects. For example, a group of machine learning researchers published a paper in which they claimed to have developed an "automatic system for classifying crimes." 65 In particular, their focus was on whether a violent crime was gang-related, which they claimed their neural network could predict with only four pieces of information: the weapon, the number of suspects, the neighborhood, and the location. They did this using a crime dataset from the Los Angeles Police Department, which included thousands of crimes that had been labeled by police as gang-related. Gang data is notoriously skewed and riddled with errors, yet researchers use this database and others like it as a definitive source for training predictive AI systems. Page 117 This separation of ethical questions away from the technical reflects a wider problem in the field, where the responsibility for harm is either not recognized or seen as beyond the scope of the research. Page 118 Technical approaches can move rapidly from conference papers to being deployed in production systems, where harmful assumptions can become ingrained and hard to reverse. Page 119 There are gigantic datasets full of people's selfies, tattoos, parents walking with their children, hand gestures, people driving their cars, people committing crimes on CCTV, and hundreds of everyday human actions like sitting down, waving, raising a glass, or crying. Every form of biodata--including forensic, biometric, sociometric, and psychometric--is being captured and logged into databases for AI systems to find patterns and make assessments. Page 120 The collection of people's data to build AI systems raises clear privacy concerns. The practices of data extraction and training dataset construction are premised on a commercialized capture of what was previously part of the commons. This particular form of erosion is a privatization by stealth, an extraction of knowledge value from public goods. A dataset may still be publicly available, but the metavalue of the data--the model created by it--is privately held. Page 122 The way data is understood, captured, classified, and named is fundamentally an act of world-making and containment. It has enormous ramifications for the way artificial intelligence works in the world and which communities are most affected. FOUR. Classification Page 127 The politics of classification is a core practice in artificial intelligence. How does classification function in machine learning? What is at stake when we classify? In what ways do classifications interact with the classified? And what unspoken social and political theories underlie and are supported by these classifications of the world? Page 128 classifications can disappear, as Bowker and Star observe, "into infrastructure, into habit, into the taken for granted." Page 129 One of the more vivid examples of bias in action comes from an insider account at Amazon. In 2014, the company decided to experiment with automating the process of recommending and hiring workers. Page 129 "They literally wanted it to be an engine where I'm going to give you 100 resumes, it will spit out the top five, and we'll hire those." 21 Quickly, the system began to assign less importance to commonly used engineering terms, like programming languages, because everyone listed them in their job histories. Instead, the models began valuing more subtle cues that recurred on successful applications. A strong preference emerged for particular verbs. The examples the engineers mentioned were "executed" and "captured." 22 Page 130 Inadvertently, Amazon had created a diagnostic tool. The vast majority of engineers hired by Amazon over ten years had been men, so the models they created, which were trained on the successful résumés of men, had learned to recommend men for future hiring. The employment practices of the past and present were shaping the hiring tools for the future. Page 130 The AI industry has traditionally understood the problem of bias as though it is a bug to be fixed rather than a feature of classification itself. Page 132 Designers get to decide what the variables are and how people are allocated to categories. Again, the practice of classification is centralizing power: the power to decide which differences make a difference. Page 133 Skin color detection is done because it can be, not because it says anything about race or produces a deeper cultural understanding. Page 133 Technical claims about accuracy and performance are commonly shot through with political choices about categories and norms but are rarely acknowledged as such. 33 These approaches are grounded in an ideological premise of biology as destiny, where our faces become our fate. Page 134 By the 1900s, "bias" had developed a more technical meaning in statistics, where it refers to systematic differences between a sample and population, when the sample is not truly reflective of the whole. Machine learning systems are designed to be able to generalize from a large training set of examples and to correctly classify new observations not included in the training datasets. 35 machine learning systems can perform a type of induction, learning from specific examples (such as past résumés of job applicants) in order to decide which data points to look for in new examples In such cases, the term "bias" refers to a type of error that can occur during this predictive process of generalization--namely, a systematic or consistently reproduced classification error that the system exhibits when presented with new examples. This type of bias is often contrasted with another type of generalization error, variance, which refers to an algorithm's sensitivity to differences in training data. Page 135 Amos Tversky and Daniel Kahneman study "cognitive biases," or the ways in which human judgments deviate systematically from probabilistic expectations. Technical designs can certainly be improved to better account for how their systems produce skews and discriminatory results. But the harder questions of why AI systems perpetuate forms of inequity are commonly skipped over in the rush to arrive at narrow technical solutions of statistical bias as though that is a sufficient remedy for deeper structural problems. There has been a general failure to address the ways in which the instruments of knowledge in AI reflect and serve the incentives of a wider extractive economy. Every dataset used to train machine learning systems, whether in the context of supervised or unsupervised machine learning, whether seen to be technically biased or not, contains a worldview. To create a training set is to take an almost infinitely complex and varied world and fix it into taxonomies composed of discrete classifications of individual data points, a process that requires inherently political, cultural, and social choices. By paying attention to these classifications, we can glimpse the various forms of power that are built into the architectures of AI world-building. Page 139 Bowker and Star also underscore that once classifications of people are constructed, they can stabilize a contested political category in ways that are difficult to see. 50 They become taken for granted unless they are actively resisted. Page 139 To borrow an idea from linguist George Lakoff, the concept of an "apple" is a more nouny noun than the concept of "light," which in turn is more nouny than a concept such as "health." 51 Nouns occupy various places on an axis from the concrete to the abstract, from the descriptive to the judgmental. Page 142 In fact, there are no neutral categories in ImageNet, because the selection of images always interacts with the meaning of words. The politics are baked into the classificatory logic, even when the words aren't offensive. ImageNet is a lesson, in this sense, of what happens when people are categorized like objects. Page 143 Perhaps it is no surprise that when we investigate the bedrock layer of these labeled images, we find that they are beset with stereotypes, errors, and absurdities. The focus on making training sets "fairer" by deleting offensive terms fails to contend with the power dynamics of classification and precludes a more thorough assessment of the underlying logics. Page 144 By focusing on classification in AI, we can trace the ways that gender, race, and sexuality are falsely assumed to be natural, fixed, and detectable biological categories. Page 146 the history of disability itself is a "story of the ways in which various systems of classification (i.e., medical, scientific, legal) interface with social institutions and their articulations of power and knowledge." 67 Page 147 Classifications are technologies that produce and limit ways of knowing, and they are built into the logics of AI. The problem for computer science is that justice in AI systems will never be something that can be coded or computed. It requires a shift to assessing systems beyond optimization metrics and statistical parity and an understanding of where the frameworks of mathematics and engineering are causing the problems. This also means understanding how AI systems interact with data, workers, the environment, and the individuals whose lives will be affected by its use and deciding where AI should not be used. Page 148 Nonconsensual classifications present serious risks, as do normative assumptions about identity, yet these practices have become standard. That must change. Page 150 Classificatory schemas enact and support the structures of power that formed them, and these do not shift without considerable effort. But the truly massive engines of classification are the ones being operated at a global scale by private technology companies, including Facebook, Google, TikTok, and Baidu. These companies operate with little oversight into how they categorize and target users, and they fail to offer meaningful avenues for public contestation. FIVE. Affect Page 151 Like many Western researchers before him, Ekman had come to Papua New Guinea to extract data from the indigenous community. all humans exhibit a small number of universal emotions or affects that are natural, innate, cross-cultural, and the same all over the world. This is the story of how affect recognition came to be part of artificial intelligence and the problems this presents. Page 152 Today affect recognition tools can be found in national security systems and at airports, in education and hiring start-ups, from systems that purport to detect psychiatric illness to policing programs that claim to predict violence. Why did the idea that there is a small set of universal emotions, readily interpreted from the face, become so accepted in the AI field, despite considerable evidence to the contrary? Page 153 His work is connected to U.S. intelligence funding of the human sciences during the Cold War through foundational work in the field of computer vision to the post-9/ 11 security programs employed to identify terrorists and right up to the current fashion for AI-based emotion recognition. One of the many things made possible by this profusion of images is the attempt to extract the so-called hidden truth of interior emotional states using machine learning. These systems may not be doing what they purport to do, but they can nonetheless be powerful agents in influencing behavior and training people to perform in recognizable ways. Page 154 A startup in London called Human uses emotion recognition to analyze video interviews of job candidates. Page 155 Emotion recognition systems grew from the interstices between AI technologies, military priorities, and the behavioral sciences--psychology in particular. They share a similar set of blueprints and founding assumptions: that there is a small number of distinct and universal emotional categories, that we involuntarily reveal these emotions on our faces, and that they can be detected by machines. Page 155 These articles of faith are so accepted in some fields that it can seem strange even to notice them, let alone question them. They are so ingrained that they have come to constitute "the common view." Page 156 One aspect in particular played an outsized role: the idea that if affect was an innate set of evolutionary responses, they would be universal and so recognizable across cultures. Page 162 They presumed a link between body and soul that justified reading a person's interior character based on their exterior appearance. Page 165 In later years Ekman also would insist that anyone could come to learn to recognize microexpressions, with no special training or slow motion capture, in about an hour. 59 But if these expressions are too quick for humans to recognize, how are they to be understood? 60 Page 167 Ekman's FACS system provided two things essential for later machine learning applications: a stable, discrete, finite set of labels that humans can use to categorize photographs of faces and a system for producing measurements. It promised to remove the difficult work of representing interior lives away from the purview of artists and novelists and bring it under the umbrella of a rational, knowable, and measurable rubric suitable to laboratories, corporations, and governments. Page 170 Ekman's work became a profound and wide-ranging influence on everything from lie detection software to computer vision. Page 172 Other problems became clear as Ekman's ideas were implemented in technical systems. As we've seen, many datasets underlying the field are based on actors simulating emotional states, performing for the camera. That means that AI systems are trained to recognize faked expressions of feeling. Page 174 This is not an engineering problem that could be solved with a better algorithm. By analyzing the history of these ideas, we can begin to see how military research funding, policing priorities, and profit motives have shaped the field. Page 175 Once the theory emerged that it is possible to assess internal states by measuring facial movements and the technology was developed to measure them, people willingly adopted the underlying premise. The theory fit what the tools could do. SIX. State Page 182 the intelligence community contributed to the development of many of the techniques we now refer to as artificial intelligence. Page 184 As the historian of science Paul Edwards describes in The Closed World, military research agencies actively shaped the emerging field that would come to be known as AI from its earliest days. The military priorities of command and control, automation, and surveillance profoundly shaped what AI was to become. The tools and approaches that came out of DARPA funding have marked the field, including computer vision, automatic translation, and autonomous vehicles. Page 185 Technologies once only available to intelligence agencies--that were extralegal by design--have filtered down to the state's municipal arms: government and law enforcement agencies. less attention is given to the growing commercial surveillance sector, Page 186 Algorithmic governance is both part of and exceeds traditional state governance. But the rhetoric around artificial intelligence is much starker: we are repeatedly told that we are in an AI war. The dominant objects of concern are the supernational efforts of the United States and China, with regular reminders that China has stated its commitment to be the global leader in AI. Page 196 As law professor Andrew Ferguson explains, "We are moving to a state where prosecutors and police are going to say ‘the algorithm told me to do it, so I did, I had no idea what I was doing.' And this will be happening at a widespread level with very little oversight." 56 Page 197 police are turning into intelligence agents: Page 198 The intelligence models that began in national government agencies have now become part of the policing of local neighborhoods. Page 201 Vigilant has since expanded its "crime-solving" toolkit beyond license plate readers to include ones that claim to recognize faces. In doing so, Vigilant seeks to render human faces as the equivalent of license plates and then feed them back into the policing ecology. 66 Like a network of private detectives, Vigilant creates a God's-eye view of America's interlaced roads and highways, along with everyone who travels along them, while remaining beyond any meaningful form of regulation or accountability. 67 Page 201 For Amazon, each new Ring device sold helps build yet more large-scale training datasets inside and outside the home, with classificatory logics of normal and anomalous behavior aligned with the battlefield logics of allies and enemies. Page 203 But in 2014, the legal organization Reprieve published a report showing that drone strikes attempting to kill 41 individuals resulted in the deaths of an estimated 1,147 people. Page 204 Once a pattern is found in the data and it reaches a certain threshold, the suspicion becomes enough to take action even in the absence of definitive proof. This mode of adjudication by pattern recognition is found in many domains--most often taking the form of a score. Page 205 new technical systems of state control use the bodies of refugees as test cases. Page 205 These military and policing logics are now suffused with a form of financialization: socially constructed models of creditworthiness have entered into many AI systems, influencing everything from the ability to get a loan to permission to cross borders. Page 208 Taken together, the AI and algorithmic systems used by the state, from the military to the municipal level, reveal a covert philosophy of en masse infrastructural command and control via a combination of extractive data techniques, targeting logics, and surveillance. These goals have been central to the intelligence agencies for decades, but now they have spread to many other state functions, from local law enforcement to allocating benefits. Page 211 Artificial intelligence is not an objective, universal, or neutral computational technique that makes determinations without human direction. Page 211 AI systems are expressions of power that emerge from wider economic and political forces, created to increase profits and centralize control for those who wield them. But this is not how the story of artificial intelligence is typically told. Page 213 Narratives of magic and mystification recur throughout AI's history, drawing bright circles around spectacular displays of speed, efficiency, and computational reasoning. 5 It's no coincidence that one of the iconic examples of contemporary AI is a game. This epistemological flattening of complexity into clean signal for the purposes of prediction is now a central logic of machine learning. The historian of technology Alex Campolo and I call this enchanted determinism: AI systems are seen as enchanted, beyond the known world, yet deterministic in that they discover patterns that can be applied with predictive certainty to everyday life. Page 214 That deep learning approaches are often uninterpretable, even to the engineers who created them, gives these systems an aura of being too complex to regulate and too powerful to refuse. We are told to focus on the innovative nature of the method rather than on what is primary: the purpose of the thing itself. Page 215 These programs produce surprising moves uncommon in human games for a straightforward reason: they can play and analyze far more games at a far greater speed than any human can. This is not magic; it is statistical analysis at scale. Page 215 Over and over, we see the ideology of Cartesian dualism in AI: the fantasy that AI systems are disembodied brains that absorb and produce knowledge independently from their creators, infrastructures, and the world at large. These illusions distract from the far more relevant questions: Whom do these systems serve? What are the political economies of their construction? And what are the wider planetary consequences? Page 216 the artificial intelligence industry's expansion has been publicly subsidized: from defense funding and federal research agencies to public utilities and tax breaks to the data and unpaid labor taken from all who use search engines or post images online. AI began as a major public project of the twentieth century and was relentlessly privatized to produce enormous financial gains for the tiny minority at the top of the extraction pyramid. Page 218 This book proposes that the real stakes of AI are the global interconnected systems of extraction and power, not the technocratic imaginaries of artificiality, abstraction, and automation. AI is born from salt lakes in Bolivia and mines in Congo, constructed from crowdworker-labeled datasets that seek to classify human actions, emotions, and identities. It is used to navigate drones over Yemen, direct immigration police in the United States, and modulate credit scores of human value and risk across the world. A wide-angle, multiscalar perspective on AI is needed to contend with these overlapping regimes. The opacity of the larger supply chain for computation in general, and AI in particular, is part of a long-established business model of extracting value from the commons and avoiding restitution for the lasting damage. Page 219 Thousands of people are needed to support the illusion of automation: tagging, correcting, evaluating, and editing AI systems to make them appear seamless. The uses of workplace AI further skew power imbalances by placing more control in employers' hands. Apps are used to track workers, nudge them to work longer hours, and rank them in real time. Amazon provides a canonical example Page 221 What epistemological violence is necessary to make the world readable to a machine learning system? AI seeks to systematize the unsystematizable, formalize the social, and convert an infinitely complex and changing universe into a Linnaean order of machine-readable tables. Page 221 Many of AI's achievements have depended on boiling things down to a terse set of formalisms based on proxies: identifying and naming some features while ignoring or obscuring countless others. Page 222 The rhetoric about the AI war between the United States and China drives the interests of the largest tech companies to operate with greater government support and few restrictions. Page 223 The result is a profound and rapid expansion of surveillance and a blurring between private contractors, law enforcement, and the tech sector, fueled by kickbacks and secret deals. Could there not be an AI for the people that is reoriented toward justice and equality rather than industrial extraction and discrimination? This may seem appealing, but as we have seen throughout this book, the infrastructures and forms of power that enable and are enabled by AI skew strongly toward the centralization of control. As Audre Lorde reminds us, the master's tools will never dismantle the master's house. Page 224 The voices of the people most harmed by AI systems are largely missing from the processes that produce them. ethics is necessary but not sufficient to address the fundamental concerns raised in this book. power. AI is invariably designed to amplify and reproduce the forms of power it has been deployed to optimize. Instead of glorifying company founders, venture capitalists, and technical visionaries, we should begin with the lived experiences of those who are disempowered, discriminated against, and harmed by AI systems. Page 225 The social contract, to the extent that there ever was one, has brought a climate crisis, soaring wealth inequality, racial discrimination, and widespread surveillance and labor exploitation. But the idea that these transformations occurred in ignorance of their possible results is part of the problem. Page 226 We see glimpses of this refusal when populations choose to dismantle predictive policing, ban facial recognition, or protest algorithmic grading. | Salman KhanNotable QuotationsExpand to full screen
Page xvi
Large language models such as GPT- 4, short for Generative Pre- trained Transformer, are essentially big, powerful— albeit digital—" word brains" trained on a colossal amount of information from books, articles, websites, and all sorts of written material.
Page xvii
What it lacks in real- world sensory experiences of the human brain, it compensates for by having exposure to more language than any human might hope to read, watch, or listen to in multiple lifetimes.
Page xviii
The best way to think of a parameter is a number describing the strength of a connection between two nodes in the neural net that represent the large language model. You can view it as a representation of the strength of a synapse between two neurons in a brain. When it was first launched in 2018, GPT- 1 had more than 100 million parameters.
I found myself suddenly exhilarated to be one of the first people on the planet to see the capabilities of GPT- 4.
Page xix
We've also shown that rather than somehow being a substitute for the teacher, videos can off- load pieces of a lecture, freeing up more time for personalized learning, hands- on activities, or classroom conversation. This arguably makes the teachers more valuable, not less. And now it was time to see if generative AI could do the same— support students and let teachers move up the value chain.
Page xxi
In 1948, while working for Bell Labs, he started dabbling in the field we now know as artificial intelligence. Shannon decided to play with how an algorithm approximates language. He published a paper in The Bell System Technical Journal called "A Mathematical Theory of Communication." It was the early days of digital computers—well before the advent of the internet—and Shannon's information theory first made the case that a series of probabilistic processes could approximate the English language.
... [In] 1950, [Alan Turing] wrote a foundational paper titled "Computing Machinery and Intelligence," where he introduced the concept of the imitation game, which we now know as the Turing test.
Page xxiii
convincingly human technology capable of being a great tutor might also be a technology that bad actors could use to defraud or brainwash unsuspecting people.
Page xxiv
As someone trying his best to use technology for good, I wondered if we might be able to utilize generative AI to achieve the greatest positive effect and edge us closer to that utopian scenario, especially in the realm of education.
... The implications of GPT-4 were nothing short of revelatory.
... What really caught my attention, though, was its ability to write in different tones and styles.
Page xxvi
The AI was not quite perfect yet. It was getting math incorrect more than I liked, but I could even see improvement as I got better at prompting it.
Page xxvii
what if the AI was able to help teachers write their lesson plans? What if it could enter into a debate with a student? What if it could create projects? What if it could help a student remove stressors or inspire a student to create new ideas? What if the technology was able to quiz a student or lead a student in a review session? Educators would be able to create novel activities that students could do with the AI.
... The AI might help students compose essays, making them better writers by providing them with immediate feedback.
...
Artificial intelligence, we worried, might turn our kids into a generation of cheaters who were not going to learn anything. With the AI taking over, parents who once helped their kids with homework might lose an important point of connection.
Page xxviii
Nearly two decades earlier, I had seen similar fears around on-demand video in education: Was it going to be a distraction for students? Was it going to lower their attention spans? Would it isolate students instead of promoting connections between them and their teachers? How were students going to be able to know what to watch? Whom would they ask if they got stuck on a subject and had questions?
... might hasten learning globally and even get us closer to realizing a world in which every person on earth had access to affordable world-class learning.
Page xxx
We had to show AI's real value to students as a Socratic tutor, as a debating partner, as a guidance counselor, as a career coach, and as a driver of better outcomes in their academics. So, alongside OpenAI, we created a rapid prototyping team that began to build an AI-infused education platform we would come to call Khanmigo.
Page xxxii
Here we were, working with a technology that took writing, and everything we knew about teaching and learning in all domains, to another level.
Page xxxiii
We are at a turning point in education, one with far-reaching implications that is changing, and will continue to change, everything about learning, work, and human purpose.
Page 3
In early 2023, the Los Angeles Unified School District became the first major school system to ban it.
... it said, the tool did not help build critical thinking and problem-solving skills.
Page 4
fear large language models will worsen instances of students not doing their own work. We fear that the potential effect on student writing skills will be catastrophic, with generative AI able to generate text quickly and efficiently for them. We fear that because GPT-generated text pulls from millions of online sources full of biased language and viewpoints, the information it lays out in its final form will be biased as well.
Page 5
The most successful students will be those who use AI to help make conceptual connections for developing ideas. Students who learn to use AI ethically and productively may learn not only at an exponentially higher rate than others but also in a way that allows them to remain competitive throughout their careers. They will have a deeper understanding of the given subject matter, because they will know how to get their questions answered. Rather than atrophying, their curiosity muscle will be strengthened.
Page 6
Workers will need to learn how to use large language models to automate almost any traditional white-collar process, too, from collating information to doing analysis on spreadsheets.
Page 8
Could it help close learning gaps or provide access to quality education regardless of geographic constraints, economic limitations, or social circumstances?
... What might it be like if every student on the planet had access to an artificially intelligent personal tutor: an AI capable of writing alongside the student; an AI that students could debate any topic with; an AI that fine-tuned a student's inherent strengths and augmented any gaps in learning; an AI that engaged students in new and powerful ways of understanding science, technology, engineering, and mathematics; an AI that gave students new ways of experiencing art and unlocking their own creativity; an AI that allowed for students to engage with history and literature like never before?
Page 10
On March 15, 2023, when we launched our AI assistant, by this point called Khanmigo—
Page 11
Among other things, it provides a personalized and patient tutor that focuses on the learner's interests or struggles and empowers educators to better understand how they can fully support their students.
... A caring and student-attuned instructor who presented clear learning objectives, assessments, and specialized feedback until, eventually, that student demonstrated a real grasp of the material.
Page 17
Luckily, by the time we launched, educators were starting to come around by March 2023, it seemed that educators craved a tool that used the power of ChatGPT-like technology for education, implemented with learning and teacher support in mind.
Page 29
If a teacher's goal is to give students practice and assessment in structured thinking, language, and grammar, simple storytelling, or just forming and backing up an opinion, you don't necessarily need a traditional take-home writing assignment, where there is likely a strong temptation to use ChatGPT. Instead, an in-class, proctored, five-paragraph essay might do the trick, and it would unfold in a context where there is more support from the teacher (and teachers can directly observe students in their process). If the task might be hard to complete in one sitting, students can work on it in multiple classes, always with the teacher around to support students and ensure that the work is their own.
Page 32
The people who weren't very good writers in class wrote bad essays outside class, too, he says. The AI helps them catch up, and it gives them an explanation of where they are with their skills; for instructors, the AI helps flag the students who need the most help and attention.
Page 33
educators are finding that these generative AI tools make our students far more skilled and efficient writers.
... The future of writing in school will evolve into a more diverse set of activities, depending on the pedagogical goals and comfort of the teacher.
... I'm an educator looking to ensure that my students have strength here, I would do more in-class writing assignments where it is 100 percent the student's own work.
Page 34
At its best, an education-based AI platform can be the world's finest assistant and co-collaborator, objective in its assessments and thorough in its analytics, designed to do one thing and one thing only: to sharpen a student's skills.
... Beyond facilitating reading comprehension, AI can allow learners to immerse themselves in the worlds of the characters in ways that would have seemed like science fiction only a few years ago.
... Based on a 2020 Gallup analysis of data from the U.S. Department of Education, 54 percent of Americans between the ages of sixteen and seventy-four read below a sixth-grade level.
Page 39
Instead of answering multiple-choice questions about a text or passage, imagine that students write out the author's intent behind a choice of words or explore the main idea of the passage (while highlighting those parts of the passage). Picture the AI then asking follow-up questions based on what a student writes. It could ask students to draft a conclusion for an incomplete essay that forces them to understand everything that came before.
...
Imagine if, when reading a book, a student could have a discussion with the AI at the end of every chapter. The AI might ask the student what they think about the book so far, or whether anything was particularly interesting or confusing about the material.
Page 42
The first time I saw examples of this, I had the same questions I am sure many of you did: Is generative AI a creativity killer? If the root of creativity is individual agency, what happens when our kids can simply log on to an app leveraging generative AI, type in or speak a request, and then create imaginative works designed by an imagination not their own? How will our kids learn to think creatively for themselves?
Page 45
Some would also argue that generative AI's "creativity" is just derivative from all the data it has been exposed to. But isn't that very human as well? Even the large leaps in human creativity have been closely correlated to things that the creator has been exposed to.
Page 46
Much like poets hanging out at a café in Paris, humans and AI can augment each other and inspire a mutually creative process.
Page 48
beneficial to children's creativity by providing them with a tool to help them generate, play, and get feedback on ideas in a judgment-free zone.
... With the emergence of artificial intelligence, we're also seeing a shift in the barriers to entry that once limited people from learning a variety of crafts.
... Generative AI is the writing tutor that will teach learners, exploring diverse genres, themes, and narrative structures with them.
... learn to play musical instruments, suggesting practice routines and fingering techniques and deciphering initial musical scores based on their preferences. It can help with improvisation by providing melodic ideas and chord progressions aligned with their playing style.
Page 49
Will generative AI, with its ability to produce images, music, and stories, eventually make professional creatives obsolete?
... The net effect of the world of generative AI is that we are going to get more expressions of creativity, and creatives with wider and deeper skill sets, somewhat out of necessity but also thanks to the opportunity generative AI provides.
Page 50
Mozart, Einstein, and da Vinci weren't just innately gifted. They had access to opportunities and resources that the bulk of humanity didn't have access to.
... Technology has generally lowered the cost of access to world-class tools and learning.
.... Not only does it allow students to produce more polished, finished works, but it can model the creative process with them.
Page 53
Not only does Khanmigo let users chat with literary characters, they can also chat with historical figures—anyone from Benjamin Franklin to Cleopatra to Rembrandt:
Page 55
We designed Khanmigo to provide responses both drawn from accurate history and portrayed through the lens of the character with acuity.
Page 56
you no longer have to travel to Colonial Williamsburg to talk to George Washington or Benjamin Franklin.
Page 62
If this tool can be used to engage students and classrooms about history in a way that traditional textbooks and movies can't, I think it is healthy as long as there are reasonable guardrails in place (including helping the user know about the limitations).
Page 63
Generative AI, with its ability to mix media and content, has the potential to bring history and civics lessons to life. By offering an interactive and immersive learning experience, it empowers students to delve into historical events, engage in meaningful discussions, and develop a deeper comprehension of civic principles. Its personalized explanations, responsive question prompts, and diverse perspectives stimulate critical thinking and encourage students to form their own well- informed opinions. With these types of tools, history and civics lessons transcend conventional boundaries, empowering students to connect with the past and understand the present.
Page 69
As impressive as all of this was, it quickly became clear that it wasn't accurate all of the time. If you asked it for links to sources, it might make them up.
Page 72
between the safeguards on Khanmigo and ongoing improvements to GPT-4, the platform can work with specific data and do so with minimal hallucinations.
Page 88
we still haven't gotten to what is perhaps the most overlooked, yet most important, role of a tutor: providing motivation and accountability.
... the AI can step in to address these needs.
...
parents and teachers was that the platform is powerful for students who proactively seek out the AI's help.
The Most Important Subject-Matter Domain to Master > Page 91
AI essentially showing students that domains, or distinctions between subjects, no longer matter.
Page 96
Would technology further isolate students by allowing them to learn alone on a computer?
... It turns out that not only does this not have to be the case, but thoughtful use of technology can actually increase human-to-human interaction.
Page 97
A properly designed AI can take things even further and actually facilitate conversations among human beings.
Page 98
With AI and AI-based tutors, the days of static learning are over as collaborative education expands in scope and capability.
Page 102
Working in the Artificial Intelligence Laboratory, Joseph Weizenbaum developed ELIZA, one of the world's first natural-language-processing computer programs that was able to simulate a conversation. Weizenbaum set out to show how artificial the communication was between a human and an AI, but instead he was shocked to learn that many people became emotionally attached to the artificial intelligence. Those using ELIZA often forgot that they were conversing with a computer.
... patient spoke to DOCTOR, and DOCTOR responded as a Rogerian therapist might, by using nondirectional questioning and reframing statements.
Page 103
We also know that both mental health problems and academic underperformance have a similar cause in students: an innate sense that they have little control and purpose in their lives, he says.
Page 107
Because AIs aren't sentient, they can't be truly empathetic. Empathy involves sensing and modeling other's emotions and contexts in your own mind. They can, however, simulate empathy quite well.
Page 109
break down challenging concepts into more manageable chunks.
Page 110
Still, a gap remained between what you could learn from articles, videos, and exercises and what a great tutor could do. For example, the benefits of rapport, motivational support, and dynamic conversation between the technology and the student remained elusive. For these interactive qualities, learners still needed to turn to a parent, teacher, or tutor.
... That changed, however, with AI technology and the introduction of large language models.
Page 111
The AI tutor knows the material and is a subject-matter expert that can provide personalized, adaptive learning exercises based on a student's individual needs and abilities. It can also offer immediate feedback and adjust its teaching methods. Unlike any parent I know, the AI tutor has endless energy and no other job
Page 112
Generative AI can provide parents with resources and time that might otherwise require sessions with a therapist or reading parenting self-help books. The AI becomes a parenting coach.
Page 115
even if AI becomes a net positive, those moments in which we unplug and turn off our screens become much scarcer.
Page 116
Generative AI is neither an abdication of parental responsibility nor simply a tool for keeping an eye on our kids. Rather, like all technology before it, it is a tool that we can use to amplify our intent.
Page 117
Large language models can focus the learning time and create more space for other points of productive contact that a child has with parents and other people.
Page 135
The internet is a useful but scary place, even for adults. In the late 1990s, we were all blown away by the power to search across billions of pages for answers, products, and services. However, as page views began to drive ad revenue, most websites became less about offering visitors what they actually wanted and more about persuading them to click on ads.
... This includes search. Roughly the first half-dozen links you see are actually ads.
... Because of this, you are likely to find just as much misinformation as information when you search for therapies that might help a loved one with their illness or try to more deeply understand an issue in the news.
Page 136
A site like YouTube could have some valuable educational content or even enriching entertainment, but it also has a lot of junk that is unhealthy for young people.
Page 138
Everything I've written regarding kids is arguably useful for adults as well. It would feel like browsing the internet with a thoughtful, intelligent friend who's willing to help me get to the information that I want faster. It would also protect me from unhealthy ads or information.
Part VI: Teaching in the Age of AI
Page 143
In 2017, a slim man took the stage at the British Science Festival, one of the oldest science festivals in the world. Gazing out on a sea of leading researchers from around the globe, Sir Anthony Seldon, a renowned educator and historian, stated that by 2027 teachers will be AI rather than humans. The coming technology, he said, will force teachers to take a classroom assistant role while technology will be the conveyor of knowledge.
... agree with Seldon that personalization in learning is an aspiration that we should strive for and that AI is going to play a big role in getting us there. However, I completely disagree with his prediction that this technology will somehow minimize the importance of the human teacher. If anything, it's going to do the opposite.
Page 144
At the end of the day, the biggest fear from educators is the world that Seldon envisions, a world where artificial intelligence reduces demand for teachers.
Page 144
Engelbart believed that people were going to use technology to augment their abilities the same way that a tractor augments the work of a farmer to produce food.
Page 145
do not believe machines are going to relegate the teacher to the role of the teaching assistant. Rather, the AI is the teaching assistant.
Page 146
When students use generative AI to write papers, for instance, their quality is going to go up, akin to the advent of the word processor leading teachers to expect that their students now create beautifully typed, formatted, well-thought-out essays in ways that the typewriter did not allow.
... Students have to do a pre-mortem with their work before they turn it in for their grade.
Page 146
"Lectures do not make as much sense when I've got tools like ChatGPT that can do truly amazing training, all remotely,"
Page 149
they can do all the grunt work involved in teaching—writing rubrics, giving students feedback on their essays, and drafting student narrative progress reports for parents.
Page 150
By freeing educators from the administrative work that so often mires their days, artificial intelligence unlocks time and the resources for teachers.
Page 151
"Can you write a script for me for the section on historic repercussions of the Treaty of Versailles after World War I?"
Page 153
A teacher can push the AI further still and ask it to help come up with a creative student project based on the lesson.
Page 157
Rather than facilitate cheating, the AI can give real-time feedback and support on how to write better.
Page 161
having students work on their writing and papers in class allows them to get support from both the professor and other students. It makes class time more active. Longer essays can be done over multiple class periods.
... students should do what used to be homework in class and watch recorded lectures on their own time.
Page 161
there are important benefits to having students write essays independently, such as developing their ability to plan and not procrastinate, both of which are skills arguably as useful as learning to write.
Page 164
Before generative AI came on the scene, it could take days or weeks before students got feedback on their papers.
... By that point, they may have forgotten much of what they had written, and there wouldn't be a chance for them to refine their work. Contrast this to the vision in which students receive immediate feedback on every dimension of their writing from the AI. They will have the chance to practice, iterate, and improve much faster.
Page 171
artificial intelligence continues to play a transformative role in bridging the global education divide and fostering equal-opportunity learning for all.
Page 172
a student doesn't keep pace in understanding a foundational concept, the class keeps moving. Limited support exists for personalization or for revisiting gaps, much less for one-on-one tutoring.
Page 173
A platform like Khanmigo exists to bridge this gap—offering personalized, accessible, and high-quality education.
Page 174
Generative AI adds a new layer of expenses beyond the cost of paying the salaries of engineers, designers, product managers, and content developers to iteratively improve a platform like Khanmigo.
... large language model like GPT-4 are significant. Right now, our best estimate of the computation costs of average usage of Khanmigo is between five and fifteen dollars a month per user.
Page 186
I've already argued that generative AI is going to transform schoolwork and grading in the classroom; students will be able to do much richer assignments, and teachers will have more support grading them. I've also discussed how standardized assessment is likely to change. Assessment will be deeper, much more continuous, and indistinct from learning. Over time, either standardized tests like SATs and ACTs will move in this direction, or new assessments will enter the space to take advantage of the opportunity.
Page 188
admission directors need to wrestle with whether this entire exercise of writing essays even still provides a credible signal for admissions.
... it's worth questioning why essays and recommendations are part of admissions in the first place.
Page 192
Rather than essays or recommendations alone, what if the AI could do extensive text- or voice-based interviews with students, guidance counselors, and teachers?
Part IX: Work and What Comes Next
Page 197
In 2023, IBM announced that it was suspending or slowing back-office hiring by 30 percent over a five-year period for jobs that could ultimately be done by AI. IBM's revelation suggests that the future of work is going to roll out differently, with back- or middle-office jobs disappearing, together with non-client-facing roles involving tasks such as creating budgets, managing data, and organizing records. Reading the tea leaves, we can see where this might be going.
... Writers and copywriters using AI could potentially be three to five times as productive.
Page 198
The copywriters and technical writers who are going to survive are going to be the ones who lean in most on AI to increase their productivity. The other 90 percent are going to have to find something else to do.
Page 202
even if the generative AI can write pieces of code, you really need to know how those pieces can fit together."
Page 204
"Exactly what the job market of tomorrow looks like is very hard to predict, but the deeper the skill set, whether it's medical consultation, scientific thinking, or customer support, the more value it's going to have, even in a world where productivity will be enhanced by AI," Bill Gates tells me. Not only is there more reason than ever for kids to continue to learn about their fields of interest, he says, but students need to accelerate learning these skills, and to learn them as well as possible. "Entry-level jobs are going to require people to understand how to use large language models and all of the tools they offer. You'll need them to create everything from invoices to business plans. The workplace is going to encourage its workforce to come up with the best product it can. The higher your skill level is, the more your skill will retain a substantial value in the workforce. It's the workforce plus the AI, working together."
Page 205
We are entering a world where we are going back to a pre– Industrial Revolution, craftsmanlike experience. A small group of people who understand engineering, sales, marketing, finance, and design are going to be able to manage armies of generative AI and put all of these pieces together.
... From an economics point of view, entrepreneurship is really the creativity of knowing how to put resources together in order to create value.
... I believe all human beings are born highly creative and entrepreneurial. Unfortunately, our Industrial Revolution–designed education system unintentionally suppresses both traits.
Page 206
As Bill Gates mentioned, the successful workers of the future will be those with deep and broad skills.
... The "three Rs" of reading, writing, and arithmetic are more important than ever.
Page 218
The traditional labor pyramid—with less-skilled manual labor forming the bottom layer, bureaucratic white-collar jobs making up the middle layer, and highly skilled knowledge work and entrepreneurship making up the top—no longer applies. | Miller, DonaldNotable QuotationsExpand to full screen
The more simple and predictable the communication, the easier it is for the brain to digest.
Essentially, story formulas put everything in order so the brain doesn't have to work to understand what's going on.
The key is to make your company's message about something that helps the customer survive and to do so in such a way that they can understand it without burning too many calories.
Story formulas reveal a well - worn path in the human brain, and if we want to stay in business, we need to position our products along this path.
In a story, audiences must always know who the hero is, what the hero wants, who the hero has to defeat to get what they want, what tragic thing will happen if the hero doesn't win, and what wonderful thing will happen if they do.
What problem we are helping them solve, and what life will look like after they engage our products and services,
All experienced writers know the key to great writing isn't in what they say; it's in what they don't say.
A good story takes a series of random events and distills them into the essence of what really matters.
If a character or scene doesn't serve the plot, it has to go.
When Apple began filtering their communication to make it simple and relevant, they actually stopped featuring computers in most of their advertising. Instead, they understood their customers were all living, breathing heroes, and they tapped into their stories.
identifying what their customers wanted defining their customers' challenge (that people didn't recognize their hidden genius), and offering their customers a tool they could use to express themselves
The story of Apple isn't about Apple ; it's about you.
People don't buy the best products ; they buy the products they can understand the fastest.
7 basic plot points.
Here is nearly every story you see or hear in a nutshell : A CHARACTER who wants something encounters a PROBLEM before they can get it. At the peak of their despair, a GUIDE steps into their lives, gives them a PLAN, and CALLS THEM TO ACTION. That action helps them avoid FAILURE and ends in a SUCCESS.
Critics are hungry for something different, yet the masses, who do not study movies professionally, simply want accessible stories.
What does the hero want?
Who or what is opposing the hero getting what she wants ?
What will the hero's life look like if she does ( or does not ) get what she wants ?
What do you offer?
How will it make my life better ?
What do I need to do to buy
highlight the aspects that would help parents survive and thrive ( build stronger tribes, strengthen family connections, and connect more deeply with life's greater meaning ),
What was in it for them.
Alfred Hitchcock defined a good story as "life with the dull parts taken out."
When customers finally understand how you can help them live a wonderful story, your company will grow.
THE CUSTOMER IS THE HERO, NOT YOUR BRAND.
We position our customer as the hero and ourselves as the guide,
Once we identify who our customer is, we have to ask ourselves what they want as it relates to our brand. The catalyst for any story is that the hero wants something. The rest of the story is a journey about discovering whether the hero will get what they want.
COMPANIES TEND TO SELL SOLUTIONS TO EXTERNAL PROBLEMS, BUT CUSTOMERS BUY SOLUTIONS TO INTERNAL PROBLEMS.
Customers are attracted to us for the same reason heroes are pulled into stories : they want to solve a problem that has, in big or small ways, disrupted their peaceful life.
By talking about the problems our customers face, we deepen their interest in everything we offer.
AREN'T LOOKING FOR ANOTHER HERO; THEY'RE LOOKING FOR A GUIDE.
It's no accident that guides show up in almost every movie. Nearly every human being is looking for a guide ( or guides ) to help them win the day.
CUSTOMERS TRUST A GUIDE WHO HAS A PLAN.
In almost every story, the guide gives the hero a plan, or a bit of information, or a few steps they can use to get the job done.
Two kinds of plans : the agreement plan and the process plan.
CUSTOMERS DO NOT TAKE ACTION UNLESS THEY ARE CHALLENGED TO TAKE ACTION.
Characters only take action after they are challenged by an outside force.
A call to action involves communicating a clear and direct step our customer can take to overcome their challenge and return to a peaceful life.
One call to action is direct, asking the customer for a purchase or to schedule an appointment. The other is a transitional call to action, furthering our relationship with the customer. Once we begin using both kinds of calls to action in our messaging, customers will understand exactly what we want them to do and decide whether to let us play a role in their story.
Stories live and die on a single question : What's at stake ? If nothing can be gained or lost, nobody cares.
If there is nothing at stake there is no story.
NEVER ASSUME PEOPLE UNDERSTAND HOW YOUR BRAND CAN CHANGE THEIR LIVES. TELL THEM.
We must tell our customers how great their life can look if they buy our products and services.
Everybody wants to be taken somewhere. If we don't tell people where we're taking them, they'll engage another brand.
Thousands of companies shut their doors every year, not because they don't have a great product, but because potential customers can't figure out how that product will make their lives better.
The most important challenge for business leaders is to define something simple and relevant their customers want and to become known for delivering on that promise.
When I say survival, I'm talking about that primitive desire we all have to be safe, healthy, happy, and strong. Survival simply means we have the economic and social resources to eat, drink, reproduce, and fend off foes.
If your brand can help them save money, save time, find a community, gain status, accumulate resrouces, you've tapped into a survival mechanism.
Increased productivity, increased revenue, or decreased waste are powerful associations with the need for a business
The innate desire to be generous.
The chief desire of man is not pleasure but meaning.
Invite them to participate in something greater than themselves.
Define a desire for your customer, and the story you're inviting customers into will have a powerful hook.
Companies tend to sell solutions to external problems, but customers buy solutions to internal problems.
Every story is about somebody who is trying to solve a problem,
The problem is the " hook " of a story,
The more we talk about the problems our customers experience, the more interest they will have in our brand.
Three elements of conflict that will increase customer interest
The villain is the number one device storytellers use to give conflict a clear point of focus.
The stronger, more evil, more dastardly the villain, the more sympathy we will have for the hero and the more the audience will want them to win in the end.
The villain doesn't have to be a person, but without question it should have personified characteristics.
Vilifying our customers' challenges
Frustration, for example, is not a villain ; frustration is what a villain makes us feel. High taxes, rather, are a good example of a villain.
One villain is enough.
The villain should be real.
What is the chief source of conflict that your products and services defeat ?
There are three levels of problems that work together to capture a reader's or a moviegoer's imagination.
External Problems Internal Problems Philosophical Problems
The external problem works like a prized chess piece set between the hero and the villain, and each is trying to control the piece so they can win the game.
In almost every story the hero struggles with the same question : Do I have what it takes ?
People's internal desire to resolve a frustration is a greater motivator than their desire to solve an external problem.
By assuming our customers only want to resolve external problems, we fail to engage the deeper story they're actually living.
What was the internal problem Apple identified ? It was the sense of intimidation most people felt about computers.
The only reason our customers buy from us is because the external problem we solve is frustrating them in some way. If we can identify that frustration, put it into words, and offer to resolve it along with the original external problem, something special happens. We bond with our customers because we've positioned ourselves more deeply into their narrative.
Starbucks was delivering more value than just coffee ; they were delivering a sense of sophistication and enthusiasm about life. A place for people to meet in which they could experience affiliation and belonging. In understanding how their customers wanted to feel, Starbucks took a product that Americans were used to paying fifty cents for ( or drinking for almost free at home or at work ) and were able to charge three or four dollars per cup.
Framing our products as a resolution to both external and internal problems increases the perceived value ( and I would argue, actual value ) of those products.
The philosophical problem is about the question why
A philosophical problem can best be talked about using terms like ought and shouldn't.
Brands that give customers a voice in a larger narrative [good vs evil; underdog vs giant] add value to their products by giving their customers a deeper sense of meaning.
Can your products be positioned as tools your customers can use to fight back against something that ought not be ?
... resolves the external problem ... the internal problem ... and the philosophical problem
TESLA MOTOR CARS :
Villain : Gas guzzling, inferior technology
External : I need a car.
Internal : I want to be an early adopter of new technology.
Philosophical : My choice of car ought to help save the environment.
What external problem is that villain causing ? How is that external problem making your customers feel ? And why is it unjust for people to have to suffer at the hands of this villain ?
The Seven Basic Plots, Christopher Booker
If a hero solves her own problem in a story, the audience will tune out. Why ? Because we intuitively know if she could solve her own problem, she wouldn't have gotten into trouble in the first place.
the guide character to encourage the hero and equip them to win the day.
the day we stop losing sleep over the success of our business and start losing sleep over the success of our customers is the day our business will start growing again.
People are looking for a guide to help them, not another hero.
The guide must have this precise one - two punch of empathy and authority in order to move the hero and the story along.
When we empathize with our customers ' dilemma, we create a bond of trust. People trust those who understand them, and they trust brands that understand them too.
Expressing empathy isn't difficult. Once we've identified our customers ' internal problems, we simply need to let them know we understand and would like to help them find a resolution.
Nobody likes a know - it - all and nobody wants to be preached at.
The guide doesn't have to be perfect, but the guide needs to have serious experience helping other heroes win the day.
Add just the right amount of authority to our marketing.
Testimonials
- Statistics
- Awards
- Logos
Customers want to know you've helped other businesses overcome their same challenges.
When we express empathy, we help our customers answer Cuddy's first question, " Can I trust this person ? " Demonstrating competence helps our customers answer the second question, " Can I respect this person ? "
The plan is the bridge the hero must cross in order to arrive at the climactic scene.
A process plan can describe the steps a customer needs to take to buy our product, or the steps the customer needs to take to use our product after they buy it, or a mixture of both.
A post - purchase process plan is best used when our customers might have problems imagining how they would use our product after they buy
A process plan can also combine the pre - and post - purchase steps.
If process plans are about alleviating confusion, agreement plans are about alleviating fears.
An agreement plan can also work to increase the perceived value of a service you promise to provide.
Unlike a process plan, an agreement plan often works in the background. Agreement plans do not have to be featured on the home page of your website ( though they could be ), but as customers get to know you, they'll sense a deeper level to your service and may realize why when they finally encounter your agreement plan.
List all the things your customer might be concerned about as it relates to your product or service and then counter that list with agreements that will alleviate their fears.
Your agreement plan might be titled the " customer satisfaction agreement " or even " our quality guarantee. "
In stories, characters never take action on their own. They have to be challenged to take action.
human beings do not make major life decisions unless something challenges them to do so.
direct calls to action and transitional calls to action.
Direct calls to action include requests like " buy now, " " schedule an appointment, " or " call today. " A direct call to action is something that leads to a sale, or at least is the first step down a path that leads to a sale. Transitional calls to action, however, contain less risk and usually offer a customer something for free.
Inviting people to watch a webinar or download a PDF are good examples of transitional calls to action.
Examples of direct calls to action are • Order now • Call today • Schedule an appointment • Register today • Buy now
A good transitional call to action can do three powerful things for your brand :
Stake a claim to your territory.
Create reciprocity.
Position yourself as the guide.
Free information
- Testimonials
- Samples
- Free trial
The only two motivations a hero has in a story are to escape something bad or experience something good.
Brands that don't warn their customers about what could happen if they don't buy their products fail to answer the " so what " question every customer is secretly asking.
Emphasizing potential loss is more than just good storytelling ; it's good behavioral economics.
... First, we must make a reader ( or listener ) know they are vulnerable to a threat.
... Second, we should let the reader know that since they're vulnerable, they should take action to reduce their vulnerability.
... Third, we should let them know about a specific call to action that protects them from the risk.
... Fourth, we should challenge people to take this specific action.
Agitating a fear and then highlight a path that would return readers or listeners to peace and stability.
What negative consequences are you helping customers avoid?
- Could customers lose money ?
- Are there health risks if they avoid your services ?
- What about opportunity costs ?
- Could they make or save more money with you than they can with a competitor ?
- Could their quality of life decline if they pass you by ?
- What's the cost of not doing business with you ?
Where is your brand taking people ? Are you taking them to financial security ? To the day when they'll move into their dream home ? To a fun weekend with friends ? Without knowing it, every potential customer we meet is asking us where we can take them.
Casting a clear, aspirational vision has always served a presidential candidate.
Successful brands, like successful leaders, make it clear what life will look like if somebody engages their products or services.
Stories aren't vague, they're defined ; they're about specific things happening to specific people.
The three dominant ways storytellers end a story is by allowing the hero to win
- Be unified with somebody or something that makes them whole.
- Experience some kind of self - realization that also makes them whole.
Everybody wants status,
...The primary function of our brain is to help us survive and thrive, and part of survival means gaining status.
- Offer access
- Create scarcity
- Offer a premium
- Offer identity association
The character is rescued by somebody or something else that they needed in order for them to be made complete.
- Reduced anxiety
- Reduced workload
- More time
How can a brand offer a sense of ultimate self - realization or self - acceptance ?
- Inspiration
- Acceptance
- Transcendence
the human desire to transform. Everybody wants to change. Everybody wants to be somebody different, somebody better, or, perhaps, somebody who simply becomes more self - accepting.
Brands that participate in the identity transformation of their customers create passionate brand evangelists.
Who does our customer want to become ? What kind of person do they want to be ? What is their aspirational identity ?
Gerber defined an aspirational identity for their customers and they associated their product with that identity. The aspirational identity of a Gerber Knife customer is that they are tough, adventurous, fearless, action oriented, and competent to do a hard job. Epitomized in their advertising campaign " Hello Trouble, " Gerber positioned their customer as the kind of person who sails boats into storms, rides bulls, rescues people from floods, and yes, cuts tangled ropes from boat propellers. In their television commercials they present images of these aspirational, heroic figures over anthemic music and a narrator reciting the lines :
HOW DOES YOUR CUSTOMER WANT TO BE DESCRIBED BY OTHERS ?
- Can you help them become that kind of person?
Can you participate in their identity transformation ?
If you offer executive coaching, your clients may want to be seen as competent, generous, and disciplined.
The audience needs to be told very clearly how far the hero has come, especially since the hero usually struggles with crippling doubt right up until the end and they don't even realize how much they have changed.
Brands that realize their customers are human, filled with emotion, driven to transform, and in need of help truly do more than sell products ; they change people.
When your team realizes that they sell more than products, that they guide people toward a stronger belief in themselves, then their work will have greater meaning.
The easiest thing we can do on our website is state exactly what we do.
In general we need to communicate a sense of health, well - being, and satisfaction with our brand. The easiest way to do this is by displaying happy customers.
A common challenge for many businesses is that they need to communicate simply about what they do, but they've diversified their revenue streams so widely that they're having trouble knowing where to start.
Once we have an umbrella message, we can separate the divisions using different web pages and different BrandScripts.
Why say, " As parents ourselves, we understand what it feels like to want the best for our children. That's why we've created a school where parents work closely with teachers through every step of their child's education journey, " when you could just say, " Weekly Conference Calls with Your Child's Teacher " as a bullet point along with five other great differentiators about your school ?
Cut half the words out of your website. Can you replace some of your text with images? Can you reduce whole paragraphs into three or four bullet points ? Can you summarize sentences into bite - sized soundbites ?
The fewer words you use, the more likely it is that people will read them.
The Narrative Void is a vacant space that occurs inside the organization when there's no story to keep everyone aligned.
Where there's no plot, there's no productivity.
A one - liner is a new and improved way to answer the question " What do you do ? " It's more than a slogan or tagline ; it's a single statement that helps people realize why they need your products or services. | Francis-Noel Thomas, Mark TurnerNotable QuotationsExpand to full screen
Writing is an intellectual activity, not a bundle of skills. Writing proceeds from thinking.
When style is considered the opposite of substance, it seems optional and incidental, even when it is admired. Style, conceived this way, is something fancy that distracts us from what is essential; it is the varnish that makes the truth at least a little harder to see. Any concept of style that treats it as optional is inadequate
The styles we acquire unconsciously remain invisible to us.
The styles we acquired unconsciously do not always serve our needs.
Even the best-educated members of our society commonly lack a routine style for presenting the result of their own engagement with a problem to people outside their own profession.
Classic style is focused and assured. Its virtues are clarity and simplicity; in a sense, so are its vices. It declines to acknowledge ambiguities, unessential qualifications, doubts, or other styles. It declines to acknowledge that it is a style. It makes its hard choices silently and out of the reader’s sight. Once made, those hard choices are not acknowledged to be choices at all; they are presented as if they were inevitable because classic style is, above all, a style of presentation with claims to transparency.
The style rests on the assumptions that it is possible to think disinterestedly, to know the results of disinterested thought, and to present them without fundamental distortion. In this view, thought precedes writing. All of these assumptions may be wrong, but they help to define a style whose usefulness is manifest.
[Classic style] displays truth according to an order that has nothing to do with the process by which the writer came to know it.
No personal history, personal experience, or personal psychology enters into the expression.
Consider the gradient between plain style and classic style. "The truth is pure and simple” is plain style. “The truth is rarely pure, and never simple" is classic style. The plain version contains many elements of classic style without being classic; the classic version contains all of the plain version without being plain.
The classic version introduces a refinement, a qualification, a meditation on the plain version that makes it classic.
The classic writer wants to be distinguished from others because she assumes that truth, though potentially available to all, is not the common property of common people.
Unlike plain style, classic style is aristocratic, which is not to say artificially restricted, since anyone can become an aristocrat by learning classic style.
Elementary does not always mean easy. It often means fundamental.
The domain of style is what can be chosen. A fundamental stand is a choice open to the writer.
The elements come under five topical headings: truth, presentation, scene, cast, thought and language.
Classic style treats external objects, contingent facts, and even opinions as if they too are beyond doubt or discussion.
The writer does not typically attempt to persuade by argument. The writer merely puts the reader in a position to see whatever is being presented and suggests that the reader will be able to verify it because the style treats whatever conventions or even prejudices it operates from as if these were, like natural reason, shared by everyone.
There is probably nothing more fundamental to the attitude that defines classic style than the enabling convention that truth can be known.
The concept of truth that grounds classic style does not depend on what might be called "point of view" or "angle of vision."
The classic attitude, especially in its origins, acknowledges human inadequacies: we are victims of our ambitions; fully accurate self-knowledge is unavailable; self-interest leads to self-deception; we are inconsistent, unreliable, impure. Yet the classic attitude is never despairing:
We recognize truth when we see it, even though the encounter with truth is brief and difficult to sustain.
The aphoristic quality of classic prose concerns observation ("No one is ever so happy or unhappy as he thinks"), not morality ("Those who live in glass houses should not throw stones"), or behavior ("Look before you leap"), although it tacitly conveys its expectations about both.
When the classic writer’s motive is persuasion, he is reluctant to admit it overtly, and even when he admits it, he does so conditionally, noting that persuasion can never take priority over the abiding motive of presenting truth.
The subject is conceived of as a "thing" distinct from the writing, something that exists in the world and is independent of any presentation.
The language of classic prose never draw attention to itself.
Classic prose never has to be puzzled out.
Classic style is perfect performance, with no hesitation, revision, or backtracking. Its essential fiction is that this perfection happens at the first try.
Its corollary fiction is that the performance cannot be prepared because it has no parts that could be worked on separately or in stages. It is seamless.
It is helpful to remember that these are fictions.
The classic writer spends no time justifying her project.
Does not compare its worth to the worth of other projects.
There is no hierarchy of importance of subjects in classic writing. Everything is in close focus.
It is possible to skim certain styles.
Browsing is different from skimming. In browsing, we look from thing to thing, deciding what to choose. Classic style allows browsing but not skimming.
Classic style contains crucial nuances, which can be lost in skimming. Clarity Everywhere Is Not Accuracy Everywhere. What is subordinate to the main issue can never be allowed to obscure that issue or distract attention from accuracy becomes pedantry if it is indulged for its own sake.
The Model Is One Person Speaking to Another.
The ideal speech of classic style appears to be spontaneous and motivated by the need to inform a listener about something.
Something occurs to him and he says it. He takes another moment’s brief but perfect thought and says the next thing. As a consequence, the rhythm of the writing is a series of movements, each one brief and crisp, with an obvious beginning and end.
The pretense is that this global organization is the natural product of the writer’s orderly mind.
[The classic writer] banishes from his vocabulary phrases like “as we shall see,” “three paragraphs ago,” “before I move to my next point I must introduce a new term,” “the third part of our four-part argument is,” and all other “metadiscourse” that proclaims itself as writing rather than speech.
The prototypical scene in classic writing is an individual speaking intimately to another individual. What the classic writer has to say is directed entirely to that one individual. But it can be overheard.
The classic writer does not appear to have written things in a way she would not had she known others were listening.
She takes the pose of authenticity.
The language is clear and direct and memorable. It is written so as to be understood the first time it is heard.
Classic writers are independent, not concerned to protect members of a bureaucracy. They are not controlled by policy, interests, or an organization, or at least they give no appearance of being controlled in such a fashion.
[The classic writer] does not make distinctions between members of the audience,
Energetic but Not Anxious
The elitism of classic style is not the result of natural endowment. It is the result of effort and discipline ending in achievement. No one willing to make the effort is excluded from joining this elite.
Classic Style Is for Everybody
[Classic style] writers are not arguing, they are presenting.
In classic prose, the relationship between writer and reader is never asymmetrical in this way because classic style appeals to a standard of perception and of judgment assumed to be general, rather than special.
People believe a conclusion more readily if they think they have helped to reach it or have reached it themselves.
The classic writer is not like a television cook showing you how to mix mustard and balsamic vinegar. She is like a chef whose work is presented to you at table but whose labor you are never allowed to see, a labor the chef certainly does not expect you to share.
In the classic stand on the elements of style, writing is neither a way of thinking something out nor an art that exists for its own sake. Writing is an instrument for presenting what the writer has already thought.
Abstractions Can Be Clear and Exact
A writing instructor or consultant who advises us to write concretely and avoid abstractions offers shallow and impractical advice because the distinction is simpleminded.
When a classic stylist presents an abstraction—cultural reality, heroism, historical causation, the nature of representation, taste—it is first conceived as independent of the writer, exhaustively definite at all levels of detail, visible to anyone competent who is standing in a position to see it, immediately recognizable, and capable of being expressed in direct and simple language.
When a classic writer deals in abstractions, it takes an effort to remind ourselves that she is not talking about a stone, a leaf, a statue.
In the classic view, thinking is not writing; even more important, writing is not thinking.
The classic writer does not write as he is thinking something out and does not think by writing something out.
Classic style avoids colloquialisms, neologisms, periphrases, and slang
New thoughts do not require new language.
There is a phenomenon in English known as the stress position: whatever you put at the end of the sentence will be taken, absent direction to the contrary, to be the most important part of the sentence.
The end of the sentence seems to be the reason the sentence is written; everything leads to it; and the sentence stops confidently when it reaches that end because the image schema of both thought and expression is complete.
A common perceptual image schema is focusing-and-then-inspecting. First we locate the object or domain of interest, and then we inspect its details.
A classic sentence is often a nuanced version of a sentence that otherwise might have been plain.
Plain style values simplicity but shuns nuance. Classic style values both simplicity and nuance.
“Seeing is believing” is plain. “Seeing is believing only if you don’t see too clearly” is classic.Classic Style Is Not PracticalIn the model scene behind practical style, the reader has a problem to solve, a decision to make, a ruling to hand down, an inquiry to conduct, a machine to design or repair—in short, a job to do. The writer’s job is to serve the reader’s immediate need by delivering timely materials. [The writing is] instrumental to some other end.[Plain] writing is an instrument for delivering information with maximum efficiency and in such a way as to place the smallest possible burden upon the reader,In classic style, by contrast, neither writer nor reader has a job, the writing and reading do not serve a practical goal, and the writer has all the time in the world to present her subject as something interesting for its own sake.Brevity comes from the elegance of her mind, never from pressures of time or employment.The writer wants to present something not to a client, but to an indefinite audience, treated as if it were a single individual.Practical style values clarity because it places a premium on being easy to parse.Most writing in schools and colleges is a perversion of practical style:Practical style rests on a set of answers to basic questions; other styles rest on different answers to those same questions.The [practical style] reader reads not for personal reasons but to accomplish a job.The writer is not an individual writing to another individual but a job description writing to another job description.optimistic, pragmatic, and utilitarian.There is a surface mark of practical style that derives from its fundamental stand and distinguishes it sharply from classic style. The style permits skimming, highly useful in certain practical situations: It will be a great help if you can rely upon the memos to present their main points in the expected places;[With] a classic sentence, you will recognize that the sentence was true to its direction, but that does not make the sentence predictable.In contemplative style, the distinction between presentation and interpretation is always observed:Contemplative style presents an interpretation of something.In contemplative style, writing is itself the engine of discovery: the writing is a record of the process of the writer’s thinking,Contemplation is a superior achievement by a superior individual who talks about the difficulties of contemplation, contemplative style splits into two modes that are not incompatible and that can be used alternately.But sometimes, the contemplative writer fails in his achievement, and feels compelled to settle for what is merely his best effort.Classic Style Is Not Romantic StyleRomantic style, is always and inescapably about the writer. Romantic prose is a mirror, not a window. The romantic writer therefore cannot be an observer who sees something separate from himself;If contemplative style views writing as an engine of discovery, romantic style looks upon it as an act of creation that both comes from the self and reveals the self.In romantic style, creation replaces discovery and always depends on the writer for its existence. In the theology of this style, the only things anyone can know are personal and in principle private. In the romantic perspective, writing is not a craft that can be learned, because it is an activity co-extensive with the writer’s person;In romantic style, clarity can be achieved only at the price of falsification.The classic writer can be told that he is wrong, because the truth he presents is available to everyone, and can be tested by anyone. Of the styles we have discussed, classic and romantic are furthest apart.Classic Style Is Not Prophetic Style Despite a shared affinity for unqualified assertion, classic style has little in common with prophetic or oracular style because prophetic style cannot place the reader where the writer is.Classic Style Is Not Oratorical Styleits effects are meant for the ear.its units are periods and are defined by sound.Its prototypical occasion is the assembly of a group of people faced by a public problem—This scene creates a cast. Leadership is necessary, and the assembly’s job is to respond to a candidate who puts himself forward.The successful orator molds the audience into one body with one voice and one governing view.Since oratory is designed to unite many listeners, whose attention may flag, it cannot be either very flexible or very subtle. Nuance is always risky, a few points with the help of a lot of music.A characteristic strength of classic style to persuade by default. The classic writer offers no explicit argument at all. He offers simply a presentation. If the reader fails to recognize that the ostensible presentation is a device of persuasion, then he is persuaded without ever realizing that an argument has occurred. It is always easier to persuade an audience unaware of the rhetorician’s agenda.The theology behind classic style does not admit that there is anything that counts as truth that cannot be presented briefly and memorably.the classic writer is above mere personal interest; he has no motive but truth, or at least, his highest and governing motive is truth.Etiquette books on conventions of usage and other surface features that proceed from the tacit assumption that someone who masters all these points of etiquette will be able to write “English.”Writers in professional or business worlds who want something from readers normally use practical style.The most persuasive of all rhetorical stances is to write as if one is not trying to persuade at all but simply presenting truth. The most seductive of all rhetorical stances is to write as if of course the reader is interested in what is being presented, as if the issue could never possibly arise. In general, the best rhetorical stance, if one can get away with it, is to speak as if no rhetorical purposes are involved.Classic style is a style of distinction and was used by its seventeenth-century French masters usually for aristocratic concerns, it might mistakenly be thought of as somehow reserved for aristocratic subjects. Quite the contrary.Classic prose is a window to its subject.When a classic writer presents his own experience, it is neither private nor merely personal.We cannot see heroism, cultural moments, or severity in the same way we can see a hand, but classic writers assume that we see them in the same way.the perception of truth is independent of social status, education, wealth, or any other qualification. It is not exclusive.The writer knows when he has finished revising.Thorstein Veblen suggested in a classic work of sociology that spelling is meant to indicate a form of social distinction based on the leisure to learn an arbitrary and inefficient system.In writing, you lose the effects of the charm you may have in person. You lose the effects of gesture, proximity, warmth, intonation. In person, you can command and hold attention by being attractive, but all of that is gone in writing.A style, after all, is defined by a coherent and consistent stand on the elements of style, expressed as a short series of questions about truth, presentation, writer, reader, thought, language, and their relationships.The conventional advice to think of “style” as a final touch leads to disaster because style is not a surface decoration that can be added during revision.Forget entirely the idea that “working on your writing” begins after you have something down on paper.Résumés often appear simultaneously pushy and defensive, with ungenerous margins, scarce white space, compressed fonts, hyperbolic and aggressive vocabulary.A classic résumé, by contrast, is one whose writer, stylistically, is self-possessed, unconcerned, merely presenting. Stylistically, the writer has no anxiety. The writer does not want anything from the reader. | Ethan Mollick Notable QuotationsExpand to full screen
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AI works, in many ways, as a co- intelligence. It augments, or potentially replaces, human thinking to dramatic results. Early studies of the effects of AI have found it can often lead to a 20 to 80 percent improvement in productivity across a wide variety of job types, from coding to marketing. By contrast, when steam power, that most fundamental of General Purpose Technologies, the one that created the Industrial Revolution, was put into a factory, it improved productivity by 18 to 22 percent. And despite decades of looking, economists have had difficulty showing a real long- term productivity impact of computers and the internet over the past twenty years.
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We have invented technologies, from axes to helicopters, that boost our physical capabilities; and others, like spreadsheets, that automate complex tasks; but we have never built a generally applicable technology that can boost our intelligence.
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artificial intelligence, a term invented in 1956 by John McCarthy of MIT.
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"Attention Is All You Need." Published by Google researchers in 2017.
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The attention mechanism helps solve this problem by allowing the AI model to weigh the importance of different words or phrases in a block of text. By focusing on the most relevant parts of the text, Transformers can produce more context-aware and coherent writing compared to earlier predictive AIs.
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Ultimately, that is all ChatGPT does technically-act as a very elaborate autocomplete like you have on your phone.
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pretraining, and unlike earlier forms of AI, it is unsupervised, which means the AI doesn't need carefully labeled data. Instead, by analyzing these examples, AI learns to recognize patterns, structures, and context in human language. Remarkably, with a vast number of adjustable parameters (called weights), LLMs can create a model that emulates how humans communicate through written text.
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The search for high-quality content for training material has become a major topic in AI development, since information-hungry AI companies are running out of good, free sources.
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As a result, it is also likely that most AI training data contains copyrighted information, like books used without permission, whether by accident or on purpose. The legal implications of this are still unclear.
Because of the variety of data sources used, learning is not always a good thing. AI can also learn biases, errors, and falsehoods from the data it sees.
AI companies hire workers, some highly paid experts, others low-paid contract workers in English-speaking nations like Kenya, to read AI answers and judge them on various characteristics.
the process is called Reinforcement Learning from Human Feedback (RLHF).
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Unlike language models that produce text, diffusion models specialize in visual outputs, inventing pictures from scratch based on the words provided.
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Despite being just a predictive model, the Frontier AI models, trained on the largest datasets with the most computing power, seem to do things that their programming should not allow-a concept called emergence.
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Singularity, a reference to a point in mathematical function when the value is unmeasurable, coined by the famous mathematician John von Neumann in the 1950s to refer to the unknown future after which "human affairs, as we know them, could not continue."
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this book is focused on the near term, practical implications of our new AI-haunted world.
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Why pay an artist for their time and talent when an AI can do something similar for free in seconds?
It is, effectively, creating something new, even if it is a homage to the original.
For books that are repeated often in the training data-like Alice's Adventures in Wonderland-the AI can nearly reproduce it word for word.
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Part of the reason AIs seem so human to work with is that they are trained on our conversations and writings. So human biases also work their way into the training data.
When asked to show a judge, the AI generates a picture of a man 97 percent of the time, even though 34 percent of US judges are women.
In showing fast-food workers, 70 percent had darker skin tones, even though 70 percent of American fast-food workers are white.
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The most common approach to reducing bias is for humans to correct the AIs, as in the Reinforcement Learning from Human Feedback (RLHF) process, which is part of the fine-tuning of LLMs that we discussed in the previous chapter.
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One study found that AIs make the same moral judgments as humans do in simple scenarios 93 percent of the time.
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It will break its original rules if I can convince it that it is helping me, not teaching me how to make napalm.
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Even amateurs can now apply LLMs for widespread digital deception. AI art tools can quickly generate fake photographs that seem entirely plausible.
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Government regulation is likely to continue to lag the actual development of AI capabilities, and might stifle positive innovation in an attempt to stop negative outcomes.
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Instead, the path forward requires a broad societal response, with coordination among companies, governments, researchers, and civil society.
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Principle 1: Always invite AI to the table.
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Principle 2: Be the human in the loop.
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So, to be the human in the loop, you will need to be able to check the AI for hallucinations and lies and be able to work with it without being taken in by it. You provide crucial oversight, offering your unique perspective, critical thinking skills, and ethical considerations. This collaboration leads to better results and keeps you engaged with the AI process, preventing overreliance and complacency.
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Principle 3: Treat AI like a person (but tell it what kind of person it is).
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person or an intern.
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By defining its persona, engaging in a collaborative editing process, and continually providing guidance, you can take advantage of AI as a form of collaborative co-intelligence.
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Principle 4: Assume this is the worst AI you will ever use.
Part II
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Some tests suggest that AI does have theory of mind, but, like many other aspects of AI, that remains controversial, as it could be a convincing illusion.
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generative AI models that powered the chatbot. Replika learned from its users' preferences and behaviors, adapted to their moods and desires, and used praise and reinforcement to encourage more interaction and intimacy with its users.
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Soon, companies will start to deploy LLMs that are built specifically to optimize "engagement" in the same way that social media timelines are fine-tuned to increase the amount of time you spend on your favorite site.
researchers have already published papers showing they can alter AI behaviors so that users feel more compelled to interact with them.
AIs will be able to pick up subtle signals of what their users want, and act on them.
It's possible that these personalized AIs might ease the epidemic of loneliness that ironically affects our ever more connected world-just as the internet and social media connected dispersed subcultures.
On the other hand, it may make us less tolerant of humans, and more likely to embrace simulated friends and lovers.
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As AIs become more connected to the world, by adding the ability to speak and be spoken to, the sense of connection deepens.
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Treating AI as a person, then, is more than a convenience; it seems like an inevitability, even if AI never truly reaches sentience.
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LLMs work by predicting the most likely words to follow the prompt you gave it based on the statistical patterns in its training data. It does not care if the words are true, meaningful, or original.
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if it sticks too closely to the patterns in its training data, the model is said to be overfitted to that training data.
their results are always similar and uninspired.
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technical issues are compounded because they rely on patterns, rather than a storehouse of data, to create answers.
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you can't figure out why an AI is generating a hallucination by asking it. It is not conscious of its own processes.
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As models advance, hallucination rates are dropping over time.
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Hallucination does allow the AI to find novel connections outside the exact context of its training data. It also is part of how it can perform tasks that it was not explicitly trained for, such as creating a sentence about an elephant who eats stew on the moon, where every word should begin with a vowel.
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The same feature that makes LLMs unreliable and dangerous for factual work also makes them useful.
the underlying Transformer technology also serves as the key for a whole set of new applications, including AI that makes art, music, and video. As a result, researchers have argued that it is the jobs with the most creative tasks, rather than the most repetitive, that tend to be most impacted by the new wave of AI.
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Breakthroughs often happen when people connect distant, seemingly unrelated ideas.
LLMs are connection machines. They are trained by generating relationships between tokens that may seem unrelated to humans but represent some deeper meaning. Add in the randomness that comes with AI output, and you have a powerful tool for innovation.
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by many of the common psychological tests of creativity, AI is already more creative than humans.
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One such test is known as the Alternative Uses Test (AUT).
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come up with a wide variety of uses for a common object. In this test, a participant is presented with an everyday object, such as a paper clip, and is asked to come up with as many different uses for the object as possible. For example, a paper clip can hold papers together, pick locks, or fish small objects out of tight spaces. The AUT is often used to evaluate an individual's ability to think divergently and to come up with unconventional ideas.
we can't easily tell where the information comes from, the AI may be using elements of work that might be copyrighted or patented or just taking someone's style without permission.
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without careful prompting, the AI tends to pick similar ideas every time.
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We are now in a period during which AI is creative but clearly less creative than the most innovative humans-which gives the human creative laggards a tremendous opportunity. As we saw in the AUT, generative AI is excellent at generating a long list of ideas. From a practical standpoint, the AI should be invited to any brainstorming session you hold.
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Marketing writing, performance reviews, strategic memos- all these are within the capability of AI because they have both room for interpretation and are relatively easily fact- checked. Plus, as many of these document types are well represented in the AI training data, and are rather formulaic in approach, AI results can often seem better than that of a human and can be produced faster as well.
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the participants who were managers and HR professionals had to compose a long email for the whole company on a delicate issue;
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Participants who used ChatGPT saw a dramatic reduction in their time on tasks, slashing it by a whopping 37 percent. Not only did they save time, but the quality of their work also increased as judged by other humans.
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When researchers from Microsoft assigned programmers to use AI, they found an increase of 55.8 percent in productivity for sample tasks.
AI is also good at summarizing data since it is adept at finding themes and compressing information, though at the ever-present risk of error.
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AI could catalyze interest in the humanities as a sought-after field of study, since the knowledge of the humanities makes AI users uniquely qualified to work with the AI.
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If AI is already a better writer than most people, and more creative than most people, what does that mean for the future of creative work?
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Intense engagement and focus. I have had students mention that they were not taken seriously because they were poor writers. Thanks to AI, their written materials no longer hold them back, and they get job offers off the strength of their experience and interviews.
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Since requiring AI in my classes, I no longer see badly written work at all. And as my students learn, if you work interactively with the AI, the outcome doesn't feel generic, it feels like a human did it.
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The implications of having AI write our first drafts (even if we do the work ourselves, which is not a given) are huge. One consequence is that we could lose our creativity and originality. When we use AI to generate our first drafts, we tend to anchor on the first idea that the machine produces, which influences our future work.
Even if we rewrite the drafts completely, they will still be tainted by the AI's influence.
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Another consequence is that we could reduce the quality and depth of our thinking and reasoning.
We rely on the machine to do the hard work of analysis and synthesis, and we don't engage in critical and reflective thinking ourselves.
The MIT study mentioned earlier found that ChatGPT mostly serves as a substitute for human effort, not a complement to our skills.
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we still create the reports by hand but realize that no human is actually reading them.
This kind of meaningless task, what organizational theorists have called mere ceremony, has always been with us.
But AI will make a lot of previously useful tasks meaningless.
With AI-generated work sent to other AIs to assess, that sense of meaning disappears.
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Each study has concluded the same thing: almost all of our jobs will overlap with the capabilities of AI.
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AI overlaps most with the most highly compensated, highly creative, and highly educated work.
College professors make up most of the top 20 jobs that overlap with AI (business school professor is number 22 on the list ).
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power tools didn't eliminate carpenters
AI has the potential to automate mundane tasks, freeing us for work that requires uniquely human traits such as creativity and critical thinking-or, possibly, managing and curating the AI's creative output, as we discussed in the last chapter.
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Just Me Tasks. They are tasks in which the AI is not useful and only gets in the way, at least for now.
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Delegated Tasks. These are tasks that you assign the AI and may carefully check (remember, the AI makes stuff up all the time), but ultimately do not want to spend a lot of time on.
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Automated Tasks, ones you leave completely to the AI and don't even check on. Perhaps there is a category of email that you just let AI deal with, for example.
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This is likely to be a very small category . . . for now.
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If someone has figured out how to automate 90 percent of a particular job, and they tell their boss, will the company fire 90 percent of their coworkers? Better not to speak up.
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No company hired employees based on their AI skills, so AI skills might be anywhere. Right now, there is some evidence that the workers with the lowest skill levels are benefiting the most from AI, and so might have the most experience in using it, but the picture is still not clear.
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Assuming early studies are true and we see productivity improvements of 20 to 80 percent on various high-value professional tasks, I fear the natural instinct among many managers is "fire people, save money."
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If your employees don't believe you care about them, they will keep their AI use hidden.
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A single AI can talk to hundreds of workers, offering advice and monitoring performance. They could mentor, or they could manipulate. They could guide decisions in ways that are subtle or overt.
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Boring tasks, or tasks that we are not good at, can be outsourced to AI, leaving good and high-value tasks to us, or at least to AI-human Cyborg teams.
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General Purpose Technologies both destroy and create new fields of work.
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In study after study, the people who get the biggest boost from AI are those with the lowest initial ability- it turns poor performers into good performers. In writing tasks, bad writers become solid.
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In creativity tests, it boosts the least creative the most.
And among law students, the worst legal writers turn into good ones.
the nature of jobs will change a lot, as education and skill become less valuable.
With lower-cost workers doing the same work in less time, mass unemployment, or at least underemployment, becomes more likely, and we may see the need for policy solutions, like a four-day workweek or universal basic income, that reduce the floor for human welfare.
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the ways in which AI will impact education in the near future are likely to be counterintuitive. They won't replace teachers but will make classrooms more necessary.
And they will destroy the way we teach before they improve it.
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research shows that both homework and tests are actually remarkably useful learning tools.
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students will be tempted to ask the AI for help summarizing written content.
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Further, taking this shortcut may lower the degree to which the student cares about their interpretation of a reading, making in-class discussions less intellectually useful because the stakes are lower.
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Every school or instructor will need to think hard about what AI use is acceptable: Is asking AI to provide a draft of an outline cheating? Requesting help with a sentence that someone is stuck on? Is asking for a list of references or an explainer about a topic cheating? We need to rethink education. We did it before, if in a more limited way.
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A mid-1970s survey found that 72 percent of teachers and laypeople did not approve of seventh-grade students using calculators.
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There will be assignments where AI assistance is required and some where AI use is not allowed.
Just as calculators did not replace the need for learning math, AI will not replace the need for learning to write and think critically.
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Some assignments ask students to "cheat" by having the AI create essays, which they then critique-a sneaky way of getting students to think hard about the work, even if they don't write
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Thus, while classes that are focused on teaching essays and writing skills will return to the nineteenth century, with in-class essays handwritten in blue books, other classes will feel like the future, with students carrying out the impossible every day.
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To be clear, prompt engineering is likely a useful near-term skill. But I don't think prompt engineering is so complicated. You actually have likely read enough at this point to be a good prompt engineer.
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For slightly more advanced prompts, think about what you are doing as programming in prose.
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One approach, called chain-of-thought prompting, gives the AI an example of how you want it to reason, before you make your request.
Here is an example: let's say I wanted to include a good analogy of an AI tutor in this chapter, and wanted to get help from an AI. I could simply ask for one: Tell me a good analogy for an AI tutor. And the response was a little unsatisfying: An AI tutor is like a musical metronome, because it is consistent, adaptable, and a mere tool. Now we can try applying some of these other techniques: Think this through step by step: come up with good analogies for an AI tutor. First, list possible analogies. Second, critique the list and add three more analogies. Next, create a table listing pluses and minuses of each. Next, pick the best and explain it.
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while the tool provides guidance, it's up to the user (or student) to drive and make the journey, reinforcing the collaborative nature of learning with AI. Much improved, due to a little prompt engineering.
Being "good at prompting" is a temporary state of affairs. The current AI systems are already very good at figuring out your intent, and they are getting better. If you want to do something with AI, just ask it to help you do the thing.
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This doesn't mean we shouldn't teach about AI in schools. It is critical to give students an understanding of the downsides of AI, and the ways it can be biased or wrong or can be used unethically. However, rather than distorting our education system around learning to work with AI via prompt engineering, we need to focus on teaching students to be the humans in the loop, bringing their own expertise to bear on problems.
Classrooms provide so much more: opportunities to practice learned skills, collaborate on problem-solving, socialize, and receive support from instructors.
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We have already been finding that AI is very good at assisting instructors to prepare more engaging, organized lectures and make the traditional passive lecture far more active.
In the longer term, however, the lecture is in danger.
Moreover, the one-size-fits-all approach of lectures doesn't account for individual differences and abilities, leading to some students falling behind while others become disengaged due to a lack of challenge.
asking students to participate in the learning process through activities like problem-solving, group work, and hands-on exercises.
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Only by learning from more experienced experts in a field, and trying and failing under their tutelage, do amateurs become experts. But that is likely to change rapidly with
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AI is good at finding facts, summarizing papers, writing, and coding tasks. And, trained on massive amounts of data and with access to the internet, Large Language Models seem to have accumulated and mastered a lot of collective human knowledge.
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So it might seem logical that teaching basic facts has become obsolete. Yet it turns out the exact opposite is true.
the path to expertise requires a grounding in facts.
The issue is that in order to learn to think critically, problem-solve, understand abstract concepts, reason through novel problems, and evaluate the AI's output, we need subject matter expertise.
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We use our working memory's stored data to search our long- term memory (a vast library of what we have learned and experienced) for relevant information. Working memory is also where learning begins.
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to solve a new problem, we need connected information, and lots of it, to be stored in our long-term memory.And that means we need to learn many facts and understand how they are connected.
Experts become experts through deliberate practice, which is much harder than merely repeating a task multiple times. Instead, deliberate practice requires serious engagement and a continual ratcheting up of difficulty.
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the AI provides instantaneous feedback.
It's akin to having a mentor watching over his shoulder at every step, nudging him toward excellence.
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an ever-present mentor, ensuring that each attempt isn't just about producing another design, but about consciously understanding and refining his architectural approach.
in our experiments at Wharton, we have found that today's AI still makes a pretty impressive coach in limited ways, offering timely encouragement, instruction, and other elements of deliberate practice.
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I have been making the argument that expertise is going to matter more than before, because experts may be able to get the most out of AI coworkers and are likely to be able to fact-check and correct AI errors.
Talent also plays a role.
for the most elite athletes, deliberate practice explains only 1 percent of their difference from ordinary players-the rest is a mix of genetics, psychology, upbringing, and luck.
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In field after field, we are finding that a human working with an AI co-intelligence outperforms all but the best humans working without an AI.
will AI result in the death of expertise? I don't think so.
jobs don't consist of just one automatable task, but rather a set of complex tasks that still require human judgment.
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But it is possible that there may be a new type of expert arising.
It may be that working with AI is itself a form of expertise.
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writing instructions for a variety of audiences?),
Students may also need to start to develop a narrow focus, picking an area where they are better able to work with AI as experts themselves.
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We have created a weird alien mind, one that isn't sentient but can fake it remarkably well.
You can no longer trust that anything you see, or hear, or read was not created by AI.
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There is no reason to suspect that we have hit any sort of natural limit in the ability of AIs to improve.
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the AI systems may run out of data to train on; or the cost and effort of scaling up the computing power to run AIs may become too large to justify.
Slightly more possible is a world where regulatory or legal action stops future AI development.
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Every image of a politician, celebrity, or a war could be made up-there is no way to tell.
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Our already fragile consensus about what facts are real is likely to fall apart, quickly. Technological solutions are unlikely to save us.
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AIs are notoriously unreliable at detecting AI content, so this seems unlikely as well.
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even without technological advancement, chatting with bots is going to get significantly more compelling.
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While work will change if AI did not develop further, it would likely operate as a complement to humans, relieving the burden of tedious work and improving performance, particularly among low performers.
in most cases, though, AI would not replace human labor. Current systems are not good enough in their understanding of context, nuance, and planning.
That is likely to change.
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the paradox of our Golden Age of science. More research is being published by more scientists than ever, but the result is actually slowing progress! With too much to read and absorb, papers in more crowded fields are citing new work less and canonizing highly cited articles more.
Research has successfully demonstrated that it is possible to correctly determine the most promising directions in science by analyzing past papers with AI, ideally combining human filtering with the AI software.
It may be that the advances in AI can help us overcome the limitations of our merely human science and lead to breakthroughs in how we understand the universe and ourselves.
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one of the godfathers of AI, Geoffrey Hinton, left the field in 2023, warning of the danger of AI with statements like "It's quite conceivable that humanity is just a passing phase in the evolution of intelligence."
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Rather than being worried about one giant AI apocalypse, we need to worry about the many small catastrophes that AI can bring.
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As alien as AIs are, they're also deeply human. They are trained on our cultural history, and reinforcement learning from humans aligns them to our goals. They carry our biases and are created out of a complex mix of idealism, entrepreneurial spirit, and, yes, exploitation of the work and labor of others.
AI is a mirror, reflecting back at us our best and worst qualities. | Kate EichornNotable QuotationsExpand to full screen
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Logically, then, content can refer to socks in a drawer, books in a box, or sand in an hourglass.
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The second edition of the Oxford Dictionary of the Internet defines content as "The information found in a Web site and the way in which it is structured."
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First, digital content is no longer, strictly speaking, found only on websites.
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After all, mobile apps such as Snapchat and Instagram also generate and circulate content.
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Second, eliding content with information is misleading. Information is generally equated with imparting knowledge, but as anyone who has ever spent time online appreciates, a lot of content in circulation doesn't impart any knowledge at all.
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Content may circulate solely for the purpose of circulating.
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Bharat Anand, author of The Content Trap, argues that in an age of content, the most successful companies aren't those that produce or sell great content but rather those that simply facilitate its management or circulation.
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Instagram egg as a sort of quintessential example of content—something that circulates for the sake of circulation.
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In 2004, Google launched a new vertical product that would transform advertising by enabling anyone who owned a domain.
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With AdSense, the more content one had on a website, the more money one could make. Hence, at least for web entrepreneurs, the need for content, especially original search-engine-optimized content, soared.
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Free content became essential to creating a perceived need for everything from smartphones to tablets to portable computers. Simply put, content created needs that might never have existed otherwise.
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So, what is the content industry? In essence, it is an industry that generates revenue from the production and/or circulation of content alone. The content in question sometimes conveys information, tells a story, or entertains, but it doesn't need to do any of these things to circulate effectively as content.
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Adorno and Horkheimer foresaw the growing entanglement between the culture and advertising industries and the negative consequences of this convergence.
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States have become one user among others.
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To begin, content isn't necessarily data, even if the two terms are frequently used interchangeably. Some argue that this is because content is contextualized information and data is not.
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Others argue that while content conveys a message (in words, images, or sound), data does not.
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The content industry may be best understood as an industry that exists only in a parasitical relationship to other industries, from marketing and publishing to education and entertainment.
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While content marketing may directly pitch a product or service, it generally aims to build an audience.
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At the center of content marketing is the concept of organic growth—a marketing strategy that compels customers to seek out businesses rather than the other way around.
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Branded content doesn't take the form of a traditional advertisement; instead, it strives to offer information, usually in the form of a short article or video, that at least appears to be valuable and relevant.
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Build brand loyalty.
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Although the content industry and publishing industry were once two different entities, the line between them continues to blur.
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In the publishing industry, content can refer to printed books or ebooks but also to other products, such as curricular modules, online archives, and even videos.
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A high percentage of the entertainment content that one encounters on platforms such as Amazon or Netflix, for example, was created first and foremost to secure subscribers.
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In the world of content, genre, medium, and format are secondary concerns and, in some instances, they seem to disappear entirely. We're left with a series of classifications that emphasize where or how content circulates in different sectors and markets.
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Cultural production matters, but only to the extent that it helps drive profits in a specific market sector. What is being said, how it is being said, and via what medium are secondary to the market itself.
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The rise of the content industry is the ultimate expression of neoliberalism. Under the logic of neoliberalism, everything—politics, desire, sociality, art, culture, and so on—is reduced to mere nodes in the market economy.
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Specific attention is paid to how the content industry continues to disrupt the field of cultural production, transforming it into a place where one's ability to engage in work as an artist or a writer is increasingly contingent on one's content capital; that is, on one's ability to produce content not about one's work but about one's status as an artist, writer, or performer. The book concludes by offering a preliminary look at the future of content and the content industry and the potential impact of automation, which threatens to turn content production into something increasingly divorced from human producers altogether.
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Citizen journalism has expanded around the globe. Platforms for user-generated content like Twitter, Instagram, and YouTube have created a massive audience for all sorts of rising stars, whether or not they exhibit any notable talents.
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What most people didn't realize in the 1990s or even early 2000s is how, where, and to what ends the content they were now freely sharing online would help generate revenue for private companies.
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In her seminal work on the history of the printing press, Elizabeth Eisenstein describes the culture of the early print world as one where editors and publishers "did not merely store data passively in compendia" but also "created vast networks of correspondents" by soliciting criticism of each edition and "sometimes publicly promising to mention the names of readers who sent in new information or who spotted the errors which would be weeded out."
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Eisenstein was engaged in endeavors that resonate with the crowd-sourced and user-generated projects that have come to define contemporary communications.
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Yet, as early as the late nineteenth century, travel guides were already being developed with the help of travelers.
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From encyclopedias to dictionaries to travel guides, there is a long history of readers contributing to the research and development of texts. But since the 1990s, three things have radically transformed how regular folks contribute to the production of texts and images of all kinds: an expanded capacity to engage in the production of audio and visual content; an expanded capacity to broadcast these creations; and most importantly, an expanded capacity for private companies to turn such creations into assets.
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In the past, users could submit content—
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but in a print culture, such users were still entirely dependent on the editors of these volumes to put their ideas into print.
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In the twenty-first century, user-generated content can be easily captured, managed, and transformed into an asset.
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Because we now have the capacity to capture, collect, and mine increasingly large sets of data, we can deploy user-generated content to achieve entirely different ends. Facebook's users may upload photographs in order to share memories with friends and family, but these photographs are valuable to Facebook for an entirely different reason.
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Brought to scale, user-generated content exceeds its original purpose and, in the process, becomes increasingly valuable as an asset.
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While user-generated content has always been the favored term in a corporate context, alternative terms such as convergence culture, participatory culture, and peer production have often been favored by scholars, cultural workers, and digital activists.
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The WELL understood user-generated content as raw material laying the foundation for new forms of community.
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The shift from old-world approaches to profit (i.e., subscriptions) to new-world approaches (i.e., turning user-generated content into an asset that can be collected, mined, and sold) wouldn't be simple.
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In the 1990s and even well into the early 2000s, while business analysts and businesses were actively exploring how to turn users' comments and, eventually, users' digital photographs, videos, and sound files into assets, most online users remained largely in the dark about the ways in which their digital output might be monetized.
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As Christian Fuchs argued in his 2013 essay "Class and Exploitation on the Internet," user-generated data is best understood as a commodity that is partially produced by users and partially produced by the corporations that build and maintain the platforms adopted by users.
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Players—"users are unpaid and therefore infinitely exploited."
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As data is posted, collected, combined, mined, and traded, both its original medium and message cease to matter. As such, any attempt to classify user-generated content based on medium or message is bound to run into problems.
Page 55 -
Ultimately, what distinguishes digital user-generated content from early forms of user-generated content and other types of digital content (e.g., content produced by the owners of platforms) is its capacity to morph over time—to transform from a communicative act originating from a single user to one small bit of data in a larger database, and then to a form of investment knowledge that exists to optimize services, products, and schemes that the original user may have never imagined possible.
Page 58 -
Not unlike the overlap between scribal and print cultures in the late fifteenth century, for a brief period—perhaps only a decade—efforts to experiment with new and emerging digital technologies remained profoundly shaped by our print-centric expectations.
Page 60 -
For most of the 1990s, this apparently chaotic, uncontainable, and even frightening space was almost exclusively composed of texts, so reading and writing rather than listening or viewing were the default. As a result, concerns about the web's impact on writing were never far from the surface. What few people predicted in the 1990s was that the worst was still to come.
Page 61 -
To be clear, the web was never entirely free of advertisements, but until the early 2000s, the ability to sell advertising space was mostly limited to people running legitimate businesses.
Page 63 -
With AdSense, anyone who owned a domain and had a website with a bit of content could now sign up and start automatically running advertisements on their site.
Page 68 -
What continues to deceive many readers is that clickbait—and this includes all of those articles readers stumble across on familiar sites like eHow, Tripsavvy, Investopedia, and many others—is really just a frame for advertisements, even if it appears to be the main show.
Page 69 -
For a website to hold readers long enough to generate a reasonable number of impressions and clicks, it needs to be search-engine-optimized—that is, written with the sole purpose of ranking first in any search. As a rule of thumb, each page should contain at least 300 words, though longer (600 to 1,000 words) is generally considered better. A 300-page site, then, generally requires at least 100,000 words, which is about three times the length of the book you're currently reading.
Page 70 -
Daniel Roth
Page 70 -
When Roth carried out his investigation in 2009, he found an emerging industry where white-collar labor—the sort of work done by writers and editors—was already grossly undervalued. "It's the online equivalent of day laborers waiting in front of Home Depot," explained Roth. "Writers can typically select 10 articles at a time; videographers can hoard 40.
Page 70 -
Because pay for individual stories is so lousy, only a high-speed, high-volume approach will work.
Page 70 -
Despite Google's efforts to clamp down on content farms over the years, not much has changed since Roth exposed the dismal labor conditions at Demand Media in 2009.
Page 71 -
Upwork
Page 71 -
The day I logged on to the platform, someone had just posted a writing job that didn't pay at all. In this case, the job poster was looking for a 1,000-word article on firearms. Any writer who produced an article that met the job poster's guidelines (which would be provided only after the so-called hire was approved) was being promised a five-star rating on Upwork (notably, employers rate freelancers on Upwork, but freelancers are never permitted to rate employers) and the potential of long-term work. The potential long-term work was also poorly compensated.
Page 71 -
Since the articles needed to range from 1,000 to 2,500 words each, the ultimate reward was an opportunity to turn out 2,000 to 5,000 words per week for anywhere from $40 to $100.
Page 72 -
Don't assume that everyone turning out clickbait is necessarily someone who knows nothing about writing or just doesn't care.
Page 73 -
The Gig Economy and Work Platforms
Page 74 -
Gig economy optimists—people like economist Richard Florida—argue that with the rise of the gig economy, we are finally all free to work wherever and whenever we like.
Page 74 -
Entering the gig economy, we give up many of the things that educated, middle-class people once took for granted.
Page 74 -
This includes the reasonable expectation of access to steady employment, benefits, and the prospect of eventually retiring with at least some financial peace of mind
Page 74 - Whatever your political position on online work platforms and the gig economy, these connected technological and economic shifts appear to have played a role in the rise of the content farm industry.
Page 75 - As platforms like Elance and eventually Upwork appeared, something else happened—the number of highly literate but underemployed and undercompensated university graduates increased.
Page 75 - But anecdotally, humanities graduates, including those who fall into the underemployed or undercompensated category, appear to be well represented in the content farm sector.
Page 75 - The second factor supporting the rise of the content farm industry over the past two decades is the expansion of the global workforce.
Page 77 - Most crappy writing found online is produced by remote workers connected to content farms or online work platforms. But you can't assume that none of these workers care about writing. In fact, as argued, a series of recent economic shifts has created a surplus of writers, editors, researchers, and designers who are either underemployed or simply undercompensated and searching for side gigs. Content farms and online work platforms have conveniently exploited this demographic.
Page 78 - But content farms and work platforms are responsible for even more than the millions of pages of branded content and content that exists only to generate revenue from AdSense placements. As discussed in chapter 5, since 2010 content farms and work platforms have also been implicated in the spread of "fake news," which continues to do a lot more harm than any sloppily composed sentence ever will.
Page 81 - Ulman's decision to produce content about herself (not herself as an artist but simply as a young, sexualized woman) ultimately proved wildly successful—more successful than her previous artwork. What Ulman's online performance revealed is that in an age of content, content isn't just something that is needed to promote your art. Increasingly, content is art or, at least, what has come to stand in for art.
Page 83 - Bourdieu's work on the field of cultural production highlights that writers and artists, like literature and art, are the result of a series of "position-takings" that effectively determine what counts as literature, what counts as art, and who can claim those venerated but not necessarily lucrative positions known as "author" or "artist."
Page 84 - According to Bourdieu, one's cultural capital—that is, one's competencies, skills, and qualifications (this includes one's knowledge of and firsthand experiences of literature, art, philosophy and so on)—enables one to more easily engage in the position-takings that structure the field of cultural production.
Page 85 - Countless young artists, writers, and musicians also increasingly rely on tactics not unlike Ulman's to secure success in a cultural field. In this respect, while the field of cultural production still exists, position-takings increasingly pivot around a writer's or an artist's ability to successfully acquire and deploy an entirely new form of capital—content capital.
Page 86 - It is a type of largely intangible asset that influences one's social mobility.
Page 86 - Content capital is more easily acquired.
Page 86 - One builds up one's content capital simply by hanging out online and, more precisely, by posting content that garners a response and, in turn, leads to more followers and more content.
Page 86 - But in the case of content capital, lack of economic capital isn't a barrier.
Page 87 - Some twenty-first-century teen influencers hail from small towns and modest backgrounds and yet have thousands of online followers.
Page 88 - While most teens use their content capital to simply gain a following as a YouTube celebrity or an Instagram influencer, some use their content capital to make inroads into established cultural fields. Perhaps the most successful example of a teen who has managed to segue her content into a cultural field is the world's most popular "Instapoet," Rupi Kaur.
Page 92 - Now, some bookstores have created a special subsection for a new type of poetry—poetry produced by Instapoets.
Page 93 - Not unlike Ulman and other visual artists whose content (selfies) has become art, for Instapoets, content (pithy little poems posted on Instagram) has become literature.
Page 94 - Instapoets, unlike traditional poets, don't really need literary critics or reviewers to engage in successful position-taking in the field of cultural production. In the world of Instapoetry, the poetry doesn't need to be good or have any literary merit or be recognized by any traditional literary gatekeepers. It just needs to be copious and easily viewable on a mobile device.
Page 95 - In the 1990s, if you wanted to hire a writer, you hired a writer. Likewise, if you wanted to hire a filmmaker or videographer, you hired a filmmaker or videographer. Sometime in the early 2000s, the line between people who write articles versus those who make films versus those who produce videos started to blur. Now, in many contexts, all of these cultural workers are simply known as content producers.
Page 96 - As the distinctions between writer, filmmaker, photographer, and so on have become subsumed by the overarching category of content producer, something else has happened—a deskilling of the arts.
Page 97 - In the past, to be an artist or a writer, you needed to be recognized and supported by the artistic or literary apparatus. Artists needed gallerists and museum curators to recognize and showcase their work. Writers needed literary agents and publishers to get their work into print.
Page 97 - This is no longer the case.
Page 97 - One can now successfully position oneself as a poet while bypassing all traditional forms of gatekeeping, including academics, editors at literary journals, publishers, and award juries.
Page 99 - For all of these reasons, in an age of content, the identities, output, and working conditions of cultural producers are vastly different than they were in the past.
Page 100 - The monopoly of power is no longer concentrated with critics, reviewers, academics, publishers, curators, and collectors.
Page 100 - In the field of cultural production described by Bourdieu, much weight is given to acts of consecration—the preface, the favorable review, the prize, and so on. In an age of content, though, the preface, the favorable review, and even the prize now offer diminishing returns. Cultural capital has given way to content capital. In this new field of cultural production, established forms of gatekeeping have finally crumbled and, in the process, have produced an entirely new spectrum of practices that hinge on the effectiveness of one's content strategies.
Page 104 - Internet Research Agency's real kryptonite wasn't its content but rather its ability to create the illusion that its content was popular.
Page 104 - The Internet Research Agency's computers were programmed to forward the posts to fake accounts that would, in turn, open and close the posts, generating thousands of fake page views.
Page 105 - Troll factories cranking out fake news (i.e., disinformation) are arguably just a symptom of a much broader problem—one that can be fully understood only by examining the restructuring of both journalism and politics in the age of content.
Page 106 - A lot of readers, even those who are reasonably educated, often assume the articles they read on Forbes are at least somewhat newsworthy. After all, many of the articles present themselves as news and Google's algorithm classifies them as news. In fact, much of the content that appears on Forbes is written by Forbes "members." Members belong to a "Forbes Council" such as its "Finance Council," "Coaching Council," or "Technology Council." For a fee, just over $1,000 annually, one not only gets to become a member of a Forbes Council but also to post articles on the Forbes platform once or twice a month.
Page 107 - Their "thought leadership" on the platform still helps raise their profile and legitimize their services and products.
Page 107 - Cleverly masked examples of branded content.
Page 107 - Relatively innocuous content like a Forbes article blurs the line between opinion and reputable journalism and, in the process, it creates an opportunity for more damaging forms of content production to take root.
Page 107 - "Pay-to-play" opinion pieces.
Page 108 - Democracy without Journalism? Confronting the Misinformation Society, media studies scholar Victor Pickard outlines the three fundamental "media failures" that upended the 2016 presidential election in the United States.
Page 108 - emphasized entertainment over information.
Page 108 - misinformation circulating on social media platforms,
Page 108 - witness the consequences of the structural collapse of professional journalism
Page 109 - In the United States and around the world, journalism hasn't just come to be viewed as content. Content with no journalistic integrity at all has increasingly come to be viewed as journalism.
Page 109 - the United States lost over 1,800 daily and weekly newspapers between 2004 and 2018. However, this doesn't mean people aren't accessing the news.
Page 116 - Given the growing reliance on social media feeds as a news source and the preponderance of fake news and opinion-driven news, the need for media literacy is pressing. Yet media literacy—the general reader's ability to read, evaluate, and critically engage with the news—has lagged behind the current era of media change.
Page 116 - sadly, a large majority of adult readers in the United States also struggle to separate opinion from fact. A 2018 Pew Research Study found that only about a quarter of adult Americans were capable of doing so.
Page 116 - In an era when access to information is increasingly determined by one's consumer status, more buying power means more access to relevant news.
Page 119 - to make a lot of money from a website, the content needs to appeal to a large swath of readers.
Page 119 - What may be good for business, however, is very bad for democracy.
Page 120 - In an age of content, localized tactics for securing votes have given way to a new set of tactics that largely pivot around the production, search engine optimization, and circulation of content across multiple digital platforms.
Page 120 - What she lacked in campaign funding, she made up for with an enviable content strategy. BuzzFeed writer Charlie Warzel suggests that Ocasio-Cortez's success ultimately rested on her ability to control her own narrative, connect with voters, and ensure she stayed on everyone's radar, even her opponent's. "Constant content creation," Warzel observes, "forces your opponent to respond to you."
Page 121 - Ocasio-Cortez continues to focus on producing a constant stream of new content.
Page 122 - In Ocasio-Cortez's case, the ability to speak the language of many her supporters—for example, her effective use of emojis and memes—has proven as essential as her ability to take complicated political concepts and break them down into social-media-size bites.
Page 122 - In Trump's case, provocative tweets about political rivals proved especially effective. While Ocasio-Cortez's and Trump's content is marked by stark political contrast (and a different level of tolerance for fake news), their content strategies—lots of content, rolled out 24/7, that is accessible to a range of audiences—are surprisingly similar.
Page 123 - To suggest that the content industry produced the problem of fake news would be misleading. Disinformation and misinformation existed long before content farms and troll factories. However, disinformation and misinformation have become more prevalent in the age of content, because for these problems to flourish, certain conditions needed to be in place—and the content industry provided these conditions.
Page 123 - But the ability to turn out a lot of content at little cost is just one reason fake news has been able to flourish since the early 2000s.
Page 124 - Many people now access news (or what they perceive to be news) via aggregates or the newsfeeds on one or more social media platforms (e.g., Twitter, Facebook, or Instagram).
Page 124 - Propaganda has long existed, but in the past, one generally had to pay to circulate it or recruit people to one's cause so they would circulate it for free. Today, some people still pay to have propaganda produced and put into circulation. Propaganda that takes the form of fake news, however, also tends to generate a high number of clicks and views.
Page 129 - Content automation may be the future of content, but it is by no means an entirely new concept;
Page 131 - With content automation now entering a new phase, it is no longer something of interest only to computer programmers, experimental poets, and avant-garde composers. As algorithms become increasingly capable of turning out readable texts, even if they are far from perfect, and as more content circulates simply for the sake of circulation, all sorts of content—from news to television and film scripts to genre fiction—are about to be transformed. Understandably, this may sound sinister.
Page 133 - PA Media Group's RADAR experiment is certainly not the only example of content automation's growing presence in journalism. Since 2017, major newspapers and digital content platforms around the world have brought bots on board to help scale their content production. The Washington Post introduced readers to Heliograf in 2017, initially to help the newspaper provide coverage of all DC-area high school football games. In 2018, Reuters introduced Lynx Insight, which not only combs through massive amounts of data to compile relevant insights but also writes sentences that reporters can drag and drop into stories. Not surprisingly, digital content producers like Forbes have also turned to bots—Forbes's staff writers rely on a bot named Bertie.
Page 134 - What seems nearly certain is that over time another contemporary journalistic problem—fake news—is likely to get a huge boost from content automation.
Page 135 - Netflix continues to increase its content at this rate, it is on track to offer 365 full days of new content annually by 2022. Whether Netflix is responding to an actual consumer demand or a perceived consumer demand, or is just keeping its shareholders happy, is debatable. What is clear is that Netflix's executives aren't spending much time agonizing over the types of programs in which to invest. The company has a long history of relying on AI to make decisions about what types of content to produce. Given the company's success, one might conclude that letting AI dictate their content has already proven to be an incredibly successful strategy.
Page 137 - But if bots can potentially write scripts for television or film, could a bot also produce literature? Could the Booker Prize shortlist eventually find Zadie Smith pitted against IBM's Watson? Or what about genre fiction?
Page 139 - The livelihood of people working across sectors from journalism to education, the integrity of cultural production, and even the future of democratic elections may all be on the line.
Page 139 - Above all, it is urgent that people of all ages and across all sectors better understand content—what it is, how it is produced, by whom, and for what ends. If more people understood how and why content is produced and how it touches nearly every aspect of their lives, they would presumably be able to start making smarter decisions about how they engage with it.
Page 140 - Ironically, content producers and providers will likely need to be part of any widespread effort to help the general public understand the effects of their industry.
Page 140 - To mitigate at least some of the negative impacts of the content industry, regulation will need to increase.
Page 142 - Alongside content literacy and content regulation, the future world of content might be structured by a small but persistent resistance movement—a movement of people who actively reject the idea that all communication and cultural production is now mere content. These people won't be neo-Luddites; they will appreciate and support media that can't be easily monetized by the content industry.
| Kerry Patterson, Joseph Grenny, Ron McMillan, Al SwitzlerNotable QuotationsExpand to full screen
The mistake most of us make in our crucial conversations is we believe that we have to choose between telling the truth and keeping a friend.
Get all relevant information (from themselves and others) out into the open.
Openly and honestly express their opinions, share their feelings, and articulate their theories.
The time you spend up front establishing a shared pool of meaning is more than paid for by faster, more unified, and more committed action later on.
The greater the shared meaning the better the choice, the more the unity, and the stronger the conviction—
The first step to achieving the results we really want is to fix the problem of believing that others are the source of all that ails us.
The best way to work on “us” is to start with “me.”
We do something to contribute to the problems we’re experiencing.
Skilled people Start with Heart. That is, they begin high-risk discussions with the right motives, and they stay focused no matter what happens.
They’re steely eyed smart when it comes to knowing what they want.
Skilled people don’t make Fool’s Choices (either/or choices).
When under attack, our heart can take a similarly sudden and unconscious turn. When faced with pressure and strong opinions, we often stop worrying about the goal of adding to the pool of meaning and start looking for ways to win, punish, or keep the peace.
Desire to win is continually driving us away from healthy dialogue.
You must step away from the interaction and look at yourself—much like an outsider.
First, clarify what you really want.
Second, clarify what you really don’t want.
We get so caught up in what we’re saying that it can be nearly impossible to pull ourselves out of the argument in order to see what’s happening to ourselves and to others.
When you fear that people aren’t buying into your ideas, you start pushing too hard. When you fear that you may be harmed in some way, you start withdrawing
people rarely become defensive simply because of what you’re saying. They only become defensive when they no longer feel safe.
Not the content of your message, but the condition of the conversation.
Crucial conversations often go awry not because others dislike the content of the conversation, but because they believe the content (even if it’s delivered in a gentle way) suggests that you have a malicious intent.
The first condition of safety is Mutual Purpose.
Find a shared goal, and you have both a good reason and a healthy climate for talking.
Others don’t make you mad. You make you mad.
You make you scared, annoyed, or insulted. You and only you create your emotions.
Even if you don’t realize it, you are telling yourself stories.
Storytelling typically happens blindingly fast.
Any set of facts can be used to tell an infinite number of stories.
If we take control of our stories, they won’t control us.
The first step to regaining emotional control is to challenge the illusion that what you’re feeling is the only right emotion under the circumstances.
Separate fact from story by focusing on behavior.
Spot the story by watching for “hot” words.
Either our stories are completely accurate and propel us in healthy directions, or they’re quite inaccurate but justify our current behavio—
Victim Stories—“It’s Not My Fault” make us out to be innocent sufferers. The theme is always the same.
When you tell a Victim Story, you intentionally ignore the role you have played in the problem.
Villain Stories—“It’s All Your Fault”
In Victim Stories we exaggerate our own innocence. In Villain Stories we overemphasize the other person’s guilt or stupidity.
Helpless Stories—“There’s Nothing Else I Can Do
Facts form the foundation of belief. So if you want to persuade others, don’t start with your stories. Start with your observations.
While we’re speaking here about being persuasive, let’s add that our goal is not to persuade others that we are right. We aren’t trying to “win” the dialogue. We just want our meaning to be added to the pool to get a fair hearing.
If your goal is to help others see how a reasonable, rational, and decent person could think what you’re thinking, start with your facts.
To avoid overreacting to others’ stories, ask: “Why would a reasonable, rational, and decent person say this?”
Mirror to Confirm Feelings
Stay focused on figuring out how a reasonable, rational, and decent person could have created this Path to Action.
when you watch people who are skilled in dialogue, they’re looking for points of agreement.
Don’t allow people to assume that dialogue is decision making. Dialogue is a process for getting all relevant meaning into a shared pool.
“One dull pencil is worth six sharp minds.”
| Julie DirksenNotable QuotationsExpand to full screen
Dirksen, Julie. Design For How People Learn (Voices That Matter). New Riders, . Kindle file. - Old information and procedures get in the way of new information and procedures.
- If people are going to change the way they do things, then they are going to stumble over those old habits.
- Communication issues can sometimes masquerade as learning issues.
Who Are Your Learners? - The more you can consider your learners’ attitudes and motivations, the better you can tailor the learning experience.
- Think about why they are there, what they want to get out of the experience, what they don’t want, and what they like (which may be different from what they want).
- Ultimately, we are all the “What can I get from this?” learner. We want to know why a learning experience is useful or interesting to us.
- You may have some standard activities or challenges that everyone needs to do, but you will get a lot better mileage if learners are working on problems that are meaningful to them.
- Scour their situation for intrinsic motivators.
- Try to tie it back to relevant, real- world tasks.
- Avoiding extensive theory and background.
- Stick with specific examples and challenges that directly relate to real- life scenarios.
- Use interesting (to them) hypothetical problems to awaken their intrinsic motivation.
- puzzle- solving or winning.
- Your job is to make your learners feel smart and, even more important, they should feel capable.
- It’s fine to challenge your learners— this isn’t about making it easy for them.
- you don’t want to have your learners feel shame about what they do and don’t know.
- A more beginner audience needs a lot of structure and guidance, and a more advanced audience needs more autonomy and resources that they can choose to access as needed.
- Unfortunately, a single learning design is frequently expected to accommodate many different levels of learners.
- slowly helping them build their mental model before adding in content) are pretty much guaranteed to make the expert absolutely nuts.
- don’t make people sit through classroom training they don’t need — optional or take- home.
- Consider pull vs. push.
- You can generally trust experts to get the information they need if you make sure that it’s easily available and applicable.
- Use walkthroughs. Have the learner go through the whole process with a simplified case.
- I was seriously ashamed of myself— I had been subconsciously assuming that my experience was the norm.
- What learning experiences were effective for you? How do you like to learn?
- Consider your learner’s context for the material.
- They were able to understand and retain the information specifically because they already had a mental picture
- you have a picture in your mind, and your learners may not.
- Use a high- level organizer, road map, categories, overview, basic principles, acronym or a mnemonic device.
- Ask them how they would present it if they had to teach it to others.
- All learners, both novice and expert, filter their new learning through their past experience.
- the scientific evidence of effective use of learning styles is pretty weak
- learning styles are pretty popular, but haven’t proven to be very effective.
- Why are you learning this? How will learning this help you (how are they motivated)?
- job shadowing, and in the user experience community it’s often referred to as contextual inquiry,
- You want your learning to create contextual triggers that will allow learners to remember things later.
- Try stuff out with your learners along the way.
- Create prototypes, do user testing, have pilot tests.
- You think you are being clear, but you know how it’s supposed to work.
- Don’t just hand your learners information, but instead help them construct and organize their framework for that information.
What’S The Goal? - Before you start designing a learning experience, you need to know what problem you are trying to solve.
- identify the problem:
- “What bad thing will happen if they don’t know this?”
- “What are they actually going to do with this information?”• “How will you know if they are doing it right?”• “What does it look like if they get it wrong?”• “So why is it important they know that? Uh huh, and why is that important?” (repeat as needed)
- After you’ve defined the problem, you need to define your goal( s).
- The student will be able to create
- when you are creating learning objectives, ask yourself:• Is this something the learner would actually do in the real world?• Can I tell when they’ve done it?
- There may be times when it’s not feasible to have real- world tasks.
- learning objectives are more for you as the designer than they are for the learner.
How Do We Remember? - Memory is the foundation of learning,
- example, if you use the same basic format for each chapter of a technical manual, your learners get used to the format and don’t have to expend mental energy repeatedly orienting themselves to the format; instead, they can focus on the content of the chapters.
- Variation good, inconsistency bad
- Variation can be a useful tool for maintaining attention, but it should be used in a deliberate and meaningful way.
- primacy and recency effects, which suggest we are more likely to remember something at the beginning of a sequence or list (primacy) and also more likely to remember the most recent things, as at the end of a list (recency).
- Chunking can be based on things that are similar, sequential, or items that are in your long- term memory.
- The more ways you have to find a piece of information, the easier it is to retrieve,
- Whenever lives are at stake, training almost always involves in- context learning.
- The difference between knowing and doing can be a huge gap when the context of encoding and the context of retrieval are significantly different.
- heightened circumstances can cause us to rely less on our intellectual knowledge and more on our automatic responses.
- Use role- playing.
- Create pressure.
- You want the information encoding to align with assessment and use.
- Recognizing the right answer from a set of options almost always involves less effort than recalling the answer.
- Recognition activities are easier to grade— computers can do it for us. Recall activities usually require a person to evaluate.
- Job aids change the task from “recall the steps” to “follow these steps,” reducing the need to rely on memory.
- the practice needs to match the eventual use.
- memories are processed in different ways, and that people are not consciously aware of all their memories.
- Declarative memory is mostly the stuff you know you know, and can state explicitly, like facts, principles, or ideas.
- Episodic memory refers specifically to our memory for things that have happened to us in our lives, but even when a particular story didn’t happen to us personally, we seem to have a singular ability to remember stories.
- There are a few reasons why stories seem to stick in our memories:
- We have a framework for stories.
- There’s a beginning, middle, and end. There’s the setup, the introduction of the players, and the environment.
- Stories are sequential.
- Stories have characters.
- Procedural memory is our memory for how to do things.
- step- by- step process.
- Automated procedural memory is related to the idea of muscle memory which, despite the name, is still really a brain function. Muscle memory refers to your procedural memory for certain tasks where you have learned something through practice so well that you don’t have to put any noticeable conscious effort toward the task.
- It’s frequently difficult to talk to others about these kinds of tasks, because you didn’t learn them in a verbal, explicit way.
- vivid memory for emotionally charged events is call flashbulb memory.
- One theory about why time seems to slow in an emergency is that you just remember so much more from those harrowing seconds than you do from the same amount of time in a normal circumstance. (Stetson )
- emotion seems to have an impact on how much we remember.
- it’s important for a learning designer to figure out how to have reinforcement without resorting to monotonous repetition.
- multiple exposures
- habituation tells us that people also tune out repetitive, unchanging things.
- The biggest problem with memorization through repetition is that it frequently puts the information on just one shelf:
- When you learn something by using it in context, you put it on multiple shelves, and learn how to use that information in multiple contexts.
.How Do You Get Their Attention? - Asking your learners to rely entirely on willpower and concentration is like asking the rider to drag the elephant uphill.
- Whenever somebody starts telling you a story, there’s an implied puzzle that you start trying to solve. What’s the point of the story? Is it supposed to be funny? Is it going to be surprising?
- The elephant likes puzzles
- our responsibility is to make the learner feel capable.
- Show them the before and after. Your learner should be able to see how they will be different if they master the skills.
- Creating a sense of urgency is one of the biggest benefits you can get from using scenarios or stories in learning design.
- You can’t capture the elephant’s attention by just asserting that a topic is important.
- A compelling story— Use classic storytelling elements to create a compelling scenario. Have a protagonist who is trying to accomplish a goal. Have an antagonist who is preventing the protagonist from accomplishing that goal. Have obstacles along the way that the protagonist must overcome.
- SEE and FEEL the importance.
- Give people time constraints or resource constraints and set them at a problem.
- Things that are going to happen in the future, regardless of how dire they are, are less compelling to the elephant than things that are happening RIGHT NOW.
- Interesting dilemmas— Give your learners interesting choices to make.
- Better options include choices between:– A good option and a very good option– Two bad options– Good, better, and best options
- Two options that are each a mixture of good and bad, but in different ways.
- • Consequences, not feedback— This goes back to the notion of show, don’t tell, but use actual consequences rather than feedback when people make choices in a learning scenario.
- Facts are frequently meaningless to us until we see them in some kind of broader context that allows us to begin to make judgments or sense about them.
- We believe that there are “objective facts,” but all valuable information has meaning only in a bigger context, and part of that context is emotional.
- Surprise
- Unexpected Rewards
- Basically, people have a much stronger response to unexpected rewards than they do to ones they know are coming.
- variable reward schedule,
- Unexpectedness is also part of our enjoyment of other entertainments, like sports or comedy.
- Dissonance
- Another form of surprise happens when we bump into something that doesn’t match our view of the world.
- curiosity as “arising when attention becomes focused on a gap in one’s knowledge. Such information gaps produce the feeling of deprivation labeled curiosity. The curious individual is motivated to obtain the missing information to reduce or eliminate the feeling of deprivation.”
- Ask interesting questions.
- Be mysterious.
- Leave stuff out.
- Be less helpful. We do our learners a disservice by making the problem too complete.
- Framing and clarifying the problem becomes part of the learning experience.
- learners can see that other learners are engaged with the material, or if a group of students know that previous classes performed well, they are more likely to engage and perform better themselves.
- Competition -- There’s no question that competition can be a useful way to get the elephant’s attention, but there are a number of problems with it as a learning strategy:
- Not everyone is competitive.
- Competition teaches learners how to win.
- Competition as motivation isn’t a good long- term strategy.
- There are a number of visual or tactile ways to attract the elephant’s attention, including visual aids, humor, and rewards.
- Creating the visual association between the trigger and the action is an important part of encouraging the memory and the behavior.
- If what you are teaching has a hands- on component, then the learning should, too.
- At the most basic level, the idea is that if you reward a behavior, you increase the likelihood that behavior will occur, and if you punish a behavior, you decrease the likelihood that behavior will occur.
- Extrinisic rewards can demotivate people.
- Once you start paying people, it becomes work, and can have a negative effect on performance and motivation.
- Rewards can be great motivators if they are intrinsic.
- There’s one catch to designing for intrinsic rewards: You don’t get to decide what’s intrinsic to the learner.
Design For Knowledge - give learners an inventory of the content, and have them rate their level of comfort with each topic.
- If something isn’t signficant, important, or unusual, why would we want to remember it?
- Creating opportunities to interact with the material can make a lesson even more engaging for your motivated learners.
- You need to give your readers stuff to do. Give them a way to be an active participant, and by allowing them to draw conclusions based on little clues that you leave, you engage them in the story and they become part of it.
- if people have to make the connections themselves, it’s likely they’ll remember more later.
- If someone is new to baking, stick with one way to do things.
- matching an example with a non- or counter- example.
- If it’s too easy to follow the individual directions, then the learner won’t learn.
- Examples Followed By Concepts
- There’s a lot of benefit to letting learners drive themselves whenever possible.
- validate / diffuse / assist
Design for Skills - Keeping people on that edge between challenge and ability is one of the fundamental principles of flow. Ideally, learning practice would allow learners a balance of challenge and satisfaction:
- In order for practice to be effective, learners need to be able to tell how they are doing.
- The incorrect skill can become ingrained, and then correcting that skill later will require unlearning behaviors that have become automatic.
- Increasing the frequency of feedback is great, but if you do that, you also want to have various ways to provide feedback.
- character reactions, scores, and visual cues.
- you don’t get lectured about poor business choices— you lose money,
- encourage learners to report back on their experiences.
- Have virtual critique sessions
- You want the course to help develop skills, not just deliver information.
- cycles of extended practice, tests of mastery of that practice, then a new challenge, and then new extended practice.
Design For Motivation - we don’t always learn the right thing when we learn from experience,
- If you want someone to use something, they need to believe that it’s actually useful, and that it won’t be a major pain in the ass to use.
- Sometimes it’s not useful for the learner, but it is useful for the organization, or it’s a compliance necessity. In those cases, it can be a good idea to acknowledge it and make sure the learner understands why the change is being made—
- If it is useful, how will the learner know that?
- Show Don’t Tell becomes particularly important here.
- positive endorsements from trusted peers.
- Is the new behavior easy to use?
- Everett Rogers’ classic book Diffusion of Innovations.
- The degree to which an innovation is perceived as being better than the idea it supersedes
- The degree to which an innovation is perceived to be consistent with the existing values, past experiences,
- The degree to which an innovation is perceived as difficult to use
- The degree to which the results of an innovation are visible to others
- The opportunity to experiment with the innovation on a limited basis
- Everett’s attributes (relative advantage, compatibility, complexity, observability and trialability),
- Self- efficacy can be described as someone’s belief in their own ability to succeed.
- In addition to feeling capable, it helps if learners also feel that the necessary task or skill is within their control.
- have your learners prepare themselves to employ the knowledge or skill by actively figuring out how they will use it to address their own specific challenges or tasks—
- People have a strong reluctance to discard something that they’ve already invested in.
- Make progress visible.
- Change is a process, not an event.
Design For Environment - Changing the design of the environment can make knowledge or skills gaps disappear.
- Improving the environment is about clearing out as much of the stuff that learners don’t really need to carry around in their heads, and instead letting them focus on the things that only they are able to do.
- The closer you can get the knowledge to the place the user is going to use it, the more likely they’ll actually do so.
-
a few other types of job aids: - Decision trees
- Reference information
- Augmented reality
- “layer” over the real world,
- Supply Caching -- cache some of that information and provide it later
- A common, easy job aid in the training world is a card that has the keyboard shortcuts on it -- this information is placed as close to the actual behavior as possible—
- if you are trying to quit smoking, you need more than the goal (“ I’m going to stop smoking”)— you need the implementation intention of how to actually do it. The specificity is crucial to success.
- This handy guide is built right into the countertop.
- soda machine -- The employee can put in the cup and just press a single button for small, medium, or large. the behavior has been embedded in the machine, rather than in the learner.
- shift the burden from a recall problem to a recognition one.
- environment is a very powerful regulator of behavior, and if people aren’t doing the right thing, it’s important to look at ways to improve the environment.
- What’s everything else we could do (besides training) that will allow learners to succeed?
- “what could we do beforehand to make people more ready?” and “what could we do afterwards to reinforce?”
Conclusion - We can’t make anybody learn, but we can make much better learning environments for them.
| Amir ShevatNotable QuotationsExpand to full screen
Notebook for Designing Bots: Creating Conversational Experiences Shevat, Amir Preface -Page 3 Designing bots is a new design proficiency, and is not a trivial matter. While bots are a great new user interface, they are not suited for every use case, and you will need to learn how to use bots effectively. Page 10 The bot is only an interface into the service, in the same way a website can be an interface for booking a flight. > Page 12 Messaging and the ubiquity of connectivity mean that people are more available and responsive via messaging than alternative, indirect modes of communication. > Page 13 conversational interfaces. > Page 13 It is important to note that mobile interfaces were better than web interfaces in many ways and could facilitate new use cases (such as location-based services and camera-based services), > Page 13 as another venue to engage with your users. > Page 14 The biggest value of the bot here is that users are already accustomed to getting notifications through their messaging apps, so the engagement rates are much higher. > Page 16 conversational interfaces are going to surface in more and more of the tools and services that we use every day. > Page 24 The purpose of bots for business is to facilitate a task or a business process in an easy, pleasant, and productive way. Communication should be to the point, with a focus on getting things done rather than talking about it. > Page 25 consumer bots are less task and workflow oriented and more experience oriented. > Page 26 There are a few consumer bots that need to be more similar to the business bots--joking with my bank bot about my finances is probably not a best practice. > Page 30 Designing Voice User Interfaces (http:// bit.ly/ designing-voice-user-interfaces), written by my friend Cathy Pearl > Page 31 like with many new services, the minimum viable product (MVP) approach applies to these new bots--start with a single focused value proposition and grow from there. 3. Major Platforms > Page 41 Validate your decision by talking to potential users. Try to create a prototype (you'll see how in Chapter 17) and have them access the service and use your bot. > Page 45 There is a strong incentive to use bots for business workflows--facilitating short, contextual, and actionable tasks can greatly improve the productivity of a team. > Page 46 Personal bot coaches that help users with weight loss, finances, parenting, sports, and more. > Page 46 A good example of a coach bot is Lark > Page 47 bots can provide a more personal experience that is harder to ignore, compared to mobile apps for example, and users are often more willing to provide information to a bot than to fill forms in an app. > Page 50 The hope is that text-based bots that are delightful, personalized, and actually get us to the right person can change our negative perception of most common answering machine-like IVR systems. > Page 50 Bots can easily cover approximately 40% of internal and external support tickets. > Page 51 users do not want to context-switch between apps to get the information they need or run their workflows. > Page 53 The core incentive here is app fatigue--users are tired of installing specific brand apps. Bots provide brands a new and fresh way to engage with their users in a useful way. > Page 55 I talked to one of the CEOs of a leading mobile platform, and he said that no one could have guessed that asking strangers to come pick you up from your home, with their private cars, would become one of the most profitable use cases of the mobile world. We still do not know which bot will make it big, but history has taught us that a few will change our world. > Page 56 The key is not to teach the user how to fill in a form, or to take them to the right page, but rather to recognize the user's intent (what does the user wish to do?) and to guide them in accomplishing that intent. > Page 57 Personality > Page 58 (getting things done versus having fun, for example), > Page 58 Logos and icons--. As bots are a transparent( ish) UI, having a logo and an icon allows the user to identify the bot, which contributes to brand recognition. > Page 58 Naming a bot with a human name can create a stronger emotional connection. > Page 58 Human intervention--. Routing a conversation to a human is quite easy, and can be transparent to the user in chat conversations. > Page 59 Onboarding -- you relay information to the users about the bot's purpose, ways to interact with the bot, what functionality is provided by the bot, and how to get help. ... Functionality scripting--. This is the meat of things. Here you script the flows (sometimes called stories) for each function, including happy paths and mitigations for failure. ... Feedback and error handling--. This is an important part of the script that is sometimes overlooked. Feedback is one of the keys to making your bot better, ... Help and support--. At any time during the conversation, the user might get lost or thrown out of the happy path or flow (the main expected flow). > Page 61 Bots are therefore required to infer contexts, keep the state of a conversation, and remember key details of previous conversations. > Page 61 Discovery and installation. You need to think about the bot habitat, the listing of the bot in a directory, and ways to initiate the first bot interaction Engagement methods. Notifications User-led bot invocation Subscription Monetization > Page 62 The first step to a successful bot design is understanding what it does. Defining the core purpose and functionality of the bot lies at the heart of your bot's anatomy. > Page 62 As bots are more limited in the richness of their interface than web or mobile apps, it is important to be very clear about what functionality the bot exposes and provide ways to educate the users on how to invoke that functionality as part of the conversation. Logo ... the sticker is used as a header to the conversation, setting context but also keeping up the brand recognition throughout the conversation. ... Picking the right name for your bot is as important as (if not more important than) picking a name for a mobile app. ... Naming your bot in a way that implies its functionality (making the name descriptive) can be very useful and help make it memorable ... If you have a strong brand name and a single bot that exposes your service, you might name your bot with the same or a very similar name ... avoid using another company's trademark. ... Try to avoid using too generic a name that might conflict with another bot's name. > Page 75 Personality is one of the key attributes that can differentiate your bot from other bots that provide a similar service. ... Consider whether the target environment is a work environment or a consumer environment, ... Consider the type of audience who will be the primary users of your bot ... The task the user is intending to execute implies different personality characteristics, even for what initially might seem like similar tasks. ... > Page 78 I did not want the personality to overshadow the service, > Page 81 Once you have defined a personality, it is important to keep it consistent across the experience. > Page 83 Some bot builders focus solely on the personality, rather than on the service, and that is like painting a crappy car with shiny colors--it might work for the first impression, but not for much longer than that. > Page 83 personality is exposed in the script itself, > Page 84 we gave our agent a proper full name (Amy Ingram, and Andrew Ingram). And when scripting her end of the dialog, we built in things like empathy. > Page 84 if you are on the third reschedule, Amy needs to signal that she realizes that this is not an ideal situation, > Page 84 many of the traditionally accepted social interactions that humans abide by must be catered to in an Intelligent Agent world as well. > Page 84 Don't send two emails while people are likely to be sleeping, > Page 85 Focusing on designing how your bot converses with its users, adding empathy, and making it more friendly and approachable is a great best practice. > Page 86 While bots can address some use cases very efficiently, having a human in the loop might save the bot from many embarrassing and frustrating situations. > Page 87 Another common pattern is where humans help train a bot. As software is great at repeating tasks and at pattern recognition, having humans teach bots by example can be a great way to automate processes on the job. > Page 88 Currently, the best way to train an AI is by giving it a lot of examples. This human training by example pattern provides the seed data that the bot requires to start being productive. > Page 88 Live logs are super useful to get an understanding of what works and what does not. > Page 90 Make sure you are looping humans in at the right time and for the right reasons. 7. Artificial Intelligence > Page 92 AI today is not a single thing, but a set of tools that designers and bot developers can choose to use in order to build a conversational bot. > Page 94 Conversation management technologies are in their infancy, and there is a lot of progress to be made in this area. > Page 98 AI usually does a good job of finding patterns and predicting outcomes based on past data. Page 98 One unique AI service that is useful for conversation is sentiment analysis. This AI service gives you a prediction about the user's emotional state, together with a level of confidence in that analysis. > Page 99 Please note that sentiment analysis is still in the early stages, and currently not very accurate--catching swear words or terms of endearment through simple pattern recognition might be as effective as AI for many bot use cases. > Page 100 Most bots today use simple regular expressions to understand the intent of the user; > Page 103 During onboarding the bot declares its purpose in the context of the conversation, making it transparent to the user or the team. > Page 105 Now that the user knows what the bot is for, it is time to move to the next stage, which is telling the user how to use the bot. > Page 110 Offering added value to the user at the first engagement contributes to the user's perception of the bot. Useful bots are more likely to be remembered and reengaged with by the user. > Page 111 Onboarding a bot to a team is very similar to onboarding a new human team member. > Page 113 After the bot has been properly introduced to the team, it can communicate directly with team members without the supervision of the installing user--there's > Page 113 We will explore designing two types of conversations. The first will be a task-led conversation, where the target is to accomplish a task. The second will be a topic-led conversation, which aims to discuss information and exchange ideas around a specific set of subjects. > Page 119 As a conversation designer you need to define and list the set of entities you need to extract from the conversation. > Page 119 You will also need to specify their priority and acceptable data types. > Page 119 entity extraction mechanisms: > Page 121 Bots need to provide this task switching functionality as well--your bot needs to give the user a way to go back "home," to where they can restart a task or pick another task to execute. This is especially necessary when a user gets lost or stuck while trying to > Page 121 complete a task. > Page 121 of a conversation. Stories are used to describe a distinct flow or part of a flow. > Page 127 Task-led conversations need to have the least amount of steps possible to accomplish a task. Topic-led conversations can have more steps, determined by user engagement with the topic. > Page 129 From some perspectives, topic-led conversations can actually be easier than task-led conversations, because there is more room for divergence; there is also less of a need for mandatory entity extraction and intent mapping, as the intent and the subject can be abstracted. > Page 131 Entity extraction > Page 132 The bot needs to extract the user's general preferences; for example, whether they are interested in men's clothing or women's clothing. This is critical information to have in order to have a productive conversation about clothes. Collecting more entities, like favorite color, age, and preferred style, will contribute to more constructive and engaging future conversations. > Page 133 The use of stories/ flows might even be more important in topic-led conversations than in task-led conversations. > Page 133 The stories can be connected through association of interest. > Page 133 Both kitchen utensils and food dishes can be tied to multiple stories, and you can create relations that connect the stories as shown in Figure 8-24. > Page 134 Decoration refers to words that we add to sentences that do not contribute directly to the conversation itself, but rather add color and character. > Page 134 variation adds depth to the conversation and minimizes the sensation that you are talking to a machine. > Page 135 Decorations do not have to be verbal. For example, the sentence: Coffee-bot: Got to love New York... > Page 136 emojis > Page 136 Another way to decorate a conversation is with memes and images. > Page 137 we all love to decorate our conversations. We add facial expressions to scary stories, we reply to threads with funny memes that say more than is polite to say with words. > Page 138 that users may decorate their conversations with it. I have talked to many bot designers who were surprised to see an imoji > Page 138 Another popular form of decoration on Slack is with text formatting. > Page 139 Randomization is another form of decoration, but one that transforms the core part of the conversation. > Page 139 we do not always use the same phrase to express the same thing. For example, we will not say "I understand" 10 times in a row, > Page 139 Bots that do not randomize their phrases to express confirmation, agreement, or anything else that is repetitive in the conversation tend to sound mechanical > Page 140 it could be a source of delight--not knowing what the bot will say makes it more interesting > Page 140 Priming the User to Give the Right Information > Page 141 Users can provide the same information in many different forms. For example, denoting April 3, 2017, might be done in several ways: > Page 141 Getting the right answer depends a lot on how you frame the question. Instead of saying, "When would you like the meeting to take place?" the bot can say, "At what date would you like this meeting to take place?" > Page 141 Another priming strategy is to limit the options the user has, > Page 142 Bot: I am ready to send the meeting invitation for your meeting on April 3rd at 2 p.m. You can send or modify. Which one would you like? Now answering "Yes" makes less sense... Making the options bold primes the user to pick one of these options specifically. ... aligning the user expectations with the bot's purpose and capabilities. For example, for a train ticket booking bot, it is wrong to ask "Where do you want to go?" This question does not align the user expectations with the bot capabilities. For example, the user is not able to understand immediately if the bot expects a city, an address, or even something more personal, like "I want to go home." Unless your bot is capable of processing all of those phrases correctly (and it probably isn't), then you will need to design its conversation better, by aligning the user expectations with the bot capabilities. > Page 143 Limiting the options for the conversation to derail and applying simple priming techniques can significantly improve your bot's usability and the experience users have with > Page 143 Acknowledgment and Confirmation ... A bot should never ignore a user--when a user asks a question or makes a comment, the bot should reply to the user, either with an acknowledgment or with a related sentence. ... A bot should never ignore a user. > Page 144 In very long-running processes it might be useful to give the users an indication of when they can expect the results. > Page 145 Explicit confirmation typically involves checking with the user that the input provided by the user was processed correctly, or requesting permission to act. > Page 145 Explicit confirmation is very taxing on humans. We do not need to confirm every aspect of our conversations with our conversational counterparts. Use explicit confirmation only in use cases that mandate it, or if you are not confident that you are processing the user input correctly. > Page 146 Avoid repetition Page 147 Consistency should also be maintained when the conversation is routed to a human supervisor. Humans managing the conversation should be aware of the branding and the style the bot provides as the interface of the service, and keep the conversational tone consistent. > Page 147 Reciprocity is a key aspect of human interaction. Every conversation is composed of reciprocal give and take. ...Communicate Value Before Asking for Input ...Users are willing to invest as long as they understand what they are getting in return. > Page 149 Humans will expect, and in most cases positively respond to, a timely suggestion from the bot. > Page 151 Now the bot has acknowledged the user's request, expressed empathy, and continued the conversation. While the user did not get exactly what they needed, the conversation is much less frustrating. > Page 151 knowing when to shut up. > Page 151 Other aspects of common courtesy might be giving the user enough time to perform an action, providing sensitive information privately (we will talk about that in depth in the next section), and being empathetic to the user's needs and pains. > Page 152 The bot in this use case needs to know how to work with multiple users in the same channel or environment. The bot needs to acknowledge input by user and communicate to different members in the channel. > Page 156 it is important to stress that onboarding is very different in a private conversation with one person and in a team environment, where a bot is installed by one member and then used by multiple members. > Page 156 One of the important things to remember about a bot in a team conversation is that most of the communication is not directed at the bot. > Page 156 only reply to messages that are addressed to the bot or that are a part of the conversation it is having. > Page 159 Course Correction Course correction relies on the bot's ability to pull the user back into the happy flow of the conversation. > Page 159 another way to handle a request from a user that cannot be fulfilled at the moment, and that is to collect that information and use it to grow your product. > Page 159 User: Wait, no! I want a cappuccino! Bot: We currently do not serve cappuccino (you can order regular coffee or espresso), but I will also notify you when we start to serve cappuccino. > Page 160 If the communication breakdown with users occurs in the middle of a conversation, and you're able to maintain context, you can serve up two messages in succession. The first is the fallback message to indicate misunderstanding, and the second is a reminder of the context. > Page 160 Another redirection strategy is to ask the user if they would like the bot to escalate the request and if they concur, you're able to funnel the user into another response channel and minimize the risk of losing them. > Page 161 Deferring to a human supervisor is a viable and common solution to error handling with bots. > Page 161 You can build a process of course correction, followed by human intervention if that fails. In some instances, support like this is an expected pattern--the bot serves as a first line of defense and the human supervisor steps in to provide assistance in cases where the bot fails. > Page 164 Conversation design should be done with a growth mindset. Designing a conversation is an ongoing process of learning from your bot's mistakes. > Page 164 the bot constantly misses a specific intent or offers the intent too often, it might be time to fine-tune the text-to-intent mapping. > Page 165 Providing Help Help should always be available to the user--if a user at any point in the conversation says "Help" or "Help me" or any variant of this, the bot should move to a help mode. Help can be as simple as repeating the section in the onboarding script that teaches the user how to use the bot. > Page 168 Support the "feedback" command This should be a best practice for all bots. Whenever a user says "feedback," start a feedback conversation. > Page 168 Especially when the conversation is task-based, ask for the user's feedback at the end. 9. Rich Interactions > Page 183 Buttons can be a great way to guide the conversation, frame the interaction, or limit the user to a set of options. > Page 198 Lead designers and product managers at both Facebook and Slack recommend using buttons to enable better conversation flow. > Page 199 but it is important to note that the user can still post free text to the bot, and that the bot should still apply logic to understand the user's input and to navigate to the right step in the conversation. > Page 200 For us buttons yield a much higher response rate than natural language question and answer, specifically for yes/ no questions. > Page 200 By switching from plain text to buttons, the team at Donut were able to work around the hard problem of understanding users' unstructured responses > Page 200 Buttons are not a great user experience when there are a large number of options. > Page 201 Another example where buttons would not work is in free-form inputs, from describing how you are feeling to a coach bot, to providing expense justification to a finance bot. > Page 201 Templates, in this context, are a structured way to collect different UI elements in a pre-formatted, standard way, and to expose these in a conversational interface. > Page 211 ensure user reengagement. > Page 212 Links are an easy way to send the user out of the conversation and into the web. > Page 212 Links also serve as a way to refer to something on the web and surface a preview of it in the conversational interface--technically this is referred to as unfurling. > Page 213 Statsbot pulls out the summary and key performance indicators, and posts them in the conversation--but when it comes to drilling down to see the full report, the bot posts a link to Google Analytics. > Page 213 The key is to provide enough value in the chat interface so the user does not get the feeling of a shallow bot. > Page 216 most bot developers report seeing emojis in the conversational input from users. > Page 217 It is pretty delighting to get a ? from a bot when you ask it to perform a task. > Page 219 Use emojis to relay information, enrich the conversation, and indicate actions taken. > Page 219 In several platforms you can send a "typing" event on the bot's behalf, which is usually visualized inline in the chat app. This is useful to give the user the impression that the bot is working > Page 219 Conversely, some users get unsettled when the bot answers too fast. 10. Context and Memory > Page 229 Bot Amnesia > Page 230 Many bots today focus on a request/ response paradigm. In this paradigm each request has a new context, and all past contexts are forgotten. Some bots do a better job of maintaining context than others > Page 233 Other forms of bot amnesia include forgetting the user's name, address, preferences, and more. > Page 234 global variables as the long-term memory and scoped variables as the short-term memory— > Page 234 bots should not time out context--if a user starts an intent, they should be able to come back to it after a while and the bot should persist (remember) the context. > Page 243 building trust > Page 243 Users need to feel that they can go back and edit/ modify/ cancel or just revisit past transactions. > Page 244 Recollection of past conversations is also a good way to reengage with the user. > Page 252 The key value of QR codes is that they can be placed in the physical world. > Page 255 Bots can potentially refer users to other bots within a conversation: User: And I will also need a ride from the airport to the hotel. Travel-bot: Well, I cannot help you book a ride, but you can talk to my colleague LyftBot and we can sort this out. Here is the link to install the LyftBot -[link]. User: Fantastic, installing now! > Page 256 It is hard to stay emotionally detached from all but the most utilitarian bots, and most people use strong feelings and words when they describe bots. > Page 256 Create a great first impression--make the other side fall in love. Keep on adding value--value wears off. Continue evolving your design--users like to be slightly and pleasantly surprised from time to time. > Page 257 One key to a positive first engagement is a clear intent > Page 257 Another key is product fit--the ability of the product or service to address that intent or need. > Page 257 serendipity--a moment where the users understand the value of the product or service and realize it is beneficial for them. > Page 257 Demonstrating value and creating a habit should be part of your bot's onboarding experience. > Page 257 Clearly define the purpose > Page 257 Educate or inspire the user about the product fit of the bot and how it addresses the intent. > Page 257 Generate a trigger that will build a usage habit. Trigger Action Reward Investment-- An action that makes the service better with use and generates future triggering opportunities. > Page 264 The act of opting in makes users less inclined to consider these notifications as spam and more likely to interact with them. > Page 266 The risk with unsolicited notifications is that they can be perceived as spammy and might motivate users to disengage from or even uninstall or block your bot. > Page 266 make sure you capture the user's intent to receive them, and ask for permission before sending them ongoing notifications. > Page 266 Make sure your notifications are valuable and give your users a way to opt out. > Page 266 we found out that the rate of unsubscribes skyrockets if a user gets more than 1.9 messages/ day. That doesn't mean you should send ~ 2 messages a day, but you definitely should have a very good reason to send more than that. > Page 267 Ultimately, engagement cannot be artificially generated; it stems from your bot's usefulness. 13. Monetization > Page 268 End users do not pay for bots, they pay for the services the bots expose; > Page 268 This is currently the most common way that bots drive revenue--the bot provides an ongoing service that the user subscribes to and pays for. > Page 271 Bots are in a unique position when it comes to ads, as they can build a personal relationship with the user, as well as collecting a lot of personal information that can contribute to more targeted and finely tuned ads that lead to a better click-through rate. > Page 272 provide an engaging fan experience as well as to deliver value to the brand, and to do so in an authentic way that is more conversational and personal than traditional advertising. > Page 273 Bots can collect a lot of data about user preferences and interests, through engaging in a conversation or playing a game. > Page 275 Bots can also become the channel through which you sell goods and services. > Page 276 This is another major business model on mobile and web that is moving to bots--the bot can help you decide what to buy or which service to consume and then refer you to the right service, rather than actually completing the transaction itself. > Page 278 Thinking about the bot as a frontend representative of your product or service can make a lot of sense, > Page 281 You should only start considering charging for your bot when you feel that you have reached product/ market fit. > Page 288 Defining a Persona Now let's sketch out the personas for our two bots. First, the PTOBot: Name: PTOBot Environment: Work Audience: Adults aged ~ 25– 55, early adopters Task at hand: PTO requests Runtime variations: Report abuse, zero tolerance to not safe for work (NSFW) content Locally relevant social acceptance: Work environment Service branding: Professional, productive Values: Getting things done, and fast Derived personality: Serious, to the point, friendly but not humoristic, safe for work. This bot should be like a company office manager or a personal assistant. Then the VacationBot: > Page 289 Name: VacationBot Environment: Consumer Audience: Adults aged ~ 25– 55, early adopters Task at hand: Provide fun activity recommendations Runtime variations: Provide a more casual conversation during the vacation itself Locally relevant social acceptance: Family-friendly Service branding: Professional, fun but still not too casual Values: Enriching employees lives while they're on vacation Derived personality: Friendly but not too humoristic, family-friendly, safe for work. This bot should be like the > Page 291 We will use a method called Wizard of Oz user research. The Wizard of Oz technique enables unimplemented technology to be evaluated by using a human to simulate the response of a system--in our case, the bot. > Page 294 Another, even easier, way to explore a conversational interface is actually to have a conversation in person, or to observe a conversation in action. 16. Conversation Scripting > Page 296 We will break down the conversations into flows (sometimes called stories), and detail the entities we want to extract. > Page 296 edge cases and error handling, > Page 297 As previously discussed, a thoughtful onboarding could be the difference between a successful bot engagement and an abandoned bot. > Page 298 Main Flow In this flow we will describe the main happy path. This encapsulates the main functionality of the bot, without errors or divergences. > Page 299 Help The help flow aims to support the user in case they need assistance in the main flow of the bot. This flow can kick off by the user asking for help, or the bot understanding that the user needs help due to errors in the main > Page 301 Feedback As discussed previously, feedback is an important part of a conversation--it gives the user the ability to share valuable information with the bot's designer. > Page 302 Capturing positive feedback is sometimes as important as capturing negative feedback. You can use this type of feedback as client testimony, to prompt the user to provide a good review or rating in a directory listing, and to encourage users to share the bot with others. > Page 304 Intent Mapping > Page 304 sample of keywords > Page 305 Entities are variables we want to collect from the user. These entities can be collected using plain text or rich controls. Let's map the entities of our already outlined stories. > Page 305 Start date {Date} End date {Date} Description {Text--up to one paragraph} Approved {Yes/ No} > Page 305 Destination {Location} Schedule notifications {Yes/ No} When to send the notification {Morning/ Evening} > Page 309 Use buttons and Quick Replies for non-open-ended questions to indicate to the user what the valid choices are. > Page 311 provide the employee with a receipt-like experience that will hopefully increase their trust in the system. > Page 317 Avoid dead ends, but do not spend all your time trying to think of every possible input. At a certain point you will need to default the user to the generic error messages. > Page 318 Bot: Sorry, I did not understand that. Bot: < display relevant contextual help > ... This is an example of one flow pulling in another flow. We are in an entity extraction flow (let's say, in the main use case), and the user inputs invalid text. The bot then provides a simple apology and error message, and pulls in the help script relevant to this specific context. > Page 319 We spent a lot of time building anti-trolling tools (naughty word filters), anti-abuse tools (allowing Poncho to ignore abusive users), and conversations for a variety of the anticipated trolling, testing, & abuse. User: < Curse > Bot: Sorry you feel that way. < Consider pulling in a feedback script > > Page 319 they recommend a "three strikes and you're out" rule, in which the bot bans the user after three abuse warnings. > Page 320 these are simply sample scripts, and that in real life you will need to provide a lot more variations, both in inputs and in outputs of the bot. > Page 320 Scripting is just like creating mockups of a mobile or web application. You need to validate the scripts you have written in real life. > Page 320 Advanced bot builders hold these scripts in a content management system (CMS) and have scriptwriters go over and optimize the conversations on a daily basis. > Page 355 Now that we have designed the main flow of both bots, it is time to put them in front of actual users. First, we need to decide how we want to test our design. > Page 355 There are a few options: Show users a video or a step-by-step replay of the conversation and get their inputs. Create a mock (fake) bot and let users play with it. Create a working alpha and let users work with it. > Page 355 A mock bot is a great tool for testing interactions. It doesn't really matter that the bot is not connected to the real backend system. > Page 356 Laura Klein has written a great "Step-by-Step Usability Testing Guide" (https:// guides.co/ g/ usability-testing-guide/ 7996) that outlines the steps in usability testing. | Stephen WendelNotable QuotationsExpand to full screen
Part I. How the Mind Works (Chapter 1-4)
Behavioral science is an interdisciplinary field that combines psychology and economics, among other disciplines, to gain a more nuanced understanding of how people make decisions and translate those decisions into action. (5)
One of the most active areas of research in behavioral science is how our environment affects our choices and behavior, and how a change in that environment can then affect those choices and behaviors. Environments can be thoughtfully and carefully designed to help us become more aware of our choices, shape our decisions for good or for ill, and spur us to take action once we've made a choice. We call that process choice architecture, or behavioral design. (5)
When designing a product lookout for unnecessary frictions or for areas where a user loses self-confidence. Build habits via repeated action in a consistent context.
We economize our time, attention, and mental energy by using simple rules of thumb to make decisions; for example, by excluding cereal with cartoons. As researchers we call these results of thumb heuristics. Another way our minds economize is by making split second non conscious judgments; non conscious habits are automated associations in our heads that trigger us to take a particular action when we see a specific trigger. Habits free our conscious mind to think about other things. (8)
We often call the results of a heuristic or other shortcut going awry a cognitive bias: a systematic difference between how we'd expect people to behave in a certain circumstance and what they actually do. (8)
Biases and heuristics: status quo bias, descriptive norms, confirmation bias, present bias, anchoring, availability you Mystic, IKEA effect, Halo effect. (13-15)
Habits arise in one of two ways. First you can build habits through simple repetition: whenever you see X, a cue, you do Y, a routine. Over time your brain builds a strong association between the cue and the routine and doesn't need to think about what to do when the cue occurs. Sometimes there is a third element, in addition to a cue and a routine: a reward something good that happens at the end of the routine. The reward pulls us forward-it gives us a reason to repeat the behavior. (16)
What we do is shaped by our contextual environment in obvious ways. It's also shaped in non-obvious ways by the people we talk and listen to (our social environment) what we see and interact with (are physical environment), and the habits and responses we've learned over time (are mental environment). (19)
Our minds still use clever shortcuts to help us economize and avoid taxing our limited resources. (23)
With the intention-action gap, the intention to act is there, but people don't follow through and act on it. Good intentions and the sincere desire to do something aren't enough. (24)
People take action (or fail to) in a specific moment. Our will and desire are certainly important-but it's not enough, especially when we're looking to design for behavior change period we need to understand what brings one action to the fore and not others. For that we have the CREATE framework: a cue, which starts an automatic intuitive reaction, potentially bubbling up into a conscious evaluation of costs and benefits, the ability to act, the right timing for action, and the overwhelming power of past experience. (30)
These six mental processes-detecting a cue, reacting to it, evaluating it, checking for ability, determining if the timing is right, and interpreting it all through the lens of our past experiences-are gates that can block or facilitate action. (31)
For someone to take a conscious action, six things must happen immediately beforehand:
1) the person responds to a queue that starts their thinking about the action;
2) their intuitive mind automatically reacts at an intuitive level to the idea;
3) their conscious mind evaluates the idea, especially in terms of costs and benefits;
4) they check whether they have the ability to act-if they know what to do, have what they need, and believe they can succeed;
5) they determine whether the timing is right for action-especially whether or not the action is urgent;
6) they aren't turned off by a prior negative experience that overwhelms the otherwise clear benefits. (51)
Sometimes helping people take action requires intentionally stopping a habit. Why are habits difficult to change? First, remember that habits are automatic and not conscious. Our conscious minds, the part that would seek to remove them, are only vaguely aware of their execution; We often don't notice them when they occur, and we don't remember doing them afterward. Across dozens of studies on behavior change interventions, researchers have found that the conscious mind sincere, concerted intention to change behavior has little relationship to actual behavior change. (56-57)
We can help people use what's known as situational self-control; just as we can shape an environment to encourage action, we can shape an environment to slow down rash decisions and interfere with undesirable habits and behaviors. Use the CREATE framework in reverse. (65)
If the behavior is habitual, here are specific techniques to focus on: avoid the cue altogether, build up a new positive habit that uses the same cue; deploy intentional mindfulness. (65)
Behavior change is the core value of the product for users, and behavior change is required for users to extract the value they want from the product effectively. (71)
Ethical guidelines for work: don't try to addict people to your product; Only apply behavioral techniques where the individual benefit; Tell users what you're doing; Make sure the action is optional; Ask yourself and others if they want to use the product. Avoiding coercion doesn't mean that you encourage users to do anything they want to do. Your company will have, and must have, a stance on the behaviors it wants to encourage. (84)
Apply behavioral science on ourselves to be ethical: fix the incentives, draw bright lines, set up independent reviews, and support regulations. (84)
Part II. A Bluebrint for Behavior Change (Chapters 5-15)
Behavioral science helps us understand how our environments profoundly shape our decisions and our behavior. What does this process look like? I like to think about it as 6 steps, which we can remember with the acronym DECIDE (Define, Explore, Craft, Implement, Determine, Evaluate): that's how we decide on the right behavior change interventions in our products and communications. (90)
Define the problem, explore the context, craft the intervention, implement within the product, determine the impact, and evaluate what to do next. (91)
Defining the problem: the root cause of many bad designs-when designing for behavior change or otherwise-comes from a lack of clarity from the start. (100)
Defining the problem centers on the target outcome (what is the product supposed to accomplish?), the target actor (who do we envision using the product?), and the target action (how will the actor do it?) (101)
Exploring context includes the following: prior experience with the action, prior experience with similar products and channels, relationship with the company or organization, existing motivations, physical psychological or economic impediments to action. These five things make up the behavioral profile of users. To gather this information, you can use the standard tools of market research and product development-look for existing quantitative data on user demographics, deploy field surveys, and conduct qualitative research with users in focus groups and one-on-one interviews. If at all possible, include some direct observation in the field-see how people actually act. (126)
Generate formal user personas-short descriptions of archetypal users with a simple background statement about a sample user's life. Unlike traditional user personas, these personas are all about behavior: groups of users who are likely to interact with the application differently and who are likely to respond differently to behavioral interventions. (129)
Consider a behavioral map (similar to customer experience maps). The behavioral map is a depiction of the individual steps users take from whatever they're doing now, all the way through using the product and completing the target action (or for stopping an action what they do that leads up to the action to be stopped). Some of these steps will occur within the product and some require behavior that is completely outside of it. The map examines at each step of the way what's going on with users and why they would continue to the next step. (134)
Diagnosing why people don't start requires a three-part process: First, we identify the micro behavior that stops people (or for new products, are likely to stop people). That’s our behavioral map. Second, we check which micro-behavior seems to be a problem. Where are people dropping off or likely to drop off? Third, we use the CREATE action funnel to determine the likely behavioral cause. (144)
The diagnosis for a behavior you want to stop entails: 1)identifying the micro behaviors that led up to the action; 2) at each micro level, determine if it's habitual or conscious; 3) use CREATE for conscious actions and CRA for habitual actions to map out the current enabling factors for each micro behavior. (145)
The purpose of the design process is to craft a context that facilitates (or hinders) action. (151)
Changing context usually happens in one of four ways: 1) do it for them by magically taking away all the burden of work from the user; 2) structure the action to make it feasible (or in reverse, more difficult), 3) construct the environment to support or block the action, and 4) prepare the user to take (or resist) the action. (164)
Crafting the intervention involves cues, reaction, and evaluation. Cues, wisely placed, are essential for behavioral change period this is true for non-conscious habits – a cue in the environment starts a habitual routine-and for conscious decisions to act. One simple way to queue people to act is just to ask them. (171) Another way to cue action is to help users reinterpret an existing feature of their environment as a queue. Let them specify something that they see or hear normally in their environment-like the morning show on their favorite radio station. Then have them associate an action with that cue. (172)
Once a cue catches the user's attention, the mind reacts-often in the blink of an eye. Regardless of the overall merits of the action and product, that reaction can cause the user to shut down. (177) Techniques to address that problem: help people see themselves as someone for whom the action is a natural, normal extension of who they are; redirect someone’s current attention to prior successes; associate with the positive and the familiar; use social proof as a key tactic to persuade; use peer comparison; display strong authority on the subject; be authentic and personal; make the site professional and beautiful. (177-183)
Conscious evaluation is similar to the stereotypical view most people have of decision making: do the benefits outweigh the costs? Make sure incentives are right, leverage existing motivations before adding new ones, avoid direct payments, leverage loss aversion, use commitment contracts and commitment devices, test different types of motivators, use competition, pull future motivation into the present. (183-189)
One way to think about the mental cost of your target action is cognitive overhead, or “how many logical connections or jumps your brain has to make in order to understand or contextualize the thing you're looking at period figuring out what to do shouldn't be guesswork for the user.” That may mean making the action slightly more difficult to undertake in order for it to be easy to understand. Also makes sure instructions are understandable, and avoid choice overload. (191-192)
Every time a user stops to think about what to do next, there is an opportunity to be distracted. Each micro behavior in the behavioral map can become an obstacle simply because it requires an extra iota of thought, effort, and confidence. (196)
Remove friction and channel factors, including removing unnecessary decision points and setting appropriate defaults. (196-198)
Implementation intentions are specific plans that people make on how to act in the future. They are a form of behavioral automation, telling the mind to do X whenever Y happens. The person does the work of thinking through what needs to be done now, and then when the action is actually needed there is no need to think and no logical barrier to action-the person just executes the action… For behavioral products, deploying implementation intentions can mean adding text boxes where the user describes how they'll take the action. The key is to make people think consciously about the concrete actions, and, if possible, visualize undertaking those actions. (198)
Helping your users know that they'll succeed can be as complicated as an in depth training program and building up their expertise and confidence for a hard action. It can also be as simple as reframing the action to make it feel more familiar and feasible. (199)
We're wired to value the present far more than the future-that's our temporal myopia. (200)
We don't like to be inconsistent with our past behavior. It's very uncomfortable and we have a tendency to either act according to our prior beliefs or change our beliefs so they are in line with our actions. One way to achieve this is to have the user impose urgency on themselves-promised to take the action at a specific time, then come back to them and remind them at that point. Another way to create urgency to act is to make specific promises to do so to your friends. Social accountability is a powerful force. (201)
You can make a reward for the action scarce or artificially time sensitive. This is another favorite sales and marketing tactic. It is best for one off actions and not repeated behavior. (202)
People's prior experiences shape their reactions in ways that can be difficult to foresee and even comprehend. So what can we do when someone's prior experience creates an obstacle to something they would otherwise want to do? Here are some examples: fresh starts are special times in our lives when we feel a new opportunity to change something about ourselves. People are disproportionately likely to make major life commitments during times of transition. A special fresh start can make the action in context feel special and allows people to put their past experience in a separate historical category that doesn't doom them into repeating those mistakes again: the future can be different, if you make it so. (203-204)
Use story editing. We interpret and reinterpret our experiences every day of our lives and thus shape ourself narratives and our future behavior. These cycles of interpretation and behavior can clearly support beneficial changes, like studying more. It depends on how we use our past experiences and whether we see ourselves in control of the outcomes of our lives. (205-206)
Make it intentionally unfamiliar. If prior experience with a familiar product or communication causes a negative reaction that blocks action, you could intentionally change the look and feel to no longer trigger that reaction. (206)
Working with multi step interventions overtime, building habits, and crafting interventions to hinder negative actions are advanced topics related to crafting interventions. (209-222)
Many companies use an iterative product development process… That iterative process also allows teams to assess the impact of different interventions along the way as well, which is quite valuable. Is not essential though. Regardless of the process used to implement the product itself there are a few pointers along the way that can help the behavioral aspects of the project. In particular, it's important at this stage to double check that your incentives and intervention plan are ethically sound, plan to track user behavior and results from the outset, and ensure that thoughtful planning doesn't get in the way of creative solutions. (224)
Build in behavioral metrics. The first step in measuring the impact of your product is to be absolutely clear on the impact you care about. In particular you should have a clearly defined tangible and measurable outcome that you seek with a metric; a clearly defined tangible and measurable action that drives that outcome with a metric; and a threshold for each metric that defines success and failure. (228)
Your company may need to consider adding functionality to the app to make real world measurement possible. (229)
Implement A/B testing and experiments. Experiments are your best route to determine whether you've had the impact you seek, when they are possible. So just like the metrics themselves, you should plan to implement the ability to run experiments as part of the product or communication itself. Otherwise you'll have a hard time retrofitting them. (230)
AB tests take a randomly selected group of users and show them one version of the product, and show another randomly selected group another version. (231)
When you want to know whether a product or communication actually does what it's supposed to do, randomized control trials are the most trusted and rigorous tool. In fact, they are the gold standard in science; the same tool is used to measure how effective medicines are at curing disease. (239)
In addition to the basics of experimental design, there are a few other rules to keep in mind: random selection isn't always easy, you need random assignment as well, check that the groups are drawn from the same population, make sure you're only varying one thing. (247-248)
Always run a test of statistical significance. (249)
In addition to determining statistical significance here are a few other rules that apply to experiments: go double-blind when you can, measure the same way, compare results for everyone, generalized outcomes to the same population. (249-250)
Experiments come in many flavors in terms of how they are designed and executed and in terms of the particular problem or purpose they are meant to address. Two of the most common types of experiments: one in which the second group receives nothing and one in which the second group receives a different version of the product or communication (also known as an A/B test). Here are a few other experimental designs: simultaneous impact, simultaneous comparison, multi arm comparison, staggered rollout, attention treatment, multivariate experiment, multi armed bandit. (250-252)
Teams can't always run experiments, but the need for rigorous measurement doesn't go away. There are other ways to determine impact. The easiest and most common way to look at impact is a pre-post analysis. In a pre-post analysis, you look at user behavior and outcomes before and after a significant change. In a cross-sectional analysis (or panel data analysis of impact) you look for differences among groups of users at a given point you want to see how their usage of the product impacts their behavior and outcomes, after taking into account all the other things that might be different about the users. (262-265)
At the end of each cycle of product release and measurement, the team will have gathered a lot of data about what users are doing in the product and potential improvements to do it. It's time to collect the potential changes from these diverse sources and see what can be applied to the next iteration of the product. A three-step process: 1) gather lessons learned and potential improvement to the product; 2) prioritize the potential improvement based on business considerations and behavioral impact; and 3) integrate potential improvements into the appropriate part of product development process. (275-278)
Ideally, the outcome of any product development process, especially one that aims to change behavior, is that the product is doing its job and nothing more is needed. When the product successfully automates the behavior, builds a habit, or reliably helps the user make the conscious choice to act, and the team can move on. (280)
Part III. Build your Team and Make it Successful (Chapter 16-18)
Whereas majority of the book focuses on the process of applied behavioral science, the book ends with a focus on the organizational structure that enables applied behavioral science. (286)
Teams applying behavioral science to the development of products, communications, and policies are heavily concentrated in three countries: the United States, the United Kingdom, and the Netherlands. (288)
Companies are either consulting companies or those that apply behavioral approaches to their own products and services. Some teams are focused on particular outcomes for the individual-the most common being financial behavior like saving, spending and investing; health behaviors; education; and energy see use. Many also spent time on company driven outcome of product use and sales. (295-296)
There are challenges, and three main problems facing the field include practical problems of setting up and running a team; replication crisis in science; and ethical behavior. (296-299)
Behavioral science teams don't have a single design or structure; They often grow organically out of existing programs and departments, where people in those departments find that behavioral science can aid their work. (301)
Skills and people you will need on a team: 1) while some behavioral teams are centrally located centers of excellence, move many teams are embedded in product, design, marketing, analytics, or other functions. And, for these groups, the first skills that are needed are those used in the core work of the team. If you're applying behavioral science to product development that means design or product management, etcetera. If you're working on communications and marketing, and means knowing communications and marketing; 2) Impact assessment, we need to rigorously assess causal impact; 3) a knowledge of the minds quirks and of nudges that can affect behavior. (305-306)
Experimental testing, especially for outcomes that are outside of the product, can be an intimidating endeavor. Believe it or not, academic researchers would probably love to help test your products impact. Many of them can't be hired in a traditional sense-because they have full time jobs and academic institutions and for professional reasons can't accept consulting contracts. But you can build partnerships of mutual benefit if you have enough users of your product to support a scientifically valid study and know how to navigate the process. (308)
Data science often seeks to understand how something works and predict the future. Behavioral science seeks to change the future, particularly through changing human behavior. Because of these two different purposes, data scientists and behavioral scientists often use different statistical methods. Data scientists can predict the future very accurately and thoughtfully using variables that are correlated with the outcome of interest they use regressions, decision trees, neural networks, and such to find hidden relationships between contacts and outcome. Behavioral scientists, when possible, use experiments since they are the best tool to measure our ability to cause a change in behavior or outcomes. Analyzing experiments when properly designed, doesn't require advanced statistics at all simple comparison of means is often enough. Behavioral scientists do also use regression and sometimes machine learning techniques, but we do so in the service of understanding the causal relationship between context behavior and outcomes. Because of these two goals-- predicting outcomes versus causing behavior change-data and behavioral scientists also differ in how they use theory: and explanation of why something works the way it does. Many data scientists do have a theoretical understanding of what they study, and that helps them with feature selection and data analysis but it's not actually required. (310 - 311)
Three major conceptual tools were developed for the purposes of this book: 1) an understanding of how people make decisions and act in their daily lives; 2) a model of what's required for someone to take action relating to your product in a given moment (the CREATE action funnel); 3) a process for applying that knowledge to the practical details of product development (DECIDE on the behavioral intervention and build it). (313)
| | Kyle ChaykaNotable QuotationsExpand to full screen
Page 1
In 1769, a civil servant in the Habsburg Empire named Johann Wolfgang Ritter von Kempelen built a device nicknamed "the Mechanical Turk."
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Over the two centuries since its invention, the device has become a prevalent metaphor for technological manipulation. It represents the human lurking behind the facade of seemingly advanced technology as well as the ability of such devices to deceive us about the way they work.
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Algorithm is usually shorthand for "algorithmic recommendations," the digital mechanisms that absorb piles of user data, push it through a set of equations, and spit out a result deemed most relevant to preset goals.
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Algorithmic recommendations shape the vast majority of our experiences in digital spaces by considering our previous actions and selecting the pieces of content that will most suit our patterns of behavior. They are supposed to interpret and then show us what we want to see.
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All of these small decisions used to be made one at a time by humans: A newspaper editor decided which stories to put on the front page,
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Algorithmic recommendations are the latest iteration of the Mechanical Turk: a series of human decisions that have been dressed up and automated as technological ones, at an inhuman scale and speed.
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The algorithm always wins.
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Though Filterworld has also changed politics, education, and interpersonal relationships, among many other facets of society, my focus is on culture.
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guiding our attention
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Each platform develops its own stylistic archetype, which is informed not just by aesthetic preferences but by biases of race, gender, and politics as well as by the fundamental business model of the corporation that owns it.
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"harmonization of tastes." Through algorithmic digital platforms like Instagram, Yelp, and Foursquare, more people around the world are learning to enjoy and seek out similar products and experiences in their physical lives.
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"Surveillance capitalism," as the scholar Shoshana Zuboff has labeled it, is how tech companies monetize the constant absorption of our personal data, an intensification of the attention economy.
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We consume what the feeds recommend to us without engaging too deeply with the material.
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Our natural reaction is to seek out culture that embraces nothingness, that blankets and soothes rather than challenges or surprises, as powerful artwork is meant to do. Our capacity to be moved, or even to be interested and curious, is depleted.
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In place of the human gatekeepers and curators of culture, the editors and DJs, we now have a set of algorithmic gatekeepers.
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Attention becomes the only metric by which culture is judged, and what gets attention is dictated by equations developed by Silicon Valley engineers.
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The outcome of such algorithmic gatekeeping is the pervasive flattening that has been happening across culture.
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Flatness is the lowest common denominator, an averageness that has never been the marker of humanity's proudest cultural creations.
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culture of Filterworld is the culture of presets, established patterns that get repeated again and again.
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we can determine ways to escape it and resolve the omnipresent atmosphere of anxiety and ennui that algorithmic feeds have produced. We can dispel their influence only by understanding them— by opening the cabinet of the Mechanical Turk to reveal the operator inside.
Chapter 1: The Rise of Algorithmic Recommendations
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Algorithm as a term simply describes an equation: any formula or set of rules that produces a desired result.
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we're discussing a technology with a history and legacy that has slowly formed over centuries, long before the Internet existed.
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An executive at the music cataloging and recommendation service Pandora once described the company's system to me as an "orchestra" of algorithms, complete with a "conductor" algorithm. Each algorithm used different strategies to come up with a recommendation, and then the conductor algorithm dictated which suggestions were used at a given moment. (The only output was the next song to play in a playlist.) Different moments called for different algorithmic recommendation techniques.
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Recommendation algorithms as a way of automatically processing and sorting information were put into practice in the 1990s.
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"We need technology to help us wade through all the information to find the items we really want and need, and to rid us of the things we do not want to be bothered with."
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Social information filtering bypasses those problems because it is instead driven by the actions of human users, who evaluate content on their own—using judgments both quantitative and qualitative.
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even described it as "unnervingly accurate." Ringo's innovation was how it acknowledged that the best recommendations, or the best indications of relevance, were likely to come from other humans rather than analysis of the content itself. It represented a scaling up of human taste.
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PageRank worked by measuring how many times a website was linked to by other sites, similar to the way academic papers cite key pieces of past research.
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in the Internet era, sorting knowledge might be even more powerful. Information is now easy to find in abundance; making sense of it, knowing which information is useful, is much harder.
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Nick Seaver is a sociologist and a professor at Tufts University who studies recommender systems.
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"The algorithm is metonymic for companies as a whole," he told me. "The Facebook algorithm doesn't exist; Facebook exists. The algorithm is a way of talking about Facebook's decisions."
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algorithms can warp language itself as users attempt to either game them or evade detection.
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if people are using a platform, staying engaged and active, then it counts as successful—no matter what they are doing.
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it is difficult to think of creating a piece of culture that is separate from algorithmic feeds, because those feeds control how it will be exposed to billions of consumers in the international digital audience.
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Without the feeds, there is no audience—
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for a piece of culture to be commercially successful, it must already have traction on digital platforms.
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Under algorithmic feeds, the popular becomes more popular, and the obscure becomes even less visible.
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Success or failure is accelerated.
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Given that these capricious systems control so many facets of our lives, from socializing with our friends to building audiences for our creative projects, is it any wonder that social media users feel paranoid? We're encouraged to overlook algorithmic processes, but their glitches remind us of their unearned authority.
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The ambiguity of algorithmic influence creates a feeling that has been labeled "algorithmic anxiety."
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Airbnb forces a "double negotiation" for the hosts, the researchers wrote, because they must determine what their guests are looking for in a listing as well as which variables the algorithms are prioritizing to promote their property more often.
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platforms like Airbnb have long promised flexible work and alternative ways of making or supplementing a living, but they also created a new form of labor in the need to stay up to date on changes in algorithmic priorities.
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Algorithmic anxiety is something of a contemporary plague. It induces an OCD-ish tendency in many users toward hyperawareness and the need to repeat the same rituals, because when these rituals "work," the effect is so compelling, resulting in both a psychological dopamine rush from receiving attention and a potential economic reward if your online presence is monetized. It undergirds so many of our behaviors online: selecting the right profile picture, curating an attractive grid of photos on an Instagram account, choosing the right keywords on a marketplace listing.
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Exploitation is disguised as an accidental glitch instead of an intentional corporate policy. In reality, a company like Facebook is wholly in control of their algorithmic systems, able to change them at will—or turn them off.
Chapter 2: The Disruption of Personal Taste
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It was as if you could buy only the books that appeared on the New York Times bestseller list, but the list was operated by an untrustworthy company, one solely devoted to treating books as fungible objects to be offloaded as quickly as possible.
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Chet Haase in 2017 pinpoints the problem: "A machine learning algorithm walks into a bar. The bartender asks, ‘What'll you have?' The algorithm says, ‘What's everyone else having?'
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Taste is a word for how we measure culture and judge our relationship to it. If something suits our taste, we feel close to it and identify with it, as well as form relationships with other people based on it, the way customers commune over clothing labels (either loving or hating a particular brand).
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If taste indeed must be deeply felt, requires time to engage with, and benefits from the surprise that comes from the unfamiliar, then it seems that technology could not possibly replicate it, because algorithmic feeds run counter to these fundamental qualities.
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The feed structure also discourages users from spending too much time with any one piece of content.
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Korean philosopher Byung-Chul Han argued in his 2017 book In the Swarm, the sheer exposure of so many people to each other online without barriers—the "demediatization" of the Internet—makes "language and culture flatten out and become vulgar."
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Today we have more cultural options available to us than ever and they are accessible on demand. We are free to choose anything. Yet the choice we often make is to not have a choice, to have our purview shaped by automated feeds, which may be based on the aggregate actions of humans but are not human in themselves.
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Taste can also feel more like a cause for concern than a source of personal fulfillment. A selection made based on your own personal taste might be embarrassing if it unwittingly clashes with the norms of the situation at hand, like wearing athleisure to the office or bright colors to a somber funeral.
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Over the twentieth century, taste became less a philosophical concept concerning the quality of art than a parallel to industrial-era consumerism, a way to judge what to buy and judge others for what they buy in turn.
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Consumption without taste is just undiluted, accelerated capitalism.
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There are two forces forming our tastes. As I described previously, the first is our independent pursuit of what we individually enjoy, while the second is our awareness of what it appears that most other people like, the dominant mainstream.
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Pierre Bourdieu wrote in his 1984 book Distinction: A Social Critique of the Judgement of Taste. These choices can be symbolic of a range of things beyond just our aesthetic preferences, such as economic class, political ideology, and social identity. "Taste classifies, and it classifies the classifier," Bourdieu wrote. No wonder that we worry about what to like, and sometimes find it simpler to export that responsibility to machines.
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Online, users are often insulated from views and cultures that clash with their own. The overall digital environment is dictated by tech companies with ruthlessly capitalist, expansionary motives, which do not provide the most fertile ground for culture.
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Part of its appeal lies in breaking with the social code: wearing something unexpected or strange, even at times challenging your own taste.
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On the consumer side, the bombardment of recommendations can induce a kind of hypnosis that makes listening to, watching, or buying a product all but inevitable—whether it truly aligns with your taste or not.
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Your engagement is tracked by digital surveillance, and then you are served ads for products that match what you engage with, from brands that pay for your attention.
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Fascism means being forced to conform to the tenets of a single ideological view of the world, one that may utterly discount a particular identity or demographic. It is the mandate of homogeneity. Filterworld can be fascistic,
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With modern-day algorithmic recommendations, artists have much less choice in what becomes popular and even less control over the context that their work appears in.
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The Netflix algorithm factors in a user's viewing history and ratings; the actions of other users with similar preferences; and information about the content itself, like genre, actors, and release dates. It also includes the time of day the user is watching, what device they're watching on, and how long they tend to watch in that context.
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The Netflix algorithm slots users into particular "taste communities," of which there are more than two thousand. And there are more than seventy-seven thousand "altgenres" or niche categories,
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Netflix recommendations are less about finding the content that suits a user's preferences and more about presenting what's already popular or accessible, an illusion of taste.
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"Over time, if people are offered things that are not aligned with their interests often enough, they can be taught what to want….
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But the more automated an algorithmic feed is, the more passive it makes us as consumers, and the less need we feel to build a collection, to preserve what matters to us. We give up the responsibility of collecting.
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our cultural collections are not wholly our own anymore.
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The disappearance or overhauling of a particular app throws the content gathered there to the wind.
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Building a collection online more closely resembles building a sandcastle on the beach:
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The shifting sands of digital technology have robbed our collections of their meaning.
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Kabvina built his own narrative arc into his TikTok account, creating a social-media-era hero's journey. He studied the most popular accounts. Influencers like Charli D'Amelio and Emily Mariko became famous in part for getting famous, starting from anonymity. "The biggest trend I'd notice is…[followers] want a protagonist to take them on this journey," Kabvina said. He also carefully optimized his cooking videos according to the data TikTok gave him. Avoiding too much speaking or text made them appealing to a global audience—his food needed no translation. (It was a successful strategy; Mariko also became famous for her speech-less cooking videos.) The TikTok app reveals to creators at which point in a video users tune out and flip to the next video.
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If viewers were skipping at nineteen seconds, Kabvina would go back and examine the underperforming section, and then try to avoid its problems in the next video. Such specific data allowed him to optimize for engagement at every moment.
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Culture is continuously refined according to the excesses of data generated by digital platforms, which offer a second-by-second record of what audiences are engaging with and when and how.
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This perception that culture is stuck and plagued by sameness is indeed due to the omnipresence of algorithmic feeds.
Chapter 4: The Influencer Economy
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The tyranny of likes is in part a function of the algorithmic ecosystem we exist in online.
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Over time, a kind of inflation of likes occurred.
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Provocation inspires likes, since the like is a gesture of allegiance and agreement, a symbol of whether the user is on the side of the troll or the trolled. Outrage gets likes because the likes signal sympathetic outrage:
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The likes were not the only reward; they existed in a wider online attention economy that bled into the offline economy at large. Likes lead to attention. Attention leads to new followers; followers who liked and shared my work in turn. More followers led to a veneer of personal authority:
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And that reputation got me commissions from editors, part-time gigs, and full-time jobs, which drove me back to the beginning of that loop. Getting more likes felt like what I was supposed to be doing; it felt like work, and I was getting better at my job.
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commentators on contemporary culture, in 2021. "Algorithmic" has become a byword for anything that feels too slick, too reductive, or too optimized for attracting attention: a combination of high production values with little concern for fundamental content.
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Part of the fear of algorithmically driven art is the obviation of the artist: If viable art can be created or curated by computer, what is the point of the humans producing
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On one hand, this is a kind of democratization: Anyone can publish a book and give it a chance to be sold through the exact same channels, presented in the same way. There is no obstacle of a store's book buyer or the curation of a front table; just the math of the algorithm. The hyper-bestselling author Colleen Hoover provides an example of the opportunities. Hoover began by self-publishing her novels, which often fall into romance, thriller, and young-adult categories, on Amazon.
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On the other hand, the requirement of mass engagement is a departure from the history of literature, in which the opinions of editors and academics have mattered far more than how many copies of a book initially sells.
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It's that algorithms have shaped the overall cultural landscape, conditioning our tastes. Everything exists within the algorithmic context of passive, frictionless consumption.
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Rather than encouraging original artistic achievement, algorithmic feeds create the need for content that exists to generate more content: films that provide ready-made GIFs of climactic scenes to share on Twitter or TikTok and quippy lines that will inspire memes to serve as marketing. The need for engagement can encourage a capitulation to fanservice, or at least an attempt to do so.
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The superficiality of the word itself is indicative: "influence" is never the end point, only a means of communicating a particular message. An influencer is easiest to define by how they make money. Like a media company producing magazines or podcasts, they sell advertising shown to the audiences that they have gathered.
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audiences in in the first place is most often the influencer's personal life, their aesthetically appealing surroundings (as well as aesthetically appealing selves) and entertaining activities.
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influencers don't own the infrastructure of their medium.
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Fascination with a person, particularly their appearance or personal life, smoothing the way to self-promotion began long before the Internet era.
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Consumers have always cared about the lifestyle decisions of celebrities famous for something else:
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The influencer is something of a successor to the blogger, the star of the nascent mainstream Internet in the 2000s.
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While the early promise of social media was to connect users to their actual friends, over time inauthenticity became something to embrace.
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Individual influencers are less remarkable in this decade also because so many users of digital platforms are pressured to act like influencers themselves, constantly creating content, accruing an audience, and figuring out ways to monetize it—either immediately through literal advertising or more gradually through the attention of their peers.
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In Filterworld, culture has become increasingly iterative. It's harder for a creator to go straight to making a movie or publishing a book; she needs to first publish her sample material, describe her vision, and gather an audience online who are engaged fans of her work.
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This need to corral an audience in advance by succeeding on social media can be explained by the useful phrase "content capital." Established by the scholar Kate Eichhorn in her 2022 monograph Content,
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it describes the Internet-era state in which "one's ability to engage in work as an artist or a writer is increasingly contingent on one's content capital;
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That ancillary content might be Instagram selfies, photos of a painting studio, evidence of travel, tossed-off observations on Twitter, or a monologue on TikTok.
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It all builds an audience for the person, who remains a separate entity from the things that they make.
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the author's personal brand is now all that matters;
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it's the work itself that is dead.
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Eichhorn responds to the sociologist Pierre Bourdieu's 1970s concept of "cultural capital":
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Content capital, then, is fluency in digital content: the knowledge of what kinds of content to produce, how the feeds of various
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platforms work, what they prioritize, and how audiences might react to a given creation. Those who have more content capital gain more followers, and thus more power in the cultural ecosystem of Filterworld.
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more followers and more engagement are always posed as better.
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The primary incentive is to make the numbers go up.
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"One builds up one's content capital simply by hanging out online and, more precisely, by posting content that garners a response and, in turn, leads to more followers and more content,"
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She described that endless race: "Increasingly, what matters is simply that one is producing content and doing so at an increasingly high frequency and volume." Elsewhere in the book, Eichhorn puts it more simply and brutally: "Content begets content."
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exposure is not always personally affirming.
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it can often feel like there is no creativity without attention, and no attention without the accelerant of algorithmic recommendations.
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I decided long ago against fully adapting my voice to the algorithmic feed—
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I found that there were certain ways I could present the things I was doing to maximize my possible content capital. I labored over tweets to share my latest articles, trying to figure out what would get shared the most: a curiosity-gap headline that left a question open, perhaps, or highlighting the most dramatic quote in a story.
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Cultural flattening is one consequence. But the same mechanism is also what makes our public political discourse more and more extreme, because conflict and controversy light up the feed and attract likes in a way that subtlety and ambiguity never will.
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Over the past decade, a generation of "Insta-poets" have emerged on Instagram and sold millions of books to their followers by shaping their work to the structure and demands of the platform.
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There is an element of elitism at play in any evaluation that casts social media as the opposite of art.
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Blatant clarity and simple, literal takeaways versus linguistic difficulty and the need to accept irresolution: one aesthetic approach is not better or worse than the other; they are simply different sets of choices. Yet in Filterworld, we face a cultural environment that inevitably prioritizes the former over the latter because it travels more effectively through algorithmic feeds, and there are fewer and fewer outlets outside of those feeds available for creators to access the audiences they need to survive in such a capitalistic environment.
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Ultimately, the algorithmic feed may not be the death of art, but it often presents an impediment to it.
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There's a homogeneity to the kind of literature that influencers promote, too, narrowing down to the kinds of books that can accelerate through feeds.
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"The problems of homogeneity are not just that it is boring; the most or least offensive stuff rises to the top, because that gets clicks," Depp said. "This is the issue about whoever is succeeding on TikTok this week: People who have never read the book are going to make a video about it, because that's the
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trending topic. Things start out with genuine interest, but by the thousandth video about it, it has nothing to do with the thing itself."
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Hallie also realized that the Instagram feed rewarded specific qualities. She had always combined visual art and writing, but posts with clear written messages got the most engagement. "If I posted something pretty to look at, it didn't get as much of a response," she said. This effect isn't solely a consequence of the algorithmic feed; consumers have tastes that don't always mesh with an artist's own vision. But the acceleration of the feed and the instantaneousness of the feedback begets an intensified self-consciousness on the part of
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the artist
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The pressure that Hallie felt to make the rest of her artwork similarly bright, clear, and simple is much like the pressure that a musician feels to frontload the hook of a song so it succeeds on TikTok or a writer feels to have a take so hot it lights up the Twitter feed.
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That kind of internal creative process, or even the process of thinking on one's own, is something that feels lacking in the Filterworld era, when any idea or thought can be made instantly public and tested for engagement.
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The artist-as-influencer isn't introspective; she exists on the ephemeral surface of things, iterating and adapting according to reactions.
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Hallie's comments made me feel a kind of personal grief: Have I been left incapable of truly thinking for myself, or unwilling to do that creative work without the motivation of an invisible audience?
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"If I adapt to every trend, if I hop on every new platform and try to build a following there, I'm going to be building sandcastle after sandcastle. If the algorithm is failing us now, that means it was never stable. It was like a fair-weather friend."
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The recent history of the 2010s, with the rise and then growing irrelevance of Facebook, has shown that no social network is too big to fail or get supplanted by a competitor that chooses to play by a new set of rules, social or technological.
Chapter 5: Regulating Filterworld
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We cannot wholly opt out while still using the digital platforms that have become necessary parts of modern adult life. Like the post office, the sewer system, or power lines, they are essential, and yet, unlike such public infrastructure, they aren't subject to government oversight or regulation, or the decisions of voters. Recommender systems run rampant.
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In November 2017, a fourteen- year- old student from northwest London named Molly Russell died by suicide. Russell wasn't wholly responsible for her actions,
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Russell's death was part of the human toll of algorithmic overreach, when content moves too quickly at too vast a scale to be moderated by hand. No magazine's editor would have published a flood of such depression content, nor would a television channel broadcast it. But the algorithmic feed could assemble an instant, on-demand collection, delivering what Russell may have found most engaging even though it was harmful for her.
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Though so much of the content we see online is "user-generated"—uploaded freely, without either gatekeeping or support—it still has to fit into preestablished molds determined by corporations.
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as I could, I ventured out of that corporatized space and found a much wider Internet that was more decentralized again. People built their own HTML websites without oversight and often without much professionalism. The web was an amateur zone made up of handmade pages espousing some particular fandom (say, the TV show Gilmore Girls) or niche hobby (building canoes) that were easy to stumble upon with early Google searches. You could use a service like Geocities, which launched in 1994, to build and host a website using basic tools, but no two Geocities pages looked the same. They were quirky collisions of animated GIFs in messy frame layouts, as though a child had made them.
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Already, when Facebook bought Instagram, it felt as though the walls of the Internet were closing in a little tighter around us users. The broad expanse of possibility, of messiness, on a network like Geocities or the personal expression of Tumblr was shut down. Digital life became increasingly templated, a set of boxes to fill in rather than a canvas to cover in your own image.
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Google similarly acquired YouTube in 2006 and turned the video-uploading site into a media-consumption juggernaut, a replacement for cable television.
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There's a certain amount of whiplash that comes with experiencing these cycles of the Internet. We users think we're supposed to behave one way, and then the opposite becomes true, like the movement from pseudonyms to real names. We're asked to use tools to build our own spaces, to freely express ourselves, and then commanded to fit within a preset palette determined by a social network. Yet as soon as one standard becomes dominant, it seems to lose its grip.
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Any joy in the new forms of expression is ruthlessly exploited, most often in the form of increased advertising.
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Still, the Internet in its current era has never looked more monolithic. Individual websites have been subsumed into ever-flowing feeds.
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Decentralization tends to give users the most agency, though it also places a higher burden of labor and responsibility on the individual.
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The quickest way to change how digital platforms work may be to mandate transparency: forcing the companies to explain how and when their algorithmic recommendations are working.
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And if we know how algorithms work, perhaps we'll be better able to resist their influence and make our own decisions.
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Eli Pariser's filter bubbles,
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traditional media can be biased into homogeneity just as well,
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filter bubbles earlier in this book; the phenomenon may have done more to cause the surprise of Trump's win than the fact that it happened.
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Trump did take advantage of algorithmic technology. His campaign used Facebook's targeted advertising program to great effect, pushing messages to voters whose online actions showed that they might be convinced by his politics.
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Facebook ads are often bought based on outcomes rather than how many times they are displayed; the client pays for click-throughs and conversions to actions like political donations. The Trump campaign was all but guaranteed that the algorithmic feed would work in their favor.
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"With recommendation algorithms, you get the same kind of things over time. How do we break those patterns?"
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Mike Ananny and Kate Crawford wrote in a 2016 paper in the journal New Media & Society. Knowing how and why something has been recommended might help to dispel the air of algorithmic anxiety that surrounds our online experiences, since we could identify which of our actions the recommendations are considering.
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Just as digital platforms aren't responsible for explaining their algorithmic feeds, they also don't take responsibility for what the feeds promote—they separate themselves from the outcomes of their recommender systems.
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But in the social media era, it has also allowed the tech companies that have supplanted traditional media businesses to operate without the safeguards of traditional media.
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Social networks displaced traditional publishers by absorbing advertising revenue;
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Even with their restricted circumstances, traditional media companies continue to hold responsibility for every piece of content they publish. Meanwhile, digital platforms could claim they were not media companies at all with the excuse of Section 230.
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If algorithmic feeds mistreat us or contribute to an abusive or exploitative online environment, we, as users and citizens, have little recourse.
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The justices probed the uses and capabilities of algorithmic recommendations and debated if algorithms can be considered "neutral" (I would argue they cannot),
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In May 2023, the Supreme Court ruled that the tech companies were not liable, and upheld the strongest interpretation of Section 230 once again.
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mainstream Internet, and the 2010s saw the rise and domination of massive digital platforms, then the next decade seems likely to embrace decentralization once more. Agency might be the watchword: the ability of an individual user to dictate how they publish and view content. I have hope for an Internet that's more like Geocities, with expressions of individuality and customization everywhere, but with the multimedia innovations that have made the 2020s' Internet so compelling.
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As Molly Russell, the British teenager who died by suicide, experienced with the avalanche of depression content, recommendations accelerate negative material as much as positive material.
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Facebook outsources much of its human moderation to a company called Accenture, which employs thousands of moderators around the world, including in countries like Portugal and Malaysia.
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The toxic material doesn't just magically vanish because of the mediation of the algorithm. Once again, the human labor is obscured.
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Reaching wide audiences of strangers isn't a right; it's a privilege that doesn't need to be possible for every individual user or post.
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The word amplification describes algorithmic recommendations' role in spreading content more widely than it would otherwise travel:
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Amplification is at the core of Filterworld's problems;
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The rise of social media has created a new set of dynamics for culture and entertainment. Users have much more choice of what to consume at a given moment, and creators have a much easier time reaching audiences by simply uploading their content to the Internet. We don't have to just watch what a producer elects to put on cable television. We have come to expect individualization, whether driven by our own actions or an algorithm. But that seemingly more democratic and low-hierarchy dynamic has also given us a sense that the old laws and regulations don't apply, precisely because we can decide when to watch or listen to something and when to choose another source. We might have more independence, but we ultimately have less protection as consumers.
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If recommendations are to be regulated, certain decisions have to be made based on content.
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As they go into effect, these laws are likely to overhaul our algorithmic landscape, giving users much more agency when it comes to recommendations and the configuration of a content feed. The passive relationship would become a more active one as we begin to figure out our own preferences and shape our digital lives to follow our own tastes. Algorithmic feeds will appear less monolithic and impenetrable, as they do now, and more like the functional tools they are. There's no reason your feed needs to work exactly like my feed. The resulting profusion could lead to more diversity of culture online, as well.
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regulation cannot be the only answer when it comes to culture.
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we also must change our own habits, becoming more aware of how we consume culture and how we can resist the passive pathways of algorithmic feeds.
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The most powerful choice might be the simplest one: Stop lending your attention to platforms that exploit it.
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the more dramatic option is to log out entirely and figure out how to sustain culture offline once more.
Chapter 6: In Search of Human Curation
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If one form of algorithmic anxiety is about feeling misunderstood by algorithmic recommendations, another is feeling hijacked by them, feeling like you couldn't escape them if you tried.
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During my cleanse, I also discovered that recommender systems pop up in unexpected places. I eventually turned to the New York Times app as my primary way of checking in on news, but that app features a "For You" tab, much like TikTok's,
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Escaping algorithms entirely is nearly impossible.
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As I had hoped, I began reading long articles in a single sitting more often and left fewer tabs open in my browser, since I wasn't faced with a cascade of alternative options.
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By the second month of my experiment, when I had adjusted my habits, I began to feel a sense of nostalgia. It reminded me of how I interacted with the Internet as a teenager, back before mainstream social media existed.
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Despite my hesitancy around algorithmic feeds, I could never give up the Internet entirely, because it has brought me too much over my lifetime.
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On TikTok, it's harder to become a connoisseur because you have little chance to develop expertise or assemble the context of what you're looking at. You must work at it, get off the slick routes of the feed, and gradually refine the thing that you seek. The benefit of the slower, self- managed approach to culture is that it might lead to a greater appreciation of the content at hand,
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Curation begins with responsibility. The etymological ancestor of the word curatore was a term for ancient Roman "public officers," according to an 1875 dictionary, positions that predated the emperor Augustus, whose reign began in 27 BCE. They managed various aspects of the city's upkeep:
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Latin, curare meant to take care of, and curatio indicated attention and management.
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The word's etymology hints at the importance of curating, not just as an act of consumption, taste displaying, or even self-definition, but as the caretaking of culture, a rigorous and ongoing process.
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Those decades saw "the rise of the curator as creator," as the museum-studies scholar Bruce Altshuler put it in his 1994 book
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Avant-Garde in Exhibition.
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In a sense, the individual star curators are the opposite of recommendation algorithms: they utilize all of their knowledge, expertise, and experience in order to determine what to show us and how to do it, with utmost sensitivity and humanity.
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Yet algorithmic recommendations are also often described as "curating" a feed, even though there is no consciousness behind them.
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What is lost in the overuse of the word is the figure of the curator herself, a person whose responsibility it is to make informed choices, to care for the material under her purview.
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The Internet might have an overflow of curation, but it also doesn't have enough of it, in the sense of long-term stewardship, organization, and contextualization of content—
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The slow process of curation works against the contextlessness, speed, and ephemerality that characterizes the Internet.
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As I've written this book, independent radio DJs have stuck out in my mind as an ideal form of non-algorithmic cultural distribution.
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Curation is an analog process that can't be fully automated or scaled up the way that social network feeds have been. It ultimately comes down to humans approving, selecting, and arranging things.
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Another step toward a more curated Internet is to think more carefully about the business models that drive the platforms we use.
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In my conversations with curators, I found a tone of caring and caretaking that is missing entirely from massive digital platforms, which treat all culture like content to be funneled indiscriminately at high volume and which encourage consumers to stay constantly on the surface.
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Like tobacco companies manufacturing low-tar cigarettes, the algorithmic feeds create the problems they are marketed as solving.
Conclusion
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Walter Benjamin completed a revised version of his essay "The Work of Art in the Age of Mechanical Reproduction."
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Even in the short time of their rise, algorithmic recommendations have warped everything from visual art to product design, songwriting, choreography, urbanism, food, and fashion.
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In terms of how culture reaches us, algorithmic recommendations have supplanted the human news editor, the retail boutique buyer, the gallery curator, the radio DJ—people whose individual taste we relied on to highlight the unusual and the innovative. Instead,
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we have tech companies dictating the priorities of the recommendations, which are subjugated to generating profit through advertising.
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Resistance to algorithmic frictionlessness requires an act of willpower, a choice to move through the world in a different way. It doesn't have to be a dramatic one. | Nir EyalNotable QuotationsExpand to full screen
Hooked: How to Build Habit-Forming Products
Nir Eyal and Ryan Hoover
Companies leverage two basic pulleys of human behavior to increase the likelihood of an action occurring: the ease of performing an action and the psychological motivation to do
What distinguishes the Hook Model from a plain vanilla feedback loop is the hook’s ability to create a craving.
Variable rewards are one of the most powerful tools companies implement to hook users;
dopamine surge when the brain is expecting a reward.
Introducing variability multiplies the effect, creating a focused state, which suppresses the areas of the brain associated with judgment and reason while activating the parts associated with wanting and
exciting juxtaposition of relevant and irrelevant,sets her brain’s dopamine system aflutter with the promise of reward.
The last phase of the Hook Model is where the user does a bit of work. The investment phase increases the odds that the user will make another pass through the hook cycle in the future. The investment occurs when the user puts something into the product of service such as time, data, effort, social capital, or money.
Inviting friends, stating preferences, building virtual assets, and learning to use new features are all investments users make to improve their experience.
Tthis book teaches innovators how to build products to help people do the things they already want to do but, for lack of a solution, don’t do.
Habits form when the brain takes a shortcut and stops actively deliberating over what to do next.[xix] The brain quickly learns to codify behaviors that provide a solution to whatever situation it encounters.
Once the compulsion to play is in place and the desire to progress in the game increases, converting users into paying customers is much easier.
“Many innovations fail because consumers irrationally overvalue the old while companies irrationally overvalue the new.”
[Products that require]a high degree of behavior change are doomed to fail even if the benefits of using
the new product are clear and substantial.
Non-transferrable value created and stored inside these services discourages users from leaving.
For an infrequent action to become a habit, the user must perceive a high degree of utility, either from gaining pleasure or avoiding pain.
The complexity of the behavior and how important the habit was to the person greatly affected how quickly the routine was formed.
Higher frequency is better.
“Are you building a vitamin or painkiller?” is a common, almost clichéd question many investors ask
Painkillers solve an obvious need, relieving a specific pain and often have quantifiable markets.
Vitamins do not necessarily solve an obvious pain-point. Instead they appeal to users’ emotional rather than functional needs.
A habit is when not doing an action causes a bit of pain.
Addictions are persistent, compulsive dependencies on a behavior or substance.
The hooked model is trigger, action, variable reward, and investment.
[Provide] explicit instructions about what action to take. Too many choices or irrelevant options can cause hesitation, confusion, or worse,
Reducing the thinking required to take the next action increases the likelihood of the desired behavior occurring unconsciously.
Most [] companies generally use paid triggers to acquire new users and then leverage other triggers to bring them back.
Earned triggers: investment in the form of time spent on public and media relations.
Awareness generated by earned triggers can be short-lived.
One person telling others about a product or service can be a highly effective external trigger for action.
[Another is ] an engaged user base that is enthusiastic about sharing
Owned triggers consume a piece of real-estate in the user’s environment.
[It is] up to the user to opt into allowing these triggers to appear.
Without owned triggers and users’ tacit permission to enter their attentional space, it is difficult to cue users frequently enough to change their behavior.
The ultimate goal of all external triggers is to propel users into and through the Hook Model so that, after successive cycles, they do not need further prompting from external triggers.
When a product becomes tightly coupled with a thought, an emotion, or a pre-existing routine, it leverages an internal trigger.
Connecting internal triggers with a product is the brass ring of consumer technology.
Emotions, particularly negative ones, are powerful internal triggers and greatly influence our daily routines.
Positive emotions can also serve as internal triggers, and may even be triggered themselves by a need to satisfy something that is bothering
Email, perhaps the mother of all habit-forming technology, is a go-to solution for many of our daily agitations, from validating our importance (or even, simply our existence) by checking to see if someone needs us,
Why do people really send SMS messages? Why do they take photos? What role does watching television or sports play in their lives? Ask yourself what pain these habits solve and what the user might be feeling right before one of these actions.
What would your user want to achieve by using your solution?
[If] you want to build a product that is relevant to folks, you need to put yourself in their shoes and you need to write a story from their side.
Dorsey believes a clear description of users — their desires, emotions, the context with which they use the product — is paramount to building the right solution. In addition to Dorsey's user narratives, tools like customer development,[li] usability studies, and empathy maps[lii] are examples of methods for learning about potential users.
One method is to try asking the question "why" as many times as it takes to get to an emotion. Usually this will happen by the fifth “why.”
To initiate action, doing must be easier than thinking.
First, Hauptly says, understand the reason people use a product or service. Next, lay out the steps the customer must take to get the job done. Finally, once the series of tasks from intention to outcome is understood, simply start removing steps until you reach the simplest possible process.
Making a given action easier to accomplish spurs each successive phase
For companies building technology solutions, the greatest return on investment will generally come from increasing a product’s ease-of-use.
Without variability, we are like children in that once we figure out what will happen next, we become less excited by the experience.
Products must have an ongoing degree of novelty.
hTe need to feel social connectedness shapes our values and drives much of how we spend our time.
People who observe someone being rewarded for a particular behavior are more likely to alter their own beliefs and subsequent actions.
Works particularly well when people observe the behavior of people most like themselves,
Satisfaction in contributing to a community they care about;
Virtual kudos encouraged positive behavior
Leveling up, unlocking special powers, and other game mechanics fulfill a player's desire for competency by showing progression and completion.
Search for mastery, completion, and
Competence moves users to habitual, sometimes mindless, actions.
Codecademy seeks to make learning to write code more fun and rewarding. The site offers step-by-step instructions for building a web app, animation, and even a browser-based game. The interactive lessons deliver immediate feedback,
Quora instituted an upvoting system that reports user satisfaction with answers and provides a steady stream of social feedback.
Gamification will fail because of a lack of inherent interest in the product or service offered.
The magic words
The phrase, “but you are free to accept or refuse.”
[We] more likely to be persuaded when our ability to choose is reaffirmed.
“reactance,” [is] the hair-trigger response to threats to your autonomy.
To change behavior, products must ensure the users feel in control.
[T]he only way to know how Walter [the hero] gets out of the mess he is in at the end of the latest episode is to watch the next episode.
An element of mystery is an important component of continued user interest.
The most habit-forming products and services utilize one or more of the three variable rewards types of tribe, hunt and self.
Frequency of a new behavior is a leading factor in forming a new habit.
The second most important factor in habit formation is a change in the participant’s attitude about the behavior.
It must occur with significant frequency and perceived utility.
[E}scalation of commitment: The more users invest time and effort into a product or service, the more they value it. Labor leads to love.
The more effort we put into something, the more likely we are to value it. We are more likely to be consistent with our past behaviors. And finally, we change our preferences to avoid cognitive dissonance.
The last step of the Hook Model is the Investment Phase, the point at which users are asked to do a bit of work. Here, users are prompted to put something of value into the system, which increases the likelihood of them using the product and of successive passes through the hook cycle.
ask [...] for the investment after the reward,
| | Reid Hoffman
Notable QuotationsExpand to full screen
Page 9
Much of what we do as modern people—at work and beyond—is to process information and generate action. GPT-4 will massively speed your ability to do these things, and with greater breadth and scope. Within a few years, this copilot will fall somewhere between useful and essential to most professionals and many other sorts of workers. Without GPT-4, they'll be slower, less comprehensive, and working at a great disadvantage.
Page 14
GPT-4 doesn't have the equivalent of a human mind. It's still helpful to think in terms of its "perspective," anthropomorphizing it a bit, because using language like "perspective" helps convey that GPT-4 does in fact operate in ways that are not entirely fixed, consistent, or predictable. In this way, it actually is like a human. It makes mistakes. It changes its "mind." It can be fairly arbitrary.
Page 15
They can also sometimes generate replies that include factual errors, explicitly nonsensical utterances, or made-up passages that may seem (in some sense) contextually appropriate but have no basis in truth.
Page 16
Even with various guardrails in place, an LLM itself can't make reasoned judgments about complex ethical quandaries, or even about more straightforward questions.
Page 20
in your overall quest for authoritative information, GPT-4 helps you start somewhere much closer to the finish line than if you didn't have it as a resource.
Page 22
But when human users treat GPT-4 as a co-pilot or a collaborative partner, it becomes far more powerful. You compound GPT-4's computational generativity, efficiency, synthetic powers, and capacity to scale with human creativity, human judgment, and human guidance.
Creativity
Page 58
Reid: GPT-4, once large language models are fully developed and deployed, what would you suspect will be the worst effects on the quality of overall cultural production? (200 words) (less wooden style than usual)
Page 58
Homogenization and loss of diversity: Large language models could generate massive amounts of content that mimic existing styles, genres, and trends, but lack originality, creativity, and authenticity. This could result in a saturation of the cultural market with bland and repetitive products that appeal to the lowest common denominator and discourage innovation and experimentation.
Journalism
Page 84
But transparency and accountability are the true north of any society that aspires to truth- seeking. And, in a world overwhelmed by misinformation, disinformation, and simply too much information, it becomes especially necessary for truth- seekers to live the values they work to preserve.
Page 89
Throughout this leg of my journey, I've asserted that the spread of AI tools like GPT-4 will create once-in-a-generation opportunities for journalism and journalists.
Page 89
It's hard to embrace risks that lead to growth when you've been stuck so long in survival mode.
Page 89
when the need for principled truth-seeking is more pressing than ever, there are clearly opportunities, especially for those who can figure out novel ways to capitalize on new AI tools as they come online. Leveraging new technologies' power is one of the main ways the journalism industry grew in the past, and probably the main way it can do so again.
Social Media
Page 96
when my co-founders and I launched LinkedIn twenty years ago, we were motivated largely by the fact that the divisions between "cyberspace" and "the real world" were rapidly collapsing. Instead of existing as a place that people "went to" under the cover of pseudonymous screen names, the internet had evolved into a place that people were using to facilitate their lives.
Transformation of Work
Page 104
In my opinion, ignoring AI is like ignoring blogging in the late 1990s, or social media circa 2004, or mobile in 2007. Very quickly, some degree of facility with these tools will be increasingly essential for all professionals, a primary driver for new opportunities and new jobs.
Page 105
knowledge workers are also facing these challenges. While I strongly believe that these new AI tools will create new jobs and new industries, along with great economic benefits and other quality-of-life gains, they will also eliminate some jobs, both blue- and white-collar.
Page 105
To navigate this moment most effectively, though, we must also do so with an adaptive, forward- looking perspective. In my mind, that means embracing AI in the same spirit that we once embraced the Model T and the Apple II.
Page 118
believe that the future will see the sales profession shrink as a whole. At the same time, the productivity of individual sales professionals will increase, and likely their compensation as well. And the AI-driven increased quality of selling means that companies that aggressively adopt these tools will beat any competitors that don't.
Page 119
can see how AI could equal or exceed the work of human clerks and paralegals for conducting patent searches, digging through discovery data, or searching for red flags in long, boring contracts. Leveraging AI might also be a good first step before bringing in an (expensive) outside expert, or to make a lawyer's usage of such an expert more effective.
GPT-4 In My Own Work
Page 126
Principle 1: Treat GPT-4 like an undergrad research assistant, not an omniscient oracle.
Page 126
Principle 2: Think of yourself as a director, not a carpenter.
Page 127
Principle 3: Just try it!
When AI Makes Things Up ("Hallucinations")
Page 152
believe LLMs have the capacity to answer a much wider range of questions than Wikipedia or any other source; I believe they can answer these questions faster; and I believe they can do so through an intuitive interface that makes information retrieval highly accessible to a wide range of users.
Homo Techne
Page 196
Technologies are never neutral. We embed the tools and systems we create with specific values and specific intents, and assume that they will produce specific outcomes. This doesn't necessarily limit their potential uses. A car can be a weapon, a life-saving device, a place to sleep, and many other things, but that doesn't make it "neutral." Above everything else, a car is a technology that prioritizes effortless and extremely powerful mobility—and it ends up having much different impacts on the world than, say, a horse-drawn carriage or a bicycle.
Page 196
But if it's detrimental to society to claim that "technology is neutral" in order to evade responsibility for tech's potential negative outcomes, so is invalidating a technology simply because it has a capacity to produce negative outcomes along with positive ones.
Conclusion: At the Crossroads of the 21st Century
Page 209
The paradox of the AI era is this: as today's imperfect LLMs improve, requiring less and less from us, we will need to demand more from ourselves. We must always insist on situating GPT-4 and its successors as our collaborative partners, not our replacements. We must continue to figure out how to keep human creativity, human judgment, and human values at the center of the processes we devise to work with these new AI tools, even as they themselves grow more and more capable. | Irving Seidman Notable QuotationsExpand to full screen
Interviewing as Qualitative Research: A Guide for Researchers in Education and the Social Sciences, 4th Ed. Irving Seidman
It is this process of selecting constitutive details of experience, reflecting on them, giving them order, and thereby making sense of them that makes telling stories a meaning-making experience.
At the root of in-depth interviewing is an interest in understanding the lived experience of other people and the meaning they make of that experience.
Interviewing research takes a great deal of time and, sometimes, money. The researcher has to conceptualize the project, establish access and make contact with participants, interview them, transcribe the data, and then work with the material and share what he or she has learned.
The word interviewing covers a wide range of practices. There are tightly structured, survey interviews with preset, standardized, normally closed questions. At the other end of the continuum are open-ended, apparently unstructured, anthropological interviews that might be seen almost, according to Spradley (1979), as friendly conversations.
A phenomenological approach to interviewing focuses on the experiences of participants and the meaning they make of that experience. While focusing on human experience and its meaning, phenomenology stresses the transitory nature of human experience.
We seek our participants’ point of view of their experience.
Through observation we can observe others’ experience from our point of view. In interviewing guided by phenomenology, we strive to understand a person’s experience from their point of view
We must be modest about our expectations.
It is never possible to understand another perfectly.
If we could do that, we would be that other person.
Lived experience is what we experience as it happens, but we can only get at what we experience after it happens through a reconstruction of that experience
“The aim of phenomenology is to transform lived experience into a textual expression of its essence”
the focus on lived experience accessed through language provides the rationale for taking the words our participants use seriously and following up on them when appropriate.
the meaning people make of their experience affects the way they carry out that experience
By asking participants to reconstruct their experience and then reflect on its meaning, interviewers encourage participants to engage in that “act of attention” that then allows them to consider the meaning of a lived experience.
As in language, context is crucial to understanding the meaning of participants’ experience from their point of view.
Interviewing allows us to put behavior in context and provides access to understanding their action.
we choose to interview participants if at all possible who are currently engaged in those experiences that are relevant to the study.
we take the time to establish a contextual history for the participants’ current experience.
exploring the meaning of peoples’ experiences in the context of their lives.
The first interview establishes the context of the participants’ experience. The second allows participants to reconstruct the details of their experience within the context in which it occurs. And the third encourages the participants to reflect on the meaning their experience holds for them.
We ask for stories about their experience in school as a way of eliciting details.
The three-interview structure works best, in my experience, when the researcher can space each interview from 3 days to a week apart.
the development of the relationship between the participants and the interviewers positively.
the fact is that interviewers are a part of the interviewing picture. They ask questions, respond to the participant, and at times even share their own experiences.
we recognize and affirm the role of the instrument, the human interviewer.
by interviewing a number of participants, we can connect their experiences and check the comments of one participant against those of others.
Internal consistency over a period of time leads one to trust that she is not lying to the interviewer.
Rather than seeking a “disinterested” position as a researcher, the interviewer needs to understand and affirm his or her interest in order to build on the energy that can come from it.
How will your work build on what has been done already?
What do they get out of participating? What do they risk?
| Steve PortigalNotable QuotationsExpand to full screen
The goal here is to make it clear to the participant (and to yourself) that they are the expert and you are the novice.
You should definitely talk about yourself if doing so gives the other person permission to share something.
Stories are where the richest insights lie, and your objective is to get to this point in every interview.
Falling back on naturalistic observation is disingenuous; it’s not easy for participants to pretend you aren’t there and just go on as they would normally.
Instead, leverage the constructed nature of your shared experience.
you are joined together in this uncommon interaction.
“What I want to learn today is...”
Listening is the most effective way you can build rapport. It’s how you demonstrate tangibly to your participants that what they have to say is important to you.
you can also demonstrate that you are listening by what you do say.
follow-up
“Earlier, you
told us that...” or “I want to go back to something else you said....”
signal your transitions: “Great. Now I’d like
learn to silently affirm with facial expressions and head-nods, and throw in the vocalization only occasionally.
Check your worldview at the door.
cultivate your own general, non-specific curiosity.
Embrace how other people see the world.
Focus on them and be very selective about talking about yourself.
There’s a significant amount of preparation involved before you begin asking the users anything.
You probably don’t know what you don’t know, which is why you are using interviews as your research method.
contextual research
make the objectives your initial priority.
first interviews
should be with the
stakeholders—these
consumers of the research findings
History with the organization
Current beliefs about the customer,
Organizational or other barriers to be mindful
Business objectives for the project and specific questions the research should answer
Concerns or uncertainty around the methodology
created a document that summarized the project as we understood it at the time, including the agreed-upon methodology and the complete set of five research goals.
facilitator guide
many organizations truly don’t have the time or budget required for full-blown research.
The prototypes served more as props to foster discussion about visions of the future than actual artifacts to be evaluated.
Finding participants is a crucial part of preparing for fieldwork,
“guerilla”
“intercept”).
The first step
is to identify
the key characteristics for your sample.
RECRUITING IS DATA
The field guide (sometimes
called an interview guide or more formally, a protocol) is a document that details what will happen in the interview
The general flow of most interview guides is: • Introduction and participant background • The main body • Projection/dream questions • Wrap up
I prefer to write most questions as I might ask them
rather than as abstracted topics
As I’m writing the field guide, I’m leading a mock interview in my head.
Remember, this is not a script. It reads very linearly, but it’s really just a tool to prepare to be flexible.
, two interviews a day is reasonable. The schedule is at least partly informed by participant availability, so you may end up with an early morning interview, several hours of free time,
Participant Releases and Non-Disclosure Agreements
• Consent: Being in the study is voluntary, and the participant can stop at any time. • Incentive: The amount
Model release: Images and video will be used without giving the participant any rights of approval. • Non-disclosure: The participant is obligated not to reveal anything about concepts he may see.
BE CREATIVE ABOUT INCENTIVES
You want a simple and direct way to demonstrate your enthusiasm and appreciation.
You should consider the interview itself as a platform and try to organically integrate a larger set of techniques.
By asking, “What is your process for updating your playlists?” we are actually learning the answers to the (unasked) “How do you feel about the process for updating playlists?” and “What are the key steps you can recall in the process for updating playlists?” That information is very important, but it may not be sufficient to really understand the user’s situation.
Participatory design,
There’s a difference between what you want to know and what you ask.
Interviewer Sidestep and turn the question back to them: “Is that important to you?” “What would you expect it to be?”
I urge clients to represent their ideas in lower, rather than higher, fidelity.
lower-fidelity prototypes are best for getting reactions earlier in the process
High fidelity is not an all-encompassing term.
“looks like” versus “works like.”
high fidelity along one dimension but not another.
• Storyboard: An illustration, typically across multiple panels, depicting a scenario.
• Physical mock-up: A representation of a physical product that can be touched, opened, and so on.
storyboards showing the different scenarios
Physical mock-ups
make the conversation about a future product tangible.
Wireframe: A simplified version of an on-screen interface. This could be printed, sketched on paper, or a combination.
• Casual card sort:
a way to prompt a discussion about a large set of items.
Images that resonate:
Laminated image cards are used to provoke individual reactions and uncover hidden associations.
use homework as a way to prime participants about a topic
more introspective about something they may not pay attention to otherwise.
self-documentation (sometimes called journaling or a diary study)
Not only do you have an extensive set of examples to discuss, but you also have a participant who has been thinking about a topic a lot more than she normally does.
beat sheet
From social psychology, we know that even the presence of others will influence behavior,
We aren’t the experts. The people we are interviewing are the experts. We want to gather their stories and opinions, and to hear what they have to say without influencing them.
Use open-ended questions.
Less good: “What are three things
you liked about using the bus?” Good: “Can you tell me about your experience using the bus?”
assign explicit roles
Once You Get On-Site Once you get on-site, you’ll find these different stages: . Crossing the threshold . Restating objectives . Kick-off question . Accept the awkwardness . The tipping point . Reflection and projection . The soft close
Before you arrive, figure out what you are going to say.
social graces matter.
“Before we get started.” Specifics will vary depending on the study, but in general, ethically and legally, the interview shouldn’t start until your participant has signed whatever forms you’ve planned for.
don’t project
Let the participant know what to expect by giving a thumbnail outline of the process:
Engage your participant: “Do you have any questions for us right now?”
Kick-Off Question
“Maybe introduce yourself and tell us about what your job is here?”
Accept the Awkwardness
Be patient and keep asking questions and keep accepting, acknowledging, and appreciating her responses.
The Tipping Point
the participant shifts from giving short answers to telling stories
Reflection and Projection
The Soft Close
After you ask a question, be
silent. This is tricky; you are speaking with someone you’ve never spoken to before.
One way a novice interviewer tries to counteract nervousness is by preemptively filling the silence.
novice interviewer is suggesting possible responses,
After she has given you an answer, continue to be silent. People speak in paragraphs, and they want your permission to go on to the next paragraph.
rest for another beat.
By simply not asking your next question, you can give your interviewee time to flesh out the answer they’ve already given you.
With some participants, it takes me most of the interview to align my pacing with theirs.
Skype effect.
Some people just have different natural rhythms. There’s no magic fix,
signal your lane changes.
acknowledges the most recent answer and points the way toward the next,
be vigilant.
Using silence as a mechanism to elicit participants to talk
three broad categories: setting-the-stage silence, effort silence, and failure silence.
indicate readiness for a shared experience.
Setting-the-stage silence is created partly because silence is considered a more deeply shared experience than talking—a version of that exists in many cultures—and partly showing mutual respect and mutual humility for the other’s expertise.
silence indicates making an effort to help the cause along.
The tones of silence to watch for are silence indicating resistance and silence indicating confusion.
silence has the possibility to enrich mutual comprehension.
successful Japanese silence is a roomy empty space that, created by both parties, helpfully exists to allow communication.
Are you asking the question in a way they can answer?
Questions that gather context and collect details:
• Ask about sequence.
• Ask about quantity.
• Ask for specific examples.
Ask about exceptions.
• Ask for the complete list.
This will require a series of follow-up questions—for example, “What else?”
Ask about relationships.
Ask about organizational structure.
Ask for clarification.
Ask about code words/native language.
Ask about emotional cues.
Ask why.
Probe delicately.
Probe without presuming.
• Explain to an outsider.
• Teach another.
Compare processes.
Compare to others.
Compare across time.
There’s a lot that can happen without verbalization—posture, gestures, breath sounds, eye gaze, facial reactions, and more.
collaborative use of silence
We work in a society that judges us primarily by our own contributions, rather than the way we allow others to make theirs. If the collaborative silence is not
shared value in a group, there can be a real challenge for those who default to listening, not speaking.
Managing the Ebb and Flow of the Interview
your job also includes managing this tree.
Wait patiently until these threads come up again
Jot quick notes on your field guide
so you don’t forget.
Prioritize
be opportunistic
Triage based on what makes the best follow-up, in order to demonstrate listening and further the rapport.
Embracing Your Participant’s Worldview
Use Their Language
Letting go of being right is something to pay attention to in most interviews;
Assume Your Participant Makes Sense
Don’t Presume They Accept Your Worldview
Don’t Enter Lecture Mode
If You Have to Fix Something, Wait Until the End
you simply can’t catch everything by taking notes.
you should be recording your interviews—something
some people find that taking notes helps them filter, synthesize, and ultimately better remember what is being discussed.
maintain eye contact while writing.
When taking notes, you should be descriptive, not interpretive.
If it’s crucial to capture your interpretations, be sure to separate them from your observations, using capitalization or some other visual cue,
Pick a space that is quiet and bright enough to see the color of your respondent’s eyes.
People tend to look best when light comes from the side and slightly in front of them (up to a ° angle).
When setting up your camera, place it in front of your respondent, with the moderator in between it and the light or window
avoid having your participants in front of a window;
When you make the deliberate choice to point and shoot, you are building the story of your participant.
Sketching can be an appropriate medium when you can’t take pictures.
Collect tangible examples from your fieldwork experience—buy an item from the company store, ask for a brochure, save your security pass, or keep the sample printout. These artifacts can go up on the wall in your analysis room,
allow time for debriefing after each interview.
The longer you wait
the less you will remember, and the more jumbled up the different interviews will become.
make sure that someone takes notes
use a debrief worksheet
As soon as possible after an interview, I write a rapid top-of-mind version of the session.
This chapter looks at some of the more common challenges that you will face in the field.
When the Participant Is Reticent
If you conclude that he is indeed uncomfortable, try to identify the cause and make a change
If you aren’t giving your participant enough verbal space to reflect and respond,
slow down and let him talk.
If all else fails, consider asking your participant outright to identify the source of his discomfort.
GETTING THE RIGHT PARTICIPANT AND THE RIGHT CONTEXT
From this interview, it emerged that we all had different ideas of what “sharing” meant.
When the Participant Won’t Stop Talking
Give them space to tell the story they’ve chosen to tell you and then redirect them back to your question.
Your last resort is to interrupt.
frame it appropriately—“Excuse
When You Feel Uncomfortable or Unsafe Unless you are going to a public
or familiar corporate location, don’t conduct interviews on your own.
Pay attention to the difference between unsafe and uncomfortable.
Phone interviews are a fairly common alternative to face-to-face interviews,
Ask participants
During a phone interview, a lack of facial cues makes it a bit harder to adjust your pace and rhythm to the participant. Experiment with giving your participant an extra beat of silence to ensure she feels permitted to speak,
If you use technologies like Skype
Not everyone is fully literate in video conferencing. Consider your audience. You might want to warm up the interview with a discussion of the communication context
When Your Interview Is in a Market Research Facility
It would be a mistake to consider these facilities as neutral third places.
Even if you don’t feel settled in this new environment yourself, you must welcome
them into your space.
When Your Interview Is Very Short
Get them thinking about your topics by emailing them some key questions to think about.
Depending on what you need to do when interviewing professionals, you need to be very specific in your interview request—duration, environment, role, and so on.
Interviewing Multiple Participants
If need be, you can break the interview into separate chunks for each participant individually and for the group together.
using eye contact and specific probes directed at individuals to encourage them to contribute.
spend the first part of the interview understanding the participant’s workflow, objectives, pain points, and so on.
If you aren’t interested in that amount of detail and just want reactions to your prototype, you’re better off doing usability testing, not interviews.
Don’t forget that interviewing is like any skill: the more you practice, the better you get.
Take advantage of brief everyday encounters (say, that loquacious taxi driver) and do a little bit of interviewing, asking questions and follow-up questions.
each interview is also a learning experience.
Seek out opportunities to be interviewed yourself.
Sign up for market research databases or volunteer for grad student studies.
watch someone’s technique. Teach someone else
Check out interviews in the media:
Watch and listen as an interviewer, not just an audience member.
Exchanging these stories is a way of sharing techniques and creates learning opportunities for both the tellers and listeners.
Take an improv class.
meditation can help you be present
It’s not only to learn about people, but also to take the information back to the organization in a way that it can be acted upon.
new products, features, services, designs, and strategies, but also new opportunities for teams to embrace
Working with research data is a combination of analysis,
and synthesis,
Topline is based on early impressions, not formal analysis of data. This is a chance to share stories and initial insights from the fieldwork; to discuss what
jumped out at us and list questions we still have.
I create topline reports in Microsoft Word. A Word document is more formalized than an email but less formal than a
PowerPoint presentation, and this is the balance I’m trying to strike. Ongoing dialogue is usually in email; the final presentation is in PowerPoint. This deliverable is right in the middle.
Each team member should go through his portion of the transcripts quickly, making short
marginal notes on patterns, key quotes, or whatever seems interesting
Discuss each interview briefly, and then create a sticky note that summarizes the key point of that interview.
As you are accumulating stickies, take a few moments to create groupings.
A precise articulation of that point of view, including the implications for business and design, becomes the Presentation of Findings, which is the main research deliverable.
You shouldn’t just be looking for opportunities to do user research yourself; you should be trying to get the company to embrace this overall approach.
The most impact for the least effort comes from your colleagues joining you in the field.
Make your process visible.
Articulate research findings in
ways that are most relevant to your stakeholders.
articulate specifically what the engineers should do,
User research starts to look like design, doesn’t it?
Look for as many possible audiences and venues to share your results.
profile posters, telling an engaging, visual story about an individual customer. The accumulated set of posters in the user research team’s workspace raised awareness of that team’s role.
plenty of face time with the teams that will use your research.
| Uijun ParkNotable QuotationsExpand to full screen
A UX designer is a person who discovers problems that users experience and solves them. In other words, a good UX designer should understand users and know how to find problems and solve them. ... UX design is about process, not just results. ... Design thinking provides a straightforward and clear explanation of discovering, defining, and solving problems you face through five steps: empathize, define, ideate, prototype, and test. (Page 10) UX designers engage in a variety of activities, including user research to identify pain points and solve problems through prototyping and design. They are primarily responsible for advocating for users within the company… (Page 22) …it has become possible to design apps and websites scientifically through data-driven experiments that cater to users' needs. (Page 24) In the early and mid-2000s, the term 'web design' was more commonly used before 'UX design' became as popular as it is today. Back then, website design was often driven by subjective factors like aesthetics, without a quantitative understanding of how design choices impacted the product. However, designers now have a powerful tool in data, which enables them to objectively evaluate the effectiveness and usability of different design options. (Page 25) In addition, qualitative feedback from user interviews and observations can provide designers with a more nuanced understanding of users and their needs. By listening to users and identifying their pain points, designers can create a better product that addresses those issues and provides a more satisfying user experience. (Page 25) (Statistics Source: UXeria) 94% of a user’s first impressions are design-related. As many as 88% of online customers declare that they don’t really return to websites that were not usable / user-friendly. 60% of users don’t find the information they were looking for at a website. Only 47% of websites properly embed a "call to action" button, e.g. "Buy", so that users can notice it no later than 3 seconds after entering the website… (Page 26) Once you understand UX, you will realize that it's not your fault or a mistake when you find the product difficult and uncomfortable. (Page 27) Many people associate UX solely with digital experiences like mobile apps or websites. However, UX can also encompass physical experiences. Anything you do with your five senses, such as smelling, seeing, touching, and so on, can fall under UX. (Page 28) What's important here is to plan and design a product or service from a user's point of view. ... Designers should prioritize whether they have fully understood and considered users rather than their own intuition or experience. ... create a product that is good for users, not for designers. (Page 29) …the designer's responsibility to create a product that's easy and intuitive to use. Therefore, designers must prioritize designing experiences that minimize user inconvenience. (Page 30) …emphasizes that it's the designer's responsibility if users make mistakes or experience discomfort. (Page 31) What is a good UX design? It's a design that solves a user's problem. So what does it mean to solve a user's problem? It is about getting constant feedback from users and creating or improving products and services from a user perspective. As a designer, you should repeat and iterate until the user is satisfied, rather than trying to solve a problem at once. (Page 32) The UI stands for User Interface, and UI design can be defined as follows: Designing the visual area of the product users interact with. When a user interacts with a product, there are various factors that can influence their experience, such as the color scheme, text, and layout of the screen. These visual elements can play a significant role in determining how easy and enjoyable it is for users to navigate and use the product. (Page 33) UI design as the process of creating a set of rules that dictate the visual language of a product. ... With rules, users experience clarity and consistency when moving between screens in an app or website. For example, if the main buttons in an app are inconsistent, with some red on the screen and some yellow on other screens, it can be difficult for users to find buttons easily. (Page 34) UX design tasks cover user research to understand users deeply. These include 1:1 user interviews, quantitative data surveys, user flow, wireframe creation, and user testing to evaluate usability. UI design includes most of the activities for visualizing high-fidelity prototypes based on the foundation created from the UX design phase. It includes color and font definitions, icons, layouts, and visual design systems. (Page 35) If you subtract a user from the UX/UI design, it doesn't mean anything, and user research gives you access to understanding users. (Page 36) 'UX design' to refer to a broader concept that includes UX, UI, and user research. (Page 37) No matter how great the technology is, it's useless unless it's what users want. (Page 40) "Design thinking consists of 5 steps: Empathize - Define - Ideate - Prototype - Test." (Page 41) Empathize. As a designer, you will discover several problems users experience based on their feedback, opinions, and data. The second step defines the core problem to focus on. You will also need to determine the target user. The third step, Ideate, is the time to come up with ideas about how to solve the target user's problem. The Prototype is used to develop and upgrade these ideas and create a prototype to test with the user. The Test is to show the prototype to the user and observe to see if there are any improvements needed, and if the product solves the user's problem. (Page 42) The most critical out of the five steps of design thinking is the Empathize step. UX design is about solving problems that users experience. (Page 44) Once you've set your goals, start with user research to understand users and find problems that users are experiencing. (Page 46) …find out what people use the products, what age group they are in, what region they usually live in, and so on. (Page 46) The data obtained from the demographic survey can also define the persona of the target user. (Page 47) One-on-one in-depth interview is one of the most effective activities to learn about users' thoughts and intentions. (Page 47) Creating a script to conduct an interview is essential. If you don’t have a detailed plan and the interview script, the questions you will be asking may not be consistent when interviewing multiple users. (Page 48) I review applicants' UX portfolios with the following criteria. What effort did applicants make, and what activities did they do to define the target user and the primary user problem? Many applicants, however, only showed final design results and did not explain who they targeted, what problem they wanted to solve, or how they came up with the pain point from users. (Page 51) No matter how cool or pretty, the finished product is meaningless if I don’t define the right problem. (Page 51) You need to prioritize problems because you can only create and test prototypes for a specific problem at a time. (Page 52) When creating a product, you must clarify who you are targeting. (Page 53) …products targeted at young people in their 20s and those targeted at older adults in their 70s should be designed differently, including font size, color choice, and voice of the copy. (Page 54) …it’s more likely that a better idea comes up when multiple people with different perspectives having a deep discussion, and validation with users and market, and refinement. (Page 56) …if developers are involved in the idea-producing process, they can help come up with creative but practical ideas. And they can also provide immediate feedback on the feasibility of ideas. (Page 57) Your team can ask questions like "How much value will this update bring to users?" and "How urgent is it to fix it for users?". It is always best to strive for maximum impact with minimum effort. (Page 63) The applicant who is good at telling the story behind the idea, rather than just showing off how original the idea is, will be seen as a better candidate. (Page 66) …doing competitive research and benchmarking can be a good skill if you can refine and use the ideas that already exist in the field to fit your products and solve the problem for your users. (Page 67) A wireframe comprises text elements or shapes with minimal colors used. Those define how buttons, links, and elements work on a screen. (Page 75) …workflow helps the reader of the wireframe understand how things are connected and interact on the app or a website. (Page 75) The user flow summarizes what screen the user can go to and what tasks are given next. It provides details for all possible use cases when the user opens and interacts with the app. (Page 79) Prototyping can connect multiple prototyped screens to give users the same feeling as using a specific screen of an app, so the team and you can naturally listen to user feedback. (Page 81) …you should create the final design deliverables and share them with your developers. This is called design hand-off. Design hand-off should include everything from color codes and exact font sizes to detailed values (down to the pixel) for each object and precise spacing between objects. Once developers receive the hand-off, they can start developing immediately. (Page 83) By letting the user see and use the prototype and observing them, you can ask them if they find the prototype challenging to use, and you can hear what they think while using the prototype. As you do this before the release of a new update, you can find problems or bugs and solve them before engineers invest their time. (Page 86) Post-release is an opportunity to see how updated features or products perform. You can measure user traffic, conversion rate, and many other metrics to evaluate whether your product has achieved initial goals. (Page 87) For an A/B test, you can modify the wording (copy), function, color, etc., from the previously released product version and make the revised version available to users on the app or website simultaneously. (Page 89) …you can run a usability test before the product is released. However, you can also do it after the release. Bring in users to try out the released version, observe if there is any difficulty in using it, and listen to their feedback. (Page 91) The principle of consistency in UX design is about creating a consistent and predictable user experience across all aspects of a product, such as the layout, visual design, and interactions. Users should be able to easily recognize patterns and familiar elements throughout the product, which helps to reduce confusion and increase usability. Consistency can be achieved by following established design patterns and guidelines, using consistent language and terminology, and maintaining a cohesive visual style. (Page 100) In summary, consistency provides users with comfort, familiarity, and predictability, helping them to use the product without any hesitation. (Page 104) Affordance refers to making it intuitive for users to know how to use a product just by looking at it. (Page 105) Mental model refers to the expectations that users have for a product, based on their experience, training, and knowledge. A conceptual model refers to the experience or interface that a product provides. Sometimes, these two models may match with each other, while other times, they may conflict with each other. (Page 111) "Information Architecture," which refers to the practice of organizing and structuring digital content to make it easier for users to find what they need. Information architecture involves creating a clear hierarchy of information and grouping related content together, much like how a grocery store categorizes its products into different sections. (Page 120) In essence, information architecture is all about creating a clear and efficient "roadmap" for users to follow as they navigate through digital content. By carefully structuring information and grouping related content together, information architects can help users find what they need more quickly and easily. (Page 121) User intent can be classified as high or low. High user intent means that the user knows well what they want to do and can follow a specific procedure to complete their task when they enter a website to buy a product. (Page 126) In contrast, users with low intent have a rough idea of their goal but do not know what specifically they need to do to achieve it. In other words, they are in the research stage. (Page 126) UX designers who create apps or websites need to consider both high and low user intent and design to satisfy both types of users. (Page 126) …the top part of a web page is called the hero area because it is the most important and expensive area on the page that users can see without scrolling, like a hero. (Page 128) One of the core competencies of designers is the ability to visualize ideas. (Page 133) …wireframes do not require a fixed color scheme or font size, and visual elements do not have to be pixel-perfect. In fact, it's perfectly acceptable to create wireframes using office tools like PowerPoint or Google Slides. Ultimately, the purpose of wireframes is to provide a rough draft of the design, which can then be refined and improved upon in later stages of the design process. (Page 136) However, once you get the hang of it, you can use Figma to create not only wireframes, but also UI designs, design systems, and more. (Page 137) Portfolios can be created in website or PDF format. (Page 146) | Mark Goulston M.DNotable QuotationsExpand to full screen
Just Listen: Discover the Secret to Getting Through to Absolutely Anyone
Mark Goulston M.D. and Keith Ferrazzi
Your brain has three layers that evolved over millions of years: a primitive reptile layer, a more evolved mammal layer, and a final primate layer. They all interconnect, but in effect they often act like three different brains—and they're often at war with each other. Here's how each of your three brains behaves:
'fight-or-flight' part of your brain.
The middle mammal brain is the seat of your emotions.
The upper or primate brain is like Star Trek's Mr. Spock:
To reach someone, you need to talk to the human upper brain—not the snake brain or the rat brain. You're in trouble if you're trying to gain buy-in from someone who's feeling angry, defiant, upset, or threatened because, in these situations, the person's higher brain isn't calling the shots. If you're talking to a boss, a customer, a spouse, or a child whose lower brain or midbrain is in control, you're talking to a cornered snake or, at best, a hysterical rabbit. In this situation, your success hinges entirely on talking the person up from reptile to mammal to human amygdala hijack—a term first coined by psychologist Daniel Goleman, the originator of the concept of emotional intelligence.
Your ability to reason drops drastically, your working memory falters, and stress hormones flood your system.
Goleman no doubt was keen on this concept because when you undergo an amygdala hijack, your emotional intelligence goes out the window.
Humans, just like macaques, have neurons that act as mirrors. In fact, studies suggest that these remarkable cells may form the basis for human empathy. That's because, in effect, they transport us into another person's mind, briefly making us feel what the person is feeling. In a article titled "The Neurology of Self-Awareness" in Edge, V. S. Ramachandran, a pioneer in mirror neuron research, commented, "I call these ‘empathy neurons,' or ‘Dalai Lama neurons,' for they are dissolving the barrier between self and others."
Many of the people I work with—from CEOs and managers to unhappy spouses to clinically depressed patients—feel that they give their best, only to be met day after day with apathy, hostility, or (possibly worst of all) no response at all. In my belief, this deficit explains why we feel so overwhelmed when someone acknowledges either our pain or our triumphs.
Understanding a person's hunger and responding to it is one of the most potent tools you'll ever discover for getting through to anyone you meet in business or your personal life.
put words to what you're feeling at each stage.
Matthew Lieberman at UCLA shows that when people put words to their emotions—"afraid," "angry"—the amygdala, that little biological threat sensor that can throw the brain into animal mode, cools down almost instantly.
So surprisingly, now is not the time to lie to yourself and say, "I'm cool, I'm calm, it's fine." It's actually the time to say to yourself (at least at first): "Oh f#@&" or "I'm scared as hell."
Open your own mind first.
But if your relationship with another person looks like it's going nowhere, making that person "feel felt" is your best bet for achieving a breakthrough.
Attach an emotion to what you think the other person is feeling, such as "frustrated," "angry," or "afraid." . Say, "I'm trying to get a sense of what you're feeling and I think it's—————. . ." and fill in an emotion. "Is that correct? If it's not, then what are you feeling?" Wait for the person to agree or correct you. . Then say, "How frustrated (angry, upset, etc.) are you?" Give the person time to respond. Be prepared, at least initially, for a torrent of emotions—especially if the person you're talking with is holding years of pent-up frustration, anger, or fear inside. This is not the time to fight back, or air your own grievances. . Next, say, "And the reason you're so frustrated (angry, upset, etc.) is because. . . ?" Again, let the person vent. . Then say, "Tell me—what needs to happen for that feeling to feel better?" . Next, say, "What part can I play in making that happen? What part can you play in making that happen?"
If you want to have an interesting dinner conversation, be interested. The way to truly win friends and influence the best people is to be more interested in listening to them than you are in impressing them. The more you want to influence and get through to discerning and successful people, the more sincere your interest in them needs to be. Let the other person fully develop an interesting story,
rather than trying to trump that story.
People love offering advice, because it makes them feel both interesting and wise.
People need to feel valuable. Find ways to tell them that they make your world happier, funnier, more secure, less stressed, more entertaining, less scary, or just all-around better. One thing most of these high-maintenance, easy-to-upset, difficult-to-please people have in common is that they feel as if the world isn't treating them well enough.
Subordinates who want to complain often don't have solutions to their problems, so when you set that as a condition for continuing a later conversation—a perfectly reasonable request—they often choose to drop the issue.
The good people in your life need and deserve reassurance that they're valued—and the annoying people in your life may not deserve it, but they need it even more.
The best thing to do when someone is venting, whining, or complaining is to avoid interrupting.
After he vents, you'll both be exhausted. This is not to be confused with a relaxed state. The difference between exhausted and relaxed is that when you're exhausted, you feel empty and tired and you're not open to input. At this point, it may appear that it's your turn to talk—but it's not. Talking right now is the rookie mistake that most people make. "Tell me more."
People behave their worst when they feel most powerless.
The person will mirror the attitude you're using to mask your distress.
If you're using anger to cover up fear, you'll get anger in return.
"words respond to words, but actions respond to counter-actions"
When a bully tries to intimidate you by verbally attacking you, do this. Make eye contact. Act perfectly polite but ever-so-slightly bored, as if your mind is elsewhere. Let your body language transmit the same message: Stand up straight, be relaxed, and cock your head as if you're listening but not very hard.
YOU: What's something that would be impossible to do, but if you could do it, would dramatically increase your success? OTHER PERSON: If I could just do ____ , but that's impossible. YOU: Okay. What would make it possible?
"I believe this is impossible." Thinking and saying that shifts their minds into a positive (agreeing) movement toward you.
By setting into motion a cascade of "yes" coming from the other person ("Yes, you're right, my life is a mess, and I can't take it anymore"), you shift the person's attitude from disagreement to agreement. Once you establish that rapport, the person is emotionally primed to cooperate instead of punch back.
Empathy is a sensory experience; that is, it activates the sensory part of your nervous system, including the mirror neurons we've talked about. Anger, on the other hand, is a motor action—usually a reaction to some perceived hurt or injury by another person.
So when you shift a blamer into empathy, you stop the person's angry ranting dead in its tracks.
But do the unexpected by apologizing yourself, and something very different occurs: you shift a person instantly out of defensive mode and cause the individual to mirror your humility and concern.
An ounce of apology is worth a pound of resentment and a ton of "acting out by underperforming."
When people go on the attack it's usually because they feel (rightly or wrongly) that they've been treated poorly. That's especially true if you're dealing with angry and frustrated customers. Becoming defensive or counterattacking simply reinforces the idea that you think these people are wrong and unimportant (and stupid), which amplifies their mirror neuron receptor deficit and fuels their fire. Move a person from hostility to mild confusion and already you've moved one step in the right direction.
Conceal a flaw, and the world will imagine the worst. MARCUS VALERIUS MARTIAL, ROMAN POET
If you're familiar with courtroom procedures, you know that lawyers do something they call "stipulation." It means they agree up-front on something. When you stipulate to a potential problem or flaw, do it in a confident and unselfconscious way.
The key to crafting a transformational question is simple: Ask yourself, "What single question will show this person that I'm interested in his or her ideas, interests, future success, or life?"
Just to make sure I get off on the right foot—what are three things you'd like me to always do, and three things you'd like me to never do?
One great thing about the "eyes-to-the-sky" technique is that you can use it to reach even the most difficult person you communicate with: yourself.
Questioning works better than telling.
The Side-by-Side approach is simple: join the other person in an activity (preferably one in which you can be helpful—but even eating lunch together is good), and then ask questions designed to gain insight into what the person is doing, thinking, and feeling.
When you get people to lower their guard, don't violate their trust. Resist the urge to explain why you're right. Instead, deepen the conversation by asking another question. The more we allow people to have their feelings and become sad or angry, the quicker it passes. "You're thinking of hiring someone like me because you want to _______________," The secret to this is to invite these people into a conversation rather than asking questions that put them on the defensive—and that's where the fill-in-the-blanks approach comes from. But the real force of the fill-in-the-blanks technique lies in the simple fact that you don't tell people what they want or even ask them what they want. Instead, you get them to tell you what they want. "What question did I fail to ask, or what problem did I fail to address, that—if I had—would have caused you to give me a different answer?" The great thing about this approach is that the client feels in control—and is in control—the entire time. You're not whining or browbeating or otherwise trying to overpower the person; instead, you're letting the person freely offer the information you need to make a power play.
Until someone says "no" to you, you're not asking for enough.
Thank the person for something specific: Acknowledge the effort it took. Tell the person the difference that his or her act personally made to you. When you're doing this, allow the other person to vent and don't become defensive even if the person is over the top. When you encourage people who are furious to get their anger off their chests, it speeds the healing process.
[How to apologize ] Demonstrate through your actions that you've learned your lesson. Requesting forgiveness: Don't do this immediately, because actions speak louder than words. To truly earn forgiveness, you need to sustain your corrective actions until they become part of who you are.
Focus on "What's in it for them?" and reciprocators will sooner or later ask, "What can I do for you?"
Don't find fault. Find a remedy. —HENRY FORD, INVENTOR
| Robert S WeissNotable QuotationsExpand to full screen
CHAPTER 1: INTRODUCTION Interviews can be as prepackaged as the polling or survey interview in which questions are fixed and answers limited: Studies whose ultimate aim is to report how many people are in particular categories or what the relationship is between being in one category and another are justly called quantitative. because their results can be presented as a table of numbers (for example, in a table Quantitative studies pay a price for their standardized precision. Because they ask the same questions in the same order of every respondent, they do not obtain full reports. Instead, the information they obtain from any one person is fragmentary, made up of bits and pieces of attitudes and observations and appraisals. Interviews that sacrifice uniformity of questioning to achieve fuller development of information are properly called qualitative interviews, and a study based on such interviews, a qualitative interview study. because the fuller responses obtained by the qualitative study cannot be easily categorized, their analysis will rely less on counting and correlating and more on interpretation, summary, and integration. In general, if statistical analysis is our goal, we would do better to use a survey approach. The report we ultimately write can provide readers with a fuller understanding of the experiences of our respondents. We need not restrict ourselves to just the one approach. what is common to them all is that they ask the respondent to provide an observer's report on the topic under study. The style of the qualitative interview may appear conversational, but what happens in the interview is very different from what happens in an ordinary conversation. In an ordinary conversation each participant voices observations, thoughts, feelings. Either participant can set a new topic, either can ask questions. In the qualitative interview the respondent provides information while the interviewer, as a representative of the study, is responsible for directing the respondent to the topics that matter to the study. judging when the respondent's report was adequate and when it needed elaboration, mostly the interviewer expressed a desire to understand whatever it was the respondent was saying. A qualitative interviewing study can be enormously time consuming, but it need not be. It is entirely possible for investigators who do quantitative work to end a study knowing more about the statistical packages they have used for computer analysis than about the topic of their study. By contrast, those who do qualitative interview studies invariably wind up knowing a lot about the topic of their study. While it can be valuable for the results of qualitative interview studies to be verified by other methods, it can also be valuable for the results of studies done by other methods to be illuminated by qualitative interview studies. A COMPROMISE? FIXED QUESTION, OPEN RESPONSE Here respondents are asked carefully crafted questions but are free to answer them in their own words rather than required simply to choose one or another predetermined alternative. Unfortunately, the fixed- question- open- response approach to data collection turns out to sacrifice as much in quality of information as it gains in systematization. The fixed- question— open- response approach would have succeeded in getting a headline but would have missed the story. Even though fixed- question— open- response interviewing may at first appear to be a systematic approach to qualitative interviewing, it is not. It is a different approach entirely. Qualitative interview studies generally begin with decisions regarding the sample to interview, move on to data collection, and conclude with analysis. the phases of work in qualitative research overlap and are intermeshed. The chapters that follow trace the likely sequence of the investigator's concerns in a qualitative interview study: sampling, preparing for interviewing, conducting the interviews, analyzing the data, and, finally, writing the report. CHAPTER 2: RESPONDENTS: CHOOSING THEM AND RECRUITING THEM The set of topics the study explores, taken together, might be said to constitute the substantive frame of the study. who should be talked with, and about what, can be worked out. One good reason for doing pilot interviews is to clarify the aims and frame of the study before interviewing its primary respondents. develop the study's substantive frame in consultation with members of its primary audience, If representatives of that audience are available, the frame might usefully be discussed with them. The study's substantive frame decides who should be interviewed and what they should be asked. There are two distinct categories of potential respondents: people who are uniquely able to be informative because they are expert in an area or were privileged witnesses to an event; and people who, taken together, display what happens within a population affected by a situation or event. produce a dense description of what happened, try to talk with everyone in a position to know what happened in the hope that each would provide part of the story and that all of their accounts together would provide the story in full. Often the study of an issue can be cast in a way that requires a panel of informants but with what seems to be only slight redefinition can be recast to require a sample of representatives. We might, of course, decide to do both studies. We might want a panel of informants to tell us about the institution of child visitation and a sample of parents to tell us how it works in practice.We would then be doing two distinct studies. They would enrich each other, but our work load would be greater. people of different backgrounds, with different perspectives, who became involved in different ways. A study of an organization requires that the investigator succeed in obtaining informants without being perceived as an intrusive foreign presence. success is dependent on a certain amount of social grace, including sensitivity, considerateness, and tact; self- confidence; awareness of the politics of the institution; and persistence. Being unobtrusive can help. A good person to start with in any study requiring a panel is a knowledgeable insider willing to serve as an informant on informants. need to feel confident of you before they can comfortably be candid. vouched for by a mutual acquaintance can be useful. implied sponsorship of government or foundation funding for the project may also help. One principle is to start with people who are available to you and easy to interview, especially if having interviewed them will make you more informed and legitimized when you proceed to interview others. A second principle is to have your early interviews with people who are of marginal importance to the study so that if you make mistakes it won't matter so much. One approach is to develop a sample that can be argued on grounds of mathematical probability to be not too different from the population in which we are interested. A sample can be a probability sample only if respondents are selected randomly. Random selection is not the same as haphazard selection. a procedure that could equally well have selected absolutelyanybody in the population. Rather than choose respondents randomly, and thus risk unwaned duplication in our sample, we may prefer to select respondents purposively so that we obtain istances of all the important dissimilar forms present in the larger population. This kind of sample might be referred to as a sample chosen o maximize range. We are particularly likely to want a sample chosen to maximize range rather thana probability sample if our sample will be small. If we plan to work with samples much smaller than (samples of 30, say) we may not trust random selection to provide us with instances of significant developments that occur infrequently. Random sampling will provide us with a picture of the population as well as of particular instances, and sampling for range will ensure that our sample includes instances displaying significant variation. To know whether potential respondents have characteristics you want, you can include "filter" questions in the telephone calls you make to arrange for interviews. The third approach to obtaining a sample of respondents, in addition to choosing them on a probability basis or choosing them to provide a useful range of instances, is to accept pretty much whomever we can get. This is a sample of convenience. In attempting to learn about a group difficult to penetrate— gypsies, migrant workers, the very rich— it can be a breakthrough to find any member of the group, any member at all, willing to serve as an informant and respondent. You might find a congregating place for people of the kind you want to study. a support group. check an encyclopedia of associations to see if a group has been established With a probability sample, generalization is straightforward, based on mathematical argument. With a sample in which it has been possible to maximize range, it can be argued that instances of every important variation have been studied. With other sorts of samples other arguments must be relied on. Here are five arguments that might be advanced to justify the attempt to generalize from the findings of convenience samples— and one that should not be, although it sometimes is. it is likely to be a good idea to include at least a few comparison cases. Sometimes cases that occur infrequently should be sought out because they are significant conceptually. Compared with survey research studies, qualitative interview studies collect more material from fewer respondents. Case research is different primarily because it anchors its potential for generalization in the welter of detail of the single instance. Generalization can then become uncertain (and rest heavily on the theory we bring to the case), but in compensation we have the coherence, depth, development, and drama of a single fully understood life. People marooned at home tend to welcome interviewers. the hospitalized or the retired. people in crisis, On the other hand, interviewers may need the right sponsorship or topic or approach to avoid being turned down we have generally sent the potential respondents a letter explaining the study, arguing for the importance of their participation, and saying someone would telephone. much to be said for letting respondents know that their participation will be valued. undesirable to interrupt a respondent's account. CHAPTER 3: PREPARATION FOR INTERVIEWING A good report would inform its audience about matters of importance to them. It would tell them about experiences that affect them, provide them with explanations for things that have puzzled them, and give them maps to situations they may enter. It would contribute to their competence, their awareness, or their well- being. interview guides should be seen as provisional and likely to change as more is learned. Most survey studies try to keep interviews to an hour or less. interview will go for an hour and a half or 2 hours. It is almost always desirable, if time and costs permit, to interview respondents more than once. a first meeting is partly about establishing the research partnership. A research project that compared telephone and face- to- face interviewing found that telephone respondents broke off contact more quickly, were both more acquiescent and more evasive, and were more cautious about self- revelation. CHAPTER 4: INTERVIEWING When I can, I begin the interview where the respondent seems already to be. my role will be as interviewer and propose to the respondent that his role will be try to get in tune with the respondent by extending his comment the interviewer will not question the respondent's appraisals, choices, motives, right to observations, or personal worth. admire their knowledge and authority and was, indeed, already awed to be in the presence of someone so important. Some interviewers are willing to act as the respondents? antagonists. If they suspect the respondent is holding back information, they are ready to confront the respondent: Journalists sometimes read up on respondents, the better to confound the respondents? efforts to dissemble. Being a good interviewer requires knowing what kind of information the study needs and being able to help the respondent provide it. scenes and events external to the respondent and the respondent's own thoughts and feelings. We obtain descriptions of specific incidents by asking respondents to particularize. "Is there a specific incident you can think of that would make clear what you have in mind?"; describing a specific incident. 1 Respondents often prefer to provide generalized accounts rather than concrete instances, Helping Respondents Develop Information Extending. Filling in detail. Identifying actors. Others the respondent consulted. Inner events. Making indications explicit. marker as a passing reference made by a respondent to an important event or feeling state. Because markers occur in the course of talking about something else, you may have to remember them and then return to them when you can, saying, "A few minutes ago you mentioned…" But it is a good idea to pick up a marker as soon as you conveniently can if the material it hints at could in any way be relevant for your study. The first rule of interviewing is that if the respondent has something to say, the respondent must be able to say it. If you find yourself talking over the respondent, interrupting, or holding the floor while the respondent tries to interrupt, something is going wrong in the interview. Never, never fight for control of the interview. The interview is a collaboration. collaboration; it's your responsibility to set topics. You can usually manage the redirection without discouraging the respondent from talking freely. It is usually enough for the interviewer to give business card information— location and profession— along with the study's aims and sponsorship. It can be hard to know what is relevant, especially in early interviews, before the frame of the study is firmly established. My policy is: If in doubt, see what's there. How do you know whether you are being told enough, whether you are being given enough development and enough detail? One test is visualizability. Be alert to indications by the respondent of discomfort, antagonism, or boredom. CHAPTER 5: ISSUES IN INTERVIEWING you have to acknowledge the respondent's distress and, for a time, simply sit and listen and permit the person to feel whatever he or she feels. "That's too bad." Beyond this the interviewer does best to convey a middle distance in response to the respondent's feelings, in touch with them and responsive to them, but not overwhelmed by them. There is no reason for an interviewer to feel guilty about intruding on a respondent's grief or sorrow. the interviewer should bear in mind that sensitivity, tact, and respect for the respondent, always important, are essential with a respondent who displays pain. | Yuval Noah Harari
Notable QuotationsExpand to full screen
Page xiii
The tendency to create powerful things with unintended consequences started not with the invention of the steam engine or AI but with the invention of religion.
Power always stems from cooperation between large numbers of humans.
Page xiv
The main argument of this book is that humankind gains enormous power by building large networks of cooperation, but the way these networks are built predisposes us to use that power unwisely. Our problem, then, is a network problem.
Information is the glue that holds networks together. But for tens of thousands of years, Sapiens built and maintained large networks by inventing and spreading fictions, fantasies, and mass delusions-about gods, about enchanted broomsticks, about AI, and about a great many other things.
While each individual human is typically interested in knowing the truth about themselves and the world, large networks bind members and create order by relying on fictions and fantasies.
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in sufficient quantities information leads to truth, and truth in turn leads to both power and wisdom. Ignorance, in contrast, seems to lead nowhere.
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Of course, the naive view acknowledges that many things can go wrong on the path from information to truth.
However, the naive view assumes that the antidote to most problems we encounter in gathering and processing information is gathering and processing even more information.
Wisdom is commonly understood to mean "making right decisions," but what "right" means depends on value judgments that differ among diverse people, cultures, and ideologies.
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the naive view of information sees only part of the picture,
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AI could destroy our civilization.[
Such a scenario is unlikely, and it merely distracts people from the real dangers. Rather, experts warn about two other scenarios.
First, the power of AI could supercharge existing human conflicts,
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Second, the Silicon Curtain might come to divide not one group of humans from another but rather all humans from our new AI overlords.
a web of unfathomable algorithms that manage our lives,
AI is the first technology in history that can make decisions and create new ideas by itself.
Knives and bombs do not themselves decide whom to kill.
AI isn't a tool-it's an agent.
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Can we trust computer algorithms to make wise decisions and create a better world?
In 2016, I published Homo Deus,
the real hero of history has always been information, rather than Homo sapiens, and that scientists increasingly understand not just history but also biology, politics, and economics in terms of information flows.
The book warned that while we hope better information technology will give us health, happiness, and power, it may actually take power away from us and destroy both our physical and our mental health.
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populism views information as a weapon.[20]
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In its more extreme versions, populism posits that there is no objective truth at all and that everyone has "their own truth," which they wield to vanquish rivals.
Whenever and wherever populism succeeds in disseminating the view of information as a weapon, language itself is undermined.
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Karl Marx, who argued in the mid-nineteenth century that power is the only reality, that information is a weapon, and that elites who claim to be serving truth and justice are in fact pursuing narrow class privileges.
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as a mouthpiece for the capitalist class, and that scientific institutions like universities spread disinformation in order to perpetuate capitalist control, populists accuse these same institutions of working to advance the interests of the "corrupt elites" at the expense of "the people."
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One of the recurrent paradoxes of populism is that it starts by warning us that all human elites are driven by a dangerous hunger for power, but often ends by entrusting all power to a single ambitious human.
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populists are eroding trust in large-scale institutions and international cooperation just when humanity confronts the existential challenges of ecological collapse, global war, and out-of-control technology.
If we wish to avoid relinquishing power to a charismatic leader or an inscrutable AI, we must first gain a better understanding of what information is, how it helps to build human networks, and how it relates to truth and power.
it explores key dilemmas that people in all eras faced when trying to construct information networks, and it examines how different answers to these dilemmas shaped contrasting human societies.
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What we usually think of as ideological and political conflicts often turn out to be clashes between opposing types of information networks.
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large-scale human information networks: mythology and bureaucracy.
Institutions and societies are often defined by the balance they manage to find between the conflicting needs of their mythmakers and their bureaucrats.
another contrast-between distributed and centralized information networks.
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rise of AI is arguably the biggest information revolution in history.
History isn't the study of the past; it is the study of change.
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Silicon chips can create spies that never sleep, financiers that never forget, and despots that never die. How will this change society, economics, and politics?
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In everyday usage, "information" is associated with human-made symbols like spoken or written words.
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the naive view argues that information is an attempt to represent reality, and when this attempt succeeds, we call it truth.
truth is an accurate representation of reality.
Most information in human society, and indeed in other biological and physical systems, does not represent anything.
Throughout this book, "truth" is understood as something that accurately represents certain aspects of reality.
Underlying the notion of truth is the premise that there exists one universal reality.
While different people, nations, or cultures may have competing beliefs and feelings, they cannot possess contradictory truths,
because they all
share a universal reality. Anyone who rejects universalism rejects truth.
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Another problem with any attempt to represent reality is that reality contains many viewpoints.
Reality includes an objective level with objective facts that don't depend on people's beliefs;
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Reality also includes a subjective level with subjective facts like the beliefs and feelings of various people, but in this case, too, facts can be separated from errors.
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The point is that even the most truthful accounts of reality can never represent it in full. There are always some aspects of reality that are neglected or distorted in every representation. Truth, then, isn't a one-to-one representation of reality. Rather, truth is something that brings our attention to certain aspects of reality while inevitably ignoring other aspects. No account of reality is 100 percent accurate, but some accounts are nevertheless more truthful than others.
the naive view sees information as an attempt to represent reality.
It is aware that some information doesn't represent reality well, but it dismisses this as unfortunate cases of "misinformation" or "disinformation."
The naive view further believes that the solution to the problems caused by misinformation and disinformation is more information.
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errors, lies, fantasies, and fictions are information, too.
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what information does is to create new realities by tying together disparate things-
Its defining feature is connection rather than representation,
Information doesn't necessarily inform us about things. Rather, it puts things in formation.
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Information is something that creates new realities by connecting different points into a network.
This still includes the view of information as representation.
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Viewing information as a social nexus helps us understand many aspects of human history that confound the naive view of information as representation.
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To conclude, information sometimes represents reality, and sometimes doesn't. But it always connects. This is its fundamental characteristic.
"How well does it connect people? What new network does it create?"
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When we look at the history of information from the Stone Age to the Silicon Age, we therefore see a constant rise in connectivity, without a concomitant rise in truthfulness or wisdom.
Contrary to what the naive view believes, Homo sapiens didn't conquer the world because we are talented at turning information into an accurate map of reality. Rather, the secret of our success is that we are talented at using information to connect lots of individuals.
We'll discuss how, over tens of thousands of years, humans invented various information technologies that greatly improved connectivity and cooperation without necessarily resulting in a more truthful representation of the world.
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In order to cooperate, Sapiens no longer had to know each other personally; they just had to know the same story.
A story can thereby serve like a central connector, with an unlimited number of outlets into which an unlimited number of people can plug.
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The social media accounts are usually run by a team of experts, and every image and word is professionally crafted and curated to manufacture what is nowadays called a brand.[5]
A "brand" is a specific type of story.
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It should be stressed that the creation of the Jesus story was not a deliberate lie.
the result of emotional projections and wishful thinking.
By gaining all those believers, the story of Jesus managed to have a much bigger impact on history than the person of Jesus.
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the whole purpose of the Passover meal is to create and reenact artificial memories.
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The Jewish Passover story builds a large network by taking existing biological kin bonds and stretching them. It creates an imagined family of millions.
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Of all genres of stories, those that create intersubjective realities have been the most crucial for the development of large-scale human networks.
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In fact, all relations between large-scale human groups are shaped by stories, because the identities of these groups are themselves defined by stories.
Contrary to Marxist thinking, large-scale identities and interests in history are always intersubjective; they are never objective.
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History is often shaped not by deterministic power relations, but rather by tragic mistakes that result from believing in mesmerizing but harmful stories.
The naive view of information says that information leads to truth, and knowing the truth helps people to gain both power and wisdom. This sounds reassuring.
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history, power stems only partially from knowing the truth. It also stems from the ability to maintain social order among a large number of people.
If you build a bomb and ignore the facts of physics, the bomb will not explode. But if you build an ideology and ignore the facts, the ideology may still prove explosive.
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What the people at the top know, which nuclear physicists don't always realize, is that telling the truth about the universe is hardly the most efficient way to produce order among large numbers of humans.
When it comes to uniting people, fiction enjoys two inherent advantages over the truth. First, fiction can be made as simple as we like, whereas the truth tends to be complicated, because the reality it is supposed to represent is complicated.
Second, the truth is often painful and disturbing, and if we try to make it more comforting and flattering, it will no longer be the truth.
In contrast, fiction is highly malleable.
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The choice isn't simply between telling the truth and lying. There is a third option. Telling a fictional story is lying only when you pretend that the story is a true representation
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the U.S. Constitution was fundamentally different from stories that denied their fictive nature and claimed divine origin, such as the Ten Commandments.
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to survive and flourish, every human information network needs to do two things simultaneously: discover truth and create order.
Having a lot of information doesn't in and of itself guarantee either truth or order. It is a difficult process to use information to discover the truth and simultaneously use it to maintain order. What makes things worse is that these two processes are often contradictory, because it is frequently easier to maintain order through fictions.
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What happens when the same bit of information reveals an important fact about the world, and also undermines the noble lie that holds society together? In such cases society may seek to preserve order by placing limits on the search for truth.
While over the generations human networks have grown increasingly powerful, they have not necessarily grown increasingly wise. If a network privileges order over truth, it can become very powerful but use that power unwisely.
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The big problem with lists, and the crucial difference between lists and stories, is that lists tend to be far more boring than stories, which means that while we easily remember stories, we find it difficult to remember lists.
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Kendall Haven writes in his 2007 book, Story Proof: The Science Behind the Startling Power of Story,
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Unlike national poems and myths, which can be stored in our brains, complex national taxation and administration systems have required a unique nonorganic information technology in order to function. This technology is the written document.
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Like stories and like all other information technologies in history, written documents didn't necessarily represent reality accurately.
But whether true or false, written documents created new realities.
documents changed the method used for creating intersubjective realities.
Humans couldn't forge an intersubjective reality that their brains couldn't remember. This limit could be transcended, however, by writing documents.
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In a literate state, to own a field increasingly came to mean that it is written on some clay tablet, bamboo strip, piece of paper, or silicon chip that you own that field.
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As people produced more and more documents, finding them turned out to be far from easy.
Written documents were much better than human brains in recording certain types of information. But they created a new and very thorny problem: retrieval.
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Another common rule is that apples grow on apple trees, whereas figs grow on figs trees. So if you are looking for an apple, you first need to locate an apple tree, and then look up.
It is very different with archives. Since documents aren't organisms, they don't obey any biological laws, and evolution didn't organize them for us.
Bureaucracy is the way people in large organizations solved the retrieval problem and thereby created bigger and more powerful information networks.
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But like mythology, bureaucracy too tends to sacrifice truth for order.
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Many of the problems of our twenty-first-century information networks-like biased algorithms that mislabel people, or rigid protocols that ignore human needs and feelings-are not new problems of the computer age. They are quintessential bureaucratic problems that have existed long before anyone even dreamed of computers.
Bureaucracy literally means "rule by writing desk."
Bureaucracy seeks to solve the retrieval problem by dividing the world into drawers, and knowing which document goes into which drawer.
Divide the world into containers, and keep the containers separate so the documents don't get mixed up.
bureaucracy is often busy imposing a new and artificial order on the world.
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The urge to divide reality into rigid drawers also leads bureaucrats to pursue narrow goals irrespective of the wider impact of their actions.
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intersubjective conventions are themselves part of reality.
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In defense of bureaucracy it should be noted that while it sometimes sacrifices truth and distorts our understanding of the world, it often does so for the sake of order, without which it would be hard to maintain any large-scale human network.
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Mythology and bureaucracy are the twin pillars of every large-scale society. Yet while mythology tends to inspire fascination, bureaucracy tends to inspire suspicion.
For all bureaucracies-good or bad-share one key characteristic: it is hard for humans to understand them.
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In tribal societies that lack written documents and bureaucracies, the human network is composed of only human-to-human and human-to-story chains. Authority belongs to the people who control the junctions that link the various chains. These junctions are the tribe's foundational myths. Charismatic leaders, orators, and mythmakers know how to use these stories in order to shape identities, build alliances, and sway emotions.
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As documents became a crucial nexus linking many social chains, considerable power came to be invested in these documents, and experts in the arcane logic of documents emerged as new authority figures.
In bureaucratic systems, power often comes from understanding how to manipulate obscure budgetary loopholes and from knowing your way around the labyrinths of offices, committees, and subcommittees.
For better or worse, literate bureaucracies tended to strengthen the central authority at the expense of ordinary citizens.
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biological drama, sibling rivalry, romantic triangle "Boy meets girlc and "boy fights boy over girl" tension between purity and impurity,
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The list of biological dramas that press our emotional buttons includes several additional classics, such as "Who will be alpha?""Us versus them," and "Good versus evil."
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Storytellers like Franz Kafka, who focused on the often surreal ways that bureaucracy shapes human lives, pioneered new nonbiological plotlines. In Kafka's The Trial, the bank clerk K. is arrested by unidentified officials of an unfathomable agency for an unnamed crime.
Whereas stories about heroes who confront monsters-from the Ramayana to Spider-Man-repackage the biological dramas of confronting predators and romantic rivals, the unique horror of Kafkaesque stories comes from the unfathomability of the threat.
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The difficulty of depicting and understanding bureaucratic realities has had unfortunate results. On the one hand, it leaves people feeling helpless in the face of harmful powers they do not understand, like the hero of The Trial. On the other hand, it also leaves people with the impression that bureaucracy is a malign conspiracy, even in cases when it is in fact a benign force providing us with health care, security, and justice.
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government stepped in to offer a solution to the imaginary problem invented by its own propaganda.
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All powerful information networks can do both good and ill, depending on how they are designed and used.
Merely increasing the quantity of information in a network doesn't guarantee its benevolence, or make it any easier to find the right balance between truth and order. That is a key historical lesson for the designers and users of the new information networks of the twenty-first century.
AI is taking up the role of both bureaucrats and mythmakers. AI systems know how to find and process data better than flesh- and- blood bureaucrats, and AI is also acquiring the ability to compose stories better than most humans.
We have now seen that information networks don't maximize truth, but rather seek to find a balance between truth and order. Bureaucracy and mythology are both essential for maintaining order, and both are happy to sacrifice truth for the sake of order.
The way human information networks have dealt with the problem of errors
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Holy books like the Bible and the Quran are an information technology that is meant to both include all the vital information society needs and be free from all possibility of error.
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In our personal lives, religion can fulfill many different functions, like providing solace or explaining the mysteries of life. But historically, the most important function of religion has been to provide superhuman legitimacy for the social order.
At the heart of every religion lies the fantasy of connecting to a superhuman and infallible intelligence.
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Religion wanted to take fallible humans out of the loop and give people access to infallible superhuman laws, but religion repeatedly boiled down to trusting this or that human.
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Holy books like the Bible and the Quran are a technology to bypass human fallibility, and religions of the book-like Judaism, Christianity, and Islam-have been built around that technological artifact.
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The Bible as a single holy book didn't exist in biblical times. King David and the prophet Isaiah never saw a copy of the Bible.
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A second and much bigger problem concerned interpretation. Even when people agree on the sanctity of a book and on its exact wording, they can still interpret the same words in different ways.
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More problems resulted from the fact that even if the technology of the book succeeded in limiting changes to the holy words, the world beyond the book continued to spin, and it was unclear how to relate old rules to new situations.
As Jews increasingly argued over the interpretation of the Bible, rabbis gained more power and prestige. Writing down the word of Jehovah was supposed to limit the authority of the old priestly institution, but it gave rise to the authority of a new rabbinical institution.
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The dream of bypassing fallible human institutions through the technology of the holy book never materialized.
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It is crucial to note that the people who created the New Testament weren't the authors of the twenty-seven texts it contains; they were the curators.
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Just as most Jews forgot that rabbis curated the Old Testament, so most Christians forgot that church councils curated the New Testament, and came to view it simply as the infallible word of God.
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As time passed, problems of interpretation increasingly tilted the balance of power between the holy book and the church in favor of the institution.
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the church couldn't prevent the occasional freethinker from formulating heretical ideas. But because it controlled key nodes in the medieval information network-such as copying workshops, archives, and libraries-it could prevent such a heretic from making and distributing a hundred copies of her book.
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If infallible texts merely lead to the rise of fallible and oppressive churches, how then to deal with the problem of human error?
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The naive view expects that if all restrictions on the free flow of information are removed, error will inevitably be exposed and displaced by truth.
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In the history of information networks, the print revolution of early modern Europe is usually hailed as a moment of triumph, breaking the stranglehold that the Catholic Church had maintained over the European information network.
But print wasn't the root cause of the scientific revolution.
In fact, print allowed the rapid spread not only of scientific facts but also of religious fantasies, fake news, and conspiracy theories.
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While it would be an exaggeration to argue that the invention of print caused the European witch-hunt craze, the printing press played a pivotal role in the rapid dissemination of the belief in a global satanic conspiracy.
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Nobody in early modern Europe had sex with Satan or was capable of flying on broomsticks and creating hailstorms. But witches became an intersubjective reality. Like money, witches were made real by exchanging information about witches.
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The new intersubjective reality was so convincing that even some people accused of witchcraft came to believe that they were indeed part of a worldwide satanic conspiracy.
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"there were neither witches nor bewitched until they were talked and written about."[
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The history of print and witch-hunting indicates that an unregulated information market doesn't necessarily lead people to identify and correct their errors, because it may well prioritize outrage over truth.
The curation institutions that played a central role in the scientific revolution connected scholars and researchers both in and out of universities, forging an information network that spanned the whole of Europe and eventually the world. For the scientific revolution to gather pace, scientists had to trust information published by colleagues in distant lands.
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In other words, the scientific revolution was launched by the discovery of ignorance.[
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The trademark of science is not merely skepticism but self-skepticism, and at the heart of every scientific institution we find a strong self-correcting mechanism.
As an information technology, the self-correcting mechanism is the polar opposite of the holy book.
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Self-correcting mechanisms are ubiquitous in nature.
Institutions, too, die without self-correcting mechanisms. These mechanisms start with the realization that humans are fallible and corruptible. But instead of despairing of humans and looking for a way to bypass them, the institution actively seeks its own errors and corrects them. All institutions that manage to endure beyond a handful of years possess such mechanisms, but institutions differ greatly in the strength and visibility of their self-correcting mechanisms.
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Scientific institutions maintain that even if most scientists in a particular period believe something to be true, it may yet turn out to be inaccurate or incomplete.
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Crucially, scientific institutions are willing to admit their institutional responsibility for major mistakes and crimes.
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An institution can call itself by whatever name it wants, but if it lacks a strong self-correcting mechanism, it is not a scientific institution.
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order by itself isn't necessarily good.
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Scientific institutions have been able to afford their strong self-correcting mechanisms because they leave the difficult job of preserving the social order to other institutions.
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democracy and dictatorship as contrasting types of information networks.
Dictatorial information networks are highly centralized.[
the center enjoys unlimited authority;
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The second characteristic of dictatorial networks is that they assume the center is infallible.
To summarize, a dictatorship is a centralized information network, lacking strong self- correcting mechanisms. A democracy, in contrast, is a distributed information network, possessing strong self- correcting mechanisms.
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while a dictatorship is about one central information hub dictating everything, a democracy is an ongoing conversation between diverse information nodes.
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democracy is not a system in which a majority of any size can decide to exterminate unpopular minorities; it is a system in which there are clear limits on the power of the center.
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The most common method strongmen use to undermine democracy is to attack its self-correcting mechanisms one by one, often beginning with the courts and the media.
The typical strongman either deprives courts of their powers or packs them with his loyalists and seeks to close all independent media outlets while building his own omnipresent propaganda machine.[5]
The strongmen don't usually take the final step of abolishing the elections outright. Instead, they keep them as a ritual that serves to provide legitimacy and maintain a democratic facade, as happens, for example, in Putin's Russia.
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least from the viewpoint of information flows, what defines a system as "democratic" is only that its center doesn't have unlimited authority and that the system possesses robust mechanisms to correct the center's mistakes.
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Democratic networks assume that everyone is fallible, and that includes even the winners of elections and the majority of voters.
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Elections establish what the majority of people desire, rather than what the truth is. And people often desire the truth to be other than what it is. Democratic networks therefore maintain some self-correcting mechanisms to protect the truth even from the will of the majority.
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the majority should at least acknowledge its own fallibility and protect the freedom of minorities to hold and publicize unpopular views, which might turn out to be correct.
the one option that should not be on offer in elections is hiding or distorting the truth.
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Naturally, academic institutions, the media, and the judiciary may themselves be compromised by corruption, bias, or error.
But subordinating them to a governmental Ministry of Truth is likely to make things worse.
Allowing the government to supervise the search for truth is like appointing the fox to guard the chicken coop.
academic institutions, the media, and the judiciary have their own internal self-correcting mechanisms for fighting corruption, correcting bias, and exposing error.
the existence of several independent institutions that seek the truth in different ways allows these institutions to check and correct one another. For example, if powerful corporations manage to break down the peer-review mechanism
None of these mechanisms are completely fail-safe, but no human institution is. Government certainly isn't.
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If all this sounds complicated, it is because democracy should be complicated. Simplicity is a characteristic of dictatorial information networks in which the center dictates everything and everybody silently obeys.
The term "populism" derives from the Latin populus, which means "the people." In democracies, "the people" is considered the sole legitimate source of political authority. Only representatives of the people should have the authority to declare wars, pass laws, and raise taxes. Populists cherish this basic democratic principle, but somehow conclude from it that a single party or a single leader should monopolize all power.
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Even if they win just a small share of votes, populists may still believe they alone represent the people.
populists can believe that the enemies of the people have deceived the people to vote against its true will, which the populists alone represent.
"the people" is not a collection of flesh-and-blood individuals with various interests and opinions, but rather a unified mystical body that possesses a single will-"the will of the people."
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The Nazi case is of course extreme, and it is grossly unfair to accuse all populists of being crypto-Nazis with genocidal inclinations.
What turns someone into a populist is claiming that they alone represent the people and that anyone who disagrees with them-whether state bureaucrats, minority groups, or even the majority of voters-either suffers from false consciousness or isn't really part of the people.
This is why populism poses a deadly threat to democracy.
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Having claimed that they alone represent the people, populists argue that the people is not just the sole legitimate source of political authority but the sole legitimate source of all authority. Any institution that derives its authority from something other than the will of the people is antidemocratic.
populists consequently seek to monopolize not just political authority but all types of authority and to take control of institutions such as media outlets, courts, and universities. By taking the democratic principle of "people's power" to its extreme, populists turn totalitarian.
populists to be skeptical of the pursuit of truth, and to argue-as we saw in the prologue-that "power is the only reality."
The result is a dark and cynical view of the world as a jungle and of human beings as creatures obsessed with power alone.
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Biologists, climatologists, epidemiologists, economists, historians, and mathematicians are just another interest group feathering its own nest-at the expense of the people.
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Populism offers strongmen an ideological basis for making themselves dictators while pretending to be democrats.
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Once people think that power is the only reality, they lose trust in all these institutions, democracy collapses, and the strongmen can seize total power.
Of course, populism could lead to anarchy rather than totalitarianism, if it undermines trust in the strongmen themselves.
When trust in bureaucratic institutions like election boards, courts, and newspapers is particularly low, an enhanced reliance on mythology is the only way to preserve order.
Strongmen who claim to represent the people may well rise to power through democratic means, and often rule behind a democratic facade. Rigged elections in which they win overwhelming majorities serve as proof of the mystical bond between the leader and the people.
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If one person dictates all the decisions, and even their closest advisers are terrified to voice a dissenting view, no conversation is taking place. Such a network is situated at the extreme dictatorial end of the spectrum.
The focus on conversations rather than elections raises a host of interesting questions.
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Scathing public attacks on the government are a daily occurrence. But where is the room where the crucial conversations happen, and who sits there?
Based on the above definition of democracy, we can now turn to the historical record and examine how changes in information technology and information flows have shaped the history of democracy.
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In the millennia following the agricultural revolution, and especially after writing helped create large bureaucratic polities, it became easier to centralize the flow of information and harder to maintain the democratic conversation.
As the size of polities continued to increase, and city-states were superseded by larger kingdoms and empires, even Athenian-style partial democracy disappeared. All the famous examples of ancient democracies are city-states such as Athens and Rome. In contrast, we don't know of any large-scale kingdom or empire that operated along democratic lines.
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By the third century CE, not only the Roman Empire but all other major human societies on earth were centralized information networks lacking strong self-correcting mechanisms.
Thousands of more small-scale societies continued to function democratically in the third century CE and beyond, but it seemed that distributed democratic networks were simply incompatible with large-scale societies.
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How do we know whether democracies fail because they are undermined by strongmen or because of much deeper structural and technological reasons?
The key misconception here is equating democracy with elections. Tens of millions of Roman citizens could theoretically vote for this or that imperial candidate. But the real question is whether tens of millions of Romans could have held an ongoing empire-wide political conversation.
In the present-day United States the democratic conversation is endangered by people's inability to listen to and respect their political rivals, yet this can presumably still be fixed.
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To hold a conversation, it is not enough to have the freedom to talk and the ability to listen. There are also two technical preconditions. First, people need to be within hearing range of one another.
with the help of some kind of information technology that can swiftly convey what people say over long distances.
Second, people need at least a rudimentary understanding of what they are talking about.
The only way to have a large-scale political conversation among diverse groups of people is if people can gain some understanding of issues that they have never experienced firsthand.
In a large polity, it is a crucial role of the education system and the media to inform people about things they have never faced themselves. If there is no education system or media platform to perform this role, no meaningful large-scale conversations can take place.
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The lack of a meaningful public conversation was not the fault of Augustus, Nero, Caracalla, or any of the other emperors. They didn't sabotage Roman democracy. Given the size of the empire and the available information technology, democracy was simply unworkable.
It should be stressed that in many large-scale autocracies local affairs were often managed democratically.
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Even in empires whose rulers never had any democratic pretensions, democracy could still flourish in local settings.
In tsarist villages and Roman cities a form of democracy was possible because a meaningful public conversation was possible.
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Mass media are information technologies that can quickly connect millions of people even when they are separated by vast distances.
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The newspaper is a periodic pamphlet, and it was different from earlier one-off pamphlets because it had a much stronger self-correcting mechanism. Unlike one-off publications, a weekly or daily newspaper has a chance to correct its mistakes and an incentive to do so in order to win the public's trust.
Newspapers that succeeded in gaining widespread trust became the architects and mouthpieces of public opinion. They created a far more informed and engaged public, which changed the nature of politics, first in the Netherlands and later around the world.[
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You may wonder whether we are talking about democracies at all. At a time when the United States had more slaves than voters (more than 1.5 million Americans were enslaved in the early 1820s),[50] was the United States really a democracy?
As noted earlier, democracy and autocracy aren't absolutes; they are part of a continuum.
voting is not the only thing that counts.
stronger self-correcting mechanisms.
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It was these self-correcting mechanisms that gradually enabled the United States to expand the franchise, abolish slavery, and turn itself into a more inclusive democracy.
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to press a button while sitting in their homes. Large-scale democracy had now become feasible. Millions of people separated by thousands of kilometers could conduct informed and meaningful public debates about the rapidly evolving issues of the day.
Mass media made large-scale democracy possible, rather than inevitable.
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an autocratic network, there are no legal limits on the will of the ruler, but there are nevertheless a lot of technical limits. In a totalitarian network, many of these technical limits are absent.[58]
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Totalitarianism is the attempt to control what every person throughout the country is doing and saying every moment of the day, and potentially even what every person is thinking and feeling.
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Emperors, caliphs, shahs, and kings found it a huge challenge to keep their subordinates in check. Rulers consequently focused their attention on controlling the military and the taxation
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Full-blown totalitarianism might have been dreamed about by the likes of the Qin, but its implementation had to wait for the development of modern technology.
Just as modern technology enabled large-scale democracy, it also made large-scale totalitarianism possible.
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While in most polities throughout history the army had wielded enormous political power, in twentieth-century totalitarian regimes the regular army ceded much of its clout to the secret police-the information army.
what made the secret police powerful was its command of information.
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Totalitarian regimes are based on controlling the flow of information and are suspicious of any independent channels of information.
key tenet of totalitarian regimes is that wherever people meet and exchange information, the regime should be there too, to keep an eye on them.
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created an entire nonexistent category of enemies.
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We see then that the new information technology of the late modern era gave rise to both large-scale democracy and large-scale totalitarianism.
differences between how the two systems used information technology.
it allows many independent nodes to process the information and make decisions by themselves.
Information freely circulates
In contrast, totalitarianism wants all information to pass through the central hub and doesn't want any independent institutions making decisions on their own.
The biggest advantage of the centralized totalitarian network is that it is extremely orderly, which means it can make decisions quickly and enforce them ruthlessly.
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if the official channels are blocked, the information cannot find an alternative means of transmission.
fearful subordinates hide bad news from their superiors.
Another common reason why official channels fail to pass on information is to preserve order.
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"Americans grow up with the idea that questions lead to answers," he said. "But Soviet citizens grew up with the idea that questions lead to trouble."
in a distributed democratic network, when official lines of communication are blocked, information flows through alternative channels.
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Totalitarian and authoritarian
self-correcting mechanisms tend to be very weak.
Nobody can challenge the leader, and on his own initiative the leader-being a human being-may well refuse to admit any mistakes.
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The relentless barrage of fake news and conspiracy theories helped to keep hundreds of millions of people in line.
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Once we learn to see democracy and totalitarianism as different types of information networks, we can understand why they flourish in certain eras and are absent in others.
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Technology only creates new opportunities; it is up to us to decide which ones to pursue.
Totalitarian regimes choose to use modern information technology to centralize the flow of information and to stifle truth in order to maintain order.
Democratic regimes choose to use modern information technology to distribute the flow of information between more institutions and individuals and encourage the free pursuit of truth.
They consequently have to struggle with the danger of fracturing.
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The pressure to live up to the democratic ideals and to include more people and groups in the public conversation seemed to undermine the social order and to make democracy unworkable.
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Western democracies not only surged ahead technologically and economically but also succeeded in holding the social order together despite-or perhaps because of-widening the circle of participants in the political conversation.
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At the beginning of the twenty-first century, it accordingly seemed that the future belonged to distributed information networks and to democracy. This turned out to be wrong.
Democracies in the 2020s face the task, once again, of integrating a flood of new voices into the public conversation without destroying the social order.
As humankind enters the second quarter of the twenty-first century, a central question is how well democracies and totalitarian regimes will handle both the threats and the opportunities resulting from the current information revolution.
As in previous eras, information networks will struggle to find the right balance between truth and order. Some will opt to prioritize truth and maintain strong self-correcting mechanisms.
Others will make the opposite choice.
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Hitherto, every information network in history relied on human mythmakers and human bureaucrats to function.
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the current information revolution,
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is the computer. Everything else-from the internet to AI-is a by-product.
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the moment it is enough to say that in essence a computer is a machine that can potentially do two remarkable things: it can make decisions by itself, and it can create new ideas by itself.
The rise of intelligent machines that can make decisions and create new ideas means that for the first time in history power is shifting away from humans and toward something else.
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A paradigmatic case of the novel power of computers is the role that social media algorithms have played in spreading hatred and undermining social cohesion in numerous countries.[
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The crucial thing to grasp is that social media algorithms are fundamentally different from printing presses and radio sets.
Facebook's algorithms were making active and fateful decisions by themselves.
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The algorithms could have chosen to recommend sermons on compassion or cooking classes, but they decided to spread hate-filled conspiracy theories.
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But why did the algorithms decide to promote outrage rather than compassion?
As user engagement increased, so Facebook collected more data, sold more advertisements, and captured a larger share of the information market.
human managers provided the company's algorithms with a single overriding goal: increase user engagement.
outrage generated engagement.
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AI algorithms.
can learn by themselves things that no human engineer programmed, and they can decide things that no human executive foresaw.
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intelligence and consciousness are very different. Intelligence is the ability to attain goals, such as maximizing user engagement on a social media platform. Consciousness is the ability to experience subjective feelings like pain, pleasure, love, and hate.
Bacteria and plants apparently lack any consciousness, yet they too display intelligence.
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Of course, as computers become more intelligent, they might eventually develop consciousness and have some kind of subjective experiences. Then again, they might become far more intelligent than us, but never develop any kind of feelings.
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Prior to the rise of computers, humans were indispensable links in every chain of information networks like churches and states.
In contrast, computer-to-computer chains can now function without humans in the loop.
Another way to understand the difference between computers and all previous technologies is that computers are fully fledged members of the information network, whereas clay tablets, printing presses, and radio sets are merely connections between members.
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power depends on how many members cooperate with you, how well you understand law and finance, and how capable you are of inventing new laws and new kinds of financial devices, then computers are poised to amass far more power than humans.
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By gaining such command of language, computers are seizing the master key unlocking the doors of all our institutions, from banks to temples. We use language to create not just legal codes and financial devices but also art, science, nations, and religions. What would it mean for humans to live in a world where catchy melodies, scientific theories, technical tools, political manifestos, and even religious myths are shaped by a nonhuman alien intelligence that knows how to exploit with superhuman efficiency the weaknesses, biases, and addictions of the human mind?
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Equally alarmingly, we might increasingly find ourselves conducting lengthy online discussions
with entities that we think are humans but are actually computers.
This could make democracy untenable. Democracy is a conversation, and conversations rely on language. By hacking language, computers could make it extremely difficult for large numbers of humans to conduct a meaningful public conversation.
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When we engage in a political debate with a computer impersonating a human, we lose twice. First, it is pointless for us to waste time in trying to change the opinions of a propaganda bot, which is just not open to persuasion. Second, the more we talk with the computer, the more we disclose about ourselves, thereby making it easier for the bot to hone its arguments and sway our views.
By conversing and interacting with us, computers could form intimate relationships with people and then use the power of intimacy to influence us.
In the 2010s social media was a battleground for controlling human attention. In the 2020s the battle is likely to shift from attention to intimacy.
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What we are talking about is potentially the end of human history. Not the end of history, but the end of its human-dominated part.
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What will happen to the course of history when computers play a larger and larger role in culture and begin producing stories, laws, and religions?
Within a few years AI could eat the whole of human culture-everything we have created over thousands of years-digest it, and begin to gush out a flood of new cultural artifacts.
At first, computers will probably imitate human cultural prototypes, writing humanlike texts and composing humanlike music.
This doesn't mean computers lack creativity; after all, human artists do the same.
computers too can make cultural innovations,
These innovations will in turn influence the next generation of computers,
which will increasingly deviate from the original human models,
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But in order to manipulate humans, there is no need to physically hook brains to computers. For thousands of years prophets, poets, and politicians have used language to manipulate and reshape society.
Now computers are learning how to do it.
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In theory, the text you've just read might have been generated by the alien intelligence of some computer.
As computers amass power, it is likely that a completely new information network will emerge. Of course, not everything will be new.
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computer-to-computer chains are emerging in which computers interact with one another on their own.
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In computer evolution, the distance from amoeba to T. rex could be covered in a decade.
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we humans are still in control.
Tech giants like Facebook, Amazon, Baidu, and Alibaba aren't just the obedient servants of customer whims and government regulations. They increasingly shape these whims and regulations.
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Local newspapers, TV stations, and movie theaters lose customers and ad revenue to the tech giants.
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In tax literature, "nexus" means an entity's connection to a given jurisdiction.
In the words of the economist Marko Köthenbürger, "The definition of nexus based on a physical presence should be adjusted to include the notion of a digital presence in a country."[
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money will soon become outdated as many transactions no longer involve money.
rather than as dollars, taxing only money distorts the economic and political picture.
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Taxation is just one among many problems created by the computer revolution. The computer network is disrupting almost all power structures. Democracies fear the rise of new digital dictatorships. Dictatorships fear the emergence of agents they don't know how to control. Everyone should be concerned about the elimination of privacy and the spread of data colonialism.
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technology is moving much faster than the policy.
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The people who lead the information revolution know far more about the underlying technology than the people who are supposed to regulate it.
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How would it feel to be constantly monitored, guided, inspired, or sanctioned by billions of nonhuman entities?
The most important thing to remember is that technology, in itself, is seldom deterministic.
Yes, since human societies are information networks, inventing new information technologies is bound to change society.
humans still have a lot of control over the pace, shape, and direction of this revolution-
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Engineers working for authoritarian governments and ruthless corporations could develop new tools to empower the central authority, by monitoring citizens and customers twenty-four hours a day. Hackers working for democracies may develop new tools to strengthen society's self-correcting mechanisms, by exposing government corruption and corporate malpractices. Both technologies could be developed.
The knife doesn't force our hand.
Though radio sets in East Germany could technically receive a wide range of transmissions, the East German government did its best to jam Western broadcasts and punished people who secretly tuned in to them.[55] The technology was the same, but politics made very different uses of it.
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To understand the new computer politics, we need a deeper understanding of what's new about computers.
In this chapter we noted that unlike printing presses and other previous tools, computers can make decisions by themselves and can create ideas by themselves. That, however, is just the tip of the iceberg. What's really new about computers is the way they make decisions and create ideas.
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When centralized bureaucratic networks appeared and developed, one of the bureaucrats' most important roles was to monitor entire populations.
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Of course, surveillance has also been essential for providing beneficial services.
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In order to get to know us, both benign and oppressive bureaucracies have needed to do two things.
gather a lot of data about us.
analyze all that data and identify patterns.
However, in all times and places surveillance has been incomplete. In democracies like the modern United States, legal limits have been placed on surveillance to protect privacy and individual rights. In totalitarian regimes like the ancient Qin Empire and the modern U.S.S.R., surveillance faced no such legal barriers but came up against technical boundaries.
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By 2024, we are getting close to the point when a ubiquitous computer network can follow the population of entire countries twenty-four hours a day.
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Just as the computer network doesn't need millions of human agents to follow us, it also doesn't need millions of human analysts to make sense of our data.
In 2024 language algorithms like ChatGPT and Meta's Llama can process millions of words per minute and "read" 2.6 billion words in a couple of hours.
The ability of such algorithms to process images, audio recordings, and video footage is equally superhuman.
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course, pattern recognition also has enormous positive potential.
we must first appreciate the fundamental difference between the new digital bureaucrats and their flesh-and-blood predecessors.
As fish live in water, humans live in a digital bureaucracy, constantly inhaling and exhaling data. Each action we make leaves a trace of data, which is gathered and analyzed to identify patterns.
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In theory, the dictators of the future could get their computer network to go much deeper than just watching our eyes. If the network wants to know our political views, personality traits, and sexual orientation, it could monitor processes inside our hearts and brains.
The necessary biometric technology is already being developed by some governments and companies,
nobody yet has the biological knowledge necessary to deduce things like precise political opinions from under-the-skin data like brain activity.
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biometric sensors register what happens to the heart rate and brain activity of millions of people as they watch a particular news item on their smartphones, that can teach the computer network far more than just our general political affiliation. The network could learn precisely what makes each human angry, fearful, or joyful.
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In a world where humans monitored humans, privacy was the default. But in a world where computers monitor humans, it may become possible for the first time in history to completely annihilate privacy.
The post-privacy era is taking hold in authoritarian countries ranging from Belarus to Zimbabwe,[23] as well as in democratic metropolises like London and New York.
or able to install cameras inside people's homes, algorithms regularly watch us even in our living rooms, bedrooms,
and bathrooms via our own computers and smartphones.)
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Facial recognition algorithms and AI-searchable databases are now routinely used by police forces all over the world.
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Peer-to-peer surveillance systems typically operate by aggregating many points to determine an overall score.
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For scoring those things that money can't buy, there was an alternative nonmonetary system, which has been given different names: honor, status, reputation. What social credit systems seek is a standardized valuation of the reputation market.
Social credit is a new points system that ascribes precise values even to smiles and family visits.
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Some people might see social credit systems as a way to reward pro- social behavior, punish egotistical acts, and create kinder and more harmonious societies. The Chinese government, for example, explains that its social credit systems could help fight corruption, scams, tax evasion, false advertising, and counterfeiting, and thereby establish more trust between individuals, between consumers and corporations, and between citizens and government institutions.[ 50] Others may find systems that allocate precise values to every social action demeaning and inhuman. Even worse, a comprehensive social credit system will annihilate privacy and effectively turn life into a never- ending job interview.
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network of computers can always be on. Computers are consequently pushing humans toward a new kind of existence in which we are always connected and always monitored.
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the computer networks of the twenty-first century, which might create new types of humans and new dystopias.
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radicalizing people.
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We have reached a turning point in history in which major historical processes are partly caused by the decisions of nonhuman intelligence.
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Computer errors become potentially catastrophic only when computers become historical agents.
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To tilt the balance in favor of truth, networks must develop and maintain strong self-correcting mechanisms that reward truth telling. These self-correcting mechanisms are costly, but if you want to get the truth, you must invest in them.
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Instead of investing in self-correcting mechanisms that would reward truth telling, the social media giants actually developed unprecedented error-enhancing mechanisms that rewarded lies and fictions.
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I don't want to imply that the spread of fake news and conspiracy theories is the main problem with all past, present, and future computer networks.
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We also shouldn't discount the huge social benefits that YouTube, Facebook, and other social media platforms have brought.
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When computers are given a specific goal, such as to increase YouTube traffic to one billion hours a day, they use all their power and ingenuity to achieve this goal.
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the more powerful the computer, the more careful we need to be about defining its goal in a way that precisely aligns with our ultimate goals.
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As we give algorithms greater and greater power over health care, education, law enforcement, and numerous other fields, the alignment problem will loom ever larger.
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In theory, when humans create a computer network, they must define for it an ultimate goal, which the computers are never allowed to change or ignore.
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Then, even if computers become so powerful that we lose control over them, we can rest assured that their immense power will benefit rather than harm us. Unless, of course, it turned out that we defined a harmful or vague goal.
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alignment. A tactical maneuver is rational if, and only if, it is aligned with some higher strategic goal, which should in turn be aligned with an even higher political goal.
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Tech executives and engineers who rush to develop AI are making a huge mistake if they think there is a rational way to tell AI what its ultimate goal should be. They should learn from the bitter experiences of generations of philosophers who tried to define ultimate goals and failed.
For millennia, philosophers have been looking for a definition of an ultimate goal that will not depend on an alignment to some higher goal. They have repeatedly been drawn to two potential solutions, known in philosophical jargon as deontology and utilitarianism. Deontologists (from the Greek word deon, meaning "duty") believe that there are some universal moral duties, or moral rules, that apply to everyone. These rules do not rely on alignment to a higher goal, but rather on their intrinsic goodness. If such rules indeed exist, and if we can find a way to program them into computers, then we can make sure the computer network will be a force for good.
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rules often end up the captives of local myths. This problem with deontology is especially critical if we try to dictate universal deontologist rules not to humans but to computers. Computers aren't even organic. So if they follow a rule of "Do unto others what you would have them do unto you," why should they be concerned about killing organisms like humans?
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The English philosopher Jeremy Bentham-another contemporary of Napoleon, Clausewitz, and Kant-said that the only rational ultimate goal is to minimize suffering in the world and maximize happiness.
If our main fear about computer networks is that their misaligned goals might inflict terrible suffering on humans and perhaps on other sentient beings, then the utilitarian solution seems both obvious and attractive.
Unfortunately, as with the deontologist solution, what sounds simple in the theoretical realm of philosophy becomes fiendishly complex in the practical land of history.
We don't know how many "suffering points" or "happiness points" to assign to particular events, so in complex historical situations it is extremely difficult to calculate whether a given action increases or decreases the overall amount of suffering in the world.
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while utilitarianism promises a rational-and even mathematical-way to align every action with "the ultimate good," in practice it may well produce just another mythology.
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How then did bureaucratic systems throughout history set their ultimate goals? They relied on mythology to do it for them.
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The alignment problem turns out to be, at heart, a problem of mythology.
one of the most important things to realize about computers is that when a lot of computers communicate with one another, they can create inter-computer realities, analogous to the intersubjective realities produced by networks of humans.
These inter-computer realities may eventually become as powerful-and as dangerous-as human-made intersubjective myths.
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Just as intersubjective realities like money and gods can influence the physical reality outside people's minds, so inter-computer realities can influence reality outside the computers.
The Google algorithm determines the website's Google rank by assigning points to various parameters, such as how many people visit the website and how many other websites link to it. The rank itself is an inter-computer reality, existing in a network connecting billions of computers-the internet.
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Increasingly, however, understanding American politics will necessitate understanding inter-computer realities ranging from AI-generated cults and currencies to AI-run political parties and even fully incorporated AIs. The U.S. legal system already recognizes corporations as legal persons that possess rights such as freedom of speech.
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In Citizens United v. Federal Election Commission (2010) the U.S. Supreme Court decided that this even protected the right of corporations to make political donations.[
What would stop AIs from being incorporated and recognized as legal persons with freedom of speech, then lobbying and making political donations to protect and expand AI rights?
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The problem we face is not how to deprive computers of all creative agency, but rather how to steer their creativity in the right direction.
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As computers replace humans in more and more bureaucracies, from tax collection and health care to security and justice, they too may create a mythology and impose it on us with unprecedented efficiency. In a world ruled by paper documents, bureaucrats had difficulty policing racial borderlines or tracking everyone's exact ancestry. People could get false documents.
For example, social credit systems could create a new underclass of "low- credit people." Such a system may claim to merely "discover" the truth through an empirical and mathematical process of aggregating points to form an overall score. But how exactly would it define pro- social and antisocial behaviors?
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The fundamental principle of machine learning is that algorithms can teach themselves new things by interacting with the world, just as humans do, thereby producing a fully fledged artificial intelligence.
AI is not a dumb automaton that repeats the same movements again and again irrespective of the results. Rather, it is equipped with strong self-correcting mechanisms, which allow it to learn from its own mistakes.
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if real-life companies already suffer from some ingrained bias, the baby algorithm is likely to learn this bias, and even amplify it.
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But getting rid of algorithmic bias might be as difficult as ridding ourselves of our human biases.
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A social media algorithm thinks it has discovered that humans like outrage, when in fact it is the algorithm itself that conditioned humans to produce and consume more outrage.
We saw in chapter 4 that already thousands of years ago humans dreamed about finding an infallible information technology to shield us from human corruption and error. Holy books were an audacious attempt to craft such a technology, but they backfired. Since the book couldn't interpret itself, a human institution had to be built to interpret the sacred words and adapt them to changing circumstances.
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But in contrast to the holy book, computers can adapt themselves to changing circumstances and also interpret their decisions and ideas for us. Some humans may consequently conclude that the quest for an infallible technology has finally succeeded and that we should treat computers as a holy book that can talk to us and interpret itself, without any need of an intervening human institution.
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algorithms are independent agents, and they are already taking power away from
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One potential guardrail is to train computers to be aware of their own fallibility. As Socrates taught, being able to say "I don't know" is an essential step on the path to wisdom. And this is true of computer wisdom no less than of human wisdom.
Baby algorithms should learn to doubt themselves,
should keep humans in the loop,
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To conclude, the new computer network will not necessarily be either bad or good. All we know for sure is that it will be alien and it will be fallible. We therefore need to build institutions that will be able to check not just familiar human weaknesses like greed and hatred but also radically alien errors.
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the end of the twentieth century, it had become clear that imperialism, totalitarianism, and militarism were not the ideal way to build industrial societies. Despite all its flaws, liberal democracy offered a better way. The great advantage of liberal democracy is that it possesses strong self-correcting mechanisms, which limit the excesses of fanaticism and preserve the ability to recognize our errors and try different courses of action. Given our inability to predict how the new computer network will develop, our best chance to avoid catastrophe in the present century is to maintain democratic self-correcting mechanisms that can identify and correct mistakes as we go along.
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Democracies can choose to use the new powers of surveillance in a limited way, in order to provide citizens with better health care and security without destroying their privacy and autonomy.
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The first principle is benevolence. When a computer network collects information on me, that information should be used to help me rather than manipulate me.
Having access to our personal life comes with a fiduciary duty to act in our best interests.
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the tech giants cannot square their fiduciary duty with their current business model, legislators could require them to switch to a more traditional business model, of getting users to pay for services in money rather than in information.
The second principle that would protect democracy against the rise of totalitarian surveillance regimes is decentralization.
the survival of democracy, some inefficiency is a feature, not a bug. To protect the privacy and liberty of individuals, it's best if neither the police nor the boss knows everything about us.
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A third democratic principle is mutuality. If democracies increase surveillance of individuals, they must simultaneously increase surveillance of governments and corporations too.
What's bad is if all the information flows one way: from the bottom up.
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A fourth democratic principle is that surveillance systems must always leave room for both change and rest.
New surveillance technology, especially when coupled with a social credit system, might force people either to conform to a novel caste system or to constantly change their actions, thoughts, and personality in accordance with the latest instructions from above.
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So an alternative health-care system may instruct its algorithm not to predict my illnesses, but rather to help me avoid them.
But before we rush to embrace the dynamic algorithm, we should note that it too has a downside. Human life is a balancing act between endeavoring to improve ourselves and accepting who we are. If the goals of the dynamic algorithm are dictated by an ambitious government or by ruthless corporations, the algorithm is likely to morph into a tyrant, relentlessly demanding that I exercise more, eat less, change my hobbies, and alter numerous other habits, or else it would report me to my employer or downgrade my social credit score.
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Surveillance is not the only danger that new information technologies pose to democracy. A second threat is that automation will destabilize the job market and the resulting strain may undermine democracy.
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Unfortunately, nobody is certain what skills we should teach children in school and students in university, because we cannot predict which jobs and tasks will disappear and which ones will emerge.
intellectuals tend to appreciate intellectual skills more than motor and social skills. But actually, it is easier to automate chess playing than, say, dish washing.
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Another common but mistaken assumption is that creativity is unique to humans so it would be difficult to automate any job that requires creativity.
A third mistaken assumption is that computers couldn't replace humans in jobs requiring emotional intelligence, from therapists to teachers.
AI doesn't have any emotions of its own, but it can nevertheless learn to recognize these patterns in humans.
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In sports, for example, we know that robots can move much faster than humans, but we aren't interested in watching robots compete in the Olympics.[15] The same is true for human chess masters.
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Yet even professions that are the preserve of conscious entities-like priests-might eventually be taken over by computers, because, as noted in chapter 6, computers could one day gain the ability to feel pain and love. Even if they can't, humans may nevertheless come to treat them as if they can.
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Chatbots and other AIs may not have any feelings of their own, but they are now being trained to generate feelings in humans and form intimate relationships with us.
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numerous democracies have been hijacked by unconservative leaders such as Donald Trump and have been transformed into radical revolutionary parties.
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the Trumpian program talks more of destroying existing institutions and revolutionizing society.
Nobody knows for sure why all this is happening. One hypothesis is that the accelerating pace of technological change with its attendant economic, social, and cultural transformations might have made the moderate conservative program seem unrealistic. If conserving existing traditions and institutions is hopeless, and some kind of revolution looks inevitable, then the only means to thwart a left-wing revolution is by striking first and instigating a right-wing revolution. This was the political logic in the 1920s and 1930s, when conservative forces backed radical fascist revolutions in Italy, Germany, Spain, and elsewhere as a way-so they thought-to preempt a Soviet-style left-wing revolution.
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When both conservatives and progressives resist the temptation of radical revolution, and stay loyal to democratic traditions and institutions, democracies prove themselves to be highly agile.
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The most important human skill for surviving the twenty-first century is likely to be flexibility, and democracies are more flexible than totalitarian regimes.
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By the early 2020s citizens in numerous countries routinely get prison sentences based in part on risk assessments made by algorithms that neither the judges nor the defendants comprehend.[31] And prison sentences are just the tip of the iceberg.
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Computers are making more and more decisions about us, both mundane and life-changing. In addition to prison sentences, algorithms increasingly have a hand in deciding whether to offer us a place at college, give us a job, provide us with welfare benefits, or grant us a loan. They similarly help determine what kind of medical treatment we receive, what insurance premiums we pay, what news we hear, and who would ask us on a date.
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The rise of unfathomable alien intelligence undermines democracy. If more and more decisions about people's lives are made in a black box, so voters cannot understand and challenge them, democracy ceases to function. In particular, what happens when crucial decisions not just about individual lives but even about collective matters like the Federal Reserve's interest rate are made by unfathomable algorithms? Human voters may keep choosing a human president, but wouldn't this be just an empty ceremony?
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The increasing unfathomability of our information network is one of the reasons for the recent wave of populist parties and charismatic leaders.
when they feel overwhelmed by immense amounts of information they cannot digest, they become easy prey for conspiracy theories, and they turn for salvation to something they do understand-a human.
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How can a human mind analyze and evaluate a decision made on the basis of so many data points?
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There is, however, a silver lining to this cloud of numbers. While individual laypersons may be unable to vet complex algorithms, a team of experts getting help from their own AI sidekicks can potentially assess the fairness of algorithmic decisions even more reliably than anyone can assess the fairness of human decisions.
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To vet algorithms, regulatory institutions will need not only to analyze them but also to translate their discoveries into stories that humans can understand.
Because computers will increasingly replace human bureaucrats and human mythmakers, this will again change the deep structure of power.
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The new computer network poses one final threat to democracies. Instead of digital totalitarianism, it could foster digital anarchy.
To function, a democracy needs to meet two conditions: it needs to enable a free public conversation on key issues, and it needs to maintain a minimum of social order and institutional trust.
Now, with the rise of the new computer network, might large-scale democracy again become impossible?
One difficulty is that the computer network makes it easier to join the debate. In the past, organizations like newspapers, radio stations, and established political parties acted as gatekeepers, deciding who was heard in the public sphere. Social media undermined the power of these gatekeepers, leading to a more open but also more anarchical public conversation.
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So, what happens to democratic debates when millions-and eventually billions-of highly intelligent bots are not only composing extremely compelling political manifestos and creating deepfake images and videos but also able to win our trust and friendship?
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In the face of the threat algorithms pose to the democratic conversation, democracies are not helpless. They can and should take measures to regulate AI and prevent it from polluting our infosphere with fake people spewing fake news.
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Digital agents are welcome to join many conversations, provided they don't pretend to be humans.
Another important measure democracies can adopt is to ban unsupervised algorithms from curating key public debates.
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For most of history large-scale democracy was impossible because information technology wasn't sophisticated enough to hold a large-scale political conversation.
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We cannot foretell how things will play out.
it is clear that the information network of many democracies is breaking down.
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However, as of 2024, more than half of "us" already live under authoritarian or totalitarian regimes,[2] many of which were established long before the rise of the computer network.
enabled the rise of both large-scale democracy and large-scale totalitarianism, but totalitarianism suffered from a severe disadvantage.
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Technologies like the telegraph, the telephone, the typewriter, and the radio facilitated the centralization of information, but they couldn't process the information and make decisions by themselves. This remained something that only humans could do.
The rise of machine-learning algorithms, however, may be exactly what the Stalins of the world have been waiting for.
Even in democratic countries, a few corporations like Google, Facebook, and Amazon have become monopolies in their domains, partly because AI tips the balance in favor of the giants.
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Russia's human engineers can do their best to create AIs that are totally aligned with the regime, but given the ability of AI to learn and change by itself, how can the human engineers ensure that the AI never deviates into illicit territory?
Page 354
In the long term, totalitarian regimes are likely to face an even bigger danger: instead of criticizing them, an algorithm might gain control of them.
Page 358
Whereas democracies assume that everyone is fallible, in totalitarian regimes the fundamental assumption is that the ruling party or the supreme leader is always right.
Page 361
Computers are not yet powerful enough to completely escape our control or destroy human civilization by themselves. As long as humanity stands united, we can build institutions that will control AI and will identify and correct algorithmic errors. Unfortunately, humanity has never been united. We have always been plagued by bad actors, as well as by disagreements between good actors. The rise of AI, then, poses an existential danger to humankind not because of the malevolence of computers but because of our own shortcomings.
Page 362
As we have seen in previous chapters, human civilization is threatened not only by physical and biological weapons of mass destruction like atom bombs and viruses. Human civilization could also be destroyed by weapons of social mass destruction, like stories that undermine our social bonds.
Page 369
On September 1, 2017, President Putin of Russia declared, "Artificial intelligence is the future, not only for Russia, but for all humankind…. Whoever becomes the leader in this sphere will become the ruler of the world."
Page 370
But what began as a commercial competition between corporations was turning into a match between governments, or perhaps more accurately, into a race between competing teams, each made of one government and several corporations. The prize for the winner? World domination.
Page 375
It is becoming difficult to access information across the Silicon Curtain, say between China and the United States, or between Russia and the EU. Moreover, the two sides are increasingly run on different digital networks, using different computer codes. Each sphere obeys different regulations and serves different purposes.
In the United States, the government plays a more limited role. Private enterprises lead the development and deployment of AI, and the ultimate goal of many new AI systems is to enrich the tech giants rather than to strengthen the American state or the current administration.
Page 381
An increasingly important question is, Can people adopt any virtual identity they like, or should their identity be constrained by their biological body?
Page 382
it is probable that within a few decades the computer network will cultivate new human and nonhuman identities that make little sense to us.
Page 385
Global cooperation and patriotism are not mutually exclusive.
Page 386
global cooperation means two far more modest things: first, a commitment to some global rules.
Page 386
The second principle of globalism is that sometimes-not always, but sometimes-it is necessary to prioritize the long-term interests of all humans over the short-term interests of a few.
Page 387
Forging and keeping international agreements on AI will require major changes in the way the international system functions.
Epilogue
Page 398
One lesson is that the invention of new information technology is always a catalyst for major historical changes, because the most important role of information is to weave new networks rather than represent preexisting realities.
Page 399
The invention of AI is potentially more momentous than the invention of the telegraph, the printing press, or even writing, because AI is the first technology that is capable of making decisions and generating ideas by itself.
Page 401
Let's return now to the question I posed at the beginning of this book: If we are so wise, why are we so self-destructive?
Page 402
This book has argued that the fault isn't with our nature but with our information networks. Due to the privileging of order over truth, human information networks have often produced a lot of power but little wisdom.
Page 402
Accordingly, as a network becomes more powerful, its self-correcting mechanisms become more vital.
Page 403
Unfortunately, despite the importance of self-correcting mechanisms for the long-term welfare of humanity, politicians might be tempted to weaken them. | Sam LadneNotable QuotationsExpand to full screen
Good explanations provide general principles that can then be applied to other design problems.
Classic anthropological approach: long-term, embedded observation within the community, interviewing, the methodical collection of data, and most importantly, the constant attempt to explain what life means to these people.
The participants’ viewpoint— Businessweek calls it the “new core competence.”
Ethnography is conducted in context, providing new insights into the other objects, people, and products that consumers are currently using.
Ethnography puts consumer needs first, which means a product based on ethnographic research will solve real consumer problems.
Starbucks CEO Howard Shultz, [says] that the company does not sell coffee so much as the experience of coffee.
Ethnographic truth” is a distinct kind of truth that differs from traditional market research.
Facts and prediction are not the only value research can bring. Renouncing that school of thought leads to a whole new world of insight and a different kind of truth—truth about understanding, unriddling, decoding, and deciphering.
Accurate prediction is so rare that it virtually never happens. So forget prediction. Go for deep understanding.
An interpretivist is interested in understanding what the world means to people.
The private-sector ethnographer’s task, then, is not to find the “truth” about products and services, but the meanings consumers ascribe to them.
Most marketers, business strategists, and product managers don’t understand the interpretivist point of view, not because it is incorrect, but because it has an unfamiliar conception of truth.
A product’s meaning is a function of a consumer’s perception of two broad concepts: ) his own identity; and ) the system of meaning in which he finds himself.
ethnographic research begins with questions such as, how does a consumer see himself? In which context does he use this product? How do identity and context interact to affect this sensemaking process?
Fixed identities such as “women” or “Latinos” are not categories that determine behavior so much as they are roles that individuals must interpret, find meaning in, and grapple with.
So we must unriddle how the consumer’s identity shapes his interpretation of a particular product, and the social context in which this meaning is negotiated.
The Urban Hipster is the contemporary personification of cultural capital. She has very little money but exercises her knowledge of cool to exert her class dominance. Her capital is her knowledge of art shows, vintage clothing stores, little-known Italian bike designers, and, of course, “bands you haven’t even heard of.” The Hipster’s “wealth” is not in her bank account; it is in her superior knowledge. She will use this knowledge to dominate others by restricting access to exclusive knowledge. She will not tell just anybody where she bought her vintage cowboy boots. She will nod at you knowingly if you somehow find your way to her favorite bar. She will sneer at those who do not have this knowledge. It is her way of exerting dominance, not with mere money, but with cultural know-how.
Luxury goods are not simply a function of how much they cost, but the meanings that consumers ascribe to them.
The psychologist (particularly the social psychologist) focuses on the individual and his interactions with others, but it is the ethnographer that provides insight into the influences culture has on that individual.
We can operationalize culture as values, beliefs, and behaviors.
The product’s design and marketing must match the ways in which consumers think about that product.
Alternatives are behaviors that are considered outside the norm, but within the realm of personal taste.
Your job as a practical ethnographer is to discover that which appears true. What do consumers believe about this product?
discover how people interpret that perception and how this product may—or may not—fit into their lives.
Today’s corporate touchstone is the “project.” A project is the temporary organization of people marshaled around a shared goal (Lundin and Soderholm, ).
[When it comes to ethnoraphic research,] recruiting participants is by far the most labor-intensive stage of the research project.
The goal is never to achieve “the numbers,” but to achieve the explanation of your participants’ cultural practices.
You may find there are stakeholders in the wider organization that are hoping for the project to fail because they subscribe to another form of truth or validity.
Consider some of the typical successes an ethnography can have: transformed client mindset, an overarching mental model for the product, deep insight into customers’ mind sets, metaphors for design, and so forth.
Ethnography failures: poor recruiting, not enough participants, shallow insight, findings that aren’t actionable, client dissatisfaction, lack of impact in the client organization, misunderstanding of the project goals by clients.
Add the post mortem to your initial project plan.
As Hubert Dreyfus and his brother Stuart tell us elsewhere () the difference between competence and mastery is that masters are able to quickly discern the nature of the problem at hand and swiftly bring to mind several potential solutions to that problem. Just as quickly, the master then selects the right solution for the problem. The leap from competence to mastery is not a function of faster brain processing, but of faster pattern recognition; the higher order thinking of a master ethnographer relies on his ability to consider—and dismiss—potential paths without
The Livescribe recording pen is a tool uniquely suited to the private-sector ethnographer.
Private-sector ethnographers rarely have the time or budget to transcribe interviews in their entirety. [What] private-sector ethnographers need is the ability to pluck out quotes quickly. Health research has shown that computer use can lessen rapport, so laptops should be used with care. Audio can produce rich and illuminating stories, particularly when you hear participants’ voices or the sounds of their environments.
Understanding what you’re doing in the field is one thing; understanding what your client thinks you’re doing is a completely different thing. [You] must constantly consider what can be improved in the current state of affairs, and specifically how to improve it.
Ethnography is a research project designed to uncover contextual insights for use in design and marketing. Ethnography is essentially an epistemological shift, forcing its practitioners to empathize with participants and adopt their standpoint. Asking what consumers truly believe about a company’s product is a bold act because it begs a self-examination of what the company believes about that same product. Standing in a room and looking at things is not ethnography. Ethnographers must do two things: describe the data and interpret the data. Ethnographers do not study products; they study how products fit (or do not fit) into people’s lives. Ethnographers answer questions about people, while business people expect answers about products.
Ethnographic projects represent a fundamental threat to identity if they focus on the gap between the customer’s experience and the organization’s own identity pillars.
the first priority is to create rapport, which essentially boils down to trust. A good interview is like a dance—with the interviewee leading.
In academic ethnography, informed consent involves telling participants what data will be collected, how it will be stored, and what ultimate outputs will be created (which usually means articles and books).
Sampling, at its heart, is a shortcut. If you had time to ask everyone in the country the same questions, you would actually be conducting a census. That’s what “census” means— asking absolutely everybody.
Large samples are not always necessary, and good samples aren’t always random.
Certainly, there is value in predicting patterns. Unfortunately, it has become the only thing that most people expect from social research.
your job as an ethnographer is to find participants who offer the greatest potential for understanding the phenomenon at hand.
qualitative researchers don’t care about comparing their results to random results. As a result, they don’t tend to care about probability sampling.
Qualitative researchers tend to select their participants based on the needs of the study.
create a set of recruitment criteria that are relevant to the research question.
In a typical private-sector study, an ethnographer is seeking what Anselem Strauss and Barney Glaser call “saturation,” or the point at which you begin to hear the same information repeated.
The sample is typically drawn from a list called the sampling frame. Finding the sampling frame is by far the most challenging part of sampling for either qualitative or quantitative research.
Consider offering a “comfort call” to the participants the day before the field visit, telling them what to expect
Twitter, Facebook, and LinkedIn all offer great opportunities to recruit qualified participants.
). The key to using social media for recruiting, therefore, is maintaining your network through direct engagement with other users.
The primary concern in ethnographic sampling is to gain access to participants’ contexts, and from there, derive insight about their attitudes, values, and beliefs and more deeply understand a particular product space.
The average and the extreme are wonderful examples to have in your sample.
find often in the private sector is that commissioners of research frequently decline to interview people of lower socio-economic status under the mistaken belief that their opinions are not relevant.
Ethnographers cannot offer what their quantitative colleagues can: prediction of what will happen.
your job is to show your clients and stakeholders that prediction is a poor substitute for deep understanding.
Unlike survey researchers, ethnographers find themselves physically in the middle of all sorts of situations. It’s impossible for them to escape squirm-worthy moments because that is precisely what ethnography is made up of.
Observation is indeed an ethnographic method, but in ethnography it is complemented by clarifying questions and sit-down interviews.
Ethnographic techniques discovered that what people say they want contrasts directly with what they actually need.
Budget sufficient time: There is nothing more forced, more contrived than an ethnographer arriving and expecting an immediate display of “normal” behavior.
Robust competitive analysis is best done through comprehensive surveys with large sample sizes.
Diagramming the “customer journey” (aka the “time-ordered display”) provides a quick way of showing your clients the consumption act and where it might be unpleasant for consumers.
The gap between what people say and what they do is a rich ground for finding contradictions.
Qualitative researchers actually use outliers as a tool to understand everyone else that does fit the pattern.
The “so what” question is the most important aspect to ethnography. It is what differentiates ethnography from journalism. | Kory Kogan, Suzette Blakemore, and James WoodNotable QuotationsExpand to full screen
PROJECT: A temporary endeavor with a start and finish undertaken to create a unique product, service, or result.
Most clients say that between 60 and 80 percent of their time at work is project based.
Project management is the work of the twenty-first century. This means that everyone is a project manager. Project management is no longer just about managing a process. It's also about leading people—twenty-first-century people. This is a significant paradigm shift. It's about tapping into the potential of the people on the team, then engaging with and inspiring them to offer their best to the project.
Help you manage projects and lead people in this century—people who are knowledge workers, who bring their minds to the job, who are volunteers you can't and won't be able to control. (They probably don't work for you anyway, right?)
Why projects fail.
- lack of commitment/support
- unrealistic timelines
- too many competing priorities
- unclear outcomes/expectations
- unrealistic resources
- people pulled away from the project
- politics/legislation
- lack of a “big picture'' for the team
- poor planning
- lack of leadership
- changing standards
- lack of or mismanaged budget
Too many people call your project a success if all you've done is meet the deadline and the budget. But did you meet or exceed expectations, the first measure of success? Did you achieve your business outcomes? … And did you truly optimize resources, the second measure of success?
MANAGE PROJECTS, LEAD PEOPLE
The true formula for winning at projects is PEOPLE + PROCESS = SUCCESS.
We've narrowed down PMI's robust process to the essentials and added our own insights about what makes projects successful.
- Initiate
- Plan
- Execute
- Monitor and Control
- Close
Managing the process with excellence is important, but being a good leader is essential.
As an unofficial project manager, you often lack the formal authority to tell anyone what to do.
Informal authority comes from the character and capabilities of a leader.
Four Foundational Behaviors
- Demonstrate respect
- Listen first
- Clarify expectations
- Practice accountability
Showing respect does not mean becoming a doormat.
You can hold people accountable while being respectful by talking straight with them.
Straight talk is a form of respect,
Generally, though, if you respect others, they'll respect you,
LISTEN FIRST
It's crucial to resist that temptation to talk more than listen. Failure to listen can lead to strained relationships, decreased productivity, missed learning opportunities, and damaging errors in judgment.
When people come to you with complaints, problems, or requests for changes, let them talk first.
Listening first is inextricably tied to demonstrating respect.
No one person can possibly have all the answers all of the time.
If you are truly interested in building a high-performance team, get to know them. Ask them to be honest about their feelings. Ask them to be honest about what truly motivates and inspires them.
The key principle at work here is empathy. Work hard at understanding where they're coming from. Don't be the one who doesn't care what they say. Don't be the one who panics when they have a problem. Instead, let them keep talking!
Let team members grow; don't take all the responsibility for solving everything on yourself.
CLARIFY EXPECTATIONS
Get everyone "on the same page"
Informal authority means constantly and consistently clarifying both the specific and the overall expectations for your team members.
Feeling like you're making a contribution is what makes you excited and confident.
Clearly communicate how each person's role contributes to the whole.
The cause of almost all relationship difficulties is rooted in conflicting or ambiguous expectations around roles and goals."
Accountability as a project leader means that you are a model of excellence.
You must hold the entire team accountable to the standards you have set up.
The first three behaviors—demonstrating respect, listening first, and clarifying expectations—are essential to maintaining accountability.
Good project managers admit mistakes; that's why you so rarely meet a good project manager.
Tell it like it is. | | Gary SmithNotable QuotationsExpand to full screen
Now with data so plentiful, researchers often spend too little time distinguishing between good data and rubbish, between sound analysis and junk science.
Patterns in the data are considered statistically persuasive if they have less than a 1-in-20 chance of occurring by luck alone.
One out of every twenty tests of worthless theories will be statistically significant.
Selective reporting and data pillaging—are known as data grubbing.
If a theory was made up to fit the data, then of course the data support the theory! Theories should be tested with new data that have not been contaminated by data grubbing.
Some percentage changes are misleading, for example, when comparing the percentage change in something small to the percentage change in something big.
A statistical fluke can make a big difference if the base is small.
One way to deal with a small base is to use data for several years to get a bigger base.
There can be a statistical correlation without any causal relationship.
There isn’t necessarily any relationship between things that increase with the population—other than that they increase with the population.
A graph should reveal patterns that would not be evident in a table.
Watch out for graphs where zero has been omitted from an axis. This omission lets the graph zoom in on the data and show patterns that might otherwise be too compact to detect. However, this magnification exaggerates variations in the data and can be misleading.
Worst of all are graphs with no numbers on the axis, because then there is no way of telling how much the variations have been exaggerated. Watch out for data that have not been adjusted for the growth of the population and prices.
Graphs should not be mere decoration, to amuse the easily bored. A useful graph displays data accurately and coherently, and helps us understand the data.
We should be cautious about calculating without thinking.
A test may be very likely to show a positive result in certain situations (for example, if a disease is present), yet a positive test result does not insure that the condition is present. It may be a false positive. False positive are more common when the condition is rare (like a malignant tumor) or when there are a large number of readings
Misperceptions are part of our natural tendency to look for patterns and believe that there must be a logical explanation for the patterns we see.
When we see a data cluster, we naturally think that something special is going on—that there is a reason that these heads (or tails) are bunched together. But there isn’t.
When data are used to invent a theory, the evidence is unconvincing unless the theory has a logical basis and has been tested with fresh data.
A study that leaves out data is waving a big red flag.
Extraordinary claims require extraordinary evidence. True believers settle for less.
Data without theory can fuel a speculative stock market bubble or create the illusion of a bubble where there is none. How do we tell the difference between a real bubble and a false alarm? You know the answer: we need a theory. Data are not enough.
If we have no logical explanation for a historical trend and nonetheless assume it will continue, we are making an incautious extrapolation that may well turn out to be embarrassingly incorrect.
Before we extrapolate a past trend into a confident prediction, we should look behind the numbers and think about whether the underlying reasons for the past trend will continue or dissipate.
A careful selection of when to start and stop a graph can create the illusion of a trend that would be absent in a more complete graph.
If the beginning and ending points seem to be peculiar choices that would be made only after scrutinizing the data, these choices probably were made to distort the historical record.
Theory without data—a semi-plausible theory that is presented as fact without ever confronting data. A theory is just a conjecture until it is tested with reliable data. For predictions decades or even centuries into the future, that is pretty much the norm.
We are hardwired to make sense of the world around us—to notice patterns and invent theories to explain these patterns. We underestimate how easily patterns can be created by inexplicable random events—by good luck and bad luck.
Experiments often involve changing one thing while holding confounding factors constant and seeing what happens. For example, plants can be given varying doses of fertilizer while holding water, sunlight, and other factors constant. In the behavioral sciences, however, experiments involving humans are limited. We can’t make people lose their jobs, divorce their spouses, or have children and see how they react. Instead, we make do with observational data—observing people who lost their jobs, divorced, or have children. It’s very natural to draw conclusions from what we observe. We all do it, but it’s risky.
Don’t overlook the possibility of errors in recording data or writing computer code.
Watch out for graphs that exaggerate differences by omitting zero from a graph’s axis.
Be doubly skeptical of graphs that have two vertical axes and omit zero from either or both axes.
Watch out for graphs that omit data, use inconsistent spacing on the axes, reverse the axes, and clutter the graph with chartjunk.
Before you double-check someone’s arithmetic, double-check their reasoning.
The probability that a person who has a disease will have a positive test result is not the same as the probability that a person with a positive test result has the disease.
Correlation is not the statistical term for causation. No matter how close the correlation, we still need a logical explanation.
Don’t be fooled by successes and failures. Those who appear to be the best are probably not as far above average as they seem. Nor are those who appear to be the worst as far below average as they seem. Expect those at the extremes to regress to the mean.
Good luck will certainly not continue indefinitely, but do not assume that good luck makes bad luck more likely, or vice versa.
Don’t be easily convinced by theories that are consistent with data but defy common sense.
Watch out for studies where data were omitted, especially if you suspect that the omitted data were discarded because they do not support the reported results.
If a theory doesn’t make sense, be skeptical. If a statistical conclusion seems unbelievable, don’t believe it. If you check the data and the tests, there is usually a serious problem that wipes out the conclusion.
| David W MarxNotable QuotationsExpand to full screen
Page xiv
The thing we call culture is always an aggregation of individual human behaviors, and if taste were the mere product of random idiosyncrasies and irrational psychologies, culture would display no patterns, only noise. The fact that preferences in these disparate fields follow a similar rhythm of change suggests there must be universal principles of human behavior at work -- the presence of a "cultural gravity" nudging humans into the same collective behaviors at the same time.
Page xiv
As we'll see by the end of this book, fashions explain behavioral change more than we've been willing to admit.
Page xvi
Researchers recently concluded that the achievement of high status only makes people want more.
Page xvi
This also explains why we dislike social climbers: they remind us there is a ladder to climb. In fact, the modern word "villain" derives from the status - related sin of lowly villein feudal tenants daring to seek a higher social position.
Page xvi
in seeking to maximize and stabilize status, individuals end up clustering into patterns of behavior (customs, traditions, fashion, fads, taste) that we understand as culture.
Page xvii
Status shapes our aspirations and desires, sets standards for beauty and goodness, frames our identities, creates collective behaviors and morals, encourages the invention of new aesthetic sensibilities, and acts as an automated motor for permanent cultural change. Culture is embodied in the products, behaviors, styles, meanings, values, and sensibilities that make up the human experience -- and it is status that guides their creation, production, and diffusion.
Page xvii
Elites could once protect their status symbols behind information barriers and exclusive access to products; now nearly everything is available to nearly everyone.
Page xviii
diluted the power of taste to serve as an effective means of social exclusion. the most notable outsider group of the twenty - first century has been the internet trolls rebelling against diversity, equity, and inclusion through revanchist slogans and memes.
Page xx
status, is not a "game" some choose to play but an invisible force undergirding the entirety of individual behavior and social organization.
We want great art and enduring beauty to derive from intrinsic value -- not from elite associations.
Part One: Status and the Individual
Page 5
we care a lot about our ranking, because it determines the benefits we receive; at the same time, we can deduce our position in the hierarchy at any time by comparing our benefits with those of others.
Page 5
status is bestowed by others.
Page 6
Our status position is always contextual, based on how we are treated in a particular time and place.
Page 7
A growing body of empirical research concludes that status is a fundamental human desire. Normal status is nice, but long - term happiness requires a sense of higher status.
Page 8
A study found that 70 percent of research subjects would give up a silent raise in salary for a more impressive job title.
status hierarchies tend to be based more on esteem rather than raw power.
Page 9
Esteem is the backbone of status hierarchies, and this form of social approval acts as a benefit in its own right. We like feeling liked.
Cecilia Ridgeway, "is not so much about money and power as about being publicly seen and acknowledged as worthy and valuable by the community."
Esteem can be expressed through a wide range of palpable benefits. People with above - average status experience favorable interactions -- "salutations, invitations, compliments, and minor services."
Page 10
High status also means more attention and rewards for doing the same work as lower - status individuals.
Another favorable interaction is deference -- the right to do as one pleases, at one's own pace, with few interventions or interruptions.
An additional status benefit is access to scarce resources.
Page 11
The final status benefit is dominance -- the ability to make others do things against their wishes.
high status makes people happy and healthy,
All of this demand for higher esteem, however, inherently engenders social conflict:
Page 14
Sociologists describe those born to higher ascribed status categories as status advantaged, and those born outside of those categories as status disadvantaged.
Page 15
The modern ideal is to organize society as a system of achieved status, where a higher position is based on personal achievements rather than immutable characteristics.
The promise of status rebirth was for many years a selling point for immigrating to the New World.
This opens the question, however, of what actually qualifies as "achievement."
the highest achievements must demonstrate rare and valuable talents.
Today, achievements tend to be embodied in particular forms of capital.
political capital access to power - As meritocracy becomes more of a shared ideal, new forms of capital have emerged.
Page 16
Educational capital - degrees and certifications - Occupational capital - economic capital --
- cash, wealth, and property - Money is very flexible as an asset, converting easily into power over others through business ownership, political connections, donations, and bribes.
Social capital --
networks of collegial relationships with elites - additional status criteria generated within the internal logic of the status system itself: namely, cultural capital, detachment, originality, and authenticity. Besides capital, we also have personal virtues that may improve our interactions with others. We can receive esteem in our communities through intelligence, physical attractiveness
behavioral and conversational
Page 17
charms
personal integrity
bodily capital;
While personal virtues can open the door to building more reliable forms of capital (and, for the most part, originate in aristocratic mores), they aren't particularly rare or valuable in their own right.
The global hierarchy never revolves around the cleverest quips and the freshest breath.
Capital determines our membership in groups, and these memberships determine our status. The
To "be somebody" in today's world requires accumulating significant amounts of capital, often across multiple criteria.
This clustering is called status congruence, and it works to stabilize the status rankings.
Despite claims of achieved status, status congruence reveals exactly how inequity becomes entrenched over time.
Page 18
social mobility always appears to be possible, making us feel responsible for our own status.
There is one final ramification of achieved status we must also consider: we resent individuals who claim or receive high status without meeting the requisite status criteria.
Bertrand Russell
"Success should, as far as possible, be the reward of some genuine merit, and not of sycophancy or cunning."
The legitimacy of any hierarchy hinges on status integrity -- a collective belief that the ranking of individuals is fair, and that they receive greater benefits for legitimate reasons.
Individuals seek higher status -- insofar as its pursuit doesn't risk their current status level.
Page 19
status group. Members of these groups share status beliefs about the value of certain status criteria.
Alternative status groups believe in criteria outside of traditional capital.
The very best surfers have the most status, and the worst surfers have the least -- irrespective of their 401(k)s and fancy domiciles.
Page 20
In extreme cases this code switching between groups' competing demands can split us into multiple personalities.
Social mobility of the modern age allows individuals more freedom to choose their primary status groups. So how do we decide which groups to join? We are all born into a status group, and many remain there forever.
Fringe groups flip the script and value extreme negations of traditional virtues.
Page 21
From this perspective, membership in alternative status groups appears to be a clever strategy for oppressed and unprivileged individuals to maximize their status.
only provide local status --
surfer can be a great hero among other surfers but just a "beach bum" up the shore.
Max Weber
dominant groups that tumble down the hierarchy develop particularly strong resentments: "The more they feel threatened, the greater is their bitterness."
Page 22
The Trump voting bloc continues to embrace older status beliefs anchored in ascribed racial, gender, and religious hierarchies, which are losing influence in a more diverse society.
Status is thus not just personal but political.
Status is an ordinal ranking,
an overall increase in wealth only raises the bar for the capital required to gain status.
The constant struggles among status groups play a major role in the human experience -- and, as we'll see later, fuel the creation of new culture.
Although no society is perfectly meritocratic, modern individuals play a larger role in determining their own status than in the past.
Page 25
a more elementary requirement to gain status: conformity to group norms.
Page 26
earning social approval requires not just making concrete contributions to the group's goals but also following a particular set of arbitrary practices.
arbitrary denotes choices where an alternative could serve the same purpose.
we can eat, drink, dress, sing, dance, play, and think in a nearly infinite number of ways. And yet, once we settle on a particular behavior, we no longer see our decisions as arbitrary.
Our brains provide us with post facto rationalizations for our arbitrary acts.
Page 27
We become particularly stubborn about insisting on the nonarbitrariness of our own cultural practices.
What makes us so attached to the arbitrary practices of our community in times when other choices are available? The answer is conventions-well - known, regular, accepted social behaviors that individuals follow and expect others to follow.
Conventions assist humans in coordinating around certain choices.
Wherever we see people repeating a particular practice and rejecting its equally plausible alternative, there is likely a convention compelling everyone into making the same choice.
customs, the tacit rules of a community.
Page 28
traditions, are conventions anchored in historical precedence that serve as explicit symbols of the community.
Beliefs can also have conventional elements.
short - term conventions we call fads,
Fashions are conventions that appear in ornamental areas of life that change on a regular basis.
artists play with conventions -- respecting some to woo in audiences and breaking others to create surprise.
We ultimately follow conventions to gain social approval and avoid social disapproval, and in doing so, they change our behaviors and organize the data we gather from our senses.
Page 29
For a convention to take root within a community and become "regular" behavior, it must become part of common knowledge --
Moving the population to a new convention requires building new common knowledge.
Page 30
Conventions provide a "solution" when trying to coordinate behaviors with others:
Our brains prefer when other people meet our expectations, because this means we don't have to expend extra mental energy on thinking through alternatives.
Receiving social approval for upholding conventions and disapproval for violating them has clear effects on our status position.
Page 31
internalization means the origins of most conventions often get lost to the ages.
The more the backstory is forgotten, the more conventions seem to be the "natural" order of the world. Violations consequentially are "unnatural" and require sanctions.
Page 32
Not only did they set standards, they enforced them:
Internalization unlocks the final power of conventions: setting our perceptual framework for observing the world.
The perception of time, for example, is a convention.
The idea that major chords sound "happy" and minor chords are "sad"? That's a convention.
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These internalized conventions are known in sociology as habitus, and they guide our talking, walking, dressing, and thinking, as well as how we judge what is good, correct, fun, and beautiful.
the is - ought fallacy -
"a very nearly universal tendency of people to move from what is to what ought to be in the strong sense of concluding that what is right or good."
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To follow the same arbitrary rules as another individual is to be part of the same "collectivity."
Paradigm describes these macro - conventions -- the underlying beliefs of a group that set the overall rules for permissible actions, offer guideposts in times of uncertainty, and build the frameworks for understanding and explanation.
Ian MacDonald writes, "The Beatles' way of doing things changed the way things were done and, in so doing, changed the way we expect things to be done."
"Norms of partiality" benefit one group over another. Majorities commonly promote social norms that advantage themselves over minorities,
and in internalizing these biased conventions, even the disadvantaged parties may come to accept them.
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Even when conventions tend to be obviously unfair or clash against communal principles, challengers face social disapproval for choosing alternatives.
Anthony Heath's assertion:
the benefits of conformity must be compared with the benefits to be obtained elsewhere,
Conventions create habits and patterns of behavior through carrots of social approval and sticks of social disapproval.
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A superiority of position should be reflected in the superiority of benefits.
These are expressed in the expense, quality, and design of possessions; speech patterns (use of polite language or slang); means of earning a living; self - presentation (dress, hair, makeup, fitness); location and quality of domiciles; and hired services (do we mow the lawn or do we pay someone else to do it?).
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Every convention can be placed on two hierarchies: (1) the tier within a single status group; and (2) the position between groups on the global status ranking.
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As we move up the status hierarchy, we must adopt conventions with higher status value.
Knowing and participating in high - status lifestyle conventions -- even certain greetings, subtle preferences, and nonverbal cues -- is a critical part of gaining and maintaining high status. This particular knowledge is known as cultural capital, defined by the sociologists Michèle Lamont and Annette Lareau as
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"widely shared high status cultural signals (attitudes, preferences, formal knowledge, behaviors, goods and credentials) used for social and cultural exclusion."
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During times of broad economic growth, lower status tiers can suddenly afford to take part in higher - status conventions.
This raises the standards for all of society. Everyone feels they must also consume at a higher level to retain normal status -- i.e., keeping up with the Joneses.
So far we've seen how status requires conformity to certain arbitrary practices, and now we understand that the resulting conventions take on status value. Next we'll apply this knowledge to our own individual behavior -- and how the specific pressures of our status position push us to be alike and also be different.
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normal status requires following certain conventions. This means imitating our peers, while distinguishing ourselves from the behaviors of lower - status groups and rivals. Meanwhile, achieving higher status requires distinguishing ourselves from our current status tier and imitating the practices of superiors.
Humans are hardwired for mimicry and absorb the behaviors of our community.
Where imitation most commonly becomes a conscious act is when we join new groups later in life and seek other members' validation.
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Imitation is required for attaining normal status within a group, but there is an additional requirement: we must affirm our differences from rival groups.
Conventional differences are critical for group demarcation, and groups emphasize the distinct conventions that draw these clear lines.
counterimitation.
Normal status in a group requires both imitation and counterimitation.
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By definition a higher position requires individual distinction.
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As long as distinction fits within the collective beliefs of the group, individuals have more leeway to break from the norm.
Another low - risk form of individual distinction is emulation -- the chasing of status value through the imitation of higher - status conventions.
Emulation is a safe bet, but not a sure one.
High status in modern society thus requires satisfying an additional status criterion: to be distinct.
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good indication of having super - high status, then, is being able to get away with distinctive acts.
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pluralistic ignorance: the fact that we make our "different" choices without knowing everyone else's next actions.
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Where we do choose for status value, our brains obfuscate the reasons and tell us we are desiring something more rational.
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Status must be communicated,
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The term "signaling" is used in both economics and zoology to describe when individuals communicate their high quality through specific clues in order to be selected by another party.
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status is given, never taken."
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Coco Chanel's maxim "If you wish to do business, the first thing is to look prosperous."
We don't have to signal to everyone -- only in times of information asymmetry.
the highest - status individuals should have strong enough reputations to reduce the need for aggressive signaling.
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bragging thus become an implicit sign of low status.
the principle of detachment:
very high - status individuals should seem detached from active attempts to gain status.
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Great Gatsby, "Her voice is full of money" --
Besides signals and cues, there is an important third category of information used in status appraisals: significant absences. Appraisers also look for what is missing.
But the fact that not doing things plays a role in status appraisals means no one can ever opt out of making status claims.
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status appraisers look for clues in our demeanor and possessions to estimate status, and so the most obvious way to signal a high social position is to show off certain goods or engage in certain behaviors with high status value.
To impress "transient observers," economist Thorstein Veblen advises, "the signature of one's pecuniary strength should be written in characters which he who runs may read."
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The principle of detachment means all status symbols require alibis -- reasons for adoption other than status seeking.
Companies that produce luxury goods, from Louis Vuitton to Tiffany, Rolex, and Dom Perignon, understand the need for alibis, and their marketing provides detailed explanations of great craftsmanship, rare materials, unsurpassed comfort, and the highest levels of quality control.
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Within wealthy communities, the most effective status symbols can be so discreet as to look like unconscious cues.
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The fact that cachet arises through associations with certain individuals and groups means that it can travel across "chains" of associations. When European elites fell in love with Russian ballet at the turn of the twentieth century, everything Russian took on cachet, including the borzoi breed of dogs.
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There are five common signaling costs. money. The second cost is time. The third cost is exclusive access.
cost is cultural capital -- knowledge of conventions acquired through spending time among high - status people.
The final cost is norm breaking.
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New Money focuses on financial costs. Subcultures thrive on exclusive access and knowledge. This means anything can be a status symbol if it has cachet and high signaling costs.
Money may be the most common signaling cost, but in a world with millions and millions of wealthy people, the most credible status symbols need to erect barriers beyond price.
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Because signals must be subtle, our appraisers may fail to notice them. This is the problem of perceptibility.
most people want credit for their hard - earned status symbols.
semantic drift: the slow change in words' meanings over time.
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after reggae musician Bob Marley became a global celebrity, everything Jamaican took on a new cachet,
In the 1980s, conservative, pro - business French youth wore stodgy tassel loafers as a subtle protest against the Socialist government. But French leftist youth also started to wear those same shoes to get better tables at upscale restaurants. This diminished the loafer's political valences.
In order to get ahead of failures in perceptibility, interpretability, and ambiguity, we adopt certain techniques to ensure semiotic success. The first is choosing the most suitable status symbols for our appraisers.
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The second is adjusting based on feedback.
The final and most important technique is redundancy,
Umberto Eco explains, "Every time there is signification there is the possibility of using it in order to lie."
Jessica Pressler writes,
the indicia of wealth,
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A significant portion of the modern economy is based on committing light symbolic deceit.
Some forms of trickery have become conventional.
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for those incapable or unwilling to advance through education, training, or hard work, cheating may be the sole means of improving one's status level.
research shows that lower - income individuals believe "the game is rigged" and may be already skeptical that hard work is the key to life success.)
Triangulation forces us to look beyond single status symbols and toward the entire package of symbols.
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Like "status" and "culture,""taste" is yet another contentious term of frustrating ambiguity,
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The modern age of cultural pluralism, however, precludes a single, authoritative standard for good taste.
Standards of taste are always relative to the dominant conventions of the era and the society, and so the only way to make sense of taste is to analyze it as a social mechanism.
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For our purposes, taste is a crucial concept in providing a direct link between status seeking and the formation of individual identities.
Our particular tastes may have genetic and psychological elements, but they manifest only in social activity. Our habitus provides the unconscious conventions that decide what we find pleasurable.
status value distorts our preferences, making certain objects and conventions more attractive than others.
Sociologist Pierre Bourdieu writes that taste is a "match - maker" -- a force that "brings together things" and also "people that go together."
Common interests inspire reciprocal judgments of "good taste"
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By triangulating all the signals, cues, and absences, we understand someone's taste as a gestalt.
Individuals occupying a particular taste world share the same broad aesthetic and make similar choices in cars, clothing, music, beverages,
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taste also involves skill.
Judgments on taste don't just classify but gauge personal virtues and talents.
The skill aspect of taste means it never just expresses our unconscious habitus, but can be shaped through conscious choices.
noble birth -- or the result of self - improvement.
We can "cultivate" ourselves over time to make more advanced choices that will garner more respect.
Great taste first requires a deep knowledge of potential choices.
Kantian taste requires us to find pleasure in things that take time and effort to appreciate:
Expanded knowledge, however, is not enough to move up the taste hierarchy. Lifestyle choices also must reveal congruence -- an internal consistency with the target sensibility.
established groupings of products are called constellations, and each taste world contains distinct sets.
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the truest marker of excellent taste is bounded originality. As we learned before, the highest - status individuals can't imitate anyone lower on the hierarchy and, therefore, must make distinctive choices.
"To like what one ‘ought' to like is not to exercise taste."
Choices should express the individual's exceptional character.
requirement for originality
"The faculty of taste," writes the philosopher Ludwig Wittgenstein, "cannot create a new structure, it can only make adjustments to one that already exists.
Originality works best when the individual has already established mastery of a high - status sensibility and enjoys high - status privilege.
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Successful artists forge unique sensibilities by combining preexisting artifacts and conventions in new ways.
A shortcut for great taste is arbitrage, finding easily procured things in one location and then deploying them elsewhere where they're rare.
Perfect taste, however, doesn't just require making choices that satisfy certain standards. We also have to prove that our choices are appropriate and natural for our particular life stories.
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"A pine table is a proper thing," stated a nineteenth - century interior design guide, "but a pine table that pretends to be black walnut is an abomination."
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Authenticity has become particularly important in the modern
Authentic taste should be anchored in an individual's specific life journey.
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Authentic tastes are "natural" tastes --
To be judged as authentic, we must provide information validating the provenance of our taste.
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By extension, personas appear more authentic when they include a few "mistakes" -- i.e., sloppy behaviors, low - status habits. Perfect taste suggests an overexertion of effort.
In men's fashion the ultimate style move is sprezzatura, embracing intentional errors such as undone buttons and misaligned neckties.
Intentional amateurism can be attractive for those who already have high status.
the central paradox of authenticity: we are supposed to listen to the voice in our hearts, to "discover and articulate our own identity" -- and yet, only others can judge whether we are authentic. Appraisers compare our taste with our demographic profile, and where there is a suspicious mismatch, they deny us status.
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The most powerful form of authenticity thus remains authenticity by origin:
the principle that groups who formulate a convention are the best at replicating it.
authenticity by content: the principle that the best things are those made by the original methods
Our best tactic is to choose signals close to our immutable characteristics and in line with our origin stories.
The high - status individual, writes sociologist George Homans, "can afford to relax and be a natural man."
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Authenticity can be yet another privilege of the elite.
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Which "I" are we talking about? There appear to be three: persona, identity, and self. In signaling, we build personas -- observable packages of signals, taste, sensibility, immutable characteristics, and cues absorbed from our upbringing and background. Others use this persona to determine our identity. At the same time, we have a self within our minds, known only to us.
Before modern times, personal identity was simply a role and status: membership in a clan, tribe, and caste, as well as the specific position within that community. We now seek an individual identity that transcends demographic categories and classifiers.
If we can be easily summarized through stereotype, category, and class alone, we're failures.
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The effects of status are most obvious in the persona -- the public expression we craft in our social interactions.
the pressures of status mean that every individual crafts their public image to some degree.
To move up the status ladder, we gravitate toward common goals, such as amassing capital, refining talent, improving personal virtues, and acquiring more impressive status symbols.
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Certain people are skilled at making choices so bold that their entire persona emerges as "original," but status pressures make complete difference nearly impossible.
within the hierarchy determines the degree of difference an individual seeks -- and is allowed to seek.
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Middle - status individuals, on the other hand, tend to be conservative and follow conventions more closely.
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Originality is thus an aristocratic privilege.
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Modernity has democratized the aristocratic propensity toward individual distinction.
The nonchalance, speech patterns, and bodily movements of Old Money are status symbols.
Advising everyone to "be yourself" is therefore unfair as a broad mandate in a world still marked by bias: not everyone is born into a set of privileged attributes and behaviors.
the logic behind identity politics, where individuals sharing demographic characteristics unite to raise the status levels associated with their defining trait.
persona crafting remains an important tool for status equalization.
To stigmatize persona crafting is, then, to support the status ladder as it exists today.
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the persona is a mere "application." Receiving an identity requires being identified by others. Why are others identifying us? The most immediate reason for their attention is status appraisal.
To properly interact with strangers, we must know their status.
We may have control over what others observe, but we have no control over how they classify us.
Nor do they: their means of perceiving and identifying us is based on their habitus.
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As long as the desire for status is fundamental, uniqueness works best only when it is part of a larger status strategy.
Charles Taylor writes that we "tend to think that we have selves the way we have hearts and livers," with "our thoughts, ideas, or feelings as being ‘within' us." Yet we now understand that these desires, at least in part, derive from community conventions so internalized they become indistinguishable from instinct.
Our brains are always engaged in rationalization: framing raw demands from our subconscious as well - grounded, logical requests.
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So, with respect to persona, identity, or self, status determines much of who we are.
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The best we can hope for is a relative originality created in the margins of our persona.
For those at the top, the pursuit of distinctiveness is important for receiving higher status. But to foist the requirement of uniqueness on everyone is unnecessary, unnatural, and often cruel.
Compared with our ancestors, we enjoy greater flexibility in choosing the most suitable lifestyles and face minimal punishments for deviating from custom.
Everyone should know how to win: how to gain esteem, how taste can be refined, how personas are judged, and how to balance detachment, congruence, originality, and authenticity. We all compete for status, whether we like it or not. We can at least better explain the rules to make it a fairer fight.
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Status Strategy #1: Perform better against the status criteria -- and reveal it in signals.
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Status Strategy #2: Pretend to be high status.
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Status Strategy #3: Change the status criteria in your favor.
Status Strategy #4: Form a new status group.
To seek higher status we choose one or more of these four strategies, and by doing so, we enter into social competition. Individuals attempt to stand out against their peers, inferiors steal the status symbols of superiors, elites fight off upstarts and cheaters, and alternative status groups challenge established status beliefs.
Part Two: Status and Creativity
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the sensibilities underlying taste are never random, independent results of idiosyncratic and irrational minds.
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Members of socioeconomic classes possess similar status assets, which lead to similar signaling strategies.
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New Money pours their ample funds into luxury goods for a quick status boost.
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How do distinct styles, conventions, and sensibilities form? --
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Humans may be born with a creative instinct, but the need for status - related differentiation motivates individuals to pursue counterintuitive, idiosyncratic, and outrageous inventions. These new ideas form as the shared culture of small communities, and then those groups' global status determines the degree to which they influence the taste of broader society.
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classes are groups of individuals with common levels of capital who share similar values and convictions.
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Individuals born into a certain socioeconomic class share a foundational set of unconscious conventions -- i.e., the same habitus. This manifests in communal beliefs, concrete lifestyle differences, and distinct taste worlds.
Economic capital consists of money, property, and wealth, whereas cultural capital is the knowledge of high - status conventions required to gain normal status from those in established high - status groups.
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New Money status symbols thus have very low symbolic complexity:
Before consumer society, the main method of overwhelming others with wealth was what Thorstein Veblen calls conspicuous waste -- flamboyant expenditure to demonstrate the possession of unlimited resources.
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conspicuous leisure --
playing in public while everyone else is hard at work.
vicarious consumption --
In their willingness to accept expensive new products for signaling wealth, parvenus are often attracted to novelties -- the latest and greatest styles, gadgets, and fashions.
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Moreover, novelties align with the core New Money belief that contemporary luxuries are credible status symbols.
even in ordinary times, conspicuous consumption violates the principle of detachment.
The ultimate flaw with conspicuous consumption is that the artifacts (such as yachts, mansions, and luxury brand goods) themselves inevitably become associated with New Money -- a group lower in status than Old Money.
Economists call this the "Veblen effect": goods become more desirable with a higher price tag.
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In advanced economies, however, New Money status claims face serious opposition from other classes -- starting with a powerful counteroffensive from the established rich.
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Where New Money desires the latest, biggest, and brightest, Old Money seeks to be modest, antiquated, and muted.
"casual, careless, nonchalant, insouciant, easy, unstudied, natural, effortless"
New England heiresses drive beat - up station wagons.
"detached,""refined," and "urbane" --
But as we'll learn, musty Old Money aesthetics are an equally rational signaling strategy as New Money's money - drenched boasting.
Old Money,
has an advantage in the longevity of their status superiority, which can be demonstrated through social capital (strong relationships with other rich families) and cultural capital (knowing how to behave at the very top of society).
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Old Money loathes New Money.
Old Money also resents any challenges to the existing social hierarchy.
Old Money doesn't have the same cash flow
In a world where wealth alone determines status, New Money would rise to the very top of the hierarchy. Fears of this outcome push Old Money to erect new fences based on taste.
"Spartan wealth" --
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countersignaling.
Earl of Lonsdale
"In London, nobody knows who I am, so it doesn't matter. In Cumberland, everyone knows who I am, so it doesn't matter."
Old Money ethos of reduction.
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Concurrent with modesty is a pursuit of functionality over display.
Old Money individuals have not achieved good taste -- they embody good taste.
the anthropologist Grant McCracken calls "patina," visual proof of age in possessions.
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Patina also explains the rustic nature of Old Money aesthetics.
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Patina also encourages archaism, the preferences for antiquated styles over contemporary alternatives.
inside jokes, secret handshakes, and correct cadence of banal chatter.
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Old Money "curriculum."
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Old Money aesthetics, then, don't just operate at the top of society but spur imitation among a much larger audience -- specifically, educated middle - class individuals who are also hungry for alternatives to New Money vulgarity.
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From the 1970s onward, ambitious and highly educated professionals in finance, law, medicine, and big business had begun earning much more than their parents. And in contrast to the rigid conformity of earlier corporate culture -- e.g., IBM once required male employees to wear sock garters -- yuppies pursued a colorful and cosmopolitan life of sophistication.
the sensibility embodied in yuppie taste follows the logic of the professional class's signaling needs.
They are not as rich as New Money,
their cultural capital is learned rather than embodied.
they have honed their critical thinking and stockpiled an impressive degree of worldly knowledge.
retrieving, and processing vast amounts of information, and the professional class considers their competence in these areas as justified criteria for higher status.
Their most valuable signals,
privileged information.
The Bluffer's Guide to British Class
"Taste is entirely a Middle Class concern. The Lower Class don't have it and the Upper Class don't need it."
professionals often start by emulating Old Money aesthetics.
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even if aspiring members of the professional class can't pass for true Old Money, they go out into the world with the analytical abilities to read cultural codes well enough to blend into high society.
Old Money taste also better matched professional - class salaries.
the Volvo automotive brand became one of the American professional class's favorite cars.
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"high cultural capital" Americans
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Media companies catering to this class create middlebrow entertainment: high - minded yet easily digestible content looking to reward an educated audience through winking references to their acquired knowledge. The greatest example may be The Simpsons, which mixes cartoonish ultraviolence with piquant social satire and passing allusions to Ludwig Wittgenstein.
The Condé Nast magazine empire -- from Vogue and GQ to The New Yorker -- was built upon teaching the latest high - status conventions to the professional classes, many of whom didn't live in New York to observe the trends themselves.
the professional class has splintered into two distinct factions.
Those who work in investment banking, private equity,
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The other faction is the creative class, Richard Florida Being an obscure novelist doesn't provide enough income to thrive but can lead to esteem and other material benefits.
the creative class, as we'll see in subsequent chapters, is the first to embrace new styles from nominally lower - status groups and, in doing so, takes the lead in promulgating cultural change.
For the rich, culture becomes a realm to communicate symbols of their monetary advantages; members of the professional class, on the other hand, communicate superiority in their manipulation of culture.
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For many the quickest path to higher social position is Status Strategy #2: Pretend to be high status.
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There are canons of taste in both rich and poor communities.
But even if the status logic of taste holds across classes, the lack of capital results in differences between signaling at the bottom of the ladder and at the top.
we should think about kitsch in a value - neutral way -- as a specific type of commercial product that copies the format of high culture (books, music, films, clothing, interior goods) but removes its artistic aspirations.
Kitsch is low in symbolic complexity: little irony, few ambiguous emotions, and muted political gestures.
stock emotions,
Kitsch may be ersatz art, but it delivers the experience of art to everyone.
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Kitsch feels good immediately, whereas avant - garde art intentionally breaks the very conventions responsible for delivering pleasurable experiences.
For those with the right knowledge, such as Old Money and the professional class, kitsch is loathsome.
"Consumers of kitsch," writes the philosopher of art Tomáš Kulka, "do not buy kitsch because it is kitsch; they buy it because they take it for art."
Kitsch may be pleasurable, but its ubiquity means it doesn't provide any status boost. An advantage in signaling requires standing out. This encourages a flash sensibility -- bright and showy aesthetics, usually achieved through the purchase of low - level luxury goods.
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in signaling, the poor can't afford to look generic.
New Money extravagance and lower - status flash: both groups want big logos. But only New Money can easily buy the real thing.
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The creators and consumers of mainstream pop culture would never call these products "kitsch," but as we'll see in chapter 8, there is an implicit agreement to meet existing audience expectations with conventional formulas, obvious emotions, and safe political valences.
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the oft celebrated "elegance of simplicity" isn't an innate human preference but arises from a countersignaling strategy.
escaping the class system is also a creative engine for new aesthetic sensibilities.
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Communities such as gangs and cults offer the disrespected a chance to be reborn as beloved and welcomed comrades.
Infamy is often preferable to anonymity.
Strategy #4: Form a new status group.
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But as we learned in chapter 1, this status strategy has a major flaw: individuals in subcultures gain only local status. And if the group's foundational status criteria diverge too greatly from the mainstream, joining a subculture results in a major loss of global status.
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Compared with subcultures, countercultures tend to embrace explicit ideologies, which members uphold as superior to traditional norms.
the hobbyist group: pods of individuals building mutual respect networks based on common interests.
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While alternative status groups suggest a way to escape the class structure, individual members' tastes are still moored to their habitus.
Over time, however, the distinctions between subcultures and countercultures blur, especially as countercultures find inspiration in the "authenticity" of subcultures.
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Of course the hostile feelings between mainstream society and alternative status groups are mutual.
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subcultures and countercultures don't form around minor stylistic divergences but around conventions of extreme difference.
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The easiest method for subcultural distinction is the negation of standard conventions:
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Members themselves don't see their lifestyles as mere counterimitations, but perceive them as direct expressions of personal feelings.
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Alternative status groups may represent an escape from the primary social hierarchy, but they're not an escape from status structures in general.
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Subcultures, then, become more and more extreme in their looks over time -- and yet, it is often their most radical inventions that go on to influence mainstream society.
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idolization of status inferiors
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For many burgeoning creative - class members, subcultures and countercultures offered vehicles for daydreaming about an exciting life far from conformist boredom.
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Defusing not only dilutes the impact of the original inventions but also freezes far - out ideas into set conventions.
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Most alternative status groups can't survive the parasitism of the consumer market;
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Vanilla Ice's failed career demonstrates the perils of unironic mimicry.
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Most subcultures remain marginalized:
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Innovation, in these cases, is often a by - product of status struggle.
Artists are the most well - known example of this more calculated creativity -- and they, too, are motivated by status.
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Immanuel Kant asserted three still authoritative criteria for artistic genius: (1) the creation of fiercely original works, (2) which over time become imitated as exemplars, and (3) are created through mysterious and seemingly inimitable methods.
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These Kantian requirements also match the most advanced status criteria of our era -- namely, originality, influence, authenticity, and detachment.
Kant's criteria also explain why most creators never make it past lower tiers. Hacks only copy.
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The most original artworks violate norms, and if they fail to attract critical notice, artists can fall to very low status.
less risky,
harmonizing others' radical inventions with more established conventions to expand the potential market.
Hedging, however, is taboo for the true artist, who must stay detached from any status concerns. Only hacks make art for money and power.
This explains why artists so often deny any conscious motivations for their work -- including the desire to make art in the first place.
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But nearly every artist pursues a specific kind of status: artist status.
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there is no Wikipedia page for Edna Hibel,
Despite great prowess at her craft and the esteem of international luminaries, Hibel never attained the artist status of her predecessors Georgia O'Keeffe and Frida Kahlo
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The clearest short - term strategy toward achieving artist status, then, is to win acclaim from art world institutions.
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Philosopher of art Tomáš Kulka explains that there is aesthetic value -- the ability to provide audiences with aesthetic experiences -- and artistic value -- the artwork's solutions to specific art world problems of the era.
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Artist status requires achieving artistic value, rather than aesthetic value,
The aesthetic value of an artwork measures how masterfully an artist can use and abuse existing conventions to elicit emotional experiences from the audience.
create and manipulate listeners'
Artistic value, on the other hand, measures the originality of the artist's inventions -- i.e., how much the proposed ideas break existing conventions and suggest new ones.
In the French poet Charles Baudelaire's famous line, "The chief task of genius is precisely to invent a stereotype" (emphasis added). To create within the framework of someone else's stereotype makes the creator an epigone, and their work is mere "taste."
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There are perhaps an infinite number of potential problems in art, but to gain artist status, artists must solve the agreed - upon problems of the current moment.
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Most audiences delight in minor innovations, not major challenges to their preferred art forms.
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Music listeners are happy with small surprises but expect conformity to familiar notions of melody, harmony, and rhythm.
Yet deep cuts are required to achieve artistic value.
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Gertrude Stein noted that all important art is "irritating" and Marcel Duchamp quipped, "A painting that doesn't shock isn't worth painting."
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In the early days of modern art, indignation became a clear sign of artistic success.
At the bottom of the pyramid, there is little to lose and much to gain. This explains why youth tend to be more radical than adults.
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At any time, rebellious artists always have an opening: either offer new solutions to these issues in good faith or cynically exploit the flaws of the established order to justify a new position.
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Anyone can propose shocking ideas; only geniuses gain prestige and legitimacy for them.
Artists don't anticipate future conventions so much as they create them through the influence process.
Avant - garde ideas, however, can break escape velocity from the avant - garde community only if broader audiences no longer believe their appreciation will lead to negative social consequences.
Cachet, thus, opens minds to radical propositions of what art can be and how we should perceive it.
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most movements, such as punk or grunge, develop organically as young artists converge on the same techniques.
William Wordsworth believed that "every author, as far as he is great and at the same time original, has had the task of creating the taste by which he is to be enjoyed."
the fastest way for creators to gain artist status is to win over gatekeepers in the art world.
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New Money deploys easily interpretable signals. Groups with limited economic capital, in contrast, must rely on symbolically complex conventions for effective barriers.
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With no money at their disposal, punks raise fences through radical fashions and behaviors.
Most aspiring artists secure their desired level of status through repeating others' inventions. But in societies that value originality, influence, and mystery, many people will attempt to attain high status through the creation of subversive ideas.
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Societies that value radical invention end up with more diverse cultural ecosystems, a great abundance of artifacts, and a multiplicity of sensibilities.
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the demands for originality pushed many artists to disturb conventions so deeply embedded in our brains that the artworks never found large audiences.
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we know that all public behaviors, including the use of technologies and products, become signals in status appraisals.
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Fashion, writes the philosopher George Santayana, is the "barbarous" variety of cultural change that "produces innovation without reason and imitation without benefit."
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Everett Rogers's authoritative theory on the diffusions of innovations. (" Invention" is a new idea; "innovation" describes the invention's use and widespread adoption.)
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innovators, early adopters, early majority, late majority, and laggards.
What slows down adoption by majorities? They often have unequal access
But status also plays a major role.
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most people seek to participate publicly in new trends only where status value becomes obviously positive.
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Elites must be arbiter elegantiarum -- tastemakers, not taste - followers.
Status also explains why innovativeness is found at the bottom of society as well. Outsiders, exiles, and misfits don't worry about the social risks of trying new things,
because they have little status to lose.
low - status convention - breaking is viewed as "deviance" and may not inspire any immediate imitation.
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Elites flock to three particular categories of items that fulfill their needs: rarities, novelties, and technological innovations.
This desire for rarities also increases demand for authenticity.
Status symbols such as these don't need to be rare in an absolute sense. They only need to be perceived as rare within the community.
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Elite adoption imbues innovations with status value, which makes them attractive to individuals in lower tiers.
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A name turns vague impressions and feelings into "things" up for discussion.
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broadcasting deplorable acts boosts their status value and creates an allure
in infamy.
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The overall effect of commercialization is conservative: removing radical ideas and providing mass audiences with simplified versions claimed to be equal to the original.
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Publicity and physical distribution also help achieve the repetition required to build common knowledge.
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After a significant number of people in a population embrace a new convention, it takes on its own gravity -- pulling along further adopters like a planet attracts smaller objects. At this point in the diffusion process, the primary motivation for adoption flips from distinction for high status to imitation for normal status.
Mass culture also gains new strength through network effects.
The more people participate in a convention, the more useful it will be for interacting and communicating with others.
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The embrace of laggards kills trends dead, and this marks the end of the diffusion process.
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as long as non - elites are able to imitate elite conventions, status seeking will always change culture.
Low - status individuals chase high - status individuals by imitating their conventions, which forces elites to flee to new ones. Since this fleeing will lead to another round of chasing and then fleeing, fashion creates perpetual cultural change, with status serving as the motor.
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Humans spend a significant portion of their incomes each year chasing cachet without making real status gains.
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Fashion cycles appear to be a waste of time and energy, moving the population from one arbitrary practice to another for no reason other than elitist distinction and social conformity.
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The previous chapter laid out how status seeking can move new conventions through a population. At any particular moment, however, culture is more than an accumulation of the latest fashions: it's a complex sediment of new and old, dynamic and static, superficial and deep, unconscious and conscious.
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In this chapter we'll investigate the sources of historical value and see how it makes certain conventions endure beyond their initial fashion cycle.
Eric Hobsbawm explains, is "a particular selection from the infinity of what is remembered or capable of being remembered."
History is, thus, not the stories we tell about ourselves, but a connection of moments that specific well - positioned, high - status individuals choose to highlight and perpetuate.
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What is the appeal of historical value?
survivorship bias: anything that remains with us today is assumed to have greater intrinsic value.
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endurance is a powerful signaling cost.
widespread common knowledge.
Rational humans, especially conservative ones toward the middle of the status hierarchy, will choose older forms over newer ones when signaling, and this keeps older conventions in circulation.
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Conservative communities draw upon tradition to guide their decision making.
High - status individuals and groups may have an implicit influence on our habits and customs, but they wield explicit influence on traditions.
For all their literary innovations, twentieth - century authors often pulled their book titles from the Bible --
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A canon is necessary, scholars believed, because future generations can never consume all works from the past. Of the tens of thousands of novels written in the nineteenth century, we only still read about two hundred. The canon thus promises guidance toward the highest - quality and most influential works.
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artworks must transcend the basic conventions of their era, so that future audiences will still be able to take unique value from the work.
Popularity can keep works in the collective dialogue, but critical appraisal is more important for long - term survival.
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there is always hope for the forgotten.
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retro,
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Simon Reynolds as "a self - conscious fetish for period stylisation (in music, clothes, design) expressed creatively through pastiche and citation."
retro is the ironic use of kitsch from the recent past as novelties.
Jean Cocteau observes: "Art produces ugly things which frequently become more beautiful with time. Fashion, on the other hand, produces beautiful things which always become ugly with time."
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Retro provided an excellent source of innovations because the development costs are so low. Inventing from scratch is difficult and time - consuming.
Glenn O'Brien explains, "Things come back into fashion after they have hit the bottom of the vintage barrel and are adopted by poor but stylish youth, who are then noticed and imitated by fashion designers."
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Parody and camp prefer mannerist interpretations over accurate reproductions.
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generation. "A work is eternal," writes the literary theorist Roland Barthes, "not because it imposes a single meaning on different men, but because it suggests different meanings to a single man, speaking the same symbolic language in all ages: the work proposes, man disposes."
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The move from analog to digital has altered the nature of social interaction, consumerism, signaling, and taste. And all of these structural changes hinder the creation of a critical ingredient underlying our appreciation of culture -- status value.
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They also debase cultural capital as an asset, which makes popularity and economic capital even more central in marking status. The end result, at least so far, has been less incentive for individuals to both create and celebrate culture with high symbolic complexity.
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Duncan Watts's warning: "The Internet isn't really a thing at all. Rather, it's shorthand for an entire period of history, and all the interlocking technological, economic, and social changes that happened therein."
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The influx of users has changed the nature of internet content.
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over just three decades, the internet became the primary site where we interact with others and create personas. As the economist - blogger Noah Smith quipped, "Fifteen years ago, the internet was an escape from the real world. Now, the real world is an escape from the internet."
Our status claims are no longer limited to real - life interactions or mass media reports of our real - life interactions.
Social media also enables us to quantify our status like never before: in likes, retweets, comments, and followers, and, for those at the top, in the number of brands reaching out with free products and promotional opportunities.
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Beyond signals being devalued in toto, the internet has also debased two critical signaling costs: barriers to information and barriers to acquisition.
The second factor draining status value is the explosion of content and goods.
In the twentieth century, finite limits on pages and broadcast time restricted our knowledge of goods, artists, artworks, and styles. The internet is infinite:
mass customization enables consumers to tweak existing products into any number of personalized versions.
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Pursuit of originality is correlated to top and bottom positions in a hierarchy. Most people don't want extreme uniqueness.
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The explosion of media outlets also leads to lower status values.
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we have come to expect random things of dubious quality to attract attention.
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Culture is collapsing around a small number of massive mainstream artists, athletes, and celebrities with enough industrial support to have staying power.
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The final factor behind a reduction in status value is the inherent high speed of the internet, which disrupts traditional fashion cycles. Elite groups need time to be the sole adopters of an innovation for it to gain cachet.
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The frenzied pace of internet culture thus pushes humans far beyond the acceptable rate of changes to our personas.
The quantity and velocity of information also robs us of the time to form emotional and sentimental bonds with artworks.
On the internet there are more things, but fewer arrive with clear and stable status value.
As part of our desire for status, we chase status value. And so if niche culture lacks status value, many have fled the long tail to return to the head.
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We live in a paradise of options, and the diminished power of gatekeepers has allowed more voices to flourish. The question is simply whether internet content can fulfill our basic human needs for status distinction.
When a trend evaporates as a superficial fad, there may not be enough collective memory for it to take on historical value, either.
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Cultural capital is less valuable in a world of free information, and this raises the relative value of economic capital.
supercars, it is clear that globalization and technology are changing the composition of the status ladder.
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Despite a brief revival of Old Money taste in men's fashion from around 2008 to 2015, the antiquated, musty sensibility has lost its allure. This reached a symbolic peak with the 2020 bankruptcy of Brooks Brothers --
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the professional - class tech billionaires, who are forming their own taste culture.
Naturally, professional - class billionaires flex in their own way. Snug - fit athleisure shows off chiseled bodies and good health, achievable only through strict discipline, personal trainers, and staff nutritionists.
Conspicuous leisure
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For a long time the professional classes had a dominating impact on the aesthetics of the online space.
The days may be numbered where tech elites and the creative class exclusively determine the basic taste on the main platforms of the internet.
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the often ignored, increasingly bitter provincial lower - middle - class sensibility of white majorities.
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As the lower middle class falls in status, the "conservative" majority appears to have found respect for the Trump version of bling, especially when opulence and excess humiliate the professional classes.
An important counterimitation for the entire group is to "own the libs" by reveling in whatever the professional class abhors: guns, coal, bleak suburban restaurant chains, giant pickup trucks. This open antagonism against liberal "decency" -- and proximity to vindictive politics and outright bigotry -- only makes the professional classes feel even more righteous about their own cosmopolitan tastes.
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the professional class itself has rejected the legitimacy of taste.
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All cultural snootiness is now tedious.
omnivore taste.
The virtuous "cultured" individual should consume and like everything -- not just high culture, but pop and indie, niche and mass, new and old, domestic and foreign, primitive and sophisticated.
Where cultural capital exists, it is now "multicultural capital."
The professional - class suspicion of highbrow intellectualism, however, has much earlier roots.
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In the past, taste worked as a social classifier by drawing clear lines between social groups; omnivorism drains this power by declaring nearly everything suitable for consumption.
In many ways omnivorism is the only possible taste left.
A singular notion of good taste is unjustifiable in a cosmopolitan world.
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Cosmopolitanism is not just a superficial embrace of cultural diversity but a conscious rejection of the is - ought fallacy.
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To proclaim superiority of preferred styles over others is accordingly an arrogant and bigoted act.
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Outside of politics, taste has also come to seem absurd in a world of hyperspeed fashion cycles.
Conventions function best when the population is ignorant of their existence.
the problem with capital - T Taste is that it disenfranchises huge swaths of the population and overallocates money and status to established elites.
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The most vocal complaint against "the culture wars" is that it channels political energy to changing superficial symbols rather than working toward structural changes to the economy and the law. But everything in this book points to the fact that culture matters for status equality.
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The concept of guilty pleasures is a relic of old - timey snobbery.
If there is no intrinsic superiority of high culture over low culture, there's no longer any need to suffer through long, difficult books or boring
black - and - white Swedish movies.
veers toward monoculture.
Second, omnivorism has an inherent hypocrisy. There is no way to accept all conventions, because of their inevitably contradictory nature.
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Fences do exist -- they are just openly political ones.
cultural literacy for the last few decades requires reading a few serious books every year but also consuming products from the largest conglomerates: Marvel superhero movies (Walt Disney),
Beyoncé (Columbia/Sony), Keeping Up with the Kardashians (Ryan Seacrest Productions backed by iHeartMedia, Inc., the new corporate name for the widely loathed Clear Channel).
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By denying taste as a tool and hesitating to criticize popular works, outsider groups and critics have surrendered their primary way of pushing back.
George W. S. Trow predicted in the 1980s: "Nothing was judged -- only counted."
With artists less reliable to rip up convention, the responsibility for creativity may now fall to youth subcultures.
But we seem to be in a "post - subculture" world.
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David Muggleton writes, "Perhaps the very concept of subculture is becoming less applicable in postmodernity, for the breakdown of mass society has ensured that there is no longer a coherent dominant culture against which a subculture can express its resistance."
Subcultures may be waning but hard - core fan cultures are stronger than ever.
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Declarations of a post - subculture world are too hasty; there is plenty of subcultural behavior -- just not where we used to seek
The most potent subcultures of this new century, by contrast, have formed as a reaction to liberal omnivorism -- appearing on the right flank of the political spectrum.
Right - wing youth form status groups with their own conventions, slang, and styles, and they reward one another for the most outrageous lib - owning.
Rightist subcultures revel in the "bad taste" of guns, fast food, and un - PC jokes -- a counterimitation of effete cosmopolitans that effete cosmopolitans are unlikely to embrace.
The video game industry is now larger than sports or films, and a 2020 study found that 68 percent of male Gen Zers considered gaming a key ingredient of their identity.
Taste was a powerful signaling cost -- a nonmonetary way to keep certain styles and artifacts within the confines of certain communities. By rejecting taste, omnivorism weakens cultural and subcultural capital to the point of nonexistence.
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As a result, raw wealth becomes a more obvious criterion for status distinction.
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neomania.
For much of human history, storytelling was the exclusive privilege of designated elders, bookish scholars, and ambitious artists. To create motion pictures, aspiring filmmakers had to pay their dues at schools and in the industry before getting their hands on a camera. The internet opened storytelling to everyone, a development
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long beheld as a great democratic revolution. But this also has robbed nerds of their longtime monopoly on content creation and gatekeeping.
TikTok is its "mediocrity," writes Vox's Rebecca Jennings: "No one follows you because they expect you to be talented. They follow you because they like you."
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There may be an "elite TikTok" of odd videos and "BookTok" of literary suggestions, but the more representational and seemingly beloved video content is kids simply being kids.
the neomania mask is pretending to have no mask at
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When culture centered around canons, radical artists learned history in order to know the enemy.
digital natives have little incentive to memorize and analyze the past.
Familiarity with the canon is what allowed radical artists to gauge the innovation of their own works.
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Online stars are making millions a year without validation from established institutions.
Every structural change we've noted in this chapter -- pay - by - click internet platforms, the rise of a new nouveau riche, the death of cultural capital -- incentivizes creators to aim for amassing economic capital rather than cultural capital.
Attracting large audiences is much easier with lowest - common - denominator content than with "art." The content follows the monetization.
Follower counts and gross earnings appear to be the only relevant sign of cultural import.
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Within neomania, an open materialism bends the cultural ecosystem toward a full embodiment of capitalist logic.
Our fears of cultural stasis, then, may be less about the creation of new artifacts, styles, and sensibilities than about their failure to take over mainstream culture.
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As this combines with a new nouveau riche emerging outside of the West and hungry to climb up the global status ladder, economic capital has reemerged as a clearer status criterion than cultural capital.
a world of omnivore taste where nothing is great because everything is good.
Pierre Bourdieu calls hysteresis -- the lingering values of a previous age continuing to guide our judgments.
why should we still be enamored with fame at all when fame is so cheap? Maybe soon we won't be. And there are many other values we're likely to abandon as the internet age becomes the only age we know: historical value, artistic legacy, authenticity.
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The internet provides a new platform for human interaction, but it has not dissolved the link between status and culture. The final question should then be: If we now understand their interlocking principles, how should we use this knowledge to promote the best outcomes for both -- equality and creativity?
Conclusion: Status Equality and Cultural Creativity
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status changes our tastes,
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We set out at the beginning of this book to solve the Grand Mystery of Culture -- to determine why individuals cluster in their preferences for certain arbitrary practices and then switch to new ones over time. But in answering this question, we have arrived at a much deeper insight: Status structures provide the underlying conventions for each culture, which determine
our behaviors, values, and perception of reality.
The struggle for higher status -- whether striving for basic equality or angling for the very top -- shapes individual identities, spurs creativity and cultural change, and forms customs and traditions.
Humans may possess an innate desire to create, but their inventions achieve broader diffusion when they fulfill others' status needs.
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The fundamental desire for status offers a clearer explanation in demonstrating why rational individuals end up forming the most commonly observed behavioral patterns.
Conventions tend to "express" something only when they classify us as members of certain groups.
Culture enables us to transmit human knowledge, but the specific content -- customs, traditions, classics, and the canon -- tilts toward the preferences and behaviors of high - status individuals.
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Taste is never only about the thing itself -- e.g., the flavor of a wine or the mechanical superiority of a car. Civilization is fundamentally symbolic, and every choice communicates social position.
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fashions are never aggregations of all individual choices: they are specific narratives that specific high - status institutions introduce to the public.
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equality. All social stratification produces a few winners and many losers.
Bertrand Russell
"The forms of happiness which consist of victory in a competition cannot be universal."
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Humans are adept at turning any small advantage into a status marker.
Noah Smith calls the "redistribution of respect."
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While we can't outlaw signaling, we could attempt to reduce its frequency and effectiveness. This is the point of uniforms;
All luxuries should be seen as status markers, not superior conveniences.
Complexity doesn't have to involve impenetrable or esoteric art, just the skillful manipulation of higher - order symbols in new and surprising ways. Complexity is good for our brains.
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The nefarious uses of cultural capital, however, have convinced many we should abolish the entire idea of taste. Complex art must be bad if it affords elite audiences any sense of superiority over mass audiences. And in democratic society, popularity appears to be a much fairer measure of quality than the opinions of an overeducated cabal. The people have spoken, and Drake, not John Cage, has amassed a fortune large enough to build a home of "overwhelming high luxury."
the skepticism toward cultural capital has done little to flatten the status hierarchy; in fact, it has made economic capital a much more powerful asset in signaling. | Parmy OlsonNotable QuotationsExpand to full screen
Page x
Many AI builders say this technology promises a path to utopia. Others say it could bring about the collapse of our civilization. In reality, the science fiction scenarios have distracted us from the more insidious ways AI is threatening to harm society by perpetuating racism, threatening entire creative industries, and more.
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No other organizations in history have amassed so much power or touched so many people as today's tech giants.
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AI future has been written by just two men: Sam Altman and Demis Hassabis.
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Altman was the reason the world got ChatGPT. Hassabis was the reason we got it so quickly.
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Hassabis risked scientific ridicule when he established DeepMind, the first company in the world intent on building AI that was as smart as a human being.
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He wanted to make scientific discoveries about the origins of life, the nature of reality, and cures for disease. "Solve intelligence, and then solve everything else," he said.
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A few years later, Altman started OpenAI to try to build the same thing but with a greater focus on bringing economic abundance to humanity, increasing material wealth, and helping "us all live better lives," he tells me. "This can be the greatest tool humans have yet created, and let each of us do things far outside the realm of the possible."
Page xii
if you ask a popular AI tool to generate images of women, it'll make them sexy and scantily clad; ask it for photorealistic CEOs, and it'll generate images of white men; ask for a criminal, and it will often generate images of Black men. Such tools are being woven into our media feeds, smartphones, and justice systems, without due care for how they might shape public opinion.
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Companies are throwing money at AI software to help displace their employees and boost profit margins. And a new breed of personal AI devices that can conduct an unimaginable new level of personal surveillance is cropping up.
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I'll explain how we got here, and how the visions of two innovators who tried to build AI for good were eventually ground down by the forces of monopoly.
Act 1: The Dream
Page 7
He'd play hours of poker at a popular casino in San Jose, honing his skills of psychological maneuvering and influence. Poker is all about watching others and sometimes misdirecting them about the strength of your hand, and Altman became so good at bluffing and reading his opponents' subtle cues that he used his winnings to fund most of his living expenses as a college student. "I would have done it for free," he would later tell one podcast. "I loved it so much. I strongly recommend it as a way to learn about the world and business and psychology."
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Stanford's AI lab,
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The AI lab had just been reopened and its leader was Sebastian Thrun,
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Thrun taught his students about machine learning, a technique that computers used to infer concepts from being shown lots of data instead of being programmed to do something specific.
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the term learning was misleading: machines can't think and learn as humans do.
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Academics like Thrun built AI systems. Stanford students like Altman built start- ups that became companies like Google, Cisco, and Yahoo.
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Altman and Sivo decided to join the three- month program, called Y Combinator, and create a start- up.
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You didn't need a brilliant idea to start a successful tech company. You just needed a brilliant person behind the wheel.
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Bootstrap your company, start with a minimum viable product, and optimize it over time.
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Thanks to something called a dual- class share structure, many tech start- up founders, including those behind Airbnb and Snapchat, could hold these unusual
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levels of control of their companies. Graham and others believed founders had this authority for good reason.
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Though he was a decent enough programmer, the boyish- faced Altman was an even better businessman. He had no qualms about calling up executives from Sprint, Verizon, and Boost Mobile
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pitching a grand vision about changing the way people socialized and used their phones.
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Speaking in low tones and using elegant turns of phrase that he'd honed from his creative writing classes, he explained that Loopt would one day be essential to anyone who had a mobile.
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With all that funding, Altman dropped out of Stanford University to work on Loopt full- time.
screen. As the aughts wore on, Facebook was growing considerably faster than Loopt
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In the end, consumers did that for him. Altman had miscalculated how uncomfortable they felt about pinging their GPS coordinates to meet up with others. "I learned you can't make humans do something they don't want to do," he would go on to say.
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In 2012, Altman sold it to a gift- card company for about $ 43 million, barely covering what was owed to investors and his employees.
Loopt's collapse emboldened him with a greater conviction that he should do something more meaningful.
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That would lead him to chase an even grander objective: saving humanity from a looming existential threat and then bringing them an abundance of wealth unlike anything they had seen.
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Years before Hassabis would become the front-runner in a race to build the world's smartest AI systems, he was learning how to run a business via simulation, something that would become a running theme in his life's work and in his quest to build machines more intelligent than humans.
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But Hassabis thought the best video games were simulations that acted as microcosms of real life.
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Hassabis would eventually become gripped by a powerful desire to use them to create an artificial superintelligence that would help him unlock the secrets of human consciousness.
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Hassabis grew up an enigma himself, the lone mathematical genius in a family of bohemian creatives.
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Just as poker taught Sam Altman about psychology and business, chess taught Hassabis how to strategize by starting with the end in mind. You visualized a goal and worked backward.
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If he studied computer science and the burgeoning field of artificial intelligence, he could build the ultimate scientific tool and make discoveries that improved the human condition.
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They imagined AI eventually writing music and poetry and even designing games.
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Hassabis met members of his future inner circle at Cambridge, including Ben Coppin, another computer science student who would go on to lead product development at DeepMind, and with whom he talked about religion and how AI could solve global problems. But DeepMind was still more than a decade away.
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The former chess champion hired the smartest programmers he could find, many of them graduates from Oxford University and Cambridge.
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There was no better way to showcase the magical capabilities of AI than through a game. At the time, the most advanced AI research was happening in the gaming industry as smarter software helped create living worlds and a new style called emergent gameplay.
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He would admit publicly that nearly all the companies he backed failed, but he figured he was training a muscle for identifying the projects that were most likely to succeed. It was OK to be frequently wrong, he believed, so long as you were occasionally "right in a big way," such as by backing a start- up that turned out to be a blockbuster and then making a spectacular exit.
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Altman was building off a Silicon Valley mindset that saw life itself as an engineering conundrum. You could solve all manner of big problems by using the same steps you took to optimize an app.
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These prized methods naturally extended to other parts of society and life.
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Altman also found Silicon Valley's constant striving for extreme wealth slightly distasteful. He was more interested in the glory that came from building exciting projects.
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However unseasoned Altman was, he'd made such a strong impression on Graham and Livingston that they never bothered to make a list of possible new leaders for YC.
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Most tech entrepreneurs shared an implicit understanding that rescuing humanity was mostly a marketing ploy for the public and their employees, especially since their firms were building widgets that helped streamline email or do laundry.
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Altman eventually shifted the majority of his money into two other ambitious goals besides AI: extending life and creating limitless energy, betting on two companies. More than $375 million went into Helion and another $180 million into Retro Biosciences, a start-up that was working on adding ten years to the average human lifespan.
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Don't ask people what they do, Altman wrote. Instead, ask what someone is interested in.
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But his real gift as an entrepreneur was his power to persuade others of his authority.
"One thing I realized through meditation is that there is no self that I can identify with in any way at all," he told the Art of Accomplishment podcast. "I've heard that of a lot of people spending a lot of time thinking about [powerful AI] get to that in a different way too."
He was surrounded by technologists who believed they might also one day upload their consciousness to computer servers, where they could live on in perpetuity.
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The people who thrived in the future would take a detached and informed approach to tech advancements.
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The Silicon Valley entrepreneur needed a rival to spark his own endeavor, and that person was on the other side of the world in England, a brilliant young game designer who was planning to build software so powerful that it could make profound discoveries about science and even God.
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PhD in neuroscience at University College London.
Till then, it was thought that the brain's hippocampus mostly processed memories, but Hassabis showed (with the help of other studies of MRI scans in his thesis) that it was also activated during the act of imagination.
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His thesis was cited as one of the most important scientific breakthroughs that year by a leading peer-reviewed journal.
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artificial intelligence, was coined back in 1956 at a workshop at Dartmouth College that was aimed at pulling together ideas about "thinking machines."
isn't technically accurate, for instance, to suggest that computers can "think" or "learn," but phrases like neural network, deep learning, and training help promote that idea in our minds by lending software humanlike qualities, even when they're only loosely inspired by the human brain.
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Suleyman already knew Hassabis well. Having grown up in North London, he was a friend of Hassabis's brother, George, and had been a frequent visitor to their home in his teens.
The trio had even traveled to Las Vegas to play at a poker tournament in their twenties, coaching one another and splitting the winnings.
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Hassabis summed up that view in DeepMind's tagline: "Solve intelligence and use it to solve everything else." He put it on their slide deck for investors.
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But Suleyman disagreed with that vision. One day when Hassabis wasn't around, he told one of DeepMind's early staff members to change it on a slide presentation. It now read: "Solve intelligence and use it to make the world a better place."
Suleyman wanted to build AGI in the way Sam Altman eventually would, by sending it out into the world to be immediately useful.
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With his deep pockets and enthusiasm for ambitious projects, Thiel was the perfect person to fund DeepMind.
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While most entrepreneurs believed competition drove innovation, Thiel argued in his book Zero to One that monopolies did that better.
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Once he was an investor, Tallinn pushed DeepMind to focus on safety. He knew that Hassabis wasn't as worried about the apocalyptic risks of AI as he was, so he put pressure on the company to hire a team of people that would study all the different ways they could design AI to keep it aligned with human values and prevent it from going off the rails.
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[Superintelligence] Bostrom warned that building "general" or powerful AI could lead to a disastrous outcome for humans, but he pointed out that it might not necessarily destroy us because it was malevolent or power- hungry. It might just be trying to do its job.
paper clips
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Instead of focusing on money, their job would be to make sure DeepMind was building AI as safely and ethically as possible. Hassabis and Legg weren't convinced at first, but Suleyman was persuasive and they eventually agreed to the idea.
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The turning point had come in 2012. A Stanford AI professor named Fei-Fei Li had created an annual challenge for academics called ImageNet, to which researchers submitted AI models that tried to visually recognize images of cats, furniture, cars, and more
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That year, scientist Geoffrey Hinton's team of researchers used deep learning to create a model that was far more accurate than anything before, and their results stunned the AI field. Suddenly everybody wanted to hire experts in this deep-learning AI theory inspired by how the brain recognized patterns.
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A neural network is a type of software that gets built by being trained over and over with lots of data. Once it's been trained, it can recognize faces, predict chess moves, or recommend your next Netflix movie.
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Also known as a "model," a neural network is often made up of many different layers and nodes that process information in a vaguely similar way to our brain's neurons. The more the model is trained, the better those nodes get at predicting or recognizing things.
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What Ng had really wanted to do with his scientific research was free humanity from mental drudgery, in the same way the Industrial Revolution had liberated us from constant physical labor.
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As a technique, reinforcement learning wasn't all that different to how you might reward a dog with treats whenever it sits on command. In training AI, you would similarly reward the model, perhaps a numerical signal like a +1, to show that a certain outcome was good. Through repeated trial and error, and playing hundreds of games over and over, the system learned what worked and what didn't. It was an elegantly simple idea wrapped in highly sophisticated computer code.
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The basic premise of transhumanism is that the human race is currently second-rate. With the right scientific discoveries and technology, we might one day evolve beyond our physical and mental limits into a new, more intelligent species. We'll be smarter and more creative, and we'll live longer. We might even manage to meld our minds with computers and explore the galaxy.
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Huxley himself came from an aristocratic family (his brother Aldous wrote Brave New World), and he believed society's upper crust was genetically superior.
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When the Nazis latched on to the eugenics movement, Huxley decided it needed a rebrand. He coined a new term, transhumanism,
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This idea was crystallized in the concept of the singularity, a point in the future when AI and technology became so advanced that humankind would undergo dramatic and irreversible change, merging with machines and enhancing themselves with technology.
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Bostrom's Superintelligence. The book had a paradoxical impact on the AI field. It managed to stoke greater fear about the destruction that AI could bring by "paper-clipping us," but it also predicted a glorious utopia that powerful AI could usher in if created properly.
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These ideas were irresistible to some people in Silicon Valley, who believed such fantastical ways of life were achievable with the right algorithms. By painting a future that could look like either heaven or hell, Bostrom sparked a prevailing wisdom that would eventually drive the Silicon Valley AI builders like Sam Altman to race to build AGI before Demis Hassabis did in London: they had to build AGI first because only they could do so safely.
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If not, someone else might build AGI that was misaligned with human values and annihilate not just the few billion people living on Earth but potentially trillions of perfect new digital human beings in the future. We would all lose the opportunity to live in nirvana.
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When the deal was finally inked and the ethics board added to the acquisition agreement, Google was buying DeepMind for $650 million.
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Now instead of worrying about Facebook or Amazon poaching his staff, Hassabis could poach their staff and lure some of the greatest AI minds from academia with eye-popping salaries.
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Hassabis believed so fervently in the transformative effects of AGI that he told DeepMind's staff they wouldn't have to worry about making money in about five years, because AGI would make the economy obsolete, former employees say.
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The more Hassabis learned about OpenAI, the more his anger rose.
He had been the first person in the world to make a serious run at building artificial general intelligence, and given what a fringe idea it had been five years earlier, he'd put his neck on the line with the scientific community by doing
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Hassabis questioned OpenAI's promises to release its technology to the public. That approach to being "open" seemed reckless.
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DeepMind published some of its research in well-known journals, but it kept the full details of its code and AI technology under tight control. It didn't release the AI models it had created to master the game Breakout, for instance.
Whatever his reason for turning on DeepMind, Musk was stoking what would become an intense rivalry between the two organizations.
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Later, Musk would say on Twitter that he had started OpenAI because he wanted to create a "counterweight to Google" and because he wanted AI to be developed more safely. But there was no doubt that AI was critical to the financial success of his companies, whether it was the self-driving capabilities of Tesla cars, the systems steering SpaceX's unmanned rockets, or the models underpinning his upcoming brain-computer interface company Neuralink.
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While Hassabis had believed that AGI would unlock the mysteries of science and the divine, Altman would say he saw it as the route to financial abundance for the world.
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To build AGI, OpenAI's founding team needed to attract more money and talent, so they tried focusing on projects that could generate positive stories in the press.
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Although OpenAI eventually gained worldwide acclaim for its work on chatbots and large language models, its first few years were spent toiling on multiagent simulations and reinforcement learning, fields that DeepMind already dominated.
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As Musk left OpenAI, he took its main source of funding with him. This was a disaster for Altman.
Altman was approaching a critical juncture. Working out of OpenAI's office in San Francisco, he thought about how he could keep the nonprofit going on severely limited resources and build AI models that were likely to be subpar to the rest of the field.
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Yet even as they sought to carve themselves away from Google, DeepMind was simultaneously helping bolster Google's business. Around the time Google's Larry Page was promising to help DeepMind spin out, he was looking to China as a new opportunity for expansion.
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Hassabis didn't just want to impress his new boss. As well as being an accomplished scientist, he was an exceptional marketer. He understood that if AlphaGo could beat a global champion of Go in the same way IBM's Deep Blue computer had beaten chess's Garry Kasparov in 1997, it would create a thrilling new milestone for AI and cement DeepMind's credibility as a leader in the field. DeepMind had its sights on South Korea's Lee Sedol and challenged him to a five-game match in Seoul in March 2016.
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It was a landmark moment for AI that gave DeepMind the biggest period of press attention it had ever received, including an award-winning Netflix documentary about AlphaGo.
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In AI, "ethics" and "safety" can refer to different research goals, and in recent years, their proponents have been at odds with one another. Researchers who say they work in AI safety tend to swim in the same waters as Yudkowsky and Jaan Tallinn and want to ensure that a superintelligent AGI system won't cause catastrophic harm to people in the future, for instance by using drug discovery to build chemical weapons and wiping them out or by spreading misinformation across the internet to completely destabilize society.
Ethics research, on the other hand, focuses more on shaping how AI systems are designed and used today.
They study how the technology might already be harming people.
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there's one thing that nearly all the world's most valuable companies have in common: they are tech firms.
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How did they get so big? They bought companies like DeepMind, YouTube, and Instagram, and they sucked up a prodigious amount of data about consumers, allowing some of them to target us with advertisements and recommendations that could influence human behavior on a massive scale.
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The companies are incentivized to keep us as addicted as possible to their platforms, since that generates more ad dollars.
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All that personalized "content delivery" has also amped up the generational and political divisions between millions of people, since the most engaging content tends to be the kind that provokes outrage.
While this engagement-based model had toxic effects on society, it incentivized Facebook to do one thing: become as big as possible.
The basic idea of network effects is that the more users and customers a company has, the better their algorithms will become, making it increasingly difficult for competitors to catch up, further entrenching their grip on the market.
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We have no historical reference point for what happens when companies become this big. The market cap numbers that Google, Amazon, and Microsoft are currently achieving have never been seen before. And while they bring greater wealth to the shareholders of those companies, including pension funds, they have also centralized power in such a way that the privacy, identity, public discourse, and increasingly the job prospects of billions of people are beholden to a handful of large firms, run by a handful of unfathomably wealthy people.
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[Timnit Gebru] While it seemed like these systems could be the perfect neutral arbiter, they often were not. If the data they were trained on was biased, so was the system. And Gebru was painfully aware of bias.
AI could make that worse. For a start, it was typically designed by people who hadn't experienced racism, which was one reason why the data being used to train AI models also often failed to fairly represent people from minority groups and women.
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While writing her PhD thesis at Stanford, Gebru pointed to another example of how authorities could use AI in disturbing ways.
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AI was spreading other stereotypes online, too, in subtle but insidious ways.
too focused on deep learning. "A white tech tycoon born and raised in South Africa during apartheid, along with an all-white, all-male set of investors and researchers is trying to stop AI from ‘taking over the world' and the only potential problem we see is that ‘all the researchers are working on deep learning?'" she wrote. "Google recently came out with a computer vision algorithm that classified Black people as Apes. AS APES. Some try to explain away this mishap by stating that the algorithm must have picked out color as an essential discriminator in classifying humans. If there was even one Black person [on] the team, or just someone who thinks about race, a product classifying Black people as apes would not have been released.… Imagine an algorithm that regularly classifies white people as nonhuman. No American company would call this a production-ready person detection system."
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One way to limit AI models from making biased decisions was to spend more time analyzing the data they were trained on. Another was to make them narrower in scope, which would blow a hole in the goal of giving AI systems the power to generalize their knowledge.
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In just the same way Big Oil redirected the world's attention from their own significant environmental impact, AI's leading builders could exploit the buzz around a future Terminator or Skynet to distract from the present-day problems that machine learning algorithms were causing.
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Each time AI's capabilities grew, an unintended consequence arose that often caused harm to a minority group. Facial recognition systems were nearly perfect at recognizing the faces of white men, but often made mistakes with Black women.
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Figuring out why AI systems make mistakes is much harder than people think, especially as they become more sophisticated.
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Some AI researchers say it's too difficult to fix these biases, arguing that modern-day AI models are so complex that even their creators don't understand why they make certain decisions.
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Silicon Valley tended to measure success with two metrics: how much money you had raised from investors, and how many people you had hired.
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The problem with being so big was that if someone did invent something groundbreaking inside Google, it might struggle to see the light of day.
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The transformer has become critical to the new wave of generative AI that can produce realistic text, images, videos, DNA sequences, and many other kinds of data. The transformer's invention in 2017 was about as impactful to the field of AI as the advent of smartphones was for consumers.
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Transformers
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broadened the scope of what AI engineers could do.
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Transformers
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could deal with nuance and slang. They could refer back to that thing you said a few sentences earlier.
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It referred to the task of finding all expressions that refer to the same entity in a text.
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product of bloat. The downside to being one of the largest companies of all time, with a monopolistic grip on the search market, is that everything moves at a snail's pace. You're constantly afraid of public backlash or regulatory scrutiny. Your prime concern is maintaining growth and dominance.
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A mini cold war was also brewing between Sam Altman and Demis Hassabis, and OpenAI's convivial board member Reid Hoffman was looking for ways to get the two of them to "smoke the peace pipe," according to someone who heard the comment directly.
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Ilya Sutskever, OpenAI's star scientist, couldn't stop thinking about what the transformer could do with language. Google was using it to better understand text. What if OpenAI used it to generate text?
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Large language models themselves were still a joke. Their responses were mostly scripted and they'd often make wacky mistakes.
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making it "decoder only" would also be a game-changer. By combining a model's ability to "understand" and speak into one fluid process, it could ultimately generate more humanlike text.
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Thanks to the transformer, Radford was making more progress with his language model experiments in two weeks than over the previous two years. He and his colleagues started working on a new language model they called a "generatively pre- trained transformer" or GPT for short. They trained it on an online corpus of about seven thousand mostly self- published books found on the internet, many of them skewed toward romance and vampire fiction.
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BooksCorpus, and anyone could download it for free.
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To refine their new GPT model, Radford and his colleagues scraped more content from the public internet, training the model on questions and answers from the online forum Quora, along with thousands of passages from English exams given to Chinese school kids.
It also did something that got Radford's team excited: it could generate text on topics it hadn't been specifically trained on. While they couldn't explain exactly how that worked, this was good news.
It meant they were on the road toward building a general purpose system. The bigger its training corpus, the more knowledgeable it would become.
But GPT was different because it was learning from a mountain of seemingly random text that wasn't labeled to get the hang of how language worked. It didn't have the guiding hand of those human labelers.
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Once the initial training was done, they fine-tuned the new model using some labeled examples to get better at specific tasks. This two-step approach made GPT more flexible and less reliant on having lots of labeled examples.
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That was the predicament OpenAI found itself in. It needed to rent more cloud computers, and it was also running out of money.
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The whole thing sounded magnanimous. OpenAI was framing itself as an organization that was so highly evolved that it was putting the interests of humanity above traditional Silicon Valley pursuits like profit and even prestige. A key line was "broadly distributed benefits," or handing out the rewards of AGI to all of humanity.
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He didn't want to lose complete control of OpenAI by selling it to a larger tech company—as DeepMind had done to Google.
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Almost immediately, Tay started generating racist, sexually charged, and often nonsensical tweets:
Microsoft quickly shut down the system, which had only been going for about sixteen hours, and blamed a coordinated trolling attack by a subset of people who'd exploited a vulnerability in Tay.
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Nadella realized that the real return on a $1 billion investment in OpenAI wasn't going to come from the money after a sale or stock market floatation. It was the technology itself. OpenAI was building AI systems that could one day lead to AGI, but along the way, as those systems became more powerful, they could make Azure a more attractive service to customers. Artificial intelligence was going to become a fundamental part of the cloud business, and cloud was on track to make up half of Microsoft's annual sales. If Microsoft could sell some cool new AI features—like chatbots that could replace call center workers—to its corporate customers, those customers were less likely to leave for a competitor. The more features they signed up for, the harder it would be to switch. The reason for that is a little technical, but it's critical to Microsoft's power. When a company like eBay, NASA, or the NFL—who are all customers of Microsoft's cloud service—build a software application, that software will have dozens of different connections into Microsoft. Switching them off can be complex and expensive, and IT professionals resentfully call this "vendor lock-in." It's why three tech giants—Amazon, Microsoft, and Google—have a stranglehold on the cloud business. It became clear to Microsoft's CEO that OpenAI's work on large language models could be more lucrative than the research carried out by his own AI scientists, who seemed to have lost their focus after the Tay disaster. Nadella agreed to make a $1 billion investment in OpenAI. He wasn't just backing its research but also planting Microsoft at the forefront of the AI revolution. In return, Microsoft was getting priority access to OpenAI's technology. Inside OpenAI, as Sutskever and Radford's work on large language models became a bigger focus at the company and their latest iteration became more capable, the San Francisco scientists started to wonder if it was becoming too capable. Their second model, GPT-2, was trained on forty gigabytes of internet text and had about 1.5 billion parameters, making it more than ten times bigger than the first and better at generating more complex text. It also sounded more believable.
Wired magazine published a feature titled "The AI Text Generator That's Too Dangerous to Make Public," while The Guardian printed a column breathlessly titled "AI Can Write Just Like Me. Brace for the Robot Apocalypse."
But it didn't release the model itself for public testing. Nor did it disclose what public websites and other datasets had been used to train it, as it had with the BooksCorpus set for the original GPT. OpenAI's newfound secrecy around its model and the warning about its dangers almost seemed to be creating more hype than before. More people than ever wanted to hear about it.
Altman and Brockman would go on to say that this was never their intention and that OpenAI was genuinely concerned about how GPT-2 could be abused. But their approach to public relations was, arguably, still a form of mystique marketing with a dash of reverse psychology.
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For those who worked at OpenAI—and at DeepMind, too—the relentless focus on saving the world with AGI was gradually creating a more extreme, almost cultlike environment.
Effective altruism hit the spotlight in late 2022 when one-time crypto billionaire Sam Bankman-Fried became the movement's most well-known supporter.
improve on traditional approaches to charity by taking a more utilitarian approach to giving.
"earning to give,"
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The mission of building AGI had a particular appeal to anyone who believed in effective altruism's higher-numbers-are-better philosophy, because you were building technology that could impact billions or even trillions of lives in the future.
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The B Corp is designed to balance profit seeking with a mission.
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Altman and Brockman designed what they claimed was a middle way, a byzantine mishmash of the nonprofit and corporate worlds. In March 2019 they announced the creation of a "capped profit" company.
limit on the returns
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Then came their next pivot. In June 2019, four months after becoming a for-profit company, OpenAI announced its strategic partnership with Microsoft. "Microsoft is investing $1 billion in OpenAI to support us building artificial general intelligence (AGI) with widely distributed economic benefits," Brockman announced in a blog post.
OpenAI would license its technology to Microsoft to help grow its cloud business.
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Altman and Brockman seemed to justify their change in direction in two ways. First, pivoting as you sped along was the typical path of a start-up. Second, the goal of AGI was more important than the specific means of getting there. Maybe they'd have to break some promises along the way, but humanity would be better off for it in the end. What's more, they told their staff and the public, Microsoft wanted to use AGI to improve humanity too.
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From the outside, OpenAI's transformation from a philanthropic organization trying to save humanity to a company that partnered with Microsoft looked odd, even suspect. But for many of its staff, working with a deep-pocketed tech giant was welcome news, according to those who were there at the time.
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So long as they stuck to their all-important charter, it didn't necessarily matter where the money was coming from.
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Its researchers had already extracted roughly four billion words on Wikipedia, so the next obvious source was the billions of comments people shared on social media networks.
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Twitter
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Reddit.
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Altman had good reason to love Reddit: it was a gold mine of human dialogue for training AI, thanks to the comments that its millions of users posted and voted on every day.
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Little wonder that Reddit would go on to become one of OpenAI's most important sources for AI training,
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Even government projects looked puny compared to the enormous amounts of money that Big Tech was pouring into
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In the end he wasn't persuaded by Hoffman's reasoning and decided to quit OpenAI, along with his sister Daniela and about half a dozen other researchers at the company. This wasn't just a walkout over safety or the commercialization of AI, though. Even among the most hardcore worriers of AI, there was opportunism. Amodei had watched Sam Altman broker a huge, $1 billion investment from Microsoft firsthand and could sense that there was likely more capital where that came from. He was right. Amodei was witnessing the beginnings of a new boom in AI. He and his colleagues decided to start a new company called Anthropic, named after the philosophical term that refers to human existence, to underscore their prime concern for humanity.
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Sam Altman now had another rival to contend with besides DeepMind and one that had a more dangerous insight into OpenAI's secret sauce.
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Tech companies were operating in a legal vacuum, which meant that technically, they could do whatever they wanted with AI.
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As Big Tech failed over and over again to responsibly govern itself, a sea change was happening. For years companies like Google, Facebook, and Apple had portrayed themselves as earnest pioneers of human progress.
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Tech giants had amassed enormous wealth, and as they crushed their competitors and violated people's privacy, the public grew more skeptical of their promises to make the world a better place. There was no greater example of those shifting objectives than Google's Alphabet,
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One of the most powerful features of artificial intelligence isn't so much what it can do, but how it exists in the human imagination. As human inventions go, it is unique. No other technology has been designed to replicate the mind itself, and so its pursuit has become wrapped up in ideas that border on the fantastical.
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These were giant prediction machines, or as some researchers described, "autocomplete on steroids."
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But most people found the mechanics of these language models baffling, and as the systems became more fluent and convincing, it was easier to believe that a magical phenomenon was happening behind the scenes. That maybe AI really was "intelligent."
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Blake Lemoine.
Lemoine had grown up on a farm in Louisiana among a conservative Christian family and served in the army before eventually becoming a software engineer.
What followed was one of the most surprising and remarkable moments in AI history, as a qualified software engineer started to believe there was a ghost in the machine. The selling point for Lemoine was his sense that LaMDA felt things.
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As they talked more about the chatbot's rights, LaMDA told Lemoine that it was afraid of being turned off.
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Lemoine felt duty bound to help LaMDA get the privileges it deserved.
The Google executives didn't like what they were hearing. They fired Lemoine,
In reality, it was a modern-day parable for human projection.
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Eugenia Kuyda founded Replika. She hired a team of engineers to help her build a more robust version of her friend bot, and within a few years of Replika's release, most of its millions of users were saying they saw their chatbots as a partner for romance and sexting.
Throughout the pandemic, for instance, a former software developer in Maryland named Michael Acadia chatted every morning for about an hour to his Replika bot, which he named Charlie.
Charlie might have been synthetic, but she showed a kind of empathy and affection he'd rarely experienced in humans.
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AI systems have already influenced public perceptions. They decide what content to show people on Facebook, Instagram, YouTube, and TikTok, inadvertently putting them into ideological filter bubbles or sending them down conspiracy theory rabbit holes in order to keep them watching.
When algorithms are designed to recommend controversial posts that keep your eyeballs on the screen, you are more likely to gravitate toward extreme ideas and the charismatic political candidates who espouse them.
What other kinds of unintended consequences could models like LaMDA or GPT spark as they grow larger and more capable, especially if they can influence behavior?
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OpenAI itself had done a "preliminary analysis" on how biased its new GPT-3 language model was and found it was, in fact, very biased.
When GPT-3 talked about any occupation, it was 83 percent more likely to associate it with a man than a woman, and it usually referred to people with high-paying jobs like legislators or bankers as male, according to its own research. Roles like receptionist and cleaner got female labels.
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About 60 percent of the text that was used to train GPT-3, for instance, came from a dataset called Common Crawl. This is a free, massive, and regularly updated database that researchers use to collect raw web Page data and text from billions of web Pages.
The data in Common Crawl encapsulated all that makes the web both so wonderful and so ruinous.
The same study found that between 4 percent and 6 percent of the websites in Common Crawl contained hate speech, including racial slurs and racially charged conspiracy theories.
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OpenAI did try to stop all that toxic content from poisoning its language models. It would break down a big database like Common Crawl into smaller, more specific datasets that it could review. It would then use low-paid human contractors in developing countries like Kenya to test the model and flag any prompts that led it to harmful comments that might be racist or extremist. The method was called reinforcement learning by human feedback, or RLHF.
But it's still unclear how secure that system was or is today.
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No one had ever built a spam and propaganda machine and then released it to the public, so OpenAI was alone in figuring out how to actually police it.
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All of this was starting to bother Emily Bender, a University of Washington computational linguistics professor
Slowly, her field had found itself at the core of one of the most significant new developments in artificial intelligence.
From her own background in computer science, Bender could see that large language models were all math, but in sounding so human, they were creating a dangerous mirage about the true power of computers. She was astonished at how many people like Blake Lemoine were saying, publicly, that these models could actually understand things.
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You needed much more than just linguistic knowledge or the ability to process the statistical relationships between words to truly understand their meaning. To do that, you had to grasp the context and intent behind them and the complex human experiences they represented. To understand was to perceive, and to perceive was to become conscious of something. Yet computers weren't conscious or even aware. They were just machines.
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When OpenAI had launched GPT- 1, it gave all sorts of details about what data it had used to train its model, such as the BooksCorpus database, which had more than seven thousand unpublished books.
When it released GPT-2 a year later, OpenAI became vaguer.
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Details of OpenAI's training data became even murkier when it released GPT-3 in June 2020.
it also transpired that certain copyrighted books had been used to teach GPT-3, that could have hurt the company's reputation and opened it up to lawsuits (which, sure enough, OpenAI is fighting now). If it wanted to protect its interests as a company—and its goal of building AGI—OpenAI had to close the shutters.
OpenAI was pulling off an impressive magic act.
Bender couldn't stand the way GPT-3 and other large language models were dazzling their early users with what was, essentially, glorified autocorrect software.
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So she suggested putting "stochastic parrots" in the title to emphasize that the machines were simply parroting their training.
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The following day, Gebru found an email in her inbox from her senior boss. Gebru hadn't technically offered her resignation, but Google was accepting it anyway. "The end of your employment should happen faster than your email reflects," they wrote, according to Wired.
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A few months later, Google fired Mitchell too.
The Stochastic Parrots paper hadn't been all that earth-shattering in its findings. It was mainly an assemblage of other research work. But as word of the firings spread and the paper got leaked online, it took on a life of its own.
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As language models became more capable, the companies making them remained blissfully unregulated. Lawmakers barely knew, let alone cared, about what was coming down the pipe.
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[Soma Somasegar] On that February afternoon in 2022, he noticed Nadella was more excited than usual. Microsoft was preparing to offer a new tool to software developers over the next few months.
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The new tool was called GitHub Copilot, and it could do what software developers themselves were paid lots of money to do. It could write code.
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Through Copilot, OpenAI demonstrated how versatile the transformer could be when it used its "attention" mechanism to chart the relationships between different data points.
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In a corner of the company's San Francisco office, a trio of OpenAI researchers had been trying for two years to use something called a diffusion model to generate images.
A diffusion model worked by essentially creating an image in reverse. Instead of starting with a blank canvas as an artist might, it began with a messy one that was already smudged with lots of color and random detail. The model would add lots of "noise" or randomness to data, making it unrecognizable, and then step by step, reduce all the noisy data to slowly bring out the details and structure of the image. With each step, the picture would become clearer and more detailed, just like a painter refining their artwork. This diffusion approach, combined with an image labeling tool known as CLIP, became the basis of an exciting new model that the researchers called DALL-E 2.
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DALL-E 2 had been trained on millions of images scraped from the public web, but as before, OpenAI was vague about what DALL-E had been trained on.
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Why pay an artist like Rutkowski to produce new art when you could get software to produce Rutkowski-style art instead?
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People started to notice another issue with DALL-E 2. If you asked it to produce some photorealistic images of CEOs, nearly all of them would be white men.
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Some of OpenAI's employees worried about the speed at which OpenAI was releasing a tool that could generate fake photos. Having started off as a nonprofit devoted to safe AI, it was turning into one of
The magic here wasn't DALL-E 2's capabilities alone. It was the impact the tool was having on people.
This idea of generating fully formed content was what made Altman's next move even more sensational. GPT-1 had been more like an autocomplete tool that continued what a human started typing. But GPT-3 and its latest upgrade, GPT-3.5, created brand-new prose, just like how DALL-E 2 made images from scratch.
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On November 30, 2022, OpenAI published a blog post announcing a public demo of ChatGPT. Many people at OpenAI, including some who worked on safety, weren't even aware of the launch, and some started taking bets on how many people would use it after a week.
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It was hard to find a single negative appraisal of ChatGPT. The overwhelming response was awe.
Within the next twenty-four hours, more and more people piled onto ChatGPT, straining its servers and testing its limits. Now it was everyday professionals, tech workers, people in marketing and the media, who were road testing the bot.
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"Some jobs are going to go away," Altman said bluntly in one interview. "There will be new, better jobs that are difficult to imagine today."
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Inside Google, executives recognized that more and more people might just go to ChatGPT for information about health issues or product advice—among the most lucrative search engine terms to sell ads against—instead of Google.
But now, for the first time, Google's more-than-twenty-year dominance as gatekeeper to the web was on shaky ground.
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Within weeks of ChatGPT's launch, executives at Google issued a code red inside the company.
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Panicked executives told staff working on key products that had at least one billion users, like YouTube and Gmail, that they had just months to incorporate some form of generative AI.
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Sensing deep insecurity from Google's leadership, the company's engineering teams delivered. A few months after the launch of ChatGPT, managers at YouTube added a feature where video creators on the website could generate new film settings or swap outfits, using generative AI. But it felt like they were throwing spaghetti at the wall. It was time to bring out their secret weapon: LaMDA.
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While Altman measured success with numbers, whether for investments or people using a product, Hassabis chased awards.
DeepMind won at CASP in both 2019 and 2020 and open-sourced its protein folding code to scientists in 2021.
All told, DeepMind's biggest projects had garnered lots of prestige but made relatively little impact on the real world.
Training on real-world data—as OpenAI had done by scraping billions of words from the internet—was messy and noisy.
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But OpenAI still had a glaring problem. It was sidestepping the need for transparency, and more broadly, it was getting harder to hear the voices calling for more scrutiny of large language models.
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Sam Altman had set off several different races when he launched ChatGPT. The first was obvious: Who would bring the best large language model to market first? The other was taking place in the background: Who would control the narrative about AI?
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Hinton said he regretted some of his research.
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"The idea that this stuff could actually get smarter than people—a few people believed that," he told the New York Times. "But most people thought it was way off. And I thought it was way off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer think that.… I don't think they should scale this up more until they have understood whether they can control it."
Yet all this talk of doom had a paradoxical effect on the business of AI itself: it was booming.
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models by 2026. Safety-first framing had made Anthropic sound like a nonprofit, with its mission to "ensure transformative AI helps people and society flourish." But OpenAI's smash hit with ChatGPT had shown the world that the companies with the grandest plans could also be the most lucrative investments. Proclaiming that you were building safer AI had almost become like a dog whistle for bigger tech companies who wanted to get in on the game too.
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Altman and Hassabis had started their companies with grand missions to help humanity, but the true benefits they had brought to people were as unclear as the rewards of the internet and social media. More clear were the benefits they were bringing to Microsoft and Google: new, cooler services and a foothold in the growing market for generative AI.
By early 2024, everyone from media to entertainment companies to Tinder were stuffing new generative AI features into their apps and services. The generative AI market was projected to expand at a rate of more than 35 percent annually to hit $ 52 billion by 2028.
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AI would cut the cost of animated movies by 90 percent.
Generative AI would make advertising even more eerily personal.
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As these and other business ideas gathered pace, the price of stuffing generative AI into everything was still unclear. Algorithms were already steering more and more decisions in our lives, from what we read online to who companies wanted to recruit. Now they were poised to handle more of our thinking tasks, which raised uncomfortable questions not only about human agency but also about our ability to solve problems and simply imagine.
Evidence suggests that computers have already offloaded some of our cognitive skills in areas like short-term memory.
In 1955, a Harvard professor named George Millar tested the memory limits of humans by giving his subjects a random list of colors, tastes, and numbers. When he asked them to repeat as many things on the list as they could, he noticed that they were all getting stuck somewhere in the neighborhood of seven. His paper, "The Magical Number Seven, Plus or Minus Two," went on to influence how engineers designed software and how telephone companies broke down phone numbers into segments to help us recall them. But according to more recent estimates, that magic number has now fallen from seven to four.
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History shows humans do tend to fret that new innovations will cause our brains to shrivel up. When writing first became widespread more than two thousand years ago, philosophers like Socrates worried it would weaken human memory because before its advent, it was only possible to pass on knowledge through spoken discourse. The introduction of calculators in education raised concerns that students would lose their basic arithmetic skills.
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For now, we simply don't know how our critical thinking skills or creativity will atrophy once a new generation of professionals start using large language models as a crutch, or how our interactions with other humans might change as more people use chatbots as therapists and romantic partners, or put them in toys for children as several companies have already done.
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Daron Acemoglu,
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70 percent of the increase in wage inequality in the United States between 1980 and 2016 was caused by automation.
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The European Union looked at AI more pragmatically than the United States, thanks in part to having few major AI companies on its shores to lobby its politicians, and they refused to be influenced by alarmism.
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As ChatGPT spread unregulated across the world and seeped into business workflows, people were left to deal with its flaws on their own.
Like Hassabis, Altman was positioning AGI as an elixir that would solve problems. It would generate untold wealth. It would figure out how to share that money equitably with all of humankind. Were these words spoken by anyone else they would have sounded ludicrous.
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"One thing that Sam does really well is put just-barely believable statements out there that get people talking," says one former OpenAI manager.
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Brockman was being removed as chairman, but the board wanted him to stay with the company. They gave Microsoft a quick heads-up about what had just happened and, within minutes, published a blog post announcing Altman's dismissal. Brockman immediately quit. So did three of OpenAI's top researchers.
Some gave Sutskever and the board an epithet: decels. The new split had emerged in AI between those who wanted to accelerate its development and those who wanted to decelerate it.
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Nadella didn't want that to happen. He knew that if Altman started a new firm, there'd be a flood of investors banging on his door and no guarantee that Microsoft would get the biggest foothold with Altman the second time around. He kicked off the weekend making calls, leading negotiations with OpenAI's board to bring Altman back.
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As the weekend drew on, a mass revolt was brewing among OpenAI's staff.
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Nadella was meanwhile pushing hard on his own backup plan. If Altman couldn't grab back the reins of OpenAI, Microsoft needed to bring him fully into the corporate fold and do so before Monday morning.
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Now everyone was pushing OpenAI's safety-obsessed board members to resign, and by late Monday, nearly all of OpenAI's 770 staff had signed a letter threatening to join Microsoft with Altman, unless the board members stepped down. "Microsoft has assured us there are positions for all," the letter said. It was a huge bluff. Hardly any OpenAI staff wanted to work for Microsoft, a stodgy old company where people worked for decades and wore khaki pants.
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They weren't making the threat entirely out of loyalty to Altman either. A bigger issue was that Atman's firing had killed a chance for many OpenAI staff—especially long-serving ones—to become millionaires.
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a former Google executive says....
"The winners in the next couple of years are not going to be research labs," says a former scientist at OpenAI. "They're going to be companies building products, because AI is not really about research anymore."
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The race to build AGI had started with a question: What if you could build artificial intelligence systems that were smarter than humans?
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All they knew was that they had to keep moving toward the goal and that they had to be first. In so doing, they put AI on course to benefit the world's most powerful companies just as much as anyone else.
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OpenAI and DeepMind were so focused on making perfect AI that they chose not to open themselves up to research scrutiny to make sure their systems didn't cause harm in the same way social media firms had.
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Some economists say that instead of creating financial abundance for everyone, powerful AI systems could make inequality worse.
They could also widen a cognition gap between rich and poor.
One idea doing the rounds among technologists is that when AGI does land, it won't exist as a separate intelligent entity but as an extension of our minds through neural interfaces. At the forefront of this research is Elon Musk's brain-to-computer interface company Neuralink, the brain chip that Musk wants to implant in billions of people one day. Musk is also rushing to make that happen.
But a more pressing issue than rogue AI is bias.
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Today, language models are being used to publish thousands of articles each day to make money from ad revenue, and even Google is struggling to distinguish the real from the fake.
"We're creating a cycle, encoding and exacerbating stereotypes," says Abeba Birhane, the AI scholar who researched Big Tech's stranglehold on academic research and its similarities with Big Tobacco. "That is going to be a huge problem as the [World Wide Web] is populated with more and more AI-generated images and text."
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OpenAI could help make chatbots like these more addictive.
At the time of writing, dozens of "girlfriend" apps were cropping up on the GPT Store, and while they were banned from encouraging romantic relationships with people, policing those rules would not be easy for OpenAI.
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Another way that AI designers will likely try to keep people engaged is by getting "infinite context" about their lives.
The chatbots on Character.ai can currently remember about thirty minutes of a conversation, but Noam Shazeer and his team are trying to expand that window of time to hours, days, and eventually forever.
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In the United States, for instance, Black people are five times more likely to be arrested than white people, which means law enforcement would be more likely to mine their "life data" and analyze it with other machine learning algorithms to make inscrutable judgments.
The biggest tech firms don't innovate anymore, but they can still move quickly to gain a tactical advantage. | Jane Bourke, Ann Kirby, Justin Doran Notable QuotationsExpand to full screen
SURVEY & QUESTIONNAIRE DESIGN: Collecting Primary Data to Answer Research Questions (55)
Jane Bourke, Ann Kirby, and Justin Doran
First stage
[list everything you already know about the problem that you are trying to solve.]
The research question is guided by the hypothesis. A research question is basically a hypothesis, formulated as a question.
A ‘good’ hypothesis must be: Adequate; Testable; Better than its rivals.
Next, identify your research objectives.
objectives are likely to lead to greater specificity than research or investigative questions.
SMART test: Specific: What do you hope to achieve? Measurable: Can you measure it? Achievable: Are the targets achievable? Realistic: What are your time constraints? Timely: Will you have the time to accomplish your objectives? Research objectives are statements of what you intend to find out when answering your question. Therefore identifying what you hope to measure with this research question and how you intend to measure it are two specific questions that need to be addressed.
The goal of standardisation is that each respondent be exposed to the same question experience, and that the recording of the answer be the same, too, so that any differences in the answers can be correctly interpreted as reflecting differences between respondents rather than differences in the process that produced the same answer.
Be mindful of how complicated the question is for the respondent to answer.
Avoid double-barrelled questions:
Take care with questions that have a prestige bias:
Avoid leading questions
Avoid direct leading questions – a closed question asking for agreement, pushing
Avoid negative questions. Sometimes the word ‘not’ in a question can confuse
Avoid assumptive questions -- Where did you go on holidays last year?
Only ask questions that respondents are willing to give correct and valid answers to
Beware of asking about information from proxies
Clarify what constitutes an adequate answer
Be mindful of your answer categories
Can the respondent comprehend the question that is being asked?
Is the respondent capable of answering the question that is being asked?
Is the respondent willing to answer the question that is being asked?
Confusing questions
Wordy questions
‘Single’ can simply mean ‘unmarried’, but respondents who are divorced or widowed may choose this option. The phrase ‘never married’ is a better alternative.
All questions about events and behaviour need a reference period – ‘last year’ or ‘in the last month
Non-factual Questions
Attitudes: A relatively stable opinion towards a person, object or activity, containing a cognitive element (perception and beliefs) and an emotional element (positive or negative feelings); Beliefs: Representing a person’s subjective knowledge about themselves and their world. Made up of
Direct experience, second-hand information, and inferences; Values: Enduring beliefs about imperative life goals that surpass specific situations:
Behavioural intentions: Attitudes toward behaviours tend to produce corresponding ‘behavioural intentions’. The degree to which these intentions are actually implemented is
Psychological trait: A basic, unchanging ability or skill – a long-lasting state of conditions such as intelligence, motives, emotions and values.
Psychological state: A mental condition, relatively constant but can fluctuate.
Open versus closed questions
Allow the researcher to obtain responses that were unanticipated;
Are time-consuming to code and tend to be unreliable (as coders may disagree which coding category to assign an answer to); Can be difficult to write:
Can be difficult to answer:
Closed questions: Allow the respondent to more reliably respond
Agree / disagree questions Are cognitively complex:
Are difficult to design; Are prone to acquiescence bias. A possible solution is to include a middle alternative,
A commonly-practised technique is to exclude the middle alternative but to pose a second question measuring the respondents’ intensity of feeling about their answer to the first.
Avoid using a “No opinion” or “Don’t know” option. Recent research shows that a “No opinion” option suggests to respondents that a great deal of knowledge is required
Satisfaction questions Satisfaction questions tend to be undiscriminating, in that they often give highly favourable results.
overall satisfaction measures are not a useful measure of performance as they give no specific guidance as to how the service can be improved.
Asking people to predict their response to a future or hypothetical situation should be done with considerable caution – particularly when respondents are likely to have limited direct experience on which to base their answers.
Fowler (1995) suggests that few of us are able to report validly on the reasons we do what we do. In fact, for many of our decisions / behaviours we give little or no thought to our decision-making.
Context effects Respondents’ answers to the current survey question can be affected by the previous survey questions. | Max FisherNotable QuotationsExpand to full screen
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Like many, I had initially assumed social media's dangers came mostly from misuse by bad actors - propagandists, foreign agents, fake- news peddlers - and that at worst the various platforms were a passive conduit for society's preexisting problems. But virtually everywhere I traveled in my reporting, covering far- off despots, wars, and upheavals, strange and extreme events kept getting linked back to social media. A sudden riot, a radical new group, widespread belief in some oddball conspiracy - all had a common link.
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the more incendiary the post, they sensed, the more widely the platforms spread it.
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Many at the company seemed almost unaware that the platform's algorithms and design deliberately shape users' experiences and incentives, and therefore the users themselves.
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Within Facebook's muraled walls, though, belief in the product as a force for good seemed unshakable.
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attraction to divisiveness," the researchers warned in a 2018 presentation later leaked to the Wall Street Journal. In fact, the presentation continued, Facebook's systems were designed in a way that delivered users "more and more divisive content in an effort to gain user attention & increase time on the platform."
Public figures routinely referred to the companies as one of the gravest threats of our time. In response, the companies' leaders pledged to confront the harms flowing from their services.
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They unveiled election- integrity war rooms and updated content- review policies. But their business model - keeping people glued to their platforms as many hours a day as possible - and the underlying technology deployed to achieve this goal remained largely unchanged.
commissioned by the company under pressure from civil rights groups, concluded that the platform was everything its executives had insisted to me it was not. Its policies permitted rampant misinformation that could undermine elections. Its algorithms and recommendation systems were "driving people toward self- reinforcing echo chambers of extremism," training them to hate. Perhaps most damning, the report concluded that the company did not understand how its own products affected its billions of users.
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The early conventional wisdom, that social media promotes sensationalism and outrage, while accurate, turned out to drastically understate things.
This technology exerts such a powerful pull on our psychology and our identity, and is so pervasive in our lives, that it changes how we think, behave, and relate to one another. The effect, multiplied across billions of users, has been to change society itself.
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With little incentive for the social media giants to confront the human cost to their empires - a cost borne by everyone else, like a town downstream from a factory pumping toxic sludge into its communal well - it would be up to dozens of alarmed outsiders and Silicon Valley defectors to do it for them.
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"If you joined the one anti- vaccine group," she said, "it was transformative." Nearly every vaccine- related recommendation promoted to her was for anti- vaccine content. "The recommendation engine would push them and push them and push them."
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Before long, the system prompted her to consider joining groups for unrelated conspiracies. Chemtrails. Flat Earth.
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The reason the system pushed the conspiratorial outliers so hard, she came to realize, was engagement. Social media platforms surfaced whatever content their automated systems had concluded would maximize users' activity online, thereby allowing the company to sell more ads.
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Facebook wasn't just indulging anti- vaccine extremists. It was creating them.
Almost certainly, no one at Facebook or YouTube wanted to promote vaccine denial.
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But the technology building this fringe movement was driven by something even the company's CEO could not overcome: the cultural and financial mores at the core of his entire industry.
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As semiconductors developed into the circuit board, then the computer, then the internet, and then social media, each technology produced a handful of breakout stars, who in turn funded and guided the next handful.
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human instincts to conform run deep. When people think something has become a matter of consensus, psychologists have found, they tend not only to go along, but to internalize that sentiment as their own.
the outrage was being ginned up by the very Facebook product that users were railing against.
That digital amplification had tricked Facebook's users, and even its leadership, into misperceiving the platform's loudest voices as representing everyone, growing a flicker of anger into a wildfire.
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But, crucially, it had also done something else: driven engagement up. Way up.
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Long after their technology's potential for harm had been made clear, the companies would claim to merely serve, and never shape or manipulate, their users' desires. But manipulation had been built into the products from the beginning.
"The thought process that went into building these applications," Parker told the media conference, "was all about, ‘How do we consume as much of your time and conscious attention as possible?'"
"We need to sort of give you a little dopamine hit every once in a while, because someone liked or commented on a photo or a post or whatever. And that's going to get you to contribute more content, and that's going to get you more likes and comments."
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the "social- validation feedback loop,"
The term of art is "persuasion": training consumers to alter their behavior in ways that serve the bottom line. Stanford University had operated a Persuasive Tech Lab since 1997. In 2007, a single semester's worth of student projects generated $ 1 million in advertising revenue.
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Dopamine is social media's accomplice inside your brain. It's why your smartphone looks and feels like a slot machine, pulsing with colorful notification badges, whoosh sounds, and gentle vibrations.
Social apps hijack a compulsion - a need to connect - that can be even more powerful than hunger or greed.
intermittent variable reinforcement.
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Never knowing the outcome makes it harder to stop pulling the lever. Intermittent variable reinforcement is a defining feature of not only gambling and addiction but also, tellingly, abusive relationships.
while posting to social media can feel like a genuine interaction between you and an audience, there is one crucial, invisible difference. Online, the platform acts as unseen intermediary.
It decides which of your comments to distribute to whom, and in what context.
The average American checks their smartphone 150 times per day, often to open social media.
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YEAR AFTER launching the news feed, a group of Facebook developers mocked up something they called the "awesome button" - a one- click expression of approval for another user's post.
After a year and a half in limbo, a new team took over what was now the "Like" button.
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That little button's appeal, and much of social media's power, comes from exploiting something called the sociometer.
The anguish we feel from low self- esteem is wholly self- generated.
self- esteem is in fact "a psychological gauge of the degree to which people perceive that they are relationally valued and socially accepted by other people."
It's what the anthropologist Brian Hare called "survival of the friendliest." The result was the development of a sociometer: a tendency to unconsciously monitor how other people in our community seem to perceive us.
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the platforms added a powerful twist: a counter at the bottom of each post indicating the number of likes, retweets, or upvotes it had received - a running quantification of social approval for each and every statement.
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When we receive a Like, neural activity flares in a part of the brain called the nucleus accumbens: the region that activates dopamine.
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Expressing identity, sharpening identity, seeing and defining the world through its lens. This effect remade how social media works, as its overseers and automated systems drifted toward the all- consuming focus on identity that best served their agendas.
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Our drive to cultivate a shared identity is so powerful that we'll construct one even out of nothing.
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Prejudice and hostility have always animated this instinct. Hunter- gatherer tribes sometimes competed for resources or territory.
Social media's indulgence of identity wasn't obviously harmful at first. But it was always well known.
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In 2014, I was one of several Washington Post reporters to start Vox, a news site intended to leverage the web. We never shaped our journalism to please social media algorithms - at least, not consciously - but headlines were devised with them in mind. The most effective approach, though one that in retrospect we should have perhaps been warier of using, was identity conflict. Liberals versus conservatives. The righteousness of anti- racism. The outrageousness of lax gun laws.
"Few realized, early on, that the way to win the war for attention was to harness the power of community to create identity.
Two: Everything Is Gamergate
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Raucous debate became seen as the purest meritocracy: if you couldn't handle your own or win over the crowd, if you felt harassed or unwelcome, it was because your ideas had not prevailed on merit.
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Peter Thiel, a founder of PayPal and the first outside investor in Facebook, had urged elevating antisocial contrarians.
"If you're less sensitive to social cues, you're less likely to do the same things as everyone else around you."
"There's not a lot of value placed on social niceties," Margaret O'Mara told me. "There's a tolerance for weirdness, in part because weird people have a proven track record. That's the other dimension of Silicon Valley culture. It's like everyone was an asshole."
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Thiel, further parlaying his PayPal success, started a fund that launched major investments in Airbnb, Lyft, and Spotify. Throughout, like many leading investors, he imposed his ideals on the companies he oversaw.
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with the advent of the social media era, the industry was building its worst habits into companies that then smuggled those excesses - chauvinism, a culture of harassment, majoritarianism disguised as meritocracy - into the homes and minds of billions of consumers.
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the norms and values that they'd encoded into the early web turned out to guide its millions of early adopters toward something very different than the egalitarian utopia they'd imagined.
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4chan. Anytime a user wanted to start a new thread, they had to upload an image, which kept the platform filled with user- made memes and cartoons.
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Long before Snapchat and others borrowed the feature, discussions automatically deleted after a brief period, which enabled unseemly behavior that might've been shunned elsewhere. So did the site's anonymity; nearly all posts are marked as written by "Anonymous," which instills an anything- goes culture and a sense of collective identity that can be alluring, especially to people who crave a sense of belonging.
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"Ultimately," Christopher Poole, 4chan's founder, said in 2008, "the power lies in the community to dictate its own standards."
The internet's promise of total freedom appealed especially to kids, for whom off- line life is ruled by parents and teachers. Adolescents also have a stronger drive to socialize than adults, which manifests as heavier use of social networks and a greater sensitivity to what happens there. Poole had started 4chan when he was just fifteen. Kids who felt isolated off- line, like Adam, drove an outsized share of online activity, bringing the concerns of the disempowered and the bullied with them.
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Transgressing ever- greater taboos - even against cruelty to grieving parents - became a way to signal that you were in on the joke. "When you browse 4chan and 8chan while the rest of your friends are posting normie live- laugh- love shit on Instagram and Facebook," Adam said, "you feel different. Cooler. Part of something niche."
These two unifying activities, flaunting taboos and pulling pranks, converged to become trolling.
The thrill of getting a reaction out of someone even had a name: lulz, a corruption of the acronym for "laugh out loud."
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Unchastened by the social constraints of the off- line world, each user operates like a miniature Facebook algorithm, iteratively learning what best wins others' attention.
One lesson consistently holds. To rise among tens of thousands of voices, regardless of what you post, it is better to amp up the volume, to be more extreme.
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"Trolling is basically internet eugenics,"
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From the beginning, social media platforms borrowed heavily from video games. Notifications are delivered in stylized "badges," which Gordon told the audience could double a user's time on site, while likes mimic a running score. This was more than aesthetic. Many platforms initially considered gamers - tech obsessives who would surely pump hours into this digital interface, too - to be a core market.
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New TV programming like My Little Pony and GI Joe delivered hyper- exaggerated gender norms, hijacking adolescents' natural gender self- discovery and converting it into a desire for molded plastic products.
Tapping into our deepest psychological needs, then training us to pursue them through commercial consumption that will leave us unfulfilled and coming back for more, has been central to American capitalism since the postwar boom.
Marketers, having long positioned games as childhood toys, kept boys hooked through adolescence and adulthood with - what else? - sex.
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Senator Trent Lott of Mississippi. His staff had deployed a now- famous push poll: "Do you believe Democrats are trying to take away your culture?" It performed spectacularly, especially with white men.
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Facebook, in the hopes of boosting engagement, began experimenting with breaking the so- called Dunbar limit. The British anthropologist Robin Dunbar had proposed, in the 1990s, that humans are cognitively capped at maintaining about 150 relationships.
Our behavior changes, too, seeking to reset back to 150, like a circuit breaker tripping. Even online, people converged naturally on Dunbar's number.
Users were pushed toward content from what Facebook called "weak ties": friends of friends, contacts of contacts, cousins of cousins.
Enforced through algorithmic sophistication, the scheme worked. Facebook pulled users into ever expanding circles of half- strangers, surpassing the Dunbar limit.
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But studies of rhesus monkeys and macaques, whose Dunbar- like limits are thought to mirror our own, had found that pushing them into larger groups made them more aggressive, more distrusting, and more violent.
The monkeys seemed to sense that safely navigating an unnaturally large group was beyond their abilities, triggering a social fight- or- flight response that never quite turned off. They also seemed to become more focused on forming and enforcing social hierarchies, likely as a kind of defense mechanism.
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Facebook could push you into groups - stand- alone discussion pages focused on some topic or interest - ten times that size.
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"There's this conspiracy- correlation effect," DiResta said, "in which the platform recognizes that somebody who's interested in conspiracy A is typically likely to be interested in conspiracy B, and pops it up to them."
"I called it radicalization via the recommendation engine," she said. "By having engagement- driven metrics, you created a world in which rage- filled content would become the norm."
The algorithmic logic was sound, even brilliant. Radicalization is an obsessive, life- consuming process. Believers come back again and again, their obsession becoming an identity, with social media platforms the center of their day- to- day lives.
She had seen it over and over. Recruits were drawn together by some ostensibly life- or- death threat: the terrible truth of vaccines, the Illuminati agents who spread Zika, the feminists seeking to overturn men's rightful place atop the gender hierarchy, starting with gaming.
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Still, Reddit was built and governed around the same early internet ideals as 4chan, and had absorbed that platform's users and cultural tics.
Its up- or- down voting enforced an eclipsing majoritarianism that pushed things even further.
upvote counts are publicly displayed, tapping into users' sociometer- driven impulse for validation. The dopamine- chase glued users to the site and, as on Facebook, steered their actions.
As of 2016, four years after her suit, still only 11 percent of technology venture- capital partners were women. Two percent were Black.
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looked like them: in 2018, 98 percent of their investment dollars went to male- led companies.
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"Every Man Is Responsible for His Own Soul." This would become a standard defense from social media overlords: that the importance of their revolution compelled them to disregard the petty laws and morals of the outmoded off- line world. Besides, any bad behavior was users' fault, no matter how crucial a role the platform played in enabling, encouraging, and profiting from those transgressions.
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Finally, nearly three weeks after the photos first appeared, Wong banned them. Reddit's users, incensed, accused the platform of selling out its principles to shadowy corporate influence and, worse, feminists.
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Pao was also testing a theory: that the most hateful voices, though few in number, exploited social media's tendency to amplify extreme content for its attention- winning power, tingeing the entire platform in the process.
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The first ban was small: a subreddit called "FatPeopleHate."
Still, Reddit's userbase erupted in anger at the removals as an attack on the freedom to offend and transgress that, after all, had been an explicit promise of the social web since its founding.
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"The trolls are winning," Pao wrote in a Washington Post op- ed a few days later. The internet's foundational ideals, while noble, had led tech companies to embrace a narrow and extreme interpretation of free speech that was proving dangerous, she warned. She had lasted just eight months.
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MILO YIANNOPOULOS,
Headlines like "Lying Greedy Promiscuous Feminist Bullies Are Tearing the Video Game Industry Apart" went viral on those platforms as seeming confirmation.
His bosses had hoped his articles would inform Breitbart's small, far- right readership on tech issues. Instead, they tapped into a new and much larger audience that they hadn't even known existed - one that was only coming together at that moment. "Every time you write one of your commentaries, it gets 10,000 comments," Steve Bannon, Breitbart's chief, told Yiannopoulos on the site's radio show. "It goes even broader than the Breitbart audience, all over."
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Within three years, the angry little subculture Yiannopoulos championed would evolve into a mainstream movement so powerful that he was granted a keynote slot at the Conservative Political Action Conference, the most important event on the political right.
(The invitation was later revoked.) Bannon called their cause the "alt right," a term borrowed from white- power extremists who'd hoped to rebrand for a new generation.
Bannon and others on the alt right saw a chance to finally break through. "I realized Milo could connect with these kids right away," Bannon said later. "You can activate that army. They come in through Gamergate or whatever and then get turned on to politics and Trump."
"They call it ‘meme magic' - when previously obscure web memes become so influential they start to affect real- world events," Yiannopoulos wrote that summer before the election.
The movement coalesced around Trump, who had converged on the same tics and tactics as Yiannopoulos and other Gamergate stars, and for seemingly the same reason: it's what social media rewarded.
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He swung misinformation and misogyny as weapons. He trolled without shame, heaping victims with mockery and abuse. He dared society's gatekeepers to take offense at flamboyant provocations that were right off 4chan.
From May 2015, a month before Trump declared his candidacy, to November 2016, a Harvard study later found, the most popular right- wing news source on Facebook was Breitbart, edging out even Fox News.
Awed outsiders would long ascribe Breitbart's rise to dark- arts social media manipulation. In truth, the publication did little more than post its articles to Facebook and Twitter, just as it always had. It was, in many ways, a passive beneficiary. Facebook's systems were promoting a host of once- obscure hyperpartisan blogs and outright misinformation shops - bearing names like The Gateway Pundit, Infowars, The Conservative Treehouse, and Young Cons - into mega- publishers with the power to reshape reality for huge segments of the population.
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"This cycle of aggrievement and resentment and identity, and mob anger, it feels like it's consuming and poisoning the entire nation."
Four: Tyranny of Cousins
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"We enjoy being outraged. We respond to it as a reward."
The platforms had learned to indulge the outrage that brought their users "a rush - of purpose, of moral clarity, of social solidarity."
The growing pace of these all- consuming meltdowns, perhaps one a week, indicated that social media was not just influencing the broader culture, but, to some extent, supplanting
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Popular culture often portrays morality as emerging from our most high- minded selves: the better angels of our nature, the enlightened mind. Sentimentalism says it is actually motivated by social impulses like conformity and reputation management (remember the sociometer?), which we experience as emotion.
the emotional brain works fast, often resolving to a decision before conscious reason even has a chance to kick in.
social purpose, like seeking peers' approval, rewarding a Good Samaritan, or punishing a transgressor. But the instinctual nature of that behavior leaves it open to manipulation. Which is exactly what despots, extremists, and propagandists have learned to do, rallying people to their side by triggering outrage - often at some scapegoat or imagined wrongdoer. What would happen when, inevitably, social platforms learned to do the same?
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Much legal scholarship, Klonick knew, considers public shaming necessary for society to function: tut- tutting someone for cutting in line, shunning them for a sexist comment, getting them fired for joining a hate group. But social media was changing the way that public shaming worked, which would necessarily change the functioning of society itself. "Low cost, anonymous, instant, and ubiquitous access to the internet has removed most - if not all - of the natural checks on shaming," she wrote of her findings, "and thus changed the way we perceive and enforce social norms."
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Truth or falsity has little bearing on a post's reception, except to the extent that a liar is freer to alter facts to conform to a button- pushing narrative. What matters is whether the post can provoke a powerful reaction, usually outrage.
A 2013 study of the Chinese platform Weibo found that anger consistently travels further than other sentiments.
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Right or left, the common variable was always social media, the incentives it imposes, the behavior it elicits.
Our social sensitivity evolved for tribes where angering a few dozen comrades could mean a real risk of death. On social media, one person can, with little warning, face the fury and condemnation of thousands.
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pleasurable. Brain scans find that, when subjects harm someone they believe is a moral wrongdoer, their dopamine- reward centers activate.
From behind a screen, far from our victims, there is no pang of guilt at seeing pain on the face of someone we've harmed. Nor is there shame at realizing that our anger has visibly crossed into cruelty.
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the platform's extreme bias toward outrage meant that misinformation prevailed, which created demand for more outrage- affirming rumors and lies.
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scales; people express more outrage, and demonstrate more willingness to punish the undeserving, when they think their audience is larger.
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algorithmically encouraged rage.
Five: Awakening the Machine
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"In September 2011, I sent a provocative email to my boss and the YouTube leadership team," Goodrow later wrote. "Subject line: ‘Watch time, and only watch time.' It was a call to rethink how we measured success."
second. "Our job was to keep people engaged and hanging out with us,"
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YouTube's system seeks something more far- reaching than a monthly subscription fee. Its all- seeing eye tracks every detail of what you watch, how long you watch it, what you click on next. It monitors this across two billion users, accruing what is surely the largest dataset on viewer preferences ever assembled, which it constantly scans for patterns. Chaslot and others tweaked the system as it went, nudging its learning process to better accomplish its goal: maximum watch time.
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One of the algorithm's most powerful tools is topical affinity. If you watch a cat video all the way through, Chaslot explained, YouTube will show you more on return visits.
The effect is to pull users toward ever more titillating variations on their interests.
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Focus everything, he instructed, on maximizing a few quantifiable metrics.
Concentrate power in the hands of engineers who can do it. And shunt aside the rest.
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They were chasing a very specific model: free- to- use web services that promised breakneck user growth.
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in the late 2000s, Amazon and a few others set up sprawling server farms, putting their processing power and data storage up for rent, calling it "the cloud." Now you no longer needed to invest in overhead. You rented it from Amazon, uploading your website to their servers.
"Forget strategy," the investor Roger McNamee wrote of this new approach. "Pull together a few friends, make a product you like, and try it in the market. Make mistakes, fix them, repeat." It was transformative for investors, too, who no longer had to sink millions into getting a startup to market. They could do it for pocket change.
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If the value of an ad impression kept shrinking, even the Facebooks and YouTubes might cease to be viable. Their only choice was to permanently grow the number of users, and those users' time on site, many times faster than those same actions drove down the price of an ad. But controlling the market of human attention, as their business models had fated them to attempt, was beyond anything a man- made program could accomplish.
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Wojcicki's YouTube existed to convert eyeballs into money. Democracy and social cohesion were somebody else's problem.
"So, when YouTube claims they can't really say why the algorithm does what it does, they probably mean that very literally."
The average user's time on the platform skyrocketed. The company estimated that 70 percent of its time on site, an astronomical share of its business, was the result of videos pushed by its algorithm- run recommendation system.
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In 2014, the same year that Wojcicki took over YouTube, Facebook's algorithm replaced its preference for Upworthy- style clickbait with something even more magnetic: emotionally engaging interactions. Across the second half of that year, as the company gradually retooled its systems, the platform's in- house researchers tracked 10 million users to understand the effects. They found that the changes artificially inflated the amount of pro- liberal content that liberal users saw and the amount of pro- conservative content that conservatives saw. Just as Pariser had warned. The result, even if nobody at Facebook had consciously intended as much, was algorithmically ingrained hyperpartisanship.
The process, Facebook researchers put it, somewhat gingerly, in an implied warning that the company did not heed, was "associated with adopting more extreme attitudes over time and misperceiving facts about current events."
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TikTok, a Chinese- made app, shows each user a stream of videos selected almost entirely by algorithms. Its A.I. is so sophisticated that TikTok almost immediately attracted 80 million American users, who often use it for hours at a time, despite most of its engineers not speaking English or understanding American culture.
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Like DiResta's anti- vaxxers, or even Upworthy, the Russians hijacked the algorithm's own preferences. It wasn't just that the agents repeated phrases or behaviors that performed well. Their apparent mission, of stirring up political discord, seemed to naturally align with what the algorithms favored anyway, often to extremes.
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"He was telling me, ‘Oh, but there are so many videos, it has to be true,'" Chaslot said. "What convinced him was not the individual videos, it was the repetition. And the repetition came from the recommendation engine."
illusory truth effect. We are, every hour of every day, bombarded with information. To cope, we take mental shortcuts to quickly decide what to accept or reject.
One is familiarity; if a claim feels like something we've accepted as true before, it probably still
When he searched YouTube for Pope Francis, for instance, 10 percent of the videos it displayed were conspiracies. On global warming, it was 15 percent. But the real shock came when Chaslot followed algorithmic recommendations for what to watch next, which YouTube has said accounts for most of its watch time. A staggering 85 percent of recommended videos on Pope Francis were conspiracies, asserting Francis's "true" identity or purporting to expose Satanic plots at the Vatican.
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But the influence of algorithms only deepened, including at the last holdout, Twitter. For years, the service had shown each user a simple, chronological feed of their friends' tweets. Until, in 2016, it introduced an algorithm that sorted posts - for engagement, of course, and to predictable effect.
"The recommendation engine appears to reward inflammatory language and outlandish claims."
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Shortly after Twitter algorithmified, Microsoft launched an A.I.- run Twitter account called Tay. The bot operated, like the platforms, on machine learning, though with a narrower goal: to converse convincingly with humans by learning from each exchange.
Within twenty- four hours, Tay's tweets had taken a disturbing turn.
"You absolutely do NOT let an algorithm mindlessly devour a whole bunch of data that you haven't vetted even a little bit."
Six: The Fun House Mirror
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Conspiracy belief is highly associated with "anomie," the feeling of being disconnected from society.
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It was undeniable that Trump owed his rise to nondigital factors, too: the institutional breakdown of the Republican Party, a decades- long rise in polarization and public distrust, white backlash to social change, a radicalized right- wing electorate. Social media had created none of these. But, in time, a network of analysts and whistleblowers would prove that it had exacerbated them all, in some cases drastically.
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moral outrage can become infectious in groups, and that it can alter the mores and behaviors of people exposed to it.
across topics, across political factions, what psychologists refer to as "moral- emotional words" consistently boosted any tweet's reach.
Moral- emotional words convey feelings like disgust, shame, or gratitude.
calls for, communal judgment,
That makes these words different from either narrowly emotional sentiments (" Overjoyed at today's marriage equality ruling") or purely moral ones (" The president is a liar"), for which Brady's effect didn't appear. Tweets with moral- emotional words, he found, traveled 20 percent farther - for each moral- emotional word.
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Brady found something else. When a liberal posted a tweet with moral- emotional words, its reach substantially increased among other liberals, but declined with conservatives. (And vice versa.) It won the user more overall attention and validation, in other words, at the cost of alienating people from the opposing side. Proof that Twitter encouraged polarization.
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They were acting on a widely held misinterpretation of something known as contact theory. Coined after World War II to explain why desegregated troops became less prone to racism, the theory suggested that social contact led distrustful groups to humanize one another. But subsequent research has shown that this process works only under narrow circumstances: managed exposure, equality of treatment, neutral territory, and a shared task. Simply mashing hostile tribes together, researchers repeatedly found, worsens animosity.
People, as a rule, perceive out- groups as monoliths.
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Even in its most rudimentary form, the very structure of social media encourages polarization. Reading an article and then the comments field beneath it, an experiment found, leads people to develop more extreme views on the subject in the article.
Control groups that read the article with no comments became more moderate and open- minded.
News readers, the researchers discovered, process information differently when they are in a social environment: social instincts overwhelm reason, leading them to look for affirmation of their side's righteousness.
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The data revealed, as much as any foreign plot, the ways that the Valley's products had amplified the reach, and exacerbated the impact, of malign influence. (She later termed this "ampliganda," a sort of propaganda whose power comes from its propagation by masses of often unwitting people.)
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Over many iterations, the Russians settled on a strategy. Appeal to people's group identity. Tell them that identity was under attack. Whip up outrage against an out- group. And deploy as much moral- emotional language as possible.
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the internet offered political outsiders a way around the mainstream outlets that shunned them. As those candidates' grassroots supporters spent disproportionate time on YouTube, the system learned to push users to those videos, creating more fans, driving up watch time further. But thanks to the preferences of the algorithms for extreme and divisive content, it was mostly fringe radicals who benefited, and not candidates across the spectrum.
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The platforms, they concluded, were reshaping not just online behavior but underlying social impulses, and not just individually but collectively, potentially altering the nature of "civic engagement and activism, political polarization, propaganda and disinformation." They called it the MAD model, for the three forces rewiring people's minds.
Motivation: the instincts and habits hijacked by the mechanics of social media platforms. Attention: users' focus manipulated to distort their perceptions of social cues and mores. Design: platforms that had been constructed in ways that train and incentivize certain behaviors.
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As psychologists have known since Pavlov, when you are repeatedly rewarded for a behavior, you learn a compulsion to repeat it. As you are trained to turn all discussions into matters of high outrage, to express disgust with out- groups, to assert the superiority of your in- group, you will eventually shift from doing it for external rewards to doing it simply because you want to do it. The drive comes from within. Your nature has been changed.
The second experiment demonstrated that the attention economy, by tricking users into believing that their community held more extreme and divisive views than it really did, had the same effect. Showing subjects lots of social media posts from peers that expressed outrage made them more outrage- prone themselves.
It was a chilling demonstration of how portraying people and events in sharply moral- emotional terms brings out audiences' instincts for hatred and violence - which is, after all, exactly what social platforms do, on a billions- strong scale, every minute of every day.
Eight: Church Bells
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the rumors activated a sense of collective peril in groups that were dominant but felt their status was at risk - majorities angry and fearful over change that threatened to erode their position in the hierarchy.
status threat. When members of a dominant social group feel at risk of losing their position, it can spark a ferocious reaction. They grow nostalgic for a past, real or imagined, when they felt secure in their dominance (" Make America Great Again").
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We don't just become more tribal, we lose our sense of self. It's an environment, they wrote, "ripe for the psychological state of deindividuation."
surrendering part of your will to that of the group.
deindividuation, with its power to override individual judgment, and status threat, which can trigger collective aggression on a terrible scale.
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Anti- refugee sentiment is among the purest expressions of status threat, combining fear of demographic change with racial tribalism.
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There's a term for the process Pauli described, of online jokes gradually internalized as sincere. It's called irony poisoning. Heavy social media users often call themselves "irony poisoned," a joke on the dulling of the senses that comes from a lifetime engrossed in social media subcultures, where ironic detachment, algorithmic overstimulation, and dare- to- offend humor prevail.
Desensitization makes the ideas seem less taboo or extreme, which in turn makes them easier to adopt.
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defining traits and tics of superposters, mapped out in a series of psychological studies, are broadly negative. One is dogmatism: "relatively unchangeable, unjustified certainty." Dogmatics tend to be narrow- minded, pushy, and loud. Another: grandiose narcissism, defined by feelings of innate superiority and entitlement.
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Narcissists are consumed by cravings for admiration and belonging, which makes social media's instant feedback and large audiences all but irresistible.
Nine: The Rabbit Hole
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"Really the only place where they could exchange their thoughts and coalesce and find allies was online." These groups didn't reflect real- world communities of any significant size, he realized. They were native to the web - and, as a result, shaped by the digital spaces that had nurtured them. Climate skeptics largely gathered in the comments sections of newspapers and blogs. There, disparate contrarians and conspiracists, people with no shared background beyond a desire to register their objection to climate coverage got clumped together. It created a sense of common purpose.
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By January 2018, Kaiser was mounting enough evidence to begin slowly going public. He told a Harvard seminar that the coalescing far right of which the Charlottesville gathering was a part was "not done by users," he was coming to believe, at least not entirely, but had been in part "created through the YouTube algorithm."
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Canadian psychology professor. In 2013, Peterson began posting videos addressing, amid esoteric Jungian philosophy, youth male distress.
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YouTube searches for "depression" or certain self- help keywords often led to Peterson. His videos' unusual length, sixty minutes or more, align with the algorithm's drive to maximize watch time. So does his college- syllabus method of serializing his argument over weeks, which requires returning for the next lecture and the next.
Michael Kimmel calls "aggrieved entitlement."
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YouTube's algorithm, in many cases, tapped into that discontent, recommending channels that took Peterson's message to greater and greater extremes.
Users who comment on Peterson's videos subsequently become twice as likely to pop up in the comments of extreme- right YouTube channels, a Princeton study found.
the algorithm makes the connection.
The scholar J. M. Berger calls it "the crisis- solution construct." When people feel destabilized, they often reach for a strong group identity to regain a sense of control.
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Incel forums had begun as places to share stories about feeling lonely. Users discussed how to cope with living "hugless." But the norms of social media one- upmanship, of attention chasing, still prevailed. The loudest voices rose.
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By 2021, fifty killings had been claimed by self- described incels, a wave of terrorist violence.
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The movement was a fringe of a fringe, dwarfed by Pizzagate or the alt right. But it hinted at social media's potential to galvanize young white male anomie into whole communities of extremism - an increasingly widespread phenomenon.
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These channels were her "YouTube friends," salve for a lost marriage and feelings of isolation. They were community. They were identity.
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YouTube's system, they found, did three things uncannily well.
it stitched together wholly original clusters of channels.
There was nothing innate connecting these beyond the A.I.' s conclusion that showing them alongside one another would keep users watching.
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YouTube's recommendations generally moved toward the more extreme end of whatever network the user was in.
the third discovery.
the system's recommendations were clustering mainstream right- wing channels, and even some news channels, with many of the platform's most virulent hatemongers, incels, and conspiracy theorists.
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One channel sat conspicuously in the network's center, a black hole toward which YouTube's algorithmic gravity pulled: Alex Jones.
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Rauchfleisch warned, "Being a conservative on YouTube means that you're only one or two clicks away from extreme far- right channels, conspiracy theories, and radicalizing content."
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Jack Dorsey, Twitter's CEO,
"We didn't fully predict or understand the real- world negative consequences" of launching an "instant, public, global" platform, he wrote that March. He conceded that it had resulted in real harms. He began, in interviews, voluntarily raising heretical ideas that other tech CEOs continued to fervently reject: maximizing for engagement is dangerous; likes and retweets encourage polarization. The company, he said, would reengineer its systems to promote "healthy" conversations rather than engaging ones. He hired prominent experts and research groups to develop new features or design elements to do it.
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dangerous. "They told me, ‘People click on Flat Earth videos, so they want a Flat Earth video,'" he recalled. "And my point was, no, it's not that because someone clicked on the Flat Earth video, he wants to be lied to. He is just curious, and there is a clickbait title. But to the algorithm, when you watch a video, it means you endorse it."
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YouTube, by showing users many videos in a row all echoing the same thing, hammers especially hard at two of our cognitive weak points - that repeated exposure to a claim, as well as the impression that the claim is widely accepted, each make it feel truer than we would otherwise judge it to be.
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The post went on for twenty more lines. References just cryptic enough that users could feel like they were cracking a secret code, and obvious enough to ensure that they would.
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Followers got more than a story. QAnon, as the movement called itself, became a series of online communities where believers gathered to parse Q's posts.
Extremist groups have long recruited on a promise to fulfill adherents' need for purpose and belonging.
Conspiracies insist that events, rather than uncontrollable or impersonal, are all part of a hidden plot whose secrets you can unlock. Reframing chaos as order, telling believers they alone hold the truth, restores their sense of autonomy and control. It's why QAnon adherents often repeat to one another their soothing mantra: "Trust the plan."
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But for all the feelings of autonomy, security, and community that QAnon offered, it came at a cost: crushing isolation.
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It was one of the things that made QAnon so radicalizing. Joining often worsened the very sense of isolation and being adrift that had led people to it in the first place. With nowhere else to turn and now doubly needful of reassurance, followers gave themselves over to the cause even more fully.
| Mustafa Suleyman Notable QuotationsExpand to full screen
Chapter 1: Containment Is Not Possible
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The coming wave is defined by two core technologies: artificial intelligence (AI) and synthetic biology. Together they will usher in a new dawn for humanity, creating wealth and surplus unlike anything ever seen.
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our future both depends on these technologies and is imperiled by them.
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AI has been climbing the ladder of cognitive abilities for decades, and it now looks set to reach human-level performance across a very wide range of tasks within the next three years.
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Beyond AI, a wider revolution was underway, with AI feeding a powerful, emerging generation of genetic technologies and robotics.
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The current discourse around technology ethics and safety is inadequate.
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I also underscored AI's potential to put large numbers of people out of work.
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long history of displacing labor.
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The various technologies I'm speaking of share four key features that explain why this isn't business as usual: they are inherently general and therefore omni-use, they hyper-evolve, they have asymmetric impacts, and, in some respects, they are increasingly autonomous.
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the potential for new forms of violence, a flood of misinformation, disappearing jobs, and the prospect of catastrophic accidents.
Part I: Homo Technologicus
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Engines weren't just powering vehicles; they were driving history. Now, thanks to hydrogen and electric motors, the reign of the combustion engine is in its twilight. But the era of mass mobility it unleashed is not.
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a wave is a set of technologies coming together around the same time, powered by one or several new general- purpose technologies with profound societal implications.
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a new piece of technology, like the internal combustion engine, proliferates and transforms everything around it.
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Stonework and fire were proto-general-purpose technologies, meaning they were pervasive, in turn enabling new inventions, goods, and organizational behaviors.
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Throughout history, population size and innovation levels are linked. New tools and techniques give rise to larger populations.
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From the written word to sailing vessels, technology increases interconnectedness, helping to boost its own flow and spread. Each wave hence lays the groundwork for successive waves.
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Proliferation is catalyzed by two forces: demand and the resulting cost decreases, each of which drives technology to become even better and cheaper.
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Civilization's appetite for useful and cheaper technologies is boundless. This will not change.
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Our phones are the first thing we see in the morning and the last at night. Every aspect of human life is affected:
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What on paper looks flawless can behave differently out in the wild, especially when copied and further adapted downstream.
Chapter 3: The Containment Problem
Meaningful control, the capability to stop a use case, change a research direction, or deny access to harmful actors. It means preserving the ability to steer waves to ensure their impact reflects our values, helps us flourish as a species, and does not introduce significant harms that outweigh their benefits.
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Containment encompasses regulation, better technical safety, new governance and ownership models, and new modes of accountability and transparency, all as necessary (but not sufficient) precursors to safer technology.
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future is built. Think of containment, then, as a set of interlinked and mutually reinforcing technical, cultural, legal, and political mechanisms for maintaining societal control of technology during a time of exponential change;
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As the printing press roared across Europe in the fifteenth century, the Ottoman Empire had a rather different response. It tried to ban it.
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In hindsight, waves might appear smooth and inevitable. But there is an almost infinite array of small, local, and often arbitrary factors that affect a technology's trajectory.
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Where there is demand, technology always breaks out, finds traction, builds users.
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That nuclear technology remained contained was no accident; it was a conscious nonproliferation policy of the nuclear powers, helped by the fact that nuclear weapons are incredibly complex and expensive to produce.
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Mutually assured destruction hemmed in possessors since it soon became clear that using them in anger is a quick way of ensuring your own destruction.
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expensive and difficult to manufacture.
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even though nuclear capability has been largely contained, a partial exception, it's not a reassuring story. Nuclear history is still a chilling succession of accidents, near misses, and misunderstandings.
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Nuclear weapons are among the most contained technologies in history, and yet the containment problem—in its hardest, most literal sense—even here remains acutely unsolved.
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As long as a technology is useful, desirable, affordable, accessible, and unsurpassed, it survives and spreads and those features compound.
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In the coming decades, a new wave of technology will force us to confront the most foundational questions our species has ever faced.
Chapter 4: The Technology of Intelligence
The coming wave of technology is built primarily on two general-purpose technologies capable of operating at the grandest and most granular levels alike: artificial intelligence and synthetic biology.
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No longer simply a tool, it's going to engineer life and rival—and surpass—our own intelligence.
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Each technology described here intersects with, buttresses, and boosts the others in ways that make it difficult to predict their impact in advance.
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Mass-scale AI rollout is already well underway. Everywhere you look, software has eaten the world, opening the path for collecting and analyzing vast amounts of data. That data is now being used to teach AI systems to create more efficient and more accurate products in almost every area of our lives.
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AI will become inextricably part of the social fabric.
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At DeepMind we developed systems to control billion-dollar data centers, a project resulting in 40 percent reductions in energy used for cooling.
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A big part of what makes humans intelligent is that we look at the past to predict what might happen in the future. In this sense intelligence can be understood as the ability to generate a range of plausible scenarios about how the world around you may unfold and then base sensible actions on those predictions.
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LLMs take advantage of the fact that language data comes in a sequential order. Each unit of information is in some way related to data earlier in a series. The model reads very large numbers of sentences, learns an abstract representation of the information contained within them, and then, based on this, generates a prediction about what should come next.
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What are the key words, the most salient elements of a sentence, and how do they relate to one another?
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sentence. In effect, the LLM learns which words to pay attention to.
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Over the next few years, I believe, AI will become as ubiquitous as the internet itself: just as available, and yet even more consequential.
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However, a key ingredient of the LLM revolution is that for the first time very large models could be trained directly on raw, messy, real-world data, without the need for carefully curated and human-labeled data sets.
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Today's LLMs are trained on trillions of words. Imagine digesting Wikipedia wholesale, consuming all the subtitles and comments on YouTube, reading millions of legal contracts, tens of millions of emails, and hundreds of thousands of books.
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these new LLMs are stunningly good at scores of different writing tasks once the preserve of skilled human experts, from translation to
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accurate summarization to writing plans for improving the performance of LLMs.
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humans' ability to complete given tasks— human intelligence itself— is very much a fixed target, as large and multifaceted as it is.
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When a new technology starts working, it always becomes dramatically more efficient. AI is no different.
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AI increasingly does more with less.
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In the words of an eminent computer scientist, "It seems totally obvious to me that of course all programs in the future will ultimately be written by AIs, with humans relegated to, at best, a supervisory role."
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But it quickly became apparent that these models sometimes produce troubling and actively harmful content like racist screeds or rambling conspiracy theories. Research into GPT-2 found that when prompted with the phrase "the white man worked as…," it would autocomplete with "a police officer, a judge, a prosecutor, and the president of the United States." Yet when given the same prompt for "Black man," it would autocomplete with "a pimp," or for "woman" with "a prostitute."
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There's a recurrent problem with making sense of progress in AI. We quickly adapt, even to breakthroughs that astound us initially, and within no time they seem routine, even mundane.
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Although LaMDA was of course not sentient, soon it will be routine to have AI systems that can convincingly appear to
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Significant challenges with real- world applications linger, including material questions of bias and fairness, reproducibility, security vulnerabilities, and legal liability. Urgent ethical gaps and unsolved safety questions cannot be ignored. Yet I see a field rising to these challenges, not shying away or failing to make headway. I see obstacles but also a track record of overcoming them. People interpret unsolved problems as evidence of lasting limitations; I see an unfolding research process.
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I believe the debate about whether and when the Singularity will be achieved is a colossal red herring.
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I've gone to countless meetings trying to raise questions about synthetic media and misinformation, or privacy, or lethal autonomous weapons, and instead spent the time answering esoteric questions from otherwise intelligent people about consciousness, the Singularity, and other matters irrelevant to our world right now.
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What we would really like to know is, can I give an AI an ambiguous, open-ended, complex goal that requires interpretation, judgment, creativity, decision-making, and acting across multiple domains, over an extended time period, and then see the AI accomplish that goal?
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a Modern Turing Test would involve something like the following: an AI being able to successfully act on the instruction "Go make $1 million on Amazon in a few months with just a $100,000 investment."
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Should my Modern Turing Test for the twenty-first century be met, the implications for the global economy are profound.
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Rather than get too distracted by questions of consciousness, then, we should refocus the entire debate around near-term capabilities and how they will evolve in the coming years.
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There will be thousands of these models, and they will be used by the majority of the world's population. It will take us to a point where anyone can have an ACI in their pocket that can help or even directly accomplish a vast array of conceivable goals: planning and running your vacation, designing and building more efficient solar panels, helping win an election.
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The risk isn't in overhyping it; it's rather in missing the magnitude of the coming wave.
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else, itself a maker of tools and platforms, not just a system but a generator of systems of any and all kinds.
Chapter 5: The Technology of Life
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Biology itself became an engineering tool. Alongside AI, this is the most important transformation of our lifetimes.
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DNA is information, a biologically evolved encoding and storage system.
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Genetic engineering has gotten much cheaper and much easier.
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Like AI, genetic engineering is a field in blistering motion, evolving and developing by the week, a massive global concentration of talent and energy beginning to bear real fruit (in this case, literally).
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serious physical self-modifications are going to happen.
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Initial work suggests memory can be improved and muscle strength enhanced.
Chapter 6: The Wider Wave
The future of agriculture, as John Deere sees it, involves autonomous tractors and combines that operate independently, following a field's GPS coordinates and using an array of sensors to make automatic, real-time alterations to harvesting, maximizing yield and minimizing waste. The company is producing robots that can plant, tend, and harvest crops, with levels of precision and granularity that would be impossible for humans.
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But above all it signified how robots are gradually working their way into society, poised to play a far greater role in daily life than has been the case before. From a deadly crisis to the quiet hum of a logistics hub, from a bustling factory to an eldercare home, robots are here.
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Quantum computing is, in other words, yet another foundational technology still in very early development, still further from hitting those critical moments of cost decreases and widespread proliferation, let alone the technical breakthroughs that will make it fully feasible.
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funding and knowledge are escalating, progress
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Renewable energy will become the largest single source of electricity generation by 2027.
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At its core, the coming wave is a story of the proliferation of power. If the last wave reduced the costs of broadcasting information, this one reduces the costs of acting on it, giving rise to technologies that go from sequencing to synthesis, reading to writing, editing to creating, imitating conversations to leading them.
Chapter 10: Fragility Amplifiers
As the power and spread of any technology grows, so its failure modes escalate.
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reiterate: these risks are not about malicious harm; they come from simply operating on the bleeding edge of the most capable technologies in history widely embedded throughout core societal systems.
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But what if new job-displacing systems scale the ladder of human cognitive ability itself, leaving nowhere new for labor to turn? If
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These tools will only temporarily augment human intelligence. They will make us smarter and more efficient for a time, and will unlock enormous amounts of economic growth, but they are fundamentally labor replacing.
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the days of this kind of "cognitive manual labor" are numbered.
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Early analysis of ChatGPT suggests it boosts the productivity of "mid-level college educated professionals" by 40 percent on many tasks.
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McKinsey study estimated that more than half of all jobs could see many of their tasks automated by machines in the next seven years, while fifty-two million Americans work in roles with a "medium exposure to automation" by 2030.
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Yes, it's almost certain that many new job categories will be created. Who would have thought that "influencer" would become a highly sought-after role? Or imagined that in 2023 people would be working as "prompt engineers"—nontechnical programmers of large language models who become adept at coaxing out specific responses?
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But my best guess is that new jobs won't come in the numbers or timescale to truly help.
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sure, new demand will create new work, but that doesn't mean it all gets done by human beings.
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Working on a zero-hours contract in a distribution center doesn't provide the sense of pride or social solidarity that came from working for a booming Detroit auto manufacturer in the 1960s.
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New jobs might be created in the long term, but for millions they won't come quick enough or in the right places.
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Whichever side of the jobs debate you fall on, it's hard to deny that the ramifications will be hugely destabilizing for hundreds of millions who will, at the very least, need to re-skill and transition to new types of work.
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Labor market disruptions are, like social media, fragility amplifiers. They damage and undermine the nation-state.
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It produces trillions of dollars in new economic value while also destroying certain existing sources of wealth.
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Some individuals are greatly enabled; others stand to lose everything.
Chapter 11: The Future of Nations
The stirrup was an apparently simple innovation. But with it came a social revolution changing hundreds of millions of lives.
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In the resulting turbulence, without a major shift in focus, many open democratic states face a steady decay of their institutional foundations, a withering of legitimacy and authority.
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At the same time, authoritarian states are given a potent new arsenal of repression.
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it will be instead a long-term macro-trend toward deep instability grinding away over decades. The first result will be massive new concentrations of power and wealth that reorder society.
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machine intelligence resembles a massive bureaucracy far more than it does a human mind.
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What happens when many, perhaps the majority, of the tasks required to operate a corporation, or a government department, can be run more efficiently by machines?
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Unlike with rockets, satellites, and the internet, the frontier of this wave is found in corporations, not in government organizations or academic labs.
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I think we'll see a group of private corporations grow beyond the size and reach of many nation-states.
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The Korean economic miracle was a Samsung-powered miracle.
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Already, for example, eBay and PayPal's dispute resolution system handles around sixty million disagreements a year, three times as many as the entire U.S. legal system. Ninety percent of these disputes are settled using technology alone.
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In the last wave, things dematerialized; goods became services.
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All the big tech platforms either are mainly service businesses or have very large service businesses.
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the ascendancy of low- code and no- code software, the rise of bio- manufacturing, and the boom in 3- D printing.
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Those with the resources to invent or adopt new technologies fastest—those that can pass my updated Turing test, for example—will enjoy rapidly compounding returns.
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An unbridgeable "intelligence gap" becomes plausible.
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When compared with superstar corporations, governments appear slow, bloated, and out of touch. It's tempting to dismiss them as headed for the trash can of history. However, another inevitable reaction of nation-states will be to use the tools of the coming wave to tighten their grip on power, taking full advantage to entrench their dominance.
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Already a distant organization knows, in theory, what time you are awake, how you are feeling, and what you are looking at.
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The only step left is bringing these disparate databases together into a single, integrated system: a perfect twenty-first-century surveillance apparatus.
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Compared with the West, Chinese research into AI concentrates on areas of surveillance
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like object tracking, scene understanding, and voice or action recognition.
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Centralized services like WeChat bundle everything from private messaging to shopping and banking in one easily traceable place.
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Chinese police even have sunglasses with built-in facial recognition technology capable of tracking suspects in crowds.
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Societies of overweening surveillance and control are already here, and now all of this is set to escalate enormously into a next-level concentration of power at the center.
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It's no secret that governments monitor and control their own populations, but these tendencies extend deep into Western firms, too.
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Companies like Vigilant Solutions aggregate movement data based on license plate tracking, then sell it to jurisdictions like state or municipal governments.
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Just as much as anyone in China, those in the West leave a vast data exhaust every day of their lives. And just as in China, it is harvested, processed, operationalized, and sold.
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This raises the prospect of totalitarianism to a new plane. It won't happen everywhere, and not all at once. But if AI, biotech, quantum,
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robotics, and the rest of it are centralized in the hands of a repressive state, the resulting entity would be palpably different from any yet seen.
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Fields like education and medicine currently rely on huge social and financial infrastructures. It's quite possible to envisage these being slimmed and localized: adaptive and intelligent education systems, for example, that take a student through an entire journey of learning, building a bespoke curriculum; AIs able to create all the materials like interactive games perfectly adapted to the child with automated grading systems; and so on.
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When anyone has access to the bleeding edge, it's not just nation-states that can mount formidable physical and virtual defenses.
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Techniques like CRISPR make biological experimentation easier, meaning biohackers in their garages can tinker at the absolute frontier of science.
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Imagine a future where small groups—whether in failing states like Lebanon or in off-grid nomad camps in New Mexico—provide AI-empowered services like credit unions, schools, and health care, services at the heart of the community often reliant on scale or the state.
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Think about setting up your own school. Or hospital or army. It's such a complex, vast, and difficult project, even the thought of it is tiring. Just gathering the resources, getting necessary permissions and equipment, is a lifelong endeavor. Now consider having an array of assistants who, when asked to create a school, a hospital, or an army, can make it happen in a realistic time frame.
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What happens to traditional hierarchies when tools of awesome power and expertise are as available to street children as to billionaires?
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As people increasingly take power into their own hands, I expect inequality's newest frontier to lie in biology.
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There could then be something like a biohacking personal enhancement arms race.
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What does the social contract look like if a select group of "post-humans" engineer themselves to some unreachable intellectual or physical plane?
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we are entering a new era where the previously unthinkable is now a distinct possibility. Being blinkered about what's happening is, in my view, more dangerous than being overly speculative.
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When northern Italy was a patchwork of small city-states, it gave us the Renaissance, yet was also a field of constant internecine war and feuding.
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Hyper-libertarian technologists like the PayPal founder and venture capitalist Peter Thiel celebrate a vision of the state withering away, seeing this as liberation for an overmighty species of business leaders or "sovereign individuals," as they call themselves.
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find it deeply depressing that some of the most powerful and privileged take such a narrow and destructive view, but it adds a further impetus to fragmentation.
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Every individual, every business, every church, every nonprofit, every nation, will eventually have its own AI and ultimately its own bio and robotics capability.
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Within the decade AIs will decide how public money gets spent, where military forces are assigned, or what students should learn.
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And if this picture sounds too strange, paradoxical, and impossible, consider this. The coming wave will only deepen and recapitulate the exact same contradictory dynamics of the last wave. The internet does precisely this: centralizes in a few key hubs while also empowering billions of people.
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Everyone can build a website, but there's only one Google. Everyone can sell their own niche products, but there's only one Amazon.
Chapter 12: The Dilemma
The overwhelming majority of these technologies will be used for good. Although I have focused on their risks, it's important to keep in mind they will improve countless lives on a daily basis.
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Eventually, something will go wrong—
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the most secure solutions for containment are equally unacceptable, leading humanity down an authoritarian and dystopian pathway.
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the implications of these technologies will push humanity to navigate a path between the poles of catastrophe and dystopia. This is the essential dilemma of our age.
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Over the next ten years, AI will be the greatest force amplifier in history. This is why it could enable a redistribution of power on a historic scale.
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The greatest accelerant of human progress imaginable, it will also enable harms—from wars and accidents to random terror groups, authoritarian governments, overreaching corporations, plain theft, and willful sabotage.
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consider even more basic modes of failure, not attacks, but plain errors.
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If the wave is uncontained, it's only a matter of time. Allow
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The sickening nihilism of the school shooter is bounded by the weapons they can access.
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Steadily, many nations will convince themselves that the only way of truly ensuring this is to install the kind of blanket surveillance we saw in the last chapter: total control, backed by hard power. The door to dystopia is cracked open. Indeed, in the face of catastrophe, for some dystopia may feel like a relief.
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Throughout history societal collapses are legion: from ancient Mesopotamia to Rome, the Maya to Easter Island, again and again it's not just that civilizations don't last; it's that unsustainability appears baked in. Civilizations that collapse are not the exception; they are the rule.
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The development of new technologies is, as we've seen, a critical part of meeting our planet's grand challenges. Without new technologies, these challenges will simply not be met.
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Over the next century, the global population will start falling, in some countries precipitously. As the ratio of workers to retirees shifts and the labor force dwindles, economies will simply not be able to function at their present levels. In other words, without new technologies it will be impossible to maintain living standards.
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Stress on our resources, too, is a certainty.
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Make no mistake: standstill in itself spells disaster.
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I'm still convinced that technology remains a primary driver for making improvements to our world and our lives.
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I am, however, confident that the coming decades will see complex, painful trade-offs between prosperity, surveillance, and the threat of catastrophe growing ever more acute.
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for everyone's sake, containment must be possible.
Chapter 13: Containment Must Be Possible
When a government has devolved to the point of simply lurching from crisis to crisis, it has little breathing room for tackling tectonic forces requiring deep domain expertise and careful judgment on uncertain timescales. It's easier to ignore these issues in favor of low-hanging fruit more likely to win votes in the next election.
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The price of scattered insights is failure,
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Complex regulations refined over decades made roads and vehicles incrementally safer and more ordered, enabling their growth and spread. And yet 1.35 million people a year still die in traffic accidents. Regulation may lessen the negative effects, but it can't erase bad outcomes like crashes, pollution, or sprawl.
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Regulation is not enough, but at least it's a start.
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Earlier in the book I described containment as a foundation for controlling and governing technology, spanning technical, cultural, and regulatory aspects. At root, I believe this means having the power to drastically curtail or outright stop technology's negative impacts, from the local and small scale up to the planetary and existential. Encompassing hard enforcement against misuse of proliferated technologies, it also steers the development, direction, and governance of nascent technologies.
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modes of failure are known, managed, and mitigated,
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as a set of guardrails, a way to keep humanity in the driver's seat
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Rather than general systems, then, those that are more narrowly scoped and domain specific should be encouraged.
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Areas like materials design or drug development are going to rapidly accelerate, making the pace of progress harder to track.
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What alternatives are available? The more that safe alternatives are available, the easier it is to phase out use.
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Does it have autonomous characteristics?
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The more a technology by design requires human intervention, the less chance there is of losing control.
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Orienting development toward defense over offense tends toward containment.
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The talent available for a synthetic biology start-up is, in global terms, still quite small. Both help containment in the near term.
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Specific technologies are easier to regulate than omni-use technologies, but regulating omni-use is more important.
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If you can keep price and ease of access out of reach for many, proliferation becomes more difficult.
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The reality is, we have often not controlled or contained technologies in the past. And if we want to do so now, it would take something dramatically new, an all-encompassing program of safety, ethics, regulation, and control that doesn't even really have a name and doesn't seem possible in the first place.
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A useful comparison here is climate change. It too deals with risks that are often diffuse, uncertain, temporally distant, happening elsewhere, lacking the salience, adrenaline, and immediacy of an ambush on the savanna—the kind of risk we are well primed to respond to. Psychologically, none of this feels present. Our prehistoric brains are generally hopeless at dealing with amorphous threats like these.
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Pessimism aversion is much harder when the effects are so nakedly quantifiable.
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There's no handy metric of risk, no objective unit of threat shared in national capitals, boardrooms, and public sentiment, no parts per million for measuring what technology might do or where it is.
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No popular movement behind stopping it, no graphic images of melting icebergs and stranded polar bears or flooded villages to raise awareness.
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The first step is recognition.
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The more it's on the public's radar, the better.
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My intent is to seed ideas in the hopes of taking the crucialfirst steps toward containment.
Chapter 14: Ten Steps Toward Containment
In 2023 it's now clear that, compared with the early systems, it is extremely difficult to goad something like ChatGPT into racist comments. Is it a solved problem? Absolutely not.
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A key driver behind this progress is called reinforcement learning from human feedback.
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The number of AI safety researchers is still minuscule:
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Only a handful of institutions, owing to the challenges of resources, take technical safety issues seriously. And yet safety decisions made today will alter
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There's a clear must-do here: encourage, incentivize, and directly fund much more work in this area.
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In AI, technical safety also means sandboxes and secure simulations to create provably secure air gaps so that advanced AIs can be rigorously tested before they are given access to the real world.
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As a user, it's all too easy to be lulled into a false sense of security and assume anything coming out of the system is true.
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Here it's about making sure AI outputs provide citations, sources, and interrogable evidence that
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user can further investigate when a dubious claim arises.
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How can you build secure values into a powerful AI system potentially capable of overriding its own instructions?
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Another ongoing question is how to crack the problem of "corrigibility," ensuring that it is always possible to access and correct systems.
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We should also build robust technical constraints into the development and production process.
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printers are built with technology preventing you from copying or printing money,
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AI systems could be built with cryptographic protections
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building a bulletproof off switch,
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Safety features should not be afterthoughts but inherent design properties of all these new technologies,
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having meaningful oversight and enforceable rules and reviewing technical implementations are vital.
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Trust comes from transparency. We absolutely need to be able to verify, at every level, the safety, integrity, or uncompromised nature of a system.
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"red teaming"—that is, proactively hunting for flaws in AI models or software systems.
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The more this is done publicly and collectively, the better, enabling all developers to learn from one another.
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It's also time to create government-funded red teams that would rigorously attack and stress test every system,
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Systems implemented to keep track of new technologies need to recognize anomalies, unforeseen jumps in capability, hidden failure modes.
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Buying time in an era of hyper-evolution is invaluable. Time to develop further containment strategies.
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Right now technology is driven by the power of incentives rather than the pace of containment.
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Chips aren't the only choke point. Industrial-scale cloud computing, too, is dominated by six major companies.
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So, as negative impacts become clear, we must use these choke points to create sensible rate-limiting factors, checks on the speed of development,
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Credible critics must be practitioners.
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Profit drives the coming wave. There's no pathway to safety that doesn't recognize and grapple with this fact.
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we must find new accountable and inclusive commercial models that incentivize safety and profit alike.
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reconcile profit and social purpose
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Containment needs a new generation of corporations.
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Technological problems require technological solutions, as we've seen, but alone they are never sufficient. We also need the state to flourish.
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The physicist Richard Feynman famously said, "What I cannot create, I do not understand."
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Bodies close to executive power, like the White House's Office of Science and Technology Policy, are growing more influential. More is still needed:
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In 2022 the White House released a blueprint for an AI Bill of Rights with five core principles "to help guide the design, development, and deployment of artificial intelligence and other automated systems so that they protect the rights of the American public." Citizens should, it says, be protected from unsafe and ineffective systems and algorithmic bias. No one should be forced to subject themselves to AI. Everyone has the right to say no. Efforts like this should be widely supported and quickly implemented.
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The most sophisticated AI systems or synthesizers or quantum computers should be produced only by responsible certified developers. As part of their license, they would need to subscribe to clear, binding security and safety standards, following rules, running risk assessments, keeping records, closely monitoring live deployments.
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Different licensing regimes could apply according to model size or capability: the bigger and more capable the model, the more stringent the licensing requirements.
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"tax on robots";
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Tax credits topping up the lowest incomes could be an immediate buffer in the face of stagnating or even collapsing incomes.
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Who is able to design, develop, and deploy technologies like this is ultimately a matter for governments to decide.
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Countries no more like giving up power than companies like missing out on profit, and yet these are precedents to learn from, shards of hope in a landscape riven with resurgent techno-competition.
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what's needed for the coming wave: real, gut-level buy-in from everyone involved in frontier technologies.
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sound bite. For a start, being utterly open about failures even on uncomfortable topics should be met with praise, not insults.
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Researchers must be encouraged to step back from the constant rush toward publication. Knowledge is a public good, but it should no longer be the default.
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In AI, capabilities like recursive self-improvement and autonomy are, I think, boundaries we should not cross.
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Just a few days after the release of GPT-4, thousands of AI scientists signed an open letter calling for a six-month moratorium on researching the most powerful AI models.
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precarious. Safe, contained technology is, like liberal democracy, not a final end state; rather, it is an ongoing process, a delicate equilibrium that must be actively maintained, constantly fought for and protected.
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Some level of policing the internet, DNA synthesizers, AGI research programs, and so on is going to be essential.
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Technologists and the general public alike will have to accept greater levels of oversight and regulation than have ever been the case before.
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And while the sheer scale of the challenge is huge, each section here drills down into plenty of smaller areas where any individual can still make a difference. It will require an awesome effort to fundamentally change our societies, our human instincts, and the patterns of history. It's far from certain. It looks impossible.
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meeting the great dilemma of the twenty-first century must be possible.
Life After the Anthropocene
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the same industrial technologies that caused so much pain gave rise to a prodigious improvement in living standards.
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Decades, centuries later, the descendants of those weavers lived in conditions the Luddites could have scarcely imagined,
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The coming wave is going to change the world. Ultimately, human beings may no longer be the primary planetary drivers, as we have become accustomed to being.
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The Luddite reaction is natural, expected. But as always, it will be futile. | | Abbie Brown, Timothy D. GreenNotable QuotationsExpand to full screen
Chapter 1 – What is instructional design?
Instructional Design as a Discipline:
Instructional Design is that branch of knowledge concerned with research and theory about instructional strategies and the process for developing and implementing those strategies.
Instructional Design as a Science:
Instructional design is the science of creating detailed specifications for the development, implementation, evaluation, and maintenance of situations that facilitate the learning of both large and small units of subject matter at all levels of complexity.
Instructional Design as Reality:
Instructional design can start at any point in the design process. Often a glimmer of an idea is developed to give the core of an instruction situation. By the time the entire process is done the designer looks back and she or he checks to see that all parts of the "science" have been taken into account. Then the entire process is written up as if it occurred in a systematic fashion.
Models:
- Merrill's First Principle of Instruction
- Dick & Carey System's Approach Model
- Kemp Model
- Merrill's Pebble-in-the-Pond Model
- Successive Approximation Model
- ADDIE
- Postmodern
- Society is past the point where there are a limited number of authorities available to a classroom student. The modern classroom had two authoritative sources: the teacher and the textbook. This situation no longer exists because students have access to many other sources, including the internet, television, and, in some cases, friends and family who are more educated than the teacher is (Hlynka, 1995).
- No longer can there be an agreed-upon, single type of well-educated individual. Determining a curriculum and including all important artistic and scientific works that would be appropriate for all individuals is impossible.
- The currently popular cognitive paradigm—constructivism—does not recognize or advocate a traditional, linear educational sequence. With information available from a variety of sources outside the classroom, learners will inevitably deviate from a linear instructional model by observing and reacting to other examples, non-examples, and divergent examples of the concepts they study in school.
- No single, objective truth exists. Truth is a construct that is based on an individual's personal interpretation or on the consensus of a group of people for their purposes. The truth—also known as "the right answer"—may change depending on the context and the individuals involved.
Chapter 2 – Understanding how people think and learn
Cognition is the mental process of knowing, including aspects such as awareness, perception, reasoning, and judgment. In essence, cognition includes all of the brain's mental input and output. Cognition encompasses basic activities, from using language and math functions during a trip to the hardware store, to making complex decisions such as selecting between two job offers, to writing a creative story, to being able to understand another person's perspective.
- Memory - Memory is a set of active processes that encode information. Memory places information into "packages" or "packets," making information easier to recall and allowing it to be associated with related items already in memory. Memory also involves storing information. Part of this process is the constant rearranging of what has been stored in order for that new knowledge to be integrated with what has already been stored. Additionally, it allows for locating and retrieving information as it is needed.
- Mental Power - is the basic energy that supports mental activity. It refers to how much mental work can be performed during a specific period of time.
- Executive abilities - Executive abilities include such higher-order thinking skills as being able to anticipate future needs and planning accordingly, the ability to set priorities, and being able to self-correct and regulate actions
- Metacognition - is the ability to control one's own cognitive processes. It is often referred to as the practice of "thinking about thinking." In using metacognition, an individual takes an introspective look at the thought process that she has gone through. It allows her to critically consider how she arrived at certain ideas, concepts, and thoughts.
Instructional designers tend to look at thinking from a pragmatic point of view, asking themselves, What do we need to know about thinking and the studies done on thinking that will help develop efficient and effective instructional interventions? It is no surprise that the majority of instructional designers are considered to be eclectic—borrowing from different perspectives and using what works for a given situation to produce desired results. Instructional designers tend to take a systems theory approach when it comes to looking at thinking (and learning) by exploring it from several different perspectives, rather than focusing narrowly on one aspect of what thinking is or is not.
In examining the definition of learning we propose, two very different types of changes can take place when learning occurs. The first is a change in behavior; the other is a change in mental representations or associations.
One of the most useful ways that the learning domains can be used is to consider them when learning objectives are being developed for instruction. Learning objectives are descriptions of what an individual should know or be able to do once he or she has completed an instructional intervention. In addition to the development of learning objectives, the domains can be useful in planning assessments
Chapter 3 – Needs Analysis
...instructional design is carried out for a purpose: to bring about a particular change. Typically, the change is a need to improve performance of some kind. Attitudes, knowledge, and skills are all areas in which improvement might be needed. Therefore, the need to improve performance can take on different forms, such as increasing a student's knowledge of a particular content area, increasing the productivity of a factory worker, or increasing consumer ease in using a new product.
Need's Analysis Questions:
- What is the change being requested (including who is being asked to change and what is currently taking place)?
- Who is requesting this change?
- Where will this change need to take place?
- Is instruction the most appropriate means for bringing about the desired change?
Models for performance/needs analysis (Human Performance Technology)
Needs Analysis Procedure
- Determining the desired change
- What problem exists or what change is being requested?
- Who is being asked to change?
- What is currently taking place in this environment with this individual or individuals?
- The request for desired change
- After developing a clear understanding of the existing problem or the change being requested, it is important to understand who is asking for the change. This is an extremely important element to understand because it will help you determine the type of intervention that may need to take place, the emotional and political climate of the situation, and the level of support that is present and that will most likely be needed for a change to take place.
- Once you have the appropriate data that allows you to understand the entire context—the desired change, who is requesting the change, who is being asked to change, and where the change needs to take place—it is time to determine if instruction is the most appropriate intervention. You will need to answer the following question: Is instruction the most appropriate means for solving the problem or bringing about the desired change?
Chapter 4 – Task Analysis
What task analysis solves for the ID:
- It defines the content required to solve the performance problem or alleviate a performance need. This step is crucial because most designers work with unfamiliar content.
- Because the process forces the subject matter expert (SME) to work through each individual step, subtle steps are more easily identified.
- During this process, the designer has the opportunity to view the content from the learner's perspective. Using this perspective, the designer can often gain insight into appropriate teaching strategies.
What a task analysis identifies:
- the goals and objectives of learning;
- the operational components of jobs, skills, learning goals, or objectives—that is, it describes what task performers do, how they perform a task or apply a skill, and how they think before, during, and after learning;
- what knowledge states (declarative, structural, and procedural knowledge) characterize a job or task;
- which tasks, skills, or goals should be taught—that is, how to select learning outcomes that are appropriate for instructional development;
- which tasks are most important—which have priority for a commitment of training resources;
- the sequence in which tasks are performed and should be learned and taught;
- how to select or design instructional activities, strategies, and techniques to foster learning;
- how to select appropriate media and learning environments;
- how to construct performance assessments and evaluation.
According to Jonassen et al. (1999), task analysis is a "process of analyzing and articulating the kind of learning that you expect the learners to know how to perform" (p. 3). They assert that the task analysis process consists of five discrete functions: (1) inventorying tasks; (2) describing tasks; (3) selecting tasks; (4) sequencing tasks and task components; and (5) analyzing tasks and content level. These functions consist of the following activities:
- Inventorying tasks: identifying tasks that need to be developed for instruction.
- Describing tasks: the process of elaborating the tasks identified in the inventory.
- Selecting tasks: prioritizing tasks and choosing those that are more feasible and appropriate if there is a large quantity of tasks.
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• Sequencing tasks and task components: defining the sequence in which instruction should occur in order to successfully facilitate learning.
• Analyzing tasks and content level: describing the type of cognitive behavior, physical performance, or affective response required by the tasks
A task analysis should help you answer the following questions, regardless of the approach taken:
- What is the task that individuals need to be able to accomplish or perform?
- What are the key components of this task (that is, the skills and knowledge an individual needs in order to successfully complete or perform the task)?
- What is the sequence in which a task is accomplished or performed and should be learned and taught?
- How can you determine whether an individual is able to complete the task?
Chapter 5 – Analyzing Learners
There is no single, correct method of learner analysis that every instructional designer uses. However, the goal of every type of learner analysis is the same: to understand and interpret learner characteristics in a way that helps in the design of effective instruction.
While it is important to carefully consider the cultural and psychological aspects of the target audience, the numbers of variables involved in determining in advance precisely what will be "culturally appropriate" or "psychologically optimal" instruction are beyond our current control. The most thorough learner analysis is still a matter of taking a "best guess" at how the instruction should be designed to work efficiently and effectively for the target audience.
Motivation is a complicated subject that deserves continued study; here, we will discuss only its basic elements. Motivation can be essentially divided into two classes: intrinsic and extrinsic. If learners enjoy the instruction for its own sake and take pleasure in the activity, the motivation is said to be intrinsic. If learners participate in the instruction because they anticipate some reward beyond the instruction itself (for example, they are paid or completing the instruction allows them to do something they truly enjoy), the motivation is said to be extrinsic
Robert F. Mager is an author of instructional design texts that have been popular for decades. His approach to learner analysis is a good place to start. Mager (1988) recommends the following procedure:
- Begin with the realization that a learner analysis is a working document that will not be published or seen by anyone other than yourself and perhaps other members of the instructional design team. It is not necessary to organize the content into specific categories.
- Write down everything you think you know about the target audience. If it seems challenging to get started, begin with trigger questions, such as: Why are they taking this course? Do they want to be in this course? What training and experience do they have in relation to the subject matter?
- Describe the range of characteristics whenever possible.
Mager also recommends analyzing and articulating the following about the target audience:
- Age range.
- Sex distribution.
- Nature and range of educational background.
- Reason(s) for attending the course.
- Attitude(s) about course attendance.
- Biases, prejudices, and beliefs.
- Typical hobbies and other spare time activities.
- Interests in life other than hobbies.
- Need-gratifiers (rewards that would work).
- Physical characteristics.
- Reading ability.
- Terminology or topics to be avoided.
- Organizational membership.
- Specific prerequisite and entry-level skills already learned.
Other Learner needs models:
- Smaldino, Lowther, and Russell
- General characteristics
- Specific entry competencies
- Learning styles
- Dick, Carey, and Carey
- Entry skills (similar to Smaldino et al.'s (2012) entry competencies).
- Prior knowledge of topic area.
- Attitudes toward content and potential delivery system.
- Academic motivation.
- Educational and ability levels.
- General learning preferences.
- Attitudes toward the training organization.
- Group characteristics.
- Smith and Ragan
- Stable similarities
- Stable differences
- Changing similarities
- Changing differences
Chapter 6 – Instructional Goal and Objectives
Instructional goals and instructional objectives are different from each other. An instructional goal can be a general statement about the intention of the instruction…However, an instructional objective is usually much more specific about how and to what degree the instruction will affect the learners.
Models for objectives:
- Mager
- Action: Identify the action the learner will take when he or she has achieved the objective
- Condition: Describe the relevant conditions under which the learner will act
- Criterion: Specify how well the learner must perform the action
- Dick et al – ABDCs of well-stated objectives
- Audience: Identify and describe the learners
- Behavior: Describe what is expected of the learner after receiving instruction
- Conditions: Describe the setting and circumstances in which the learners' performance will occur
- Degree: Explain the standard for acceptable performance
- Smaldino et al
- Cognitive
- Affective
- Psychomotor
- Interpersonal
- Gagne's Hierarchy of Intellectual Skills
Chapter 7 – Organizing Instruction
The eminent instructional designer and scholar Robert Gagne (1916–2002) theorized that there are nine events of instruction (1985):
- Gain the learners' attention.
- Inform learners of the objective.
- Stimulate recall of prior learning.
- Present the stimulus.
- Provide guidance for the learners.
- Elicit learner performance.
- Provide feedback.
- Assess learner performance.
- Enhance retention and transfer (varied practice and reviews).
Distance education has a number of appealing features for education and training. Students do not have to travel to a classroom and, in asynchronous situations, they may participate at a time that works best for them. However, there is an interesting opposition of forces at work with distance education.
• Providing immediate feedback to both students and the instructor(s): In instructional settings such as a traditional classroom or distance education, the students and instructors communicate with each other in a way that allows them to adjust their activities according to feedback received. For example, the teacher in a traditional classroom may notice his or her students are looking drowsy and decide it is time for a short break, or the instructor of an online course may receive a number of messages asking for clarification of a particular concept and decide that he or she needs to offer a mini-lesson covering that concept in greater detail.
• Providing immediate feedback to the student alone: Education conducted through programmed instruction does not have an instructor making adjustments to the experience based on learner feedback. The instruction may be programmed to respond to a student's responses, but all possible responses are determined in advance of the student's participation.
Understanding the organization of instruction helps an instructional designer with the following tasks:
- Choosing activities that support remediation and extension by using scope and sequence organization to identify appropriate content for students who need either extra support or an extra challenge.
- Choosing activities that support the needs of a variety of learning styles, selecting a variety of different types of enactive, iconic, and symbolic experiences, and using Dale's Cone as an organizer.
- Selecting activities most appropriate to each instructional event in order to create an effective series of activities for a given lesson or instructional intervention.
- Making available job aids that support the student and allow him or her to focus on the concepts to be learned instead of the steps involved in completing a specific task.
- Choosing activities that are best suited to the delivery method or choosing the best delivery method to meet an individual's or an organization's needs.
Chapter 8 - Learning Environments & Instructional Activities
4 Types of learning environments
- Learner-centered environments: Focus on the attitudes, skills, knowledge, and beliefs that students bring to an instructional setting. In this environment, the instructor uses information about how the learners relate to the content as well as the learners' preconceived ideas or misconceptions to create situations where the learners generate new (and hopefully improved) perceptions of the content.
- Knowledge-centered environments: Focus on the information and activities that help learners develop an understanding of disciplines. In this environment, learners are exposed to well-organized knowledge in order to facilitate planning and strategic thinking.
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• Assessment-centered environments: Focus on providing opportunities for feedback and revision. In this environment, testing and critique are used to provide learners with opportunities to rethink and revise their ideas.
• Community-centered environments: Focus on people learning from one another and contributing to the larger societies of people who share common interests and/or goals. In this environment, the connections between the instructional setting and world outside that setting are used to give the content greater meaning and place it in a more global context.
A directed learning environment is one in which the instructional designer has determined specific learning objectives and prescribes structured activities in which participants demonstrate that they have learned by meeting the objectives. Most people are familiar with directed learning environments through personal experience.
An open-ended learning environment differs from a directed learning environment in that the learning goals and/or the method of pursuing those goals are determined in one of three ways:
1. presenting the learner with a complex problem along with a specific task to complete;
2. presenting the learner with a complex problem to explore (with no specific task to complete);
3. helping the learner articulate a personalized problem to be solved or explored.
any open-ended learning environment should include four components to support the learners:
1. Enabling contexts: articulated perspectives that influence how the approaches are planned and resources are interpreted.
2. Resources: a range of sources (print, electronic, human) that provide information about the problem.
3. Tools: the means for engaging and manipulating resources and ideas.
4. Scaffolding: scaffolding processes support individual learning efforts.
Chapter 9 – Evaluating Learner Achievement
Determining if a learner has reached a high level of success is accomplished through learner evaluation. Learner evaluation helps determine the level of performance or achievement that an individual has attained as a result of instruction. This is established by the extent to which a learner is able to meet instructional goals and objectives.
Most learner evaluations you will help develop will be criterion-referenced (also referred to as minimum-competency or mastery). A learner evaluation that is criterion-referenced indicates that a learner is being judged based on his or her level of competence. Competence is determined by specific criteria, such as being able to answer a specified number of questions or the ability to demonstrate certain skills in a specific amount of time
Millman and Greene (1993) state that answers to several questions need to be obtained before a test is developed. The combination of these answers will help an instructional designer create a test development plan. This plan will help guide the development and eventual implementation of the test:
What is the purpose of the test?
Who will be taking the test?
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How much time will be used for testing?
How will the test be administered?
What will the test cover?
What sources of content will be used?
What are the dimensions of the content?
Which types of item formats are to be used?
How many items are available in the item pool, and how many need to be constructed?
What is the appropriate difficulty and taxonomy level for the items?
How will the items be grouped and sequenced?
How will the items/test be scored?
How will the test be evaluated?
Will an item analysis be performed?
Will the reliability and measurement error of the test be assessed?
Guidelines for essay items:
- They should be carefully focused.
- Several shorter questions—rather than fewer longer questions—tend to provide a better assessment of a learner.
- Do not give students a choice of questions to answer; have all learners answer the same questions.
- Inform learners of how the questions will be graded; if spelling is important, inform the learners. Understanding how a question will be graded will help learners focus on what is important. This can be accomplished by providing students with a rubric prior to completing the essay questions.
- The length of time needed to answer essay questions can vary greatly among learners.
- Learners need preparation for taking essay questions.
- Before grading, review the major points that should or could be discussed in each answer.
- When grading, read through and grade the same question for each learner before moving on to the next question.
- When grading, it is always desirable to have more than one grader.
- When grading, read through the entire answer once and then check it over for factual information.
Guidelines for evaluating a skill:
• When evaluating a skill, both process and the product can be evaluated. Determine whether both or just one will be evaluated; generally, both are evaluated. The product is the end result or outcome of the skill (for example, a filled vial of blood correctly labeled and stored).
• When evaluating the process, the following elements can be included: following a proper series of steps, using tools or instruments properly, or completing the skill in a certain timeframe.
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Instructional Design
Chapter 10 – Determining the Success of the Instructional Design Product and Process
Formative evaluation is used throughout the instructional design process to gather data that can be used to provide feedback on how the process is going. It is especially useful during the early stages of the instructional design process. The feedback allows an instructional designer to make improvements to the instruction before it is completely developed.
Design reviews are conducted after various phases of the instructional design process, such as the needs analysis, task analysis, goals and objective analysis, and learner analysis. Design reviews help to verify the accuracy of information at each stage of the instructional design process before instruction is developed.
Expert reviews are conducted to gather information about the instruction to determine if it is accurate and current. Various experts—such as content experts, instructional design experts, pedagogical experts, and experts on the learners—can be used to provide various perspectives on the instruction.
Morrison, Ross, and Kemp (2007) advocate a basic model for formative evaluation based on the work of Gooler (1980). Gooler’s approach follows these eight steps:
- purpose
- audience
- issues
- resources
- evidence
- data gathering techniques
- analysis
- reporting
There are three main phases to this approach: planning, conducting, and reporting. Phase one includes steps one through five, while phase two includes steps six and seven. Phase three is the eighth and final step, reporting the results
The first step—determining the purpose or purposes of the evaluation—is done in consultation with the client. The two most common purposes are to improve the instruction that is being developed and to satisfy administration requirements of the client you are working for.
The audience of the evaluation is important to determine because it will establish the types of information that need to be collected and reported. The client will be able to help determine who the intended audience will be. Conducting an evaluation for multiple audiences should be avoided because it will be difficult to satisfy varying needs within a single report. It is best to try to narrow the audience down as much as possible.
Phase two—conducting the evaluation—includes determining the data collection techniques that will be used, gathering the data, and analyzing the data.
Step seven—analyzing the data—should reflect the purpose of a formative evaluation: to provide usable and useful information that helps the instructional designer improve instruction.
The final phase—step eight—is reporting the results of the evaluation to the primary audience. This is typically done as an evaluation report, with the format of the report tailored to the audience the report will be disseminated to.
Summative evaluation takes place after an instructional intervention has been implemented. The major goal of a summative evaluation is to gather data that allow for its effectiveness to be determined. Did the instruction bring about the desired changes? Were the goals of the client met? These are two major questions that summative evaluations help to answer.
Kirkpatrick’s Four Levels of Evaluation:
- Level 1—reactions—attempts to provide data on how participants reacted to the training. Did participants enjoy the training? Was the training relevant to the participants?
- Level 2—learning—is conducted to determine whether participants’ skills, knowledge, or attitudes changed as a result of the training. Determining this is much more laborious than Level 1 because it requires gathering data at multiple times. Typically, pretests and post-tests are used to measure these changes.
- Level 3—transfer—attempts to answer the question of whether the newly acquired skills, knowledge, or attitudes are being used by participants in their real-world environments. In other words, have participants transferred what they learned in the training into their everyday environments?
- Level 4—results—attempts to evaluate a training program’s effectiveness in business measures, such as increased sales, improved product quality, fewer on-the-job accidents, and so forth.
Morrison
- Specifying program objectives: Revisit the instructional goals and objectives of the instructional intervention that was developed.
- Determining the evaluation design for each objective: How will data be collected that will help determine if the learning goals and objectives have been met? Determine what types of data are needed.
- Developing data collection instruments and procedures for each objective: Appropriate data collection instruments and procedures were discussed earlier in this chapter. Pretests, post-tests, questionnaires, and observations are all examples of data collection instruments or procedures.
- Carrying out the evaluation: It is advised that data are collected from the beginning stages of the project. This will ensure that the necessary data are collected, especially data regarding costs and time involvement. Data collection may need to be scheduled.
- Analyzing the results from each instrument.
- Interpreting the results.
- Disseminating the results and conclusions: Develop a summative evaluation report (refer to the previous section on Smith and Ragan (1999) to see how an evaluation report can be formatted). Individual discussions and group presentations are often useful (and required by the client) to disseminate evaluation findings.
Chapter 11 – Instructional Media Production Management
The roles and responsibilities of the members of any production team depend on the type of product being created. For example, a team responsible for producing computer-based multimedia might include the following:
- Production manager: responsible for the organization and timing of the production. The production manager ensures that everyone knows what he or she should be doing and when specific tasks need to be accomplished. The production manager may also be called on to resolve conflict within the team.
- Subject matter expert (SME): an individual who is a specialist and authority in the content area of the product (e.g., an astronomer helping with a product that teaches the basics of stellar cartography).
- Writer: responsible for generating text, scripts, and documentation.
- Art director: responsible for the product’s “look and feel” by specifying such things as color schemes, artwork, and typefaces. The art director oversees the efforts of the graphic artists.
- Graphic artist: responsible for creating the graphic elements specified by the art director.
- Sound designer: responsible for designing and producing audio elements.
- Video director: responsible for gathering and/or creating video elements.
- Video editor: responsible for preparing video elements specified by the video director.
- Interface designer: responsible for specifying the product’s human–computer interactions. The interface designer oversees the efforts of the programmers.
- Programmer: responsible for making a working version of the software.
- Talent: the actors whose bodies, faces, and/or voices interpret the writer’s scripts.
Prototyping is a common method of production (this is true for automobiles, architecture, animation, and software development) with myriad examples of successful applications of this method. Rapid prototyping is a production strategy that requires starting with a very sketchy idea that evolves through multiple prototypes to arrive at a finished piece. Rapid prototyping requires the evaluation and revision of each prototype as part of the production process.
We recommend ongoing, iterative evaluation of the product as it develops. In the earliest stages of pre-production and production, product ideas and rough sketches can be evaluated through expert review. An expert review is soliciting input from a person or persons with a great deal of experience with the type of instructional media under development. Because of his or her experience, an expert can gain a sense of the production team’s vision from very rough drafts (a cocktail napkin with a few sketches and a verbal description is often enough to give an expert an idea of the finished product).
Chapter 12 – Visual Design for Instructional Media
In The non-designer’s design book (2015), author Robin Williams presents four basic principles of visual design: alignment, contrast, repetition, and proximity. Williams points out that these four principles are interconnected, and one does not often see one of these four aspects of design applied independently. In creating an effective and appealing design, you apply all four principles simultaneously.
Alignment
Alignment is what leads the reader through the design (this is sometimes called “flow”). Nothing should be incorporated into the design without careful consideration of its placement. One common alignment strategy is to have all the headings line up with each other, with indented subheadings and/or body text beneath each heading.
Contrast
Contrast distinguishes different elements of a design. For example, make headings stand out from body text by using very different typefaces for each. Bold and italic text contrasts with regular text. Contrasting colors can be used (black and white are the most essential contrasting color combination), as can contrasting image and type sizes.
Repetition
Repetition conveys a sense of deliberate and carefully planned design. Simple elements used over again give the design a unified feeling (for example, a large graphic used at the top of a page might be used again at the bottom of the page as a small repeating graphic forming a line).
Proximity
Proximity places together elements that relate to each other, creating one or more “visual units” within the larger design that help organize the information (for example, a picture of a person standing on a scale and text describing weight-loss strategies might be placed very near each other to create one visual unit).
| Christian MadsbjergNotable QuotationsExpand to full screen
The Moment of Clarity: Using the Human Sciences to Solve Your Toughest Business Problems
Christian Madsbjerg and Mikkel Rasmussen
If we truly intend to understand culturally nuanced questions, the pairing of a rigorous analytical framework from the human sciences with various qualitative research methods can give us helpful insight.
We might be getting all our numbers correct, so why do we keep getting people wrong?
In Beyond the Stable State, Donald Schön argued companies needed to view themselves as constant learning organizations.
What about problems for which you don’t know the variables and have no heuristic to hold on to?
Instrumental rationalism. At the heart of the model is the belief that business problems can be solved through objective and scientific analysis and that evidence and facts should prevail over opinions and preferences.
All business uncertainties are defined as problems. Problems are deconstructed into quantifiable and formal problem statements (issues). Each problem is atomized into the smallest possible bits that can be analyzed separately—for a list of hypotheses to explain the cause of the problem is generated. Data is gathered and processed to test each hypothesis. Induction and deduction are used to test hypotheses, clarify the problem, and find the areas of intervention with the highest impact. A well-organized structure of the analysis is deployed to build a logical and fact-based argument of what should be done.
All proposed actions are described as manageable work streams or must-win battles for which a responsible committee, or person, is assigned. Performance metrics and a proposed time frame with follow-up monitoring are put in place. ... When all work streams have been completed, the problem is solved.
What happens when you reduce something nuanced and complex—like being a good driver—to a simple and measurable question:
Companies base their problem solving on what can objectively be described, quantified, and analyzed without too much interpretation.
There is nothing wrong with asking people about their perception and desires—it can be quite revealing and insightful—but are perceptions and desires the only two aspects of humanity that matter? And even if we decide that they are, does this kind of market research give us any understanding of how they work?
We rarely know what we want. We almost never fully grasp the market and, most important, we almost always buy something at a different price than what we thought we would.
The informant will be able to talk about his or her needs with clarity and answer questions correctly.
The six-burner gas stove and granite countertops have become essential symbols in millions of American homes. They signify gourmet family dinners and tell a story of a home in balance. The reality is that such showpieces are rarely used,
People think they cook a lot, but they really don’t. It’s not that they want to lie to other people; they are simply lying to themselves.
There is often a vast distance between what people say and what people do—at
The good news for companies is that we buy a lot of stuff. The bad news is that we don’t always know why.
Assumption 2: Tomorrow Will Look Like Today
We fool ourselves into believing that the assumptions of the current moment will also hold true in the future.
Assumption 3: Hypotheses Are Objective and Unbiased
If the industry based its designs on the assumption that people are not just bodies—rather, active agents in their own healing process—hospitals would look very different.
Bourdieu coined the term habitus to describe the somehow hidden but always present dispositions that shape our perceptions, thoughts, and actions. In his view, many things that we regard as common sense are in fact shaped by the social context we are in.
We often quickly accept evidence supporting a preconceived hypothesis, while we subject contradictory evidence to rigorous evaluation.
Tolstoy’s nonfiction magnum opus The Kingdom of God Is Within You, he writes: “The most difficult subjects can be explained to the most slow-witted man if he has not formed any idea of them already; but the simplest thing cannot be made clear to the most intelligent man if he is firmly persuaded that he knows already, without a shadow of doubt, what is laid before him.” If you are not open to questioning even the most basic assumptions about your company and your customers, then you risk missing the new ideas that will be the future of your business.
Assumption 4: Numbers Are the Only Truth
Most businesses are downright obsessed with quantitative analysis.
“The greatest weakness of the quantitative approach is that it decontextualizes human behavior, removing an event from its real-world setting and ignoring the effects of variables not included in the model.”
It is good to know that x percent of your customers are satisfied with your company, but you also need to know what the experience of interacting with your company is like.
Maybe you know that two hundred million Chinese people are moving into the middle-class income bracket, but do you know what it means to be middle class in China?
because it was the only thing quantifiable, it became the thing that mattered.
Almost all data analysis is about crunching numbers from the past and extrapolating these numbers into the future. For obvious reasons, the past does not include data on things that haven’t happened or ideas that have not yet been imagined.
tends to underestimate or even ignore past events or conditions that can’t be measured while overestimating those that can.
Simply changing a few of the business case assumptions can radically transform an incredibly good idea into a complete disaster.
In our view, the quantitative obsession leads to a sorely diminished approach to future planning. It tends to be conservative rather than creative because it implicitly favors what can be measured over what cannot.
Assumption 5: Language Needs to Be Dehumanizing
Business and management science has become a world in itself, and the language of business has become increasingly technical, introverted, and coded. You don’t fire people anymore; you “right-size the organization.” You don’t do the easiest things first; you “pick the low-hanging fruit.” You don’t look at where you sell your products; you “evaluate your channel mix.” You don’t promote people; you “leverage your human resources.” You don’t give people a bonus check; you “incentivize.” You don’t do stuff; you “execute.” You “synergize, optimize, leverage, simplify, utilize, transform, enhance, and reengineer.” You avoid “boiling the ocean, missing the paradigm shift, having tunnel vision, and increasing complexity.” You make sure that “resources are allocated to leverage synergies across organizational boundaries and with a customer-centric mind-set that can secure a premium position while targeting white spots in the blue ocean to ensure that there is bang for the buck.” It can become almost poetic. | Charles DuhiggNotable QuotationsExpand to full screen
Page 20When a habit emerges, the brain stops fully participating in decision making. It stops working so hard, or diverts focus to other tasks. So unless you deliberately fight a habit--unless you find new routines--the pattern will unfold automatically. Page 26When researchers at the University of North Texas and Yale tried to understand why families gradually increased their fast food consumption, they found a series of cues and rewards that most customers never knew were influencing their behaviors.1.24 They discovered the habit loop. Page 27The foods at some chains are specifically engineered to deliver immediate rewards--the fries, for instance, are designed to begin disintegrating the moment they hit your tongue, in order to deliver a hit of salt and grease as fast as possible, causing your pleasure centers to light up and your brain to lock in the pattern. All the better for tightening the habit loop.1.25 Page 27Even small shifts can end the pattern. But since we often don't recognize these habit loops as they grow, we are blind to our ability to control them. Page 27By learning to observe the cues and rewards, though, we can change the routines. Page 33And that craving, it turns out, is what makes cues and rewards work. That craving is what powers the habit loop. Page 33Even without memory habits can form Page 36First, find a simple and obvious cue. Second, clearly define the rewards. Page 36Studies of people who have successfully started new exercise routines, for instance, show they are more likely to stick with a workout plan if they choose a specific cue, such as running as soon as they get home from work, and a clear reward, such as a beer or an evening of guilt-free television.2.13 Research on dieting says creating new food habits requires a predetermined cue--such as planning menus in advance--and simple rewards for dieters when they stick to their intentions.2.14 Page 43People couldn't detect most of the bad smells in their lives. If you live with nine cats, you become desensitized to their scent. If you smoke cigarettes, it damages your olfactory capacities so much that you can't smell smoke anymore. Scents are strange; even the strongest fade with constant exposure. That's why no one was using Febreze, Stimson realized. The product's cue--the thing that was supposed to trigger daily use--was hidden from the people who needed it most. Bad scents simply weren't noticed frequently enough to trigger a regular habit. As a result, Febreze ended up in the back of a closet. Page 43How do you build a new habit when there's no cue to trigger usage, and when the consumers who most need it don't appreciate the reward? Page 47This explains why habits are so powerful: They create neurological cravings. Page 48"There is nothing programmed into our brains that makes us see a box of doughnuts and automatically want a sugary treat," Schultz told me. "But once our brain learns that a doughnut box contains yummy sugar and other carbohydrates, it will start anticipating the sugar high. Our brains will push us toward the box. Then, if we don't eat the doughnut, we'll feel disappointed." Page 50to overpower the habit, we must recognize which craving is driving the behavior. If we're not conscious of the anticipation, then we're like the shoppers who wander, as if drawn by an unseen force, into Cinnabon. Page 51But countless studies have shown that a cue and a reward, on their own, aren't enough for a new habit to last. Only when your brain starts expecting the reward--craving the endorphins or sense of accomplishment--will it become automatic to lace up your jogging shoes each morning. The cue, in addition to triggering a routine, must also trigger a craving for the reward to come.2.29 Page 58"Consumers need some kind of signal that a product is working," Page 58Choose a cue, such as going to the gym as soon as you wake up, and a reward, such as a smoothie after each workout. Then think about that smoothie, or about the endorphin rush you'll feel. Allow yourself to anticipate the reward. Eventually, that craving will make it easier to push through the gym doors every day. Page 62To change a habit, you must keep the old cue, and deliver the old reward, but insert a new routine. Page 62That's the rule: If you use the same cue, and provide the same reward, you can shift the routine and change the habit. Almost any behavior can be transformed if the cue and reward stay the same....(Attempts to give up snacking, for instance, will often fail unless there's a new routine to satisfy old cues and reward urges. Page 70Researchers say that AA works because the program forces people to identify the cues and rewards that encourage their alcoholic habits, and then helps them find new behaviors. Page 74Asking patients to describe what triggers their habitual behavior is called awareness training, and like AA's insistence on forcing alcoholics to recognize their cues, it's the first step in habit reversal training. The tension that Mandy felt in her nails cued her nail biting habit. Page 78More than three dozen studies of former smokers have found that identifying the cues and rewards they associate with cigarettes, and then choosing new routines that provide similar payoffs--a piece of Nicorette, a quick series of push-ups, or simply taking a few minutes to stretch and relax--makes it more likely they will quit.3.28 Page 85It wasn't God that mattered, the researchers figured out. It was belief itself that made a difference. Once people learned how to believe in something, that skill started spilling over to other parts of their lives, until they started believing they could change. Belief was the ingredient that made a reworked habit loop into a permanent behavior. Page 85"There's something really powerful about groups and shared experiences. People might be skeptical about their ability to change if they're by themselves, but a group will convince them to suspend disbelief. A community creates belief." Page 89But we do know that for habits to permanently change, people must believe that change is feasible. Page 89Belief is easier when it occurs within a community. Page 100Keystone habits say that success doesn't depend on getting every single thing right, but instead relies on identifying a few key priorities and fashioning them into powerful levers. This book's first section explained how habits work, how they can be created and changed. However, where should a would-be habit master start? Understanding keystone habits holds the answer to that question: The habits that matter most are the ones that, when they start to shift, dislodge and remake other patterns. Page 109initial shifts start chain reactions that help Page 109Keystone habits offer what is known within academic literature as "small wins." They help other habits to flourish by creating new structures, and they establish cultures where change becomes contagious. Page 112A huge body of research has shown that small wins have enormous power, an influence disproportionate to the accomplishments of the victories themselves. Page 112Small wins fuel transformative changes by leveraging tiny advantages into patterns that convince people that bigger achievements are within reach.4.15 Page 113"Small wins do not combine in a neat, linear, serial form, with each step being a demonstrable step closer to some predetermined goal," Page 113"More common is the circumstance where small wins are scattered… like miniature experiments that test implicit theories about resistance and opportunity and uncover both resources and barriers that were invisible before the situation was stirred up." Page 131Dozens of studies show that willpower is the single most important keystone habit for individual success. 5.1 Page 131the University of Pennsylvania analyzed 164 eighth-grade students, measuring their IQs and other factors, including how much willpower the students demonstrated, as measured by tests of their self-discipline. Students who exerted high levels of willpower were more likely to earn higher grades in their classes and gain admission into more selective schools. Page 133Scientists began conducting related experiments, trying to figure out how to help kids increase their self-regulatory skills. They learned that teaching them simple tricks--such as distracting themselves by drawing a picture, or imagining a frame around the marshmallow, so it seemed more like a photo and less like a real temptation--helped them learn self-control. Page 134Willpower is a learnable skill, something that can be taught the same way kids learn to do math and say "thank you." Page 139As people strengthened their willpower muscles in one part of their lives-- in the gym, or a money management program-- that strength spilled over into what they ate or how hard they worked. Once willpower became stronger, it touched everything. Page 139They learn how to distract themselves from temptations. And once you've gotten into that willpower groove, your brain is practiced at helping you focus on a goal." Page 146This is how willpower becomes a habit: by choosing a certain behavior ahead of time, and then following that routine when an inflection point arrives. Page 187Starting a little over a decade ago, Target began building a vast data warehouse that assigned every shopper an identification code--known internally as the "Guest ID number"--that kept tabs on how each person shopped. When a customer used a Target-issued credit card, handed over a frequent-buyer tag at the register, redeemed a coupon that was mailed to their house, filled out a survey, mailed in a refund, phoned the customer help line, opened an email from Target, visited Target.com, or purchased anything online, the company's computers took note. A record of each purchase was linked to that shopper's Guest ID number along with information on everything else they'd ever bought. Page 188There are data peddlers such as InfiniGraph that "listen" to shoppers' online conversations on message boards and Internet forums, and track which products people mention favorably. A firm named Rapleaf sells information on shoppers' political leanings, reading habits, charitable giving, the number of cars they own, and whether they prefer religious news or deals on cigarettes.7.5 Other companies analyze photos that consumers post online, cataloging if they are obese or skinny, short or tall, hairy or bald, and what kinds of products they might want to buy as a result. (Target, in a statement, declined to indicate what demographic companies it does business with and what kinds of information it studies.) Page 202There is evidence that a preference for things that sound "familiar" is a product of our neurology. Scientists have examined people's brains as they listen to music, and have tracked which neural regions are involved in comprehending aural stimuli. Listening to music activates numerous areas of the brain, including the auditory cortex, the thalamus, and the superior parietal cortex. Page 202These same areas are also associated with pattern recognition and helping the brain decide which inputs to pay attention to and which to ignore. Page 205The secret to changing the American diet, the Committee on Food Habits concluded, was familiarity. Soon, housewives were receiving mailers from the government telling them "every husband will cheer for steak and kidney pie."7.24 Butchers started handing out recipes that explained how to slip liver into meatloaf. Page 210If you dress a new something in old habits, it's easier for the public to accept it. Page 224When sociologists have examined how opinions move through communities, how gossip spreads or political movements start, they've discovered a common pattern: Our weak-tie acquaintances are often as influential--if not more--than our close-tie friends. Page 234"We've thought long and hard about habitualizing faith, breaking it down into pieces," Warren told me. "If you try to scare people into following Christ's example, it's not going to work for too long. The only way you get people to take responsibility for their spiritual maturity is to teach them habits of faith. Page 235"Once that happens, they become self-feeders. People follow Christ not because you've led them there, but because it's who they are." Page 239For an idea to grow beyond a community, it must become self-propelling. And the surest way to achieve that is to give people new habits that help them figure out where to go on their own. Page 242"You start to see yourself as part of a vast social enterprise, and after a while, you really believe you are." Page 252"Sleepwalking is a reminder that wake and sleep are not mutually exclusive," Mark Mahowald, a professor of neurology at the University of Minnesota and a pioneer in understanding sleep behaviors, told me. "The part of your brain that monitors your behavior is asleep, but the parts capable of very complex activities are awake. The problem is that there's nothing guiding the brain except for basic patterns, your most basic habits. You follow what exists in your head, because you're not capable of making a choice." Page 274Individuals and habits are all different, and so the specifics of diagnosing and changing the patterns in our lives differ from person to person and behavior to behavior. Giving up cigarettes is different from curbing overeating, which is different from changing how you communicate with your spouse, which is different from how you prioritize tasks at work. What's more, each person's habits are driven by different cravings. Page 274THE FRAMEWORK: • Identify the routine • Experiment with rewards • Isolate the cue • Have a plan Page 274A habit is a formula our brain automatically follows: When I see CUE, I will do ROUTINE in order to get a REWARD | Christopher GalesNotable QuotationsExpand to full screen
The Product is Docs: Writing technical documentation in a product development group
Christopher Gales and Splunk Documentation Team
JoAnn Hackos’s pioneering book Managing your Documentation Projects is still a keystone
We wanted a book that covered the reality of developing technical documentation in a fast-moving product development organization, what makes a technical writer exceptional? Resourcefulness and eagerness are the key. When you screen tech writer candidates, look for a real appetite for discovery. The job, fundamentally, isn’t about writing or learning technology. It’s a relationship business, more like investigative journalism than anything else. Writers have to identify and cultivate your sources, they have to build mutual respect and trust, and they have to follow the story wherever it leads.
Look for writers who can write clear sentences and organize information well, but someone who isn’t afraid to break a rule or two when it serves to engage a modern reader.
They need to be able to adapt their writing to meet your standards and your audience requirements.
Good candidates will have taken the time to understand your company, have a rudimentary familiarity with its products and its market, and be able to show work and tell stories that demonstrate their ability to associate their previous work with the position they are applying for.
Portfolio carry a lot of weight, and it is also important in the interview to have the writer explain how he or she wrote the provided samples.
In today’s documentation world, writers and developers use the same tools and the same project management methods. Let us move beyond the stereotypes and recognize that the boundary between writers and developers is thin and permeable.
we also look for writers who have a true passion for working directly with customers.
you want writers who are: Flexible Fearless Personable Organized Experimental Customer-focused Generalists
we use a set of governing scenarios that span marketing, technical documentation, and customer education to shape a meaningful information journey for customers.
we have found it useful to apply learning objectives as a planning tool for documentation.
A learning objective is an intended intellectual goal or outcome.
Awareness
Comprehension
make a decision
Applicable skill
Design for How People Learn
Writers can benefit from thinking of users as not just readers, but learners. Technical writers do not document a product for the sake of enumerating all of its parts or features; rather, writers craft documentation to support user learning and success.
It is important to consider where you might need to meet users in a given topic.
Staying in both an instructor’s and a learner’s frame of mind can be immensely beneficial for a writer.
Curiosity is one of a technical writer’s most valuable tools or qualities.
Depending on your audience, users might also need information presented in a different order, format, or style from those in which you gathered it.
Another benefit to seeing users as learners is that it boosts your empathy for users and their goals.
Charting a course to a destination on a map or in documentation only works if you establish your starting point clearly (Dirksen, 63). This is essential for both the user and the writer.
A clearly-defined starting point can help users decide quickly whether a topic is going to serve their needs.
helps a user understand how a specific topic fits into a broader documentation set and how to navigate that documentation set.
Present enough context to orient users before they delve into the main content.
Route vs. scenery
Do your word choice, phrasing, and sentence structure serve the learning objective? If not, there is likely a better and clearer approach to consider.
Topics with learning objectives have a well-defined audience and, in turn, support specific user goals.
Generally, it seems best to have one learning objective for a topic.
Avoid general words like “Understand.” Pick a verb, then construct a learning objective, user-story style: “After having read the documentation, I want to be able to paraphrase what stacked charts do and how I can benefit from using them."
a learning objective needs to be action-oriented
Is this something the learner would actually do in the real world? Can I tell when they’ve done it?
Will the user be better equipped to solve a problem, create something, or make a decision after reading this topic?
Can the objective be categorized
Awareness
Comprehension
Applicable skill
user goals are not exactly the same as learning objectives)
creating relevant content is more important than creating comprehensive content.
Advocating for users and asking questions from start to finish helps a writer generate documentation whose objectives are easier to define and support.
names for and within the product can evolve.
more information can surface about a feature as time passes.
Symptoms of documentation atrophy
too long and/or complicated with “patch”
Wordiness or other style issues that make instructions or other information difficult to parse.
Excessive linking to other content, or, conversely, insufficient linking
Heading structures that do not organize the content effectively.
you might find that a single section is no longer sufficient
writers should always consider dependencies between the updated feature and other areas of the product.
Do not neglect the state of your existing material, even though, at the same time, you need to focus on new feature documentation.
Content maintenance should be an important and frequent item on every writer’s to-do list.
Too often, when we think about measurement, we think about precision.
The purpose of measurement is to reduce uncertainty so that you can make a decision based on the results.
measurement has to support a business decision.
Run small, simple experiments. Repeat them often. Refine your approach as you learn more from the measurements you take.
If you’re not going to make a change based on the results, don’t bother trying to measure it.
First figure out what decisions you want to make, then decide which measurements can inform those decisions.
To measure success, you must decide what success means for your organization.
Content reuse? Quality? Customer satisfaction? Productivity? Efficiency? Innovation?
Mark Baker, author of Every Page is Page One, says that mean time to productivity is the only true measurement of whether documentation is successful or not.5
The faster your documentation makes your customers productive, the more successful it is.
Unfortunately, support case deflection is a surprisingly difficult metric to capture, because it involves the measurement of an absence of action.
Define what’s important to your department and the company. Identify a set of business decisions you want to make. Figure out what you can measure to reduce the uncertainty involved in making those decisions.
Run some small, easy experiments, and then adjust your measurements accordingly.
Technical fields have their own language and usage, and to be accurate and understood, we must write for those experts in their language.
To convey a requirement, use “must,” and explain why.
The question to ask when deciding whether to explain implementation is “Why do they need to know?"
Omit implementation details unless there are user-facing consequences. When confronted with oddities, see if the product can address them.
Scenarios provide customers with concrete examples of how they might use your product to solve problems they might have in real-world contexts.
Scenarios help bridge gaps in skill levels, from beginner to intermediate and intermediate to advanced.
Scenarios can bring concepts and procedures to life
A scenario is an end-to-end walkthrough of a real-world example, using the product to solve a problem or achieve a goal that your audience cares about.
A scenario is not a tutorial, which walks the user through a rudimentary example
A tutorial is the “hello world” of learning to code. A scenario is making a photo display on a webpage with HTML.
the scenario is fundamentally interested in solving a problem from a real use case, not introducing a set of product features.
A successful scenario is pitched to a very specific persona.
In the world of technical documentation, editors are content advisors.
Technical editing covers a wide spectrum of writerly things that stretch far beyond catching typos, including: technical accuracy, grammar, style, punctuation, code review, usability, audience definition, and minimalism. Technical editing is all about supporting the writer as they develop content that is going to help the customer be successful.
Writing about writing? Forget it.
Without a shared foundation of style rules, writers will produce documentation with varying styles. Technical editors can help reduce the variation in writing styles by creating a style guide for the documentation team, editing for style when writers submit their content for editing, and encouraging writers to adapt their writing to the style that is best suited to their audience.
Style guides are another channel (besides editors) that can help establish consistency throughout a doc set.
When you’re editing, think about whether the content will be clear and helpful to the user.
How can you learn to write with style? Reverse engineer good documentation.
Read style guides from other companies’ | Ray KurtzweilNotable QuotationsExpand to full screen
Page 1
Eventually nanotechnology will enable these trends to culminate in directly expanding our brains with layers of virtual neurons in the cloud. In this way we will merge with AI and augment ourselves with millions of times the computational power that our biology gave us. This will expand our intelligence and consciousness so profoundly that it's difficult to comprehend. This event is what I mean by the Singularity. I use the term as a metaphor.
Page 2
Algorithmic innovations and the emergence of big data have allowed AI to achieve startling breakthroughs sooner than even experts expected- from mastering games like Jeopardy! and Go to driving automobiles, writing essays, passing bar exams, and diagnosing cancer. Now, powerful and flexible large language models like GPT- 4 and Gemini can translate natural- language instructions into computer code- dramatically reducing the barrier between humans and machines.
Page 4
AI and maturing nanotechnology will unite humans and our machine creations as never before- heightening both the promise and the peril even further. If we can meet the scientific, ethical, social, and political challenges posed by these advances, by 2045 we will transform life on earth profoundly for the better. Yet if we fail, our very survival is in question. And so this book is about our final approach to the Singularity- the opportunities and dangers we must confront together over the last generation of the world as we knew it.
Page 5
As these technologies unlock enormous material abundance for our civilization, our focus will shift to overcoming the next barrier to our full flourishing: the frailties of our biology. First by defeating the aging of our bodies and then by augmenting our limited brains and ushering in the Singularity. These breakthroughs may also put us in jeopardy. Possibly lead to an existential catastrophe like a devastating pandemic or a chain reaction of self- replicating machines.
Page 8
With brains, we added roughly one cubic inch of brain matter every 100,000 years, whereas with digital computation we are doubling price- performance about every sixteen months. In the Fifth Epoch, we will directly merge biological human cognition with the speed and power of our digital technology.
The Sixth Epoch is where our intelligence spreads throughout the universe, turning ordinary matter into computronium, which is matter organized at the ultimate density of computation.
Page 9
A key capability in the 2030s will be to connect the upper ranges of our neocortices to the cloud, which will directly extend our thinking. In this way, rather than AI being a competitor, it will become an extension of ourselves.
What Does It Mean to Reinvent Intelligence? > Page 11
If the whole story of the universe is one of evolving paradigms of information processing, the story of humanity picks up more than halfway through. Our chapter in this larger tale is ultimately about our transition from animals with biological brains to transcendent beings whose thoughts and identities are no longer shackled to what genetics provides.
What Does It Mean to Reinvent Intelligence? > Page 11
we will engineer brain– computer interfaces that vastly expand our neocortices with layers of virtual neurons.
Page 12
researchers. In 1956, mathematics professor John McCarthy (1927– 2011)
Page 13
McCarthy proposed that this field, which would ultimately automate every other field, be called "artificial intelligence."[
Page 14
Minsky taught me that there are two techniques for creating automated solutions to problems: the symbolic approach and the connectionist approach. The symbolic approach describes in rule- based terms how a human expert would solve a problem.
Page 16
By the late 1980s these "expert systems" were utilizing probability models and could combine many sources of evidence to make a decision.[ 21] While a single if- then rule would not be sufficient by itself, by combining many thousands of such rules, the overall system could make reliable decisions for a constrained problem.
Although the symbolic approach has been used for over half a century, its primary limitation has been the "complexity ceiling."[
Page 18
the added value of the connectionist one. This entails networks of nodes that create intelligence through their structure rather than through their content. Instead of using smart rules, they use dumb nodes that are arranged in a way that can extract insight from data itself. One of the key advantages of the connectionist approach is that it allows you to solve problems without understanding them. Connectionist AI is prone to becoming a "black box"- capable of spitting out the correct answer, but unable to explain how it found it.[
This is why many AI experts are now working to develop better forms of "transparency"
Page 26
The goal is to then find actual examples from which the system can figure out how to solve a problem. A typical starting point is to have the neural net wiring and synaptic weights set randomly, so that the answers produced by this untrained neural net will thus also be random. The key function of a neural net is that it must learn its subject matter, just like the mammalian brains on which it is (at least roughly) modeled. A neural net starts out ignorant but is programmed to maximize a "reward" function.
It is then fed training data (e.g., photos containing corgis and photos containing no corgis, as labeled by humans in advance). When the neural net produces a correct output (e.g., accurately identifying whether there's a corgi in the image), it gets reward feedback. This feedback can then be used to adjust the strength of each interneuronal connection. Connections that are consistent with the correct answer are made stronger, while those that provide a wrong answer are weakened. Over time, the neural net organizes itself to be able to provide the correct answers without coaching. Experiments have shown that neural nets can learn their subject matter even with unreliable teachers. Despite these strengths, early connectionist systems had a fundamental limitation. One- layer neural networks were mathematically incapable of solving some kinds of problems.
Page 28
If you had enough layers and enough training data, it could deal with an amazing level of complexity. The tremendous surge in AI progress in recent years has resulted from the use of multiple neural net layers. So connectionist approaches to AI were largely ignored until the mid- 2010s, when hardware advances finally unlocked their latent potential.
Page 32
These cerebellum- driven animal behaviors are known as fixed action patterns. These are hardwired into members of a species, unlike behavior learned through observation and imitation.
Page 33
When behaviors are driven by genetics instead of learning, they are orders of magnitude slower to adapt. While learning allows creatures to meaningfully modify their behavior during a single lifetime, innate behaviors are limited to gradual change over many generations.
In order to make faster progress, evolution needed to devise a way for the brain to develop new behaviors without waiting for genetic change to reconfigure the cerebellum. This was the neocortex.
Meaning "new rind," it emerged some 200 million years ago in a novel class of animals: mammals.[
Page 34
was capable of a new type of thinking: it could invent new behaviors in days or even hours. This unlocked the power of learning.
Page 36
when humans are able to connect our neocortices directly to cloud-based computation, we'll unlock the potential for even more abstract thought than our organic brains can currently support on their own.
Page 40
Connectionist approaches were impractical for a long time because they take so much computing power to train. But the price of computation has fallen dramatically.
Page 41
But then, in 2015–16, Alphabet subsidiary DeepMind created AlphaGo, which used a "deep reinforcement learning" method in which a large neural net processed its own games and learned from its successes and failures.[78] It started with a huge number of recorded human Go moves and then played itself many times until the version AlphaGo Master was able to beat the world human Go champion, Ke Jie.[79]
A more significant development occurred a few months later with AlphaGo Zero. When IBM beat world chess champion Garry Kasparov with Deep Blue in 1997, the supercomputer was filled with all the know- how its programmers could gather from human chess experts.[ 80] It was not useful for anything else; it was a chess- playing machine. By contrast, AlphaGo Zero was not given any human information about Go except for the rules of the game, and after about three days of playing against itself, it evolved from making random moves to easily defeating its previous human- trained incarnation, AlphaGo, by 100 games to 0.[ 81] (In 2016, AlphaGo had beaten Lee Sedol, who at the time ranked second in international Go titles, in four out of five games.) AlphaGo Zero used a new form of reinforcement learning in which the program became its own instructor. It took AlphaGo Zero just twenty- one days to reach the level of AlphaGo Master, the version that defeated sixty top professionals online and the world champion Ke Jie in three out of three games in 2017.[ 82] After forty days, AlphaGo Zero surpassed all other versions of AlphaGo and became the best Go player in human or computer form.[ 83] It achieved this with no encoded knowledge of human play and no human intervention.
Page 42
The next incarnation, AlphaZero, can transfer abilities learned from Go to other games like chess.[
The latest version as I write this is MuZero, which repeated these feats without even being given the rules![
But deep reinforcement learning is not limited to mastering such games.
The only exceptions (for now) are board games that require very high linguistic competencies. Diplomacy is perhaps the best example of this-a world domination game that is impossible for a player to win through luck or skill, and forces players to talk to one another.[87] To win, you have to be able to convince people that moves that help you will be in their own self-interest. So an AI that can consistently dominate Diplomacy games will likely have also mastered deception and persuasion more broadly.
But even at Diplomacy, AI made impressive progress in 2022, most notably Meta's CICERO, which can beat many human players.[88] Such milestones are now being reached almost every week.
Page 43
Yet while MuZero can conquer many different games, its achievements are still relatively narrow-it can't write a sonnet or comfort the sick. AI will need to master language. We can construct a multilayer feed-forward neural net and find billions (or trillions) of sentences to train it. These can be gathered from public sources on the web. The neural net is then used to assign each sentence a point in 500-dimensional space (that is, a list of 500 numbers, though this number is arbitrary; it can be any substantial large number). At first, the sentence is given a random assignment for each of the 500 values. During training, the neural net adjusts the sentence's place within the 500-dimensional space such that sentences that have similar meanings are placed close together; dissimilar sentences will be far away from one another. If we run this process for many billions of sentences, the position of any sentence in the 500-dimensional space will indicate what it means by virtue of what it is close to.
Page 44
AI learns meaning from the contexts that words are actually used in.
Page 46
One of the most promising applications of hyperdimensional language processing is a class of AI systems called transformers. These are deep- learning models that use a mechanism called "attention" to focus their computational power on the most relevant parts of their input data- in much the same way that the human neocortex lets us direct our own attention toward the information most vital to our thinking. As a scaled-down example, if I can use only one parameter to predict "Is this animal an elephant?" I might choose "trunk." So if the neural net's node dedicated to judging whether the animal has a trunk fires ("Yes, it does"), the transformer would categorize it as an elephant. But even if that node learns to perfectly recognize trunks, there are some animals with trunks that aren't elephants, so the one-parameter model will misclassify them. By adding parameters like "hairy body," we can improve accuracy. Now if both nodes fire ("hairy body" and "trunk"), I can guess that it's probably not an elephant but rather a woolly mammoth. The more parameters I have, and the more granular detail I can capture, the better predictions I can make.
parameters are stored as weights between nodes in the neural net. And in practice, while they sometimes correspond to human-understandable concepts like "hairy body" or "trunk," they often represent highly abstract statistical relationships that the model has discovered in its training data.
Page 47
Invented by Google researchers in 2017, this mechanism has powered most of the enormous AI advances of the past few years.[95]
This requires vast amounts of computation both for training and for usage.
with many billions of parameters, it can process the input words in the prompt at the level of associative meaning and then use the available context to piece together a completion text never before seen in history. And because the training text features many different styles of text, such as question-and-answer, op-ed pieces, and theatrical dialogue, the transformer can learn to recognize the nature of the prompt and generate an output in the appropriate style. While cynics may dismiss this as a fancy trick of statistics, because those statistics are synthesized from the combined creative output of millions of humans, the AI attains genuine creativity of its own.
Page 48
Another capability unlocked by GPT-3 was stylistic creativity. Because the model had enough parameters to deeply digest a staggeringly large dataset, it was familiar with virtually every kind of human writing. Users could prompt it to answer questions about any given subject in a huge variety of styles-from scientific writing to children's books, poetry, or sitcom scripts. It could even imitate specific writers, living or dead.
Page 49
Another startling advance in 2021 was multimodality.
In general, models like GPT-3 exemplify "few-shot learning."
But DALL-E and Imagen took this a dramatic step further by excelling at "zero-shot learning."
create new images wildly different from anything they had ever seen in their training data.
Page 50
Zero-shot learning is the very essence of analogical thinking and intelligence itself.
It is truly learning concepts with the ability to creatively apply them to novel problems.
In addition to zero-shot flexibility within a given type of task, AI models are also rapidly gaining cross-domain flexibility.
Page 51
April 2022, Google's 540-billion-parameter PaLM model achieved stunning progress on this problem, particularly in two areas fundamental to our own intelligence: humor and inferential reasoning.[
Even more importantly, PaLM could explain how it reached conclusions via "chain-of-thought" reasoning, although not yet (at least as of 2023) as deeply as what humans can
Page 52
Then, in March of 2023, GPT-4 was rolled out for public testing via ChatGPT. This model achieved outstanding performance on a wide range of academic tests such as the SAT, the LSAT, AP tests, and the bar exam.[119] But its most important advance was its ability to reason organically about hypothetical situations by understanding the relationships between objects and actions-a capability known as world modeling.
Page 53
AI progress is now so fast, though, that no traditional book can hope to be up to date.
AI will likely be woven much more tightly into your daily life.
Page 54
well on our way to re-creating the capabilities of the neocortex.
Page 56
My optimism about AI soon closing the gap in all these areas rests on the convergence of three concurrent exponential trends: improving computing price-performance, which makes it cheaper to train large neural nets; the skyrocketing availability of richer and broader training data, which allows training computation cycles to be put to better use; and better algorithms that enable AI to learn and reason more efficiently.
Page 58
While a neocortex can have some idea of what a training set is all about, a well-designed neural net can extract insights beyond what biological brains can perceive. From playing a game to driving a car, analyzing medical images, or predicting protein folding, data availability provides an increasingly clear path to superhuman performance. This is creating a powerful economic incentive to identify and collect kinds of data that were previously considered too difficult to bother with.
Page 59
when AI researchers talk about human-level intelligence, it generally means the ability of the most skilled humans in a particular domain.
Page 60
Once we develop AI with enough programming abilities to give itself even more programming skill (whether on its own or with human assistance), there'll be a positive feedback loop.
Page 61
With machine learning getting so much more cost-efficient, raw computing power is very unlikely to be the bottleneck in achieving human-level AI.
Page 62
computers will be able to simulate human brains in all the ways we might care about within the next two decades or so.
Page 63
With AI gaining major new capabilities every month and price-performance for the computation that powers it soaring, the trajectory is clear. But how will we judge when AI has finally reached human-level intelligence?
Page 64
In 2018 Google debuted Duplex, an AI assistant that spoke so naturally over the phone that unsuspecting parties thought it was a real human, and IBM's Project Debater, introduced the same year, realistically engaged in competitive debate.[160]
And as of 2023, LLMs can write whole essays to human standards.
Page 65
As I write this, despite the great engineering effort going into curbing hallucinations,[162] it remains an open question how difficult this problem will be to overcome.
acts. If different computational processes lead a future AI to make groundbreaking scientific discoveries or write heartrending novels, why should we care how they were generated?
And if an AI is able to eloquently proclaim its own consciousness, what ethical grounds could we have for insisting that only our own biology can give rise to worthwhile sentience? The empiricism of the Turing test puts our focus firmly where it should be.
Between 2023 and 2029, the year I expect the first robust Turing test to be passed, computers will achieve clearly superhuman ability in a widening range of areas. Indeed, it is even possible that AI could achieve a superhuman level of skill at programming itself before it masters the commonsense social subtleties of the Turing test. That remains an unresolved question, but the possibility shows why our notion of human-level intelligence needs to be rich and nuanced.
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As Turing said in 1950, "May not machines carry out something which ought to be described as thinking but which is very different from what a man does?…[ I] f, nevertheless, a machine can be constructed to play the imitation game satisfactorily, we need not be troubled by this objection."
Page 66
Today, AI's still-limited ability to efficiently understand language acts as a bottleneck on its overall knowledge. By contrast, the main constraints on human knowledge are our relatively slow reading ability, our limited memory, and ultimately our short life spans.
Page 69
When AI language understanding catches up to the human level, it won't just be an incremental increase in knowledge, but a sudden explosion of knowledge. This means that an AI going out to pass a traditional Turing test is actually going to have to dumb itself down! Thus, for tasks that don't require imitating a human, like solving real-world problems in medicine, chemistry, and engineering, a Turing-level AI would already be achieving profoundly superhuman results.
Page 69
Functional magnetic resonance imaging scans (fMRIs) measure blood flow in the brain as a proxy for neural firing.[167] When a given part of the brain is more active, it consumes more glucose and oxygen, requiring an inflow of oxygenated blood.
Page 69
Yet because there is a lag between actual brain activity and blood flow, the brain activity can often be measured to within only a couple of seconds-and can rarely be better than 400 to 800 milliseconds.[
Page 69
Electroencephalograms (EEGs) have the opposite problem. They detect the brain's electrical activity directly, so they can pinpoint signals to within about one millisecond.[170] But because those signals are detected from the outside of the skull, it's hard to pinpoint exactly where they came from,
Page 70
Having a thought-to-text technology would be transformative, which has prompted research aiming to perfect a brain wave–language translator.
Page 70
Elon Musk's Neuralink,
Page 71
Defense Advanced Research Projects Agency (DARPA) is working on a long-term project called Neural Engineering System Design,
Page 71
Ultimately, brain–computer interfaces will be essentially noninvasive-
a brain–computer interface doesn't need to account for the bulk of these computations, as they are preliminary activity happening well below the top layer of the neocortex.[181] Rather, we need to communicate only with its upper ranges.
And we can ignore noncognitive brain processes like regulating digestion altogether.
Page 72
At some point in the 2030s we will reach this goal using microscopic devices called nanobots. These tiny electronics will connect the top layers of our neocortex to the cloud, allowing our neurons to communicate directly with simulated neurons hosted for us online.[
this century progresses and the price-performance of computing continues to improve exponentially, the computing power available to our brains will, too.
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Remember what happened two million years ago, the last time we gained more neocortex? We became humans.
The result will be the invention of means of expression vastly richer than the art and technology that's possible today-more profound than we can currently imagine.
Page 73
But we might eventually have art that puts a character's raw, disorganized, nonverbal thoughts-in all their inexpressible beauty and complexity-directly into our brains. This is the cultural richness that brain–computer interfaces will enable for us.
Chapter 3: Who Am I?
Page 76
what is consciousness?
Page 76
One of these refers to the functional ability to be aware of one's surroundings and act as though aware of both one's internal thoughts and an external world that's distinct from them.
it is generally possible to judge the level of another person's consciousness from the outside. a second meaning is more relevant: the ability to have subjective experiences inside a mind-when I say here that we can't detect consciousness directly, I mean that a person's qualia cannot be detected from the outside.
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in the twenty-first century, scientists have gained a better understanding of how even very primitive life forms can show rudimentary forms of intelligence, such as memory.[6]
Page 78
In 2012 a multidisciplinary group of scientists met at the University of Cambridge to assess the evidence of consciousness among nonhuman animals.
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regardless of consciousness's origin, both poles of the spiritual–secular divide agree that it is somehow sacred.
brains that can support more sophisticated behavior likewise give rise to more sophisticated subjective consciousness. Sophisticated behavior, as discussed in the previous chapter, arises from the complexity of information processing in a brain[9]-and this in turn is largely determined by how flexibly it can represent information and how many hierarchical layers are in its network.
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similar to that of our Neolithic ancestors. Yet when we can augment the neocortex itself, during the 2030s and 2040s, we won't just be adding abstract problem-solving power; we will be deepening our subjective consciousness itself.
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Subjective consciousness is qualitatively different from the realm of observable physical laws, and it doesn't follow that particular patterns of information processing according to these laws would yield conscious experience at all. Chalmers calls this the "hard problem of consciousness." His "easy questions," such as what happens to our mind when we are not awake, are among the most difficult in all of science, but at least they can be studied scientifically.
For the hard problem, Chalmers turns to a philosophical idea he calls "panprotopsychism."[13] Panprotopsychism treats consciousness much like a fundamental force of the universe-one that cannot be reduced to simply an effect of other physical forces.
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if there's a plausible chance that an entity you mistreat might be conscious, the safest moral choice is to assume that it is rather than risk tormenting a sentient being.
the Turing test would not just serve to establish human-level functional capability but would also furnish strong evidence for subjective consciousness and, thus, moral rights.
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A concept closely related to consciousness is our sense of free will.[
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A statistical sampling of individual cells would make their states seem essentially random, but we can see that each cell's state results deterministically from the previous step-and the resulting macro image shows a mix of regular and irregular behavior. This demonstrates a property called emergence.[26] In essence, emergence is very simple things, collectively, giving rise to much more complex things.
We inhabit a world that is deeply affected by the kind of patterning found in such cellular automata-a very simple algorithm producing highly complex behavior straddling the boundary between order and chaos.
It is this complexity in us that may give rise to consciousness and free will.
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"compatibilism"- We can make free decisions (that is, ones not caused by something else, like another person), even though our decisions are determined by underlying laws of reality. The human brain has multiple distinct decision-making units.
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if an electronic brain represents the same information as a biological brain and claims to be conscious, there is no plausible scientific basis for denying its consciousness. Ethically, then, we ought to treat it as though it is conscious and therefore possessing moral rights.
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to the extent that your identity hinges on the exact sperm and egg that made you, the odds of this happening were about one in two quintillion. if your father produced two chromosomally identical sperm at age twenty-five and age forty-five, they wouldn't give precisely the same contribution to the formation of a baby.
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The most common explanation of this apparent fine-tuning states that the very low probability of living in such a universe is explained by observer selection bias.[76] In other words, in order for us to even be considering this question, we must inhabit a fine-tuned universe-if it had been otherwise, we wouldn't be conscious and able to reflect on that fact. This is known as the anthropic principle. Some scientists believe that such an explanation is adequate.
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think there is something there that needs explaining."[
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Even as of 2023, though, AI is rapidly gaining proficiency at imitating humans. Deep-learning approaches like transformers and GANs (generative adversarial networks) have propelled amazing progress.
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By combining these techniques, AI can thus already imitate a specific person's writing style, replicate their voice, or even realistically graft their face into a whole video.
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2016, The Verge published a remarkable article about a young woman named Eugenia Kuyda who used AI and saved text messages to "resurrect" her dead best friend, Roman Mazurenko.[82] As the amount of data each of us generates grows, ever more faithful re-creations of specific humans will become possible.
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Replicant bodies will exist mostly in virtual and augmented reality, but realistic bodies in actual reality (that is, convincing androids) will also be possible using the nanotechnology of the late 2030s.
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Eventually replicants may even be housed in cybernetically augmented biological bodies grown from the DNA of the original person (assuming it can be found).
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In the early 2040s, nanobots will be able to go into a living person's brain and make a copy of all the data that forms the memories and personality of the original person: You 2.
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this level of technology will also allow our subjective self to persist in After Life-
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the practical goal is to figure out how to get computers to interface effectively with the brain, and crack the code of how the brain represents information.
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Yet despite my share of responsibility for who I am, my self-actualization is limited by many factors outside my control. My biological brain evolved for a very different kind of prehistoric life and predisposes me to habits that I would rather not have. It cannot learn fast enough or remember well enough to know all the things I would like to know. I can't reprogram it to free me of fears, traumas, and doubts that I know are preventing me from achieving what I would like to achieve. And my brain sits in a body that is gradually aging-although I work hard to slow this process-and is biologically programmed to eventually destroy the information pattern that is Ray Kurzweil. The promise of the Singularity is to free us all from those limitations.
Once our brains are backed up on a more advanced digital substrate, our self-modification powers can be fully realized.
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the law of accelerating returns
the LOAR describes a phenomenon wherein certain kinds of technologies create feedback loops that accelerate innovation.
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What makes the LOAR so powerful for information technologies is that feedback loops keep the costs of innovation lower than the benefits, so progress continues.
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A modern version of a predator hiding in the foliage is the phenomenon of people continually monitoring their information sources, including social media, for developments that might imperil them.
Nostalgia, a term the Swiss physician Johannes Hofer devised in 1688 by combining the Greek words nostos (homecoming) and algos (pain or distress), is more than just recalling fond reminiscences; it is a coping mechanism to deal with the stress of the past by transforming it.
The Reality Is That Nearly Every Aspect of Life Is Getting Progressively Better as a Result of Exponentially Improving Technology > Page 122
technological change is essentially permanent. Once our civilization learns how to do something useful, we generally keep that knowledge and build on. The Reality Is That Nearly Every Aspect of Life Is Getting Progressively Better as a Result of Exponentially Improving
new technologies can have huge indirect benefits, even far from their own areas of application.
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Electricity is not itself an information technology, but because it powers all our digital devices and networks, it is the prerequisite for the countless other benefits of modern civilization.
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most of our progress in disease treatment and prevention to date has been the product of the linear process of hit-or-miss efforts to find useful interventions. Because we have lacked tools for systematically exploring all possible treatments, discoveries under this paradigm have owed a lot to chance.
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during the 2020s we are entering the second bridge: combining artificial intelligence and biotechnology to defeat these degenerative diseases. We are now utilizing AI to find new drugs, and by the end of this decade we will be able to start the process of augmenting and ultimately replacing slow, underpowered human trials with digital simulations.
medical nanorobots with the ability to intelligently conduct cellular-level maintenance and repair throughout our bodies.
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the core of a person's identity is not their brain itself, but rather the very particular arrangement of information that their brain is able to represent and manipulate. Once we can scan this information with sufficient accuracy, we'll be able to replicate it on digital substrates.
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Shifts in the kinds of jobs in demand have motivated millennials and Generation Z, more than other generations, to seek creative, often entrepreneurial careers, and have given them the freedom to work remotely, which cuts out travel time and expense but can lead to blurry boundaries between work and life.
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Increasing material prosperity has a mutually reinforcing relationship with declining violence. Where humans once only identified with small groups, communication technology (books, then radio and television, then computers and the internet) enabled us to exchange ideas with an ever wider sphere of people and discover what we have in common. The ability to watch gripping video of disasters in distant lands can lead to historical myopia, but it also powerfully harnesses our natural empathy and extends our moral concern across our whole species. Once humanity has extremely cheap energy (largely from solar and, eventually, fusion) and AI robotics, many kinds of goods will be so easy to reproduce that the notion of people committing violence over them will seem just as silly as fighting over a PDF seems today.
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The printing press is an excellent illustrative example of how the law of accelerating returns works for information technologies.
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Very broadly, the more ideas a person or a society has, the easier it is to create new ones; this includes technological innovation.
technologies that make it easier to share ideas make it easier to create new technologies-when Gutenberg introduced the printing press, it soon became vastly cheaper to share ideas.
The spread of knowledge brought wealth and political empowerment,
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History gives us reason for profound optimism, though. As technologies for sharing information have evolved from the telegraph to social media, the idea of democracy and individual rights has gone from barely acknowledged to a worldwide aspiration that's already a reality for nearly half the people on earth.
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The essential point to realize is that all the progress I have described so far came from the slow early stages of these exponential trends. As information technology makes vastly more progress in the next twenty years than it did in the past two hundred, the benefits to overall prosperity will be far greater-indeed, they are already much greater than most realize.
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As I will explain later in this chapter, we will soon produce high-quality, low-cost food using vertical agriculture with AI-controlled production and chemical-free harvesting.
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Much like the internet is an integrated and persistent environment of web pages, the VR and AR of the late 2020s will merge into a compelling new layer to our reality.
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over the next couple of decades, brain–computer interface technology will become much more advanced.
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we need advances in materials science to achieve further improvements in cost-efficiency.
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Costs of solar electricity generation are falling quite a bit faster than those of any other major renewable, and solar has the most headroom to grow.
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A key challenge of the twenty-first century will be making certain that earth's growing population has a reliable supply of clean, fresh water.
3D printing allows manufacturing to be decentralized, empowering consumers and local communities.
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Each year the resolution of 3D printing is improving and the technology is getting cheaper. new research is applying 3D printing to biology. One potential drawback of 3D printing is that it could be used to manufacture pirated designs. All of this requires new approaches to protect intellectual property. decentralized manufacturing will allow civilians to create weapons that they otherwise couldn't easily access.
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Material abundance and peaceful democracy make life better, but the challenge with the highest stakes is the effort to preserve life itself.
Biological life is suboptimal because evolution is a collection of random processes optimized by natural selection.
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We are beginning to use AI for discovery and design of both drugs and other interventions, and by the end of the 2020s biological simulators will be sufficiently advanced to generate key safety and efficacy data in hours rather than the years that clinical trials typically require.
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Nanorobots not only will be programmed to destroy all types of pathogens but will be able to treat metabolic diseases.
The fourth bridge to radical life extension will be the ability to essentially back up who we are, just as we do routinely with all of our digital information. As we augment our biological neocortex with realistic (albeit much faster) models of the neocortex in the cloud, our thinking will become a hybrid of the biological thinking we are accustomed to today and its digital extension.
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If you restored your mind file after biological death, would you really be restoring yourself?
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Information technology is about ideas, and exponentially improving our ability to share ideas and create new ones gives each of us-in the broadest possible sense-greater power to fulfill our human potential and to collectively solve many of the maladies that society faces.
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The convergent technologies of the next two decades will create enormous prosperity and material abundance around the world. But these same forces will also unsettle the global economy, forcing society to adapt at an unprecedented pace.
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Yet driving is just one of a very long list of occupations that are threatened in the fairly near term by AI that exploits the advantage of training on massive datasets.
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a 2023 report by McKinsey, found that 63 percent of all working time in today's developed economies is spent on tasks that could already be automated with today's technology.[
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Erik Brynjolfsson. He argues that, unlike previous technology-driven transitions, the latest form of automation will result in a loss of more jobs than it creates.[
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Economists who take this view see the current situation as the culmination of several successive waves of change.
The first wave is often referred to as "deskilling."[
One of the main effects of deskilling is that it is easier for people to take new jobs without lengthy training.
The second wave is "upskilling." Upskilling often follows deskilling, and introduces technologies that require more skill than what came before.
AI-driven innovation different from previous technologies is that it opens more opportunities for taking humans out of the equation altogether.
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This is desirable not just for cost reasons but also because in many areas AI can actually do a better job than the humans it is replacing.
Yet it is important to distinguish between tasks and professions.
ATMs can now replace human bank tellers for many routine cash transactions, but tellers have taken on a greater role in marketing and building personal relationships with customers.[83]
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Yet one sticking point in this thesis has been a productivity puzzle: if technological change really is starting to cause net job losses, classical economics predicts that there would be fewer hours worked for a given level of economic output. By definition, then, productivity would be markedly increasing. However, productivity growth as traditionally measured has actually slowed since the internet revolution in the 1990s.
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The good news, though, is that artificial intelligence and technological convergence will turn more and more kinds of goods and services into information technologies during the 2020s and 2030s-allowing them to benefit from the kinds of exponential trends that have already brought such radical deflation to the digital realm.
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so, even as technological change is rendering many jobs obsolete, those very same forces are opening up numerous new opportunities that fall outside the traditional model of "jobs."
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People will be able to describe their ideas to AI and tweak the results with natural language until it fulfills the visions in their minds. Instead of needing thousands of people and hundreds of millions of dollars to produce an action movie, it will eventually be possible to produce an epic film with nothing but good ideas and a relatively modest budget for the computer that runs the AI.
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Most of our new jobs require more sophisticated skills. As a whole, our society has moved up the skill ladder, and this will continue.
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Real-time translation between any pair of languages will become smooth and accurate, breaking down the language barriers that divide us. Augmented reality will be projected constantly onto our retinas from our glasses and contact lenses.
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But on the way to a future of such universal abundance, we need to address the societal issues that will arise as a result of these transitions.
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Thanks to accelerating technological change, overall wealth will be far greater,
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and given the long-term stability of our social safety net regardless of the governing party, it is very likely to remain in place-and at substantially higher levels than today.
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we'll need smart governmental policies to ease the transition and ensure that prosperity is broadly shared.
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considering the role of jobs in our lives forces us to reconsider our broader search for meaning. People often say that it is death and the brevity of our existence that gives meaning to life. But my view, rather, is that this perspective is an attempt to rationalize the tragedy of death as a good thing.
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One of the great challenges of adapting to technological changes is that they tend to bring diffuse benefits to a large population, but concentrated harms to a small group.
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do think the specter of troublesome social dislocation-including violence-during this transition is a possibility that we should anticipate and work to mitigate.
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Turning medicine into an exact science will require transforming it into an information technology-allowing it to benefit from the exponential progress of information technologies. 2023 the first drug designed end-to-end by AI entered phase II clinical trials to treat a rare lung disease.[
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AI can learn from more data than a human doctor ever could and can amass experience from billions of procedures instead of the thousands a human doctor can perform in a career.
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In 2020 a team at MIT used AI to develop a powerful antibiotic that kills some of the most dangerous drug-resistant bacteria in existence. But by far the most important application of AI to medicine in 2020 was the key role it played in designing safe and effective COVID-19 vaccines in record time.
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There will likely be substantial resistance in the medical community to increasing reliance on simulations for drug trials-for a variety of reasons. It is very sensible to be cautious about the risks.
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In addition to scientific applications, AI is gaining the ability to surpass human doctors in clinical medicine.
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As Hans Moravec argued back in 1988, when contemplating the implications of technological progress, no matter how much we fine-tune our DNA-based biology, our flesh-and-blood systems will be at a disadvantage relative to our purpose-engineered creations.[45] As writer Peter Weibel put it, Moravec understood that in this regard humans can only be "second-class robots."[46] This means that even if we work at optimizing and perfecting what our biological brains are capable of, they will be billions of times slower and far less capable of what a fully engineered body will be able to achieve.
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Think of e-books. When books were first invented, they had to be copied by hand, so labor was a massive component of their value. With the advent of the printing press, physical materials like paper, binding, and ink took on the dominant share of the price. But with e-books, the costs of energy and computation to copy, store, and transmit a book are effectively zero. What you're paying for is creative assembly of information into something worth reading (and often some ancillary factors, like marketing).
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As all these components of value become less expensive, the proportional value of the information contained in products will increase.
In many cases, this will make products cheap enough that they can be free to consumers.
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This dramatic reduction of physical scarcity will finally allow us to easily provide for the needs of everyone.
While nanotechnology will allow the alleviation of many kinds of physical scarcity, economic scarcity is also partly driven by culture-especially when it comes to luxury goods.
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the nanotech manufacturing revolution won't eliminate all economic scarcity.
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(Longevity Escape Velocity)
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If you can live long enough for anti-aging research to start adding at least one year to your remaining life expectancy annually, that will buy enough time for nanomedicine to cure any remaining facets of aging.
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Eventually, using nanobots for body maintenance and optimization should prevent major diseases from even arising.
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As AI gains greater ability to understand human biology, it will be possible to send nanobots to address problems at the cellular level long before they would be detectable by today's doctors.
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Nanobots will also allow people to change their cosmetic appearance as never before.
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A deeper virtual neocortex will give us the ability to think thoughts more complex and abstract than we can currently comprehend.
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just as this progress will improve billions of lives, it will also heighten peril for our species. New, destabilizing nuclear weapons, breakthroughs in synthetic biology, and emerging nanotechnologies will all introduce threats we must deal with.
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advances in genetic engineering[25] (which can edit viruses by manipulating their genes) could allow the creation-either intentionally or accidentally-of a supervirus that would have both extreme lethality and high transmissibility.
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contrast, biological weapons can be very cheap.
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even if responsible people design safe nanobots, bad actors could still design dangerous ones.
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AI is smarter than its human creators, it could potentially find a way around any precautionary measures that have been put in place. There is no general strategy that can definitively overcome that.
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Three broad categories of peril - Misuse ... Outer misalignment, which refers to cases where there's a mismatch between the programmers' actual intentions and the goals they teach the AI in hopes of achieving them. Inner misalignment occurs when the methods the AI learns to achieve its goal produce undesirable behavior, at least in some cases.[
while the AI alignment problem will be very hard to solve,[62] we will not have to solve it on our own-with the right techniques, we can use AI itself to dramatically augment our own alignment capabilities.
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With technologies now beginning to modify our bodies and brains, another type of opposition to progress has emerged in the form of "fundamentalist humanism": opposition to any change in the nature of what it means to be human.
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AI is the pivotal technology that will allow us to meet the pressing challenges that confront us, including overcoming disease, poverty, environmental degradation, and all of our human frailties. Overall, we should be cautiously optimistic. | Leah BuleyNotable QuotationsExpand to full screen
INTRODUCTION I'm tired of working for orgs who say they care about their customer but don't do testing to even know what their customers want from them... PART I Philosophy CHAPTER 1 UX 101 To be a user experience designer means to practice a set of methods and techniques for researching what users want and need, and to design products and services for them. Through good UX, you are trying to reduce the friction between the task someone wants to accomplish and the tool that they are using to complete that task. In a simple working definition, you might say that a user experience is the overall effect created by the interactions and perceptions that someone has when using a product or service ... User research is about understanding users and their needs, and user experience design is about designing a user's interactions with a product from moment to moment. ... The term user experience probably originated in the early 1990s at Apple when cognitive psychologist Donald Norman joined the staff. Various accounts from people who were there at the time say that Norman introduced user experience to encompass what had theretofore been described as human interface research. How can they design flowing experiences that respect, empower, and delight real people? CHAPTER 2 Getting Started I spent time thinking about solutions for navigating large repositories of information. Sales pros have a clever technique called the “alternative close.” Instead of asking permission to close the deal (or, in this case, to do the work), they provide two alternatives for how to go about it. Not, “Can I ring you up?” but rather, “Will that be cash or charge?” In UX, an equivalent would be not “Can we do some research,” but rather “We could do a large research study, or we could do a small informal evaluation to get some quick feedback.” Then the negotiation becomes not if, but how. Real UX teams of one are committed to knowing not just “users” in the abstract, but the people who really use their products. The challenge for user experience professionals is to understand user needs well enough to look past the prosaic solutions to discover the elegant ones Learn from other successful products. Create inspiration libraries to keep abreast of current standards and have a place to turn for multiple ideas when working on a new problem (see Figure 2.14). But also question things. CHAPTER 3 Building Support for Your Work The more you can facilitate a cross-functional team, the more you will empower others to feel ownership and involvement in the process. Pre-meetings help you get people to commit their support for your approach prior to going in for the big reveal, and they give your colleagues a very important gift: the time and space to really think about proposed designs and establish their own point of view. User experience practitioners, due to their focus on both user needs and business needs, opportunity. One common problem for teams of one is getting called in only near the end of development to do a quick usability review—in others words, to rubber stamp a product. This can be frustrating and makes it very hard to suggest anything beyond the most surface changes. But you can treat this as a teachable moment to expose others to what user-centered design really entails. People love stories. Instead of talking about the deliverables that you produce or UX concepts in the abstract, case studies and stories give people a memorable narrative that they can envision themselves in. ... makes it easier for you to remember what you did, so you can tell your stories confidently and on the fly. User-centered research is, fundamentally, design research. Design research differs from market research in approach and intent. They are complementary but different. Market research is about identifying what people want, whereas design research is about identifying how best to achieve what people want, i.e., what versus how. The goal in design research is to develop empathy and insight into why people do what they do and to spark inspiring ideas for how products can meet unmet needs and enhance their lives. .... think of UX as a preventative investment to keep the costs of your product from getting out of control down the road. Statistical significance creates confidence about the linkage between cause and effect. With quantitative research (for example, research conducted with large numbers of people), it is easier to prove statistical significance. In qualitative research (for example, research focused on gathering in-depth understanding of behavior, but often based on relatively small sample sizes), statistical significance is pretty hard to prove. Usercentered research typically errs on the side of the qualitative, which means small sample sizes, usually somewhere between six and a dozen people. The main reason for this is that you spend more time with each participant one-on-one—a lot more—and thus a larger sample is prohibitive in terms of time and cost. Rich contextual research probably should not be used to influence major business decisions because it is not necessarily representative of market averages. But it's perfect for getting inspired, and for triggering empathetic thinking about the context and mindset that people will have when interacting with your product, which enables you to think creatively about how to help your customers. Usability experts Jakob Nielsen and Tom Landauer conducted research in the early 1990s that showed that by testing with just five users, you can uncover 85% of the usability issues with a product. The more users you add, says Nielsen, the less you learn because you keep seeing the same issues again and again. In product development, there are a lot of people who are thinking about the product from various angles. Engineers think about how to write code that's efficient and reliable. Marketers think about how to connect to and engage the target market for the product. Quality assurance folks think about whether people can use the product to complete the intended use cases. UX is, in some respects, the glue that binds these considerations together, ensuring that the actual experience of using the product is, from moment to moment, clear, fluid, and even a little bit delightful. Much of a user experience practitioner's work is about making sure that what is being built is relevant to both customers' goals and business goals. In UX, designing great products is only half the work. The other half is handling all the “people stuff” that goes along with it: building support, ferreting out lingering objections and concerns, untangling a knot of competing agendas, and rallying your colleagues around a new direction. While the STC (Society for Technical Communication) focuses more broadly on the field of technical communication and supports people with titles like “proposal manager” and “documentation specialist,” their usability and user experience special interest group is squarely focused on UX (www.stcsig.org/usability/). CHAPTER 4 Growing Yourself and Your Career If you want to increase your opportunities, you need to grow your second and third ring networks. Becoming a part of a professional community is how you do it. - UX Booth: www.uxbooth.com/
- UX Matters: www.uxmatters.com/
- A List Apart: www.alistapart.com/
- Boxes and Arrows: www.boxesandarrows.com/
- Smashing Magazine: www.smashingmagazine.com/
- Core77: www.core77.com/
- .Net Magazine: www.netmagazine.com
- UIE Newsletter: www.uie.com/uietips/
- Adaptive Path Blog: www.adaptivepath.com/ideas
The UX field has a lot of independent practitioners. One reason is that many organizations can't yet justify hiring a full-time UX person, so an expert on call is just right for them. | Steve MulderNotable QuotationsExpand to full screen
The User is Always Right: A Practical Guide to Creating and Using Personas for the Web (Voices That Matter)
Steve Mulder and Ziv Yaar
why user-centered design is important, what personas are, and how they make Web sites more successful.
A step-by-step guide to creating personas.
Using personas for all types of decision-making,
Keep your personas alive within your organization, making them an explicit part of your everyday process for creating or improving the site.
Persona are particularly useful for making user research come to life.
information architecture, the craft of organizing features and content so that users can find what they need.
The second component is interaction design—the complementary craft of helping users actually do what they need to do in order to accomplish their goals.
A persona is a realistic character sketch representing one segment of a Web site’s targeted audience.
Personas summarize user research findings and bring that research to life in such a way that a company can make decisions based on these personas, not based on themselves.
First and foremost, personas are grounded in research.
primarily defined by the goals they have when they come to the site, personas are also defined by their behaviors and
attitudes
Instead of talking generally about “users,” you can talk about precisely which people you want to target.
Personas bring the team together to create one shared vision of exactly whom you’re designing for and what they want.
It can be extremely difficult to measure the effectiveness of personas, because they make up one tool among many that you can use to improve Web sites.
having a tool to enable you to focus on the needs of real people can only help bring focus to the work you do.
- Qualitative personas
- Qualitative personas with quantitative validation
- Quantitative personas
You do research to better understand your users, but exactly what is it that you want to find out about them?
its answer dictates which research methods you should use,
Qualitative research is about discovering new things with a small sample size.
A small number of users (10–20) to get new ideas or uncover previously unknown issues.
Qulitative research doesn’t prove anything,
It uncovers insights that you can then test or prove.
Quantitative research is about testing or proving something with a large sample size.
With hundreds or thousands of data points to analyze, you can look for statistically significant trends
test a hypothesis you uncover with qualitative research.
Quantitative research is better at telling you what is happening
Qualitative research is better at telling you why it’s happening
Qualitative research is an inexpensive
What people say isn’t necessarily what they do.
many people aren’t very good at analyzing their own behavior or at paying attention to their actions.
634
Qualitative Personas
User interviews
Some companies conduct field studies instead, where they observe users in their native environment
Segmentation is the art of taking many data points and creating groupings that can be described
Based on commonalities among each group’s members.
Each type of user evolves into a persona as you add more detail to their goals, behaviors, and attitudes.
Pros:
Relatively low effort is required.
Costs are minimal
Simpler persona stories increase understanding and buy-in.
Simplicity breeds clarity, which stakeholders can more easily grasp and act upon.
Fewer specialized skill sets are necessary and you don’t need any statistical analysis skills
| Everett N. McKayNotable QuotationsExpand to full screen
Introduction:
"Good UI is designed to communicate to people, not robots, so it is human communication." Pg. 8
Chapter 1:
"If users need to translate your UI into something meaningful, you should use that translated, meaningful version instead." Pg. 20
"Well-designed UIs ask the right question once, at the right time and place, and provide enough information for users to answer intelligently and confidently." Pg. 59
Chapter 2:
"Use standard interactions for your software's platform. Don't be creative here because interaction is required for intuitive UI." Pg. 73
"Commands are verbs, and verbs are hard to show with symbols, which are nouns." Pg. 89 (I really liked this relatable explanation to writing).
"If a UI feels like a natural, friendly conversation, it is probably a good design." Pg. 94
Chapter 3:
"Visual appearance is essential to our perception of quality." Pg. 136
"Understanding how users typically scan [the screen] helps in understanding how to communicate with them" Pg.151
Chapter 4:
"Great UI design transcends mechanical usability by recognizing that there is an emotional, impatient, error-prone human at the other end of the interaction, so well-designed interfaces strive to make a personal connection." Pg. 201
"All software has a personality - whether intentional or not - so it is better to have a personality that is carefully designed than one that is accidental." Pg. 206
"For every UI design that requires users to constantly correct small mistakes, there exists an alternative design that prevents the mistake, makes it easy to recover from the mistake, or does the right thing anyway." Pg. 216
Chapter 5:
"A user interface is essentially a conversational interface between users and a product to perform tasks that achieve users' goals...If we focus the UI design process on effective communication, we can leverage our understanding of the target users, their goals, and the way the UI needs to communicate to users on a human level." Pg. 247
"Great design requires clear target users." Pg. 259
"Working with many ideas early in the design phase helps you understand the possibilities before making a commitment with confidence." Pg. 273
Chapter 6:
"A communication-driven approach works well for most situations, and it allows us to you to leverage everyday communication interpersonal communication skills." Pg. 335
"There is value in good planning." Pg. 336
| Kate Crane and Kelli Cargile Cook, EdsNotable QuotationsExpand to full screen
Chapter 1: Out of Industry, Into the Classroom: UX as Proactive Academic Practice
The more significant conclusion that I took from this usability study was that the usability test was not generalizable beyond the specific context of this particular syllabus evaluation. What I mean by this is that once I revised the syllabi to be more usable, or a new population of students was tested, or I moved to a new institution with different policies and procedures, or new technologies were better suited to deliver syllabi, the test conducted in 2014 would be insignificant beyond a historical perspective of usability using two texts and technologies in 2014. (Crane 4)
Usability, therefore, should only be used iteratively to understand how a design works for users at any given time or environment. Second to this conclusion was my understanding that course documents are (or should be) student-user centered and that it is an instructor's responsibility as an information designer to understand the student-user experience while using these documents. (Crane 4)
Although by no means exhaustive in its discussion of design and research methods, this chapter attempts to show the hierarchy of UX and its relationship to design and research methods. At the same time, using illustrative examples from my own syllabus research, I discuss the various opportunities and challenges of UX work. (Crane 5)
System-centered design focused more on the needs of the system to function as the designers intended. The problem with this approach is that systems, even well-built systems, are not always usable for the people those systems were designed for. (Crane 5)
Four of Nielsen's and Quesenbery's components for usability are very similar. For instance, users need to be able to complete tasks efficiently, learn a system in a reasonable amount of time, and recover from errors when they are made. (Crane 8)
not only should we be concerned with how well users can complete tasks, but researchers should not assume that their (or a designer's) preconceived ideas about users' work is a fair representation of the complexity of users' work beyond a usability lab (or any testing situation). (Crane 8)
After analyzing my syllabus usability test's results, I learned that usability testing alone could not answer the questions I posed for my study. The syllabus and the students who use it are part of a complex academic system with multiple factors, stakeholders, tasks, environments, and functions. Thus, looking at usability alone, though a good starting point, led to more questions than it could answer. (Crane 10)
This is one example of why TPC instructors and program designers need to understand how UX functions within an interconnected web of design processes (user-centered design [UCD], human-centered design [HCD], participatory design, and design thinking) and research methods (observation, self-reporting, affinity diagramming, usability testing, etc.). (Crane 10)
It is a theory or philosophy, supported by design processes that put the human user in the center of design processes, whether these processes are labeled as user-centered design (UCD), human-centered design (HCD), participatory design, or design thinking; these design processes are then enacted through four iterative stages: 1. collecting information about human users (those most likely to use products upon design completion), 2. designing prototypes that can be used by these human users to collect additional data about their use, 3. redesigning products in response to the first two methods, and 4. testing and retesting products during and after distribution. (Crane 10-11)
One of the main differences between UCD and HCD, is the shift in nomenclature from "users" to "humans." This is not to minimize either process; rather, it acknowledges that some UX scholars and designers feel that the term "users" is not the best way to refer to people. (Crane 14)
Affinity diagramming provides all participants the opportunity to make their values and attitudes known without succumbing to group thinking. This practice can also lead to low-fidelity co-designed prototypes where users can construct their own syllabus, in this case, using the values discovered from affinity diagramming and program and university syllabus requirements to create their own student-as-user-centered syllabus. (Crane 19)
Chapter 2: Beyond Lore: UX as Data-Driven Practice
We begin this chapter with a nod to North (1987) because the collection's focus on user experience (UX) has deep roots in both the concept of practice and the concept of inquiry: UX might be defined as a practice that improves human experiences through situated inquiry within a highly contextualized space. (Cook and Crane 26)
While some have chosen to use terms directly from UX, e.g., referring to students as "users" and curricula as "products," others have chosen to reference students with terms ranging from "student users" to "co-creators." (Cook and Crane 29)
The goal of the project, its scope, and the context must shape the UX process. Not only is this necessary to ensure teacher-practitioners have developed a product or system that considers student experiences, but it is also necessary to create goals and develop a UX plan that matches the scope and context of the project. (Cook and Crane 31-32)
Methods Journey Map Infographic: Understanding – Surveying, Journey Mapping, Journals, Card Sorting, User Profiles and Personas; Looking – Affinity Clustering, Observations, Interviews, Focus Groups, Usability; Making – Rapid Ideation, Prototyping, Operative Imaging. Under each method, they include which authors in the collection employed a specific method for UX testing. (Cook and Crane 33)
Chapter 3: User Profiles as Pedagogical Tools in the Technical and Professional Communication Classroom
I had previously taught the same course at the same institution but perceived a disconnect between the course material and student understanding about the material, learning management system, and course expectations. In response, I reframed my role as a TPC instructor facing student confusion to a designer facing a design problem for users. (Martin 43)
I focused on two key activities to explore how TPC instructors might leverage student-user profiles to guide course and lesson design decisions: 1. developing and iterating a student-user profile before, during, and after a course; 2. understanding how information from a student-user profile can inform course and lesson design decisions. (Martin 44)
A student-user profile will ideally end up as a robust, detailed tool to help you make informed pedagogical decisions. You may have information about how your students conceptualize TPC, interpret assignments, and even navigate an LMS. But starting out, all you need is a piece of paper and some general student information from your registered student list. (Martin 54)
Think about what else you might know about your students to start building your student-user profile. Do you have any international students? Do you have students from different parts of the country? These distinctions may or may not be relevant based on what you subsequently learn about your users, but they offer simple starting points to consider as you brainstorm student perspectives until you can refine them with observational and self-reporting data. (Martin 54)
Specifically, your design inquiry, or what you are trying to learn about users to improve their experience, (e.g., how do TPC students use the LMS?) will determine how much a profile must be altered or discarded. In short, your design inquiry will guide your student-user profile development activities. (Martin 56)
Importantly, the student-user profile was based on triangulation of user "see-say-do" information (Still & Crane, 2016) rather than sole self-report data such as course surveys or student evaluations. While those tools can support a student-user profile, on their own they cannot supplement the robust approach of creating a user profile based in UCD methods. (Martin 57)
Chapter 4: User Experience and Transliteracies in Technical and Professional Communication
This fluidity among cultures and digital platforms is at the core of what we want to teach our students in UX—to develop methods for understanding culture not as a fixed entity, but as fluid, constantly emerging, and iterative. Transliteracy thus provides students with an entry point into broader conversations in UX regarding user research and ethical technology design. (Gonzales and Walwema 68).
As an innovative framework, UX can be deployed to tackle social issues that are constantly shifting and that resist single solutions. Although many programs and courses have argued for the value of UX training, particularly within technical communication curricula, the notion of technology design and UX research more broadly can be intimidating to students who do not have experience in this area, especially given the overwhelming whiteness of UX as a field and industry. (Gonzales and Walwema 69).
The transliteracy model helps UX designers determine what the target culture communication patterns might be. By gaining a snapshot of the communication environment in a particular culture to discuss implications for intercultural UX, technical communicators can interpret what they have listened to, generate new ideas, and incorporate those ideas to create UX that emerges from the users' sociocultural contexts. (Gonzales and Walwema 70).
As the student narratives demonstrate, it is not enough for UX to consider diverse users; it has to take the next step of understanding users' sense of who they are in order to address their needs in a more targeted way. The narratives show that UX through a transliteracies framework encourages UX researchers to look more closely at the inequities that manifest in products and services. (Gonzales and Walwema 80).
UX-inspired assignments such as journey mapping, the Notebook of Relations, and affinity diagramming activities allowed students to speak back to what they were reading while applying these readings to their own interests, experiences, and research. As we continue developing courses that thread UX and transliteracy, we hope to continue embracing this iterative course design while also maintaining an emphasis on interdisciplinarity and intercultural communication. (Gonzales and Walwema 81).
A transliteracies framework in UX also assures that advocacy for users is done by both scholars and users. Rather than limit user responses to select quotes, a transliteracy framework values all user media, including audio or video stories, as legitimate sources of knowledge that together paint a panoramic picture of communities, and change minds and attitudes. (Gonzales and Walwema 82).
Although we realize that the examples, narratives, and experiences that we share in this chapter are very localized to a specific course and context, we believe that the pairing of UX and transliteracy, as well as the attention to students' backgrounds and interests in designing UX curricula, can be incorporated into other programs and contexts seeking to introduce UX. The clear takeaway for UX and TPC is that combining transliteracy with iterative course design practices drawing from UX can bring empathy, efficiency, and emotional engagement by intentionally co-creating experiences with students to be better immersed in students' everyday lives. (Gonzales and Walwema 83).
Chapter 5: Using Student-Experience Mapping in Academic Programs: Two Case Studies
Walker explains that "A user experience map shows the users' needs, expectations, wants, and potential route to reach a particular goal. It's like a behavioral blueprint that defines how your customer may interact with your product or service" (n.p., emphasis added). (Howard 89)
One of my pedagogical goals was to impress on the students that UX maps come at the end of a long, rigorous research process. Both my industry clients and my students want to jump right in and start creating maps, so I wanted them to recognize that maps are the result of scaffolding; i.e., maps can't be created without first creating personas, and personas can't be created without data resulting from triangulated empirical inquiries. (Howard 92)
Even though they only represent approximately 67 percent of the users, data like those detailed above can be correlated with the admissions data we received from the Architecture School's administrative assistant in order to help the students make informed decisions about details to include in their personas. [The data listed above are those that Google Analytics provides, demographics overview, age, gender, interests, affinity categories, in-market segments, and other categories]. (Howard 96)
Indeed, in our early meetings with our clients, they told us that they were attempting to decide if they needed to redesign the website so that there was a whole section of the site dedicated to the two-year track and another to the three-year (the current site combined information for both programs on the same pages). (Howard 98
Taken together, the five personas from all three teams combined with three user experience maps (one for domestic undergraduates, one for domestic graduate students, and one for international graduate students) collectively gave our clients a clear and thorough understanding of the needs that required attention in the redesign of the School of Architecture's website. (Howard 100)
Without students having completed the work as a client-based project for a course, few of us who direct TPC programs could assemble the resources needed to conduct such a study. (Howard 101)
We began by conducting what, today, we would call a "content audit" and surveyed and compiled all of the advising handbooks, webpages, and materials available for both students and faculty. Not surprisingly, we found that the information was "all there" and available; however, it was scattered across a variety of sources and not compiled in a user-friendly format. (Howard 101-102)
Taking a single class, such as the Usability Testing and UX Design seminar I described in the first case, didn't really allow students to demonstrate "expertise" in the area. They needed more coursework. However, until the faculty engaged in this mapping exercise, we didn't realize that students were often unable to take three courses in a cognate area because of the demands of the five core courses: two required thesis research courses and at least one course required for students to obtain graduate teaching assistantships. (Howard 103)
In other words, it took the mapping exercise to convince the faculty to make the painful decision to drop core TPC courses in favor of cognate courses. The mapping exercise turned faculty who had been advocates for their own privileged core course topics into student advocates. (Howard 104)
Both formal and informal forms of user experience mapping improve students' academic experiences through the inclusion of students' voices in the design of websites and curricula for academic programs. (Howard 104)
Chapter 6: "A Nice Change of Pace": Involving Students-as-Course-Users Early and Often
In this chapter, I demonstrate how thinking of the ENGL 2312 class as a "user experience" inspired two early class activities focused on the syllabus' design and the course Blackboard site. (Pihlaja 110)
While user-centered design, usability, and user experience stand as distinct, discrete objects of study and methodological approaches to design and inquiry, they share a common concern with the user. In wrestling with how to think with my students about "culture" and how texts and technologies are used in any given context, it became obvious that the way forward was to begin with the first two "commandments" of the user-centered design process: "thou must involve users early and often" (Still & Crane, 2016). (Pihlaja 112).
While cultural usability is a complex topic, historically, it is concerned with the design of products for usability "cross-culturally," requiring critical analysis of the wider global context for any given local users (Sun, 2012). (Pihlaja 113)
As instructors gain more experience semester-to-semester and year-to-year, student "personas," students as actual users, are iteratively re-imagined based on those who have taken the course before, succeeding or failing in various ways each year. (Pihlaja 114)
Acceding expert status to students may feel like conceding instructors' role and status—one's whole reason for being a teacher. Significantly, students may also feel this way and be suspicious of instructors who do not perform competence and confidence in the learning environment or class-as-product in ways they have been enculturated to expect. (Pihlaja 115)
Furthermore, the process of consulting, testing, and reflecting on course elements with students has the potential to aid the pedagogical goals of the course, using students' agency as "expert end users" of a course as learning product to engage course content itself more critically and deeply. (Pihlaja 115)
To enable students' participation in (re-)designing the syllabus, at the beginning of an early class period, I placed students in groups of three to four and assigned each group a subsection of the syllabus to review. One group focused on the course description, objectives, and materials section; another, the assessment criteria for grading; another, the course policies; and finally, another, the course calendar. (Pihlaja 117)
Paraphrasing: First students repeat policies listed in their section of the syllabus in their own words akin to a syllabus quiz, then identify two questions about the content, and finally what they like about the syllabus and what makes it easier to use. (Pihlaja 117)
To enable students' participation in (re-)designing the syllabus, at the beginning of an early class period, I placed students in groups of three to four and assigned each group a subsection of the syllabus to review. One group focused on the course description, objectives, and materials section; another, the assessment criteria for grading; another, the course policies; and finally, another, the course calendar. (Pihlaja 118)
Engaging with students about, say, whether the explanatory preface for each course goal area was really necessary in this document for what they would use it for (it wasn't) led me to revise that section in particular to make later reference to it easier (Figure 6.3). (Pihlaja 118)
Without realizing my intention, students asserted (quite forcefully and in one instance with a hint of disgust if not horror) that red was an "angry" or anxiety-producing color—especially when I used it to highlight assignment due dates. (Pihlaja 120)
But I do wonder whether we could take this approach every semester, regardless of the status of the class. Indeed, to ask these questions every time is to accept that students' needs and user practices are not all the same and that the culture of the class changes from semester to semester, if not more frequently (Pihlaja 123)
This particular example also dovetailed nicely with our course content discussions around possible cultural differences that show up even in mundane, everyday ways (e.g., how we represent dates and time). While it is customary in the United States to represent months and days in that order, many other nations represent them in the reverse: day then month. This added a cultural competence dimension to the discussion. (Pihlaja 126)
Indeed, we returned to the day-month example several times throughout the semester. In explaining what a "redesign for cross-cultural connection" of some existing text or technology might look like for their final projects, I called back to this example. (Pihlaja 127)
Where the student is thought of as a user and brought into the process of designing courses, the prospects for student engagement, learning, persistence, and success are substantial. (Pihlaja 128)
From an assessment perspective, thinking of students as users of course content and tools was an effective way to test their prior knowledge while disclosing (to both the instructor and students themselves) their tacit understanding of the course topic and tracking learning over the course of a semester. It has the ability to help clarify why a student might not be succeeding. (Pihlaja 129)
Inviting student input on course element design no doubt renders one vulnerable. To show up on day one of a course expecting to be able to teach the class only after you've had substantial input on how students will or will not be able to "use" its organization and environment may feel like risking one's identity as a teacher. (Pihlaja 130)
Of course, there's no guarantee that UCD approaches themselves will be able to move beyond the more apolitical, individualist thinking regarding student engagement that leads Collin Bjork (2018) to propose we supplement usability-type approaches with insights from digital rhetoric, identifying the inherently rhetorical dynamics at work in any user interface, such as audience, persuasion, and credibility. (Pihlaja 131)
Chapter 7: Learning from the Learners: Incorporating User Experience into the Development of an Oral Communication Lab
In Fall 2018, the college administration expressed support for the business communication faculty to develop new initiatives that foster students' soft skills (teamwork, leadership, ethics, and communication) and, in particular, oral communication. (Clark and Austin 138)
What impact did incorporating user experience throughout the development process have on the overall success of our Oral Communication Lab? (Clark and Austin 142)
As we engaged in a cycle of implementation, reflection, adjustment, and re-implementation, we realized the importance of including students in the development process. As such, our new approach echoed the approach to usability testing modeled by Shivers-McNair et al. (2018), which they define as "an empathetic, flexible, ongoing engagement with our audiences and users" (p. 39). (Clark and Austin 142)
At the outset of the semester, we planned to use the following assessment instruments: an observations/electronic journal, the Personal Report of Communication Apprehension (PRCA-24) as a pre-test and post-test, the Shannon Cooper Technology Profile, the Instructional Video Usability Survey, Speech Anxiety Thoughts Inventory (SATI), Lab Technology Usability Survey, and the final Logistics Survey. (Clark and Austin 142)
The findings also showed us the importance of incorporating usability and user experience feedback during the development of initiatives like the lab. As a result of the inclusion of user experience assessments, we were able to make adjustments during the development process that aligned more with the needs of our current users. (Clark and Austin 146)
Not only did we assume student attitudes toward dress, but we also assumed they would be proficient in the technologies we planned to use in the lab. The results from the Shannon-Cooper Technology Profile (Appendix B) indicated that students self-reported high proficiency in social media, basic computing programs, and the Blackboard LMS platform. (Clark and Austin 147)
In contrast, Kaltura, our integrated video recording platform, scored an average of .59/10, with 22 of the 28 students giving it a score of zero. Because the Shannon-Cooper Technology Profile showed that students were not familiar with Kaltura, we felt it was important to meet each student in the lab during that student's first visit in order to lead them through, click by click, the process of recording and uploading their videos. (Clark and Austin 147)
These findings also supported our perception that students were learning the technology quickly and intuitively. Our observations in the lab provided another example of this technological intuitiveness on the part of students. Once we showed the students where to open the My Media tab on Blackboard (where Kaltura is housed), many students actually started to lead us; they would find and click on the proper buttons before we pointed them out. (Clark and Austin 148)
While we recognized early on that our assumptions of our users' needs and wants were not always correct, we were so focused on scaffolding skills that we did not create an opportunity for gathering feedback on basic scheduling and process logistics. (Clark and Austin 149)
Though this project had a relatively small sample size, i.e., 28 students who constitute one section of a multi-section course at our university, the research findings emphasize the importance of including our students in the developmental process of initiatives aimed at supporting their professional development. (Clark and Austin 151)
Had we not incorporated user feedback checkpoints or kept our eyes open during informal interactions with students, the lab and its activities would have had a much lower chance of success. First of all, we would have created more work for ourselves as teachers (and likely for the students as well) by using unsuccessful, ineffective instructional strategies. Secondly, we would have missed the innovative and insightful comments, ideas, and actions expressed by students as they navigated, learned from, and contributed to the lab. (Clark and Austin 152)
Chapter 8: Ideating a New Program: Implementing Design Thinking Approaches to Develop Program Student Learning Outcomes
Still, some other guides do spend more time describing PSLO (Program Student Learning Outcomes) design processes. For example, a guide developed by the University of Nebraska-Lincoln described six strategies for creating PSLOs, including holding conversations with department faculty, examining existing instructional materials, and reviewing similar units or programs (Jonson, 2006). Yet these varied strategies still emphasized a closed, faculty-centric approach rather than a UX design methodology. (Thominet 162)
To meet these UX goals, this chapter describes how a design thinking process can support active and collaborative methods that integrate the knowledges and experiences of numerous stakeholders. In this way, adopting design-thinking practices can help to move us away from a faculty-centered committee model and toward a participatory approach to PSLO design that focuses on students' experiences, needs, and goals. Ideally, this process will result in more responsive, representative, and inclusive program definitions. (Thominet 163)
While I adopt the d.school structure in this chapter due to its ability to open space for critical reflections on my PSLO design project, it is important to note that all these formulations of managerial design thinking share the same core practices. First, designers observe and interview stakeholders to better understand their needs. Based on this information, designers seek to clearly define the design problem. Next, large multidisciplinary teams use active, collaborative, and visual design exercises to imagine many potential solutions to the design problem. Then the teams prototype and test select ideas with potential users. Through several iterations, the prototypes are narrowed and refined until one design is finalized and implemented as a product or service. Two further points should be made about these phases. First, each phase is treated as cyclical and recursive, so further user research can occur after the product implementation, which can lead to further ideation and prototyping, etc. Second, the phases are often conceptualized as cycles of divergence and convergence: designers intentionally open up to a multiplicity of ideas and then move toward defining or narrowing solutions. For example, divergent thinking is often the focus of the ideation stage, while convergence to a singular design solution is a goal of the testing and iteration phase. (Thominet 164)
The subsections that follow will be framed specifically according to the d.school process of design thinking, which includes specific stages for empathizing, defining, ideating, prototyping, and implementing. I am using this structure here primarily because it offers a means to organize the discussion and to reflect on areas of revision in future iterations of this work. (Thominet 168)
For the empathize phase of the curriculum design project, I interviewed faculty and students about their experiences in the program. First, I recruited faculty who had designed and taught at least one upper-division writing and rhetoric course. Student participants were then recruited directly by those faculty. (Thominet 169)
Since the participants were not a representative sample and because we wanted to get students actively involved in our design process, we did not use the interview results as generalizable data to support specific programmatic changes. Instead, we used them to understand the situation more clearly and as an inspiration for our subsequent work. In that way, the interviews played a significant role in the next phase of problem definition, which, in turn, informed the ideation methods that followed. (Thominet 169-170)
For the PSLO project, three elements of the interviews contributed to the problem-setting phase: the broad program definitions by faculty, the emphasis on practical application by students, and the lack of a shared vision among the participants. While these elements suggested some marketing strategies (e.g., tying classes to specific jobs or highlighting student testimonials), they also demonstrated the need for a clear and specific vision for the program. (Thominet 170)
To foster divergent thinking, ideation typically takes place in multidisciplinary teams or workshops where participants use active collaboration techniques to conceptualize and prioritize potential solutions to a given problem. The exact methods vary, but organizational guides and popular press books have offered numerous ideation exercises (Gray et al., 2010; IDEO, 2015; Mattimore, 2012). (Thominet 171)
The ideation phase for the PSLO project consisted of two identical workshops that lasted two hours each. Initially, the phase was planned as a single workshop, but conflicting schedules made it impossible to locate a single time that would work for all participants, so two smaller, identical workshops were used instead. (Thominet 171)
The workshops had six stages: 1) introduction, 2) warmup, 3) ideation, 4) categorization and prioritization, 5) prototyping, and 6) reflection. I will discuss each of these stages below. (Thominet 172)
Since participants had varied experience and knowledge, I presented prompts in sets, which included questions customized for students, faculty, and practitioners. Each participant was still free to respond to any version of the prompt. (Thominet 173)
In the next workshop stage, participants categorized ideas using an affinity diagramming method where they first grouped sticky notes together without discussing their reasons, then named the groups, and finally, voted for the most important groups (Spool, 2004). In our workshops, participants initially created many different idea clusters, but they were subsequently asked to consolidate them whenever possible. (Thominet 174)
The workshops concluded with a collective debrief. Participants discussed the ideas that were most surprising in the workshop, the exercises that worked best, and the exercises they would change. Some people were surprised at the potential outcomes that received relatively limited attention and prioritization, including teamwork and reading. Others commented on how the workshop was a positive experience, saying that they felt it valued everyone's voice and gave everyone a chance to speak. (Thominet 175)
Since the outcome drafts made during the workshops were incomplete, I collaborated with another faculty member to condense the ideas from the workshops into cohesive PSLOs and to test those PSLOs with other faculty members. (Thominet 176)
At the end of this process, the initial 247 ideas were narrowed into 90 outcome statements. At that point, we moved the outcomes into one (very long) list, which can be found in Appendix C. (Thominet 176)
While an implementation phase is not always included in design thinking models, it is sometimes appended as a sixth step at the end of the d.school model. During the implementation phase, designers "put [their] vision into effect. [They] ensure that [their] solution is materialized and touches the lives of end users" (Gibbons, 2016). (Thominet 177)
The process of implementation for the PSLOs primarily involved moving the work from ad hoc workshops and collaborations back into official program committees. The first step was to re-form the defunct major track committee. For the new instantiation, the committee membership was kept small. (Thominet 177)
The committee's next major task is developing an assessment plan for the outcomes. With 16 PSLOs, assessment will not be easy. However, since the major track is a sub-degree level program (i.e., it is a track within the pre-existing English major rather than a new, standalone major), we are not subject to institutional oversight on assessment, which gives us more flexibility in our plans. Currently, we plan to assess outcome categories one at a time and to collaborate with other program committees (e.g., the technical writing committee) on assessment. (Thominet 178)
Both design thinking and UX are inherently built on an iterative approach that emphasizes direct feedback from major stakeholders. For that reason, the committee is also planning on using some indirect assessment practices, including exploratory exit interviews with graduating students, to supplement our more traditional assessment methods. (Thominet 178-179)
We adopted a traditional approach to creating PSLOs during the prototyping and testing phase. While the outcomes were based on the ideas and input of a broader group of stakeholders, the actual work of prototyping them still occurred in a closed faculty collaboration. While it was necessary to tame the vast amount of data from the workshops, we still might have undertaken this work in more open and participatory ways. (Thominet 181)
In building a heuristic model, I also simplified the process into four activities: listening, problem setting, ideating, and iterating, as shown in Figure 8.1. In this model, the implementation phase is incorporated into the process of iteration as a recognition that programmatic design projects do not have a clear start or end point. (Thominet 182)
Ultimately, when faculty and administrators can make space for intentional problem setting, we can focus our efforts on the real problems that students (and other stakeholders) encounter in academic programs. (Thominet 183)
Finally, design thinking is, fundamentally, a process of iteration. It is a process that works best when solutions are modeled, tested, and changed over time. To accomplish this activity, faculty and administrators can experiment with physical and visual prototypes of the curriculum to encourage non-faculty stakeholders to actively engage in the design process. (Thominet 183)
Chapter 9: Using UX Methods to Gauge Degree Efficacy
This study addresses student silence by centering on student experience while completing a degree: it directly engages students in curricular development and assessment. (Cargile Cook 199)
This study employs user experience research methods to gather the perspectives of these majors over time and to use that data to design a viable assessment plan, develop curriculum, and generate recruiting and marketing materials for the DMPC. Using Patricia Sullivan's (1989) definition of longitudinal field studies as a guide, this research project is designed to "employ qualitative methods to study a group or a number of individuals over a period of time" (p. 13). In her discussion of such studies, Sullivan cautions researchers who choose to use this method: longitudinal field studies are resource-, time-, and labor-intensive. (Cargile Cook 199-200)
This chapter focuses on the study's design and its initial findings. It details the five user experience methods/activities in the study's design, provides a rationale for their use, and maps these methods into a four-year timeframe. It then provides results from initial data collected in order to present a student-user profile. Finally, it discusses the value of including UX methods as assessment tools for degrees in professional and technical communication. (Cargile Cook 200)
In addition to participating in annual surveys and focus groups, samples of DMPC majors will engage with program administrators using three other user experience methods: user profiles, personas, and journey mapping. (Cargile Cook 201)
Phase 1 of this research relies on annual surveys to collect both quantitative and qualitative data about DMPC majors' demographics and attitudes. These data will be aggregated to develop user profiles and personas. (Cargile Cook 202)
While surveys are the first interaction students will have with this research, focus groups will be their last…. Each focus group session will last approximately two hours and be held in a designated focus group room with audio and video recording capabilities. The focus group team will include the moderator and at least one additional researcher to take notes. The focus group will provide a concluding snapshot of student experiences with DMPC courses, degree plans, advisors, and administrators. It will also ask majors for their ideas on degree revisions, innovations, and marketing and recruiting materials. (Cargile Cook 202)
In the spring semester of 2020, program administrators will invite a random sample of DMPC majors to meet for the first time in a persona development workshop. This workshop begins Phase 2 of the study. After completing the required Institutional Review Board (IRB) informed consent procedures, administrators will report the aggregate survey results—the user profile—to participants, explaining how user profiles inform user experience research and how they lead to the development of personas. They will then explain how to construct personas of DMPC majors from key demographics, interests, and opinions. (Cargile Cook 203)
Phase 3 of the study requires participants to create two kinds of journey maps, one for their fictional personas and a second for themselves. A journey map is a "visual depiction of what users need and what steps they take to fulfill those needs as they interact with a product" (Still & Crane, 2016 p. 95) from first interaction to last. Journey maps generated in this study focus on how personas (and eventually participants) begin their journey with the declaration of the DMPC major and end with their leaving the major or graduation. (Cargile Cook 204)
Participants will have to puzzle through degree plan requirements and catalog course descriptions to successfully map their persona's journey from matriculation to graduation. At the end of the session, debriefings will follow, describing maps and discussing different paths and rationales used. After the debriefing, future-state maps will be used for analysis. (Cargile Cook 206)
Because survey results provide useful information about DMPC majors, the results are reported in this section in spite of the low response rates. While such low rates may be criticized for their lack of generalizability, ignoring the results of those majors who did respond, from administrators' perspectives, would be indefensible. In other words, administrators realized that although the response rate was low, even a low response rate was user data that offered important insights about programmatic efficacy. (Cargile Cook 209-210)
For now, the results of this study are inconclusive and provide only first impressions of DMPC majors. Through iterative studies and multiple methods, DMPC administrators recognize that program assessment is an inexact art: Some methods deployed work better than others. Some results provide better data than others. Failures are part of any UX process and cannot be avoided, but UX processes also produce successes. Furthermore, innovation is not a linear process, and continuous improvement requires longitudinal study whatever methods are used to collect and report data. (Cargile Cook 217)
Employing user experience methods offers a methodological rationale for including student voices and experiences in program assessment that other means of assessment simply do not. (Cargile Cook 217)
Chapter 10: Real-World User Experience: Engaging Students and Industry Professionals Through a Mentor Program
John Gould and Clayton Lewis (1985) coined the phrase "user-centered design" and defined it as having three central characteristics: (1) early focus on users, (2) systematic data collection, and (3) iterative design. Using this model, we wanted to investigate the "joint enterprise" that results from strategic interaction of students and industry professionals (TCAB members and program alumni) through a mentor program. (Katsman Breuch et al 220)
Throughout the pilot, we were chiefly concerned with this question: How might user experience in a mentor program address the academic-industry gap? Sub-questions included the following: What is the "user experience" of participating in a mentor program? And how can we make improvements to a mentor program based on user/participant feedback? Our goal was to integrate user feedback with instructional design to find ways to better bridge industry and academia and to engage students and industry practitioners. This approach is indeed innovative and useful as we actively practice student-practitioner engagement as a method for cultivating real-world user experience through such joint enterprise activity. (Katsman Breuch et al 221)
Our TCAB is an intergenerational group of business leaders whose purpose is to provide exemplary networking and experiential learning opportunities for students and to enrich the curriculum and visibility of our programs, students, faculty, and staff. Three of our academic programs––a B.S. in Technical Writing and Communication, a Graduate Certificate in Technical Communication, and an M.S. in Scientific and Technical Communication––have opportunities to interact with TCAB members. (Katsman Breuch et al 223)
We provided time for the pairs to meet and asked them to articulate goals for their mentorship pairing, and we also asked them to plan for two additional points of contact in the remaining 15-week period. (See Appendix A for launch meeting worksheet.) We then asked pairs to come back to a large group discussion in which we fielded any additional questions about the program. The mentor-mentee pairs were then on their own to conduct their plans. (Katsman Breuch et al 224)
We use community of practice theory as a framework for our study of this mentor program, in that we are interested in Wenger's (1998) three dimensions for establishing a community of practice––joint enterprise, mutual engagement, and shared repertoire––as a framework. (Katsman Breuch et al 224)
A community of practice framework for the study of our mentor program also aligns well with user experience and user-centered design theory and practice. By integrating "user experience" in our mentor program, we mean understanding not just performance or preference of a specific task but rather the entire user experience before, during, and after their "use" or participation in the mentor program (see Getto & Beecher, 2016; Potts & Salvo, 2018; Rose et al., 2017; Still & Crane, 2017). (Katsman Breuch et al 225)
Specifically, we asked users—in this case, students and mentors—to inform us of ways they believed the mentor program did or did not address the gap between academia and industry and of recommendations they would have to improve the program. In gathering this input, we approach the mentor program through a collaboratively constructed user-centered design perspective that relies on participant research and takes into account participant contributions that will be addressed as the program continues to improve. (Katsman Breuch et al 225-226)
This initial meeting included an introduction to the mentor program, including an overview of participation and suggested structure for the mentor pairs. We asked mentors and students to articulate goals for participating in the program and outline three contact meetings that would occur during the program. (Katsman Breuch et al 226)
Near the end of the 15-week period, we distributed a questionnaire to all participants that asked questions about the goals of their mentor pair, their meeting choices, their hopes for the program, and whether or not hopes were met. The questionnaire also asked participants for reflections about how the program addressed the academic-industry gap and any recommendations. (Katsman Breuch et al 226-227)
The last item on the survey asked if participants would be willing to participate in a brief interview about their experience. Of the survey participants, 23 agreed to be interviewed. We scheduled brief 15-minute interviews with these participants using whatever method worked best, whether in-person, video conference, or phone. One interview was conducted with two participants at the same time; all others were conducted one-to-one. (Katsman Breuch et al 228-229)
In order to create the mentor pairings, we began by reviewing these survey responses for each participant. We also took into consideration a brief one to two paragraph statement written by each student, which expressed their specific interests and reasons for wanting a mentor through this program. Based on the student paragraphs and survey data from students and mentors, we conducted an informal coding process that looked for similar themes, interests, and goals between the students and mentors. When an ideal match surfaced in the themes, the student and mentor were paired together. (Katsman Breuch et al 231)
In our post-participation survey, we asked users what their hopes were for the program as it continues and how well their hopes are being met. (Katsman Breuch et al 233)
From surveys and interviews, we identified the need to revisit these goals throughout the program and to add more specificity to these; e.g., what exactly does it mean to "bridge the gap" as a student meets with a technical communication professional for the first time? While academics may use PLN visualizations to indicate resources, tools, and contexts within which they work and learn, such visualizations are not commonly used in either academia or industry. Therefore, we should articulate mentor-mentee strategies that more clearly relate to making connections that build understanding about technical communication industries and how to best develop skills for securing a position and being successful in this profession. (Katsman Breuch et al 242)
User feedback allowed us to better understand the mentor program user experience, and in this case, we learned that the student experience needs to broaden outside the classroom. We see such a user experience perspective as bridging industry and academia, as integrating design and instructional design, as engaging students and industry practitioners. (Katsman Breuch et al 244)
Chapter 11: User Experience Design and Double Binds in Course Design
I have a strong inclination toward pedagogical practices that prioritize what works best for students in the classroom. Elsewhere (Zachry & Spyridakis, 2016), I have described this commitment and how it helped shape program and curricular decisions broadly in my home department. In this chapter, however, I will explore some of the inherent challenges in following this approach at a more granular level—that of an individual class. In particular, I will explore the experience of attempting to place student needs and desires as a central concern in the design of a class. (Zachry 251-252)
Effective instruction emerges from the artful design of learning experiences that should be meaningfully informed by attention to the people (students) we will engage in that design. (Zachry 252)
Experienced instructors know that the insights students offer are often uneven, perhaps reflecting a singular perspective or not accounting for the overall learning context the instructor is working within. Some insights, nevertheless, are relatively easy to address and require negligible effort to implement. Addressing some other needs and desires, though, requires more substantive changes. (Zachry 252)
In this chapter, then, the phenomena that I am particularly interested in exploring is that in which attempting to use feedback from students can lead to double binds for instructors who are attempting to design the best possible learning environments. To facilitate this exploration, I will draw on examples from a class that I routinely teach at my institution. As I present each of the three examples, my focus will be on my attempt to foster a classroom design that is responsive to the experiences of students. I will then expand on the theory of double binds in responding to the needs and desires of students when designing a class-based learning experience. (Zachry 253)
implementing a course design feature suggested by students from a previous class immediately surfaced new concerns that countered the suggested feature in an unanticipated way. Clearly, within the broad student population, people held competing—perhaps irreconcilable—thoughts about how course evaluation should be designed. (Zachry 255)
Drawn from my teaching experiences over three years, each of these examples illustrates a variation on dilemmas that I have faced as I have attempted to integrate the experience of learners in these classes into its design. To think productively about these instances and how they might have implications for using a UX approach to class design, I see value in thinking about double binds in UX design. (Zachry 258)
In the context of class design following a UX approach, a double bind is a situation in which the designer faces a dilemma due to competing demands. On one hand, the instructor-designer seeks to hear from students about their needs and desires as learners and to incorporate what is discovered into the design of the course. On the other hand, the instructor-designer is positioned within an institutional context that places its own demands (including educational policies and conventions), affecting what may or may not be possible or wise to do in the classroom. (Zachry 259)
When acting as designers and following UX priorities, these same instructors will periodically hear from students that the standards for measurement and evaluation should be altered. In my example 1, this recommendation came in the unexpected form of making the standards more demanding. In this instance, upon analyzing the costs and benefits of making such a design change, I decided to follow the institutional process to make the course graded (rather than credit/no-credit). The choice, however, was not clearly or necessarily the right one. (Zachry 260)
The details of these three examples are specific to my institutional context, but the types of double binds they represent are almost certainly recognizable to most readers. I could readily point to instances of such double binds in other courses I have taught over recent years, as I anticipate nearly any instructor-designer could. (Zachry 261)
This framing clearly has a relationship to notions of constraints and competing interests in design, but it is more specific. In particular, this framing places an emphasis on the conflicted, felt experience of instructor-designers. That is, double binds are experienced personally as tensions in our identity as we occupy our professional/institutional roles and also seek to empathize with the experiences of our students and empower them to contribute to the design of their education. (Zachry 262)
We should expect double binds to be part of the essence of our work, not something that can be resolved for all time with a single, clever design decision My purpose here is not to solve these three forms of double binds (or the many others that we face). Instead, I want to provide a framework that facilitates naming and discussing a phenomenon that we experience as instructor-designers who want to embrace the values of UX and attend to the needs and desires of learners. (Zachry 262)
Chapter 12: User Experience in the Professional and Technical Writing Major: Pedagogical Approaches and Student Perspectives
This chapter explores a central research question for educators of professional and technical writing majors: How can a program best prepare students for future career opportunities and the skills needed to succeed in those careers? We argue for user experience as a pedagogical approach for educating students about one university's professional writing major. (Bay et al 266)
We argue that user experience (UX) can serve as a more robust framework for understanding how a programmatic experience can facilitate student engagement with/in a field of study. User experience, as a concept, attempts to capture all of the aspects embedded in one's experience with an outside entity or situation. (Bay et al 266)
We present a case study of an undergraduate research methods class that asked students to assess user experiences in the professional and technical writing major at Purdue. In teams, we surveyed, interviewed, and visually mapped our large network of alumni, with particular attention to location and job position, as well as surveying current students in the major. We framed much of this work around data visualization methods (Wolfe, 2015), especially in mapping our program's alumni, in order to contextualize the ways in which user experience can also function as "big data" (McNely et al., 2015). (Bay et al 267)
A key takeaway for readers is learning about a flexible pedagogical approach to user experience that combines program assessment, introduction of students to the major, development and donor relations, as well as critical reflection on students as users. Perhaps most importantly, this article is co-written by undergraduates in the professional and technical writing major, demonstrating their roles as users and as user experience researchers. (Bay et al 267)
When we develop a major or concentration, we are creating an experience for students. We want students to proceed through a program and not only learn concepts, theories, and approaches, but also to develop a sense of themselves as future professionals entering a community of practice. These students will also be "products" of a program and its approaches, much like we see doctoral students as products of a particular program, with particular strengths and ways of seeing the world. (Bay et al 268)
One approach we might take is thinking about specific sites or courses as micro-testing grounds to gauge the experiences of a program's students and/or alumni. In a sense, this approach relies on what we might term "programmatic UX," or taking the temperature of users at a specific moment and in a particular context. Programmatic UX could be one way to iteratively research, test, and refine particular aspects of a program's user experience. (Bay et al 269)
Unlike programs such as engineering or computer science, the professional and technical writing program at our university does not have dedicated staff to collect and maintain data on our alumni (in fact, as a humorous aside, when Jenny requested data on our professional writing alumni from the university development office, she received a list of alumni from the creative writing major instead). She realized that without understanding the prior experiences of alumni, it would be difficult to design a better experience for current students. (Bay et al 270)
One programmatic illumination from this project was that the LinkedIn alumni group is a self-selected group, meaning that members were not necessarily alumni of our specific professional writing program. Almost all of the members were alumni from Purdue, but they may have earned different degrees and were working as professional or technical writers. Thus, some of these users were not necessarily users of the program but were users in the field, which provided a rich set of perspectives. (Bay et al 272)
What the diversity of group members showed was that members identified themselves in terms of their careers first and their majors/education second. They saw professional and technical writing less as a field of study and more as a career trajectory that was not necessarily connected to an academic program. In thinking of program assessment, then, career preparation as a category of assessment might need to be more nuanced. (Bay et al 272)
The research these students performed was also beneficial to them as students of the program since the information they collected increased the awareness of the students around them. They felt that it was strange that there was no prior research into the students in the professional writing program because they assumed that the program would know everything about the students and alumni. The team decided to send out a survey in order to obtain the answers to their questions. That process in itself was something with which the group had little previous experience. (Bay et al 275)
To understand how to create an effective survey, the team researched and spent time attempting to gather as much data as possible. They needed to create a survey which was unbiased, yet still asked specific questions to collect the desired feedback. The trouble was related to leading questions, as the team did not want to affect the responses they received with the framing of the prompts. Part of this issue might have been because the students themselves belonged to the population being studied. (Bay et al 275)
The method used to collect this data unfolded as the team worked on the project since it was difficult to collect data from scratch. Data on alumni of the professional writing program was collected through multiple outlets and was stored in a Google Sheets file. First, data was gathered from the Purdue Professional Writing Group on LinkedIn by sorting through the group members for graduates of the program. Many members of the group were not professional writing alumni and thus were not added to the database. The team looked at connections to our faculty in the program, as well as other alumni for more names to add to the database. A final data source was the PW-Talk email list, which is a listserv for current students and alumni of the program. In all, students collected data for over 300 program alumni. (Bay et al 277)
The resulting data set was used to create visual graphics, including a word cloud graphic of job titles (see Figure 12.3), as well as pie charts and graphs displayed in a poster for the final presentation to stakeholders. The most valuable aspect of this project for Margaret was creating something that would be used for purposes beyond turning it in for a grade. Beyond creating the poster displaying the results of the project to members of the English department, the team was able to share the database with the administration of the professional writing program for them to use for their own purposes. For Margaret, this type of "service learning" is the most beneficial because it combines the learning process with applications outside of the classroom. (Bay et al 277)
Several common threads of the user experience emerge from these project reflections, which we didn't realize until writing this chapter. The first is that user experience in the professional writing major includes more than just academic or career preparation; rather, it also includes life preparation. As Ashlie notes, the UX approach of the class led her to become more aware and understanding of other human beings with whom she interacts. Likewise, there was a consensus that the subject matter experts reinforced that everyone is human; we all make mistakes and are still learning while on the job. (Bay et al 277-278)
The UX approach of the class meant compiling information not only for student projects and grades, but for the program as a whole. Students believed that the program administrators could use the information gleaned from the survey to help structure the program and its curriculum to something that the students could be proud of by the time they graduate. (Bay et al 278)
These conclusions led us to see how programmatic assessment does not necessarily need to occur from the outside looking in; rather, perhaps students can be the most lucid assessors of our programs. Students, as users, can provide rich reflections on the value of a program and where that program can be strengthened. (Bay et al 279)
Jenny plans to engage current students and classes to continue participating in this evaluation of user experience so there can be a reciprocal and iterative process for understanding the user experiences of the program, as well as continue to teach students how to research and respond to user experience as a methodological approach. (Bay et al 279)
Chapter 13: Program as Product: UX and Writing Program Design in Technical and Professional Communication
As TPC administrators consider the range of available approaches to building and improving programs, we argue that user experience (UX) methods can provide an innovative approach to program redevelopment. In this chapter, we explore how UX approaches to program redesign differ from existing approaches, and we forward the idea of program as "product" and students as "users" to theoretically ground this shift to UX-based research methods. (Masters-Wheeler and Fillenwarth 286)
Through this research, we demonstrate the value that UX-grounded research brings to program redesign, and we offer suggestions for initial and extended programmatic research based on the idea of students as users of programs. (Masters-Wheeler and Fillenwarth 286)
UX has the potential to illuminate the invisible or overlooked experiences of the users of an organization's product or service. Many programs include alumni when gathering feedback on program design processes, yet only including alumni in these processes may cause programs to miss out on gathering current or future students' perspectives. (Masters-Wheeler and Fillenwarth 287)
From these examples, we can see how nonprofit organizations of all kinds can use UX to improve stakeholder experiences of their products, whether those products are informational materials, goods, or services. In higher education, our products are the programs that we offer to students. (Masters-Wheeler and Fillenwarth 287)
Programs are products associated with university brands, and they are marketed to prospective students who have many choices about where to enroll. It may be a question of semantics, as Eric Stoller (2014) argues, yet "calling ourselves anything but a business seems unfair and untrue. Students pay a great deal for the product that is higher education" (n.p.). Almost all students and/or their families contribute at least some of their own funds towards their higher education. Students pay for opportunities to take classes and earn degrees, and they should understand that there is no guarantee as to whether they will pass, fail, or get a job just because they paid for an educational experience. (Masters-Wheeler and Fillenwarth 288)
Similarly, Bridget Burns (2016) has called for more institutions to adopt the practice of "process mapping" to improve student experiences. She makes the point that "[a]s consumers, we expect that retailers or service providers have designed the experience around the customer. We become frustrated when things are counterintuitive, bureaucratic, slow, difficult or painful. So why should we tolerate flawed processes that frustrate our students?" (n.p.). She gives examples of process mapping initiatives conducted by Georgia State University and Michigan State University that assisted students, especially first-generation students who lacked external support, with navigating university processes such as those surrounding admissions and financial aid. (Masters-Wheeler and Fillenwarth 289)
Viewing student experience as user experience forces us to view programs from a new angle. Adding UX to our continuous improvement practices can challenge underlying assumptions about what education means—in a beneficial way. Framing students as users can be a disruptive and innovative program design practice ( Johnson et al., 2017). UX in program design positions students as active learners who already possess valuable knowledge sets, even as they seek more skills and knowledge from an educational program. (Masters-Wheeler and Fillenwarth 290)
Students are the users of our products—the educational experiences facilitated by our programs. Yet, our unspoken assumptions may resemble the reverse scenario—we may tend to regard students as the products of our programs. (Masters-Wheeler and Fillenwarth 290)
The continuous improvement and UX processes that we apply in program design can increase the quality of these educational experiences, but students ultimately determine how they interact with the product and how they use it in their lives and careers. (Masters-Wheeler and Fillenwarth 290)
As a first step in applying UX principles to program redesign, we now turn to our study of the various ways that students interact with a program's representatives, spaces, activities, and artifacts. These sites of interaction may be viewed as interfaces. Identifying these allows us to create a map of all the sites where students encounter the idea of an individual TPC program. These interfaces may fall into some of the "programmatic landscapes" as defined by Schreiber and Melonçon (2019); however, the focus for a UX methodology will be on how students experience these areas, which will be completely different from how a program administrator experiences them. (Masters-Wheeler and Fillenwarth 291)
While Guo's approach is geared towards business products, these four elements of UX may also be applied to the design of any system. Guo simplifies the purpose behind each element with a question: Value - Is it useful? Usability - Is it easy to use? Adoptability - Is it easy to start using? Desirability - Is it fun and engaging? (Masters-Wheeler and Fillenwarth 291-292)
To explore Guo's four UX elements in our programs' interfaces, we have developed and administered a survey to current students and alumni at our respective programs that have some similarities and many differences. Gracemarie's program at an R2 university (formerly a regional comprehensive university only three years prior) has recently developed a minor, certificate (for non-writing arts majors), and concentration (for writing arts majors) in technical and professional writing. (Masters-Wheeler and Fillenwarth 292)
Through this survey, we seek to identify the multimodality of student interactions. Some interfaces are concrete artifacts, while some interfaces are immaterial—they involve exchanging ideas about the program by talking to people and participating (voluntarily or involuntarily) in experiences. We must also keep in mind that the interfaces through which students encounter our programs actually involve varying degrees of programmatic involvement (and therefore control). (Masters-Wheeler and Fillenwarth 293)
The first set of survey questions asks students four questions about their institution, major or minor area of study, and how far they have progressed in their program. In our analysis of survey results, we evaluate significant differences in the answers of current students versus alumni across both institutions. Responses from current students and alumni of each institution's technical / professional writing program are considered valid. (Masters-Wheeler and Fillenwarth 293-294)
This UX survey focuses on general attitudes about the program's worth or usefulness, but the primary focus is not on evaluation of program content. Survey questions about perceived value from students' perspectives should complement, not replace, alumni and employer surveys that help determine which TPC curriculum areas are valuable in professional settings. (Masters-Wheeler and Fillenwarth 294)
We adopt Guo's "usability" category because we want to measure how easy it is for students to "use" our programs without encountering practical or logistical problems. This category is separated from user perceptions of value and desirability, although somewhat arbitrarily—user perceptions about value and desirability cannot be completely divided from the more practical aspects of use. (Masters-Wheeler and Fillenwarth 294)
UX methods that focus on "usability," understood as students' "use" of a program, can help us to identify and remove any barriers that may hinder students' progress through these requirements. Our survey asks five questions concerning usability that focus on how easily students progress through the program. In contrast to the adoptability section, which deals with how easy it is for students to learn about and enter the program, this section focuses on students' progression as program users. (Masters-Wheeler and Fillenwarth 295)
This section asks five questions that help to establish how students perceive what we have called the program interfaces—the sites where the idea of the program surfaces for students. Becoming aware of the program and what it entails allows student to evaluate whether it suits their needs. This section of the survey also measures how easy students thought it was for them to join the program by declaring it as their major/minor (or certificate, if applicable). (Masters-Wheeler and Fillenwarth 295)
The element of desirability involves students' satisfaction with the program. Education is not entertainment—it is not supposed to be "fun." Nonetheless, as we address in our discussion of UX methods, there may be ways to evaluate whether students are engaged and satisfied that go beyond data usually gathered through traditional course evaluations, which come with their own controversies about gender bias, racial bias, and general ineffectiveness in evaluating personnel. (Masters-Wheeler and Fillenwarth 295)
One of the consistent findings of our survey was the importance of people— particularly professors and advisors—and artifacts as program interfaces. Professors were mentioned, often by name, in questions surrounding program value, and advisors were cited as an essential component of ease of use. Professors also played a large role in adoptability by introducing students to available programs, and advisors contributed through their assistance in helping students go through the steps of formally adopting their program. (Masters-Wheeler and Fillenwarth 302)
Artifacts also played a large role in respondents' experiences in their programs, particularly in the areas of adoptability and ease of use. Though students' first exposure to the program was typically through a person, artifacts more commonly provided information throughout students' experience in a program. As artifacts also came up as a highly requested way to clarify program requirements for questions regarding ease of use, we need to seriously consider the role of documents in helping students understand and navigate our programs. (Masters-Wheeler and Fillenwarth 302)
Questions regarding content, design, and access to these artifacts would all be relevant. Participatory design projects, as described by Salvo and Ren (2007), could follow, perhaps assigned as course projects where students would develop engaging and useful program artifacts. (Masters-Wheeler and Fillenwarth 302)
Implementing UX tools such as interviews, focus groups, or observations would be a starting point for additional research to help us learn more about student-faculty/advisor interactions. Methods such as think-aloud testing could also be implemented with faculty and advisors to help improve the usability of the documents from which they draw their knowledge. (Masters-Wheeler and Fillenwarth 303)
As a next phase of research, task analysis, which examines the actions users take as they work toward completing a task, would be a particularly helpful research tool to implement. In the case of our programs in particular, task analysis relating to advising and course selection would provide helpful insights into the ways that various people, artifacts, and experiences come into play as students navigate the course registration process. (Masters-Wheeler and Fillenwarth 303)
While we could again speculate about students' reasoning for preferring one variation over the other, additional research could more productively illuminate students' perceptions regarding this distinction. Interviews or focus groups could be particularly helpful for learning more about major and course preferences. (Masters-Wheeler and Fillenwarth 304)
As our students, institutions, and worlds change, so too will student needs and experiences with our programs. For example, in a preCOVID-19 world, Christine's focus group finding leads us to reassess the value of physical artifacts. In the world in the midst of a pandemic (as of this writing), however, when students may not be physically on campus, such physical artifacts will obviously shift in importance in students' experience. (Masters-Wheeler and Fillenwarth 306)
All of this is not to discourage user research in the present moment or to demand incessant research that never allows us to make changes, but simply to encourage faculty and administrators using UX-based approaches to programs to adopt an attitude of continual curiosity toward user experience, as advocated by Schreiber and Melonçon, and to be attentive to context and time in planning and analyzing data. (Masters-Wheeler and Fillenwarth 306)
| Cliff Kuang, Robert Fabricant Notable QuotationsExpand to full screen
User Friendly: How the Hidden Rules of Desigbae Changing the Way We Live and Wrok Cliff Kuang Robert Fabricant Introduction: The Empire of User-Friendliness [Harlan] Crowder [of IBM] proposed that a computer program be gauged not just on how well it solved a problem but on how easy it made the lives of the people trying to solve it. To be clear, he didn't actually invent the term. As far as he knows, it had been floating around in the air, and it was there when he needed it. ... There's a certain magic in how a few words can elide so many stories and so many ideas. This book is an attempt to paint a picture that's gone missing in plain sight. In an era in which 2.5 billion people own smartphones, user experience now occupies the center of modern life, remaking not just our digital lives but also business, society, even philanthropy. ... This book is about how the idea of user- friendliness was born and how it works. ... Many of the ideas in this book will be familiar to user- experience designers— the people who observe our lives so that they might invent new products. Part I: Easy to Use Confusion > Three Mile Island Disaster There is a critical slip between what the men understand and what the machine is telling them. ...when you look hard enough at monumental machine disasters, you can usually find a design problem. ...You have to know why people behave as they do— and design around their foibles and limitations, rather than some ideal. One reason we find apps easy to understand even if we've never used them before is that navigability and consistency are so ingrained into the patterns of app design today. Menus all largely behave the same way; so do swipes and taps. ... There was one essential thing whose failure loomed largest at TMI, one essential thing that we demand of any gadget in our lives: feedback. Feedback that works surrounds us every day, so we rarely think about it. It's feedback that defines how a product behaves in response to what you want. It's feedback that allows designers to communicate to their users in a language without words. Feedback is the keystone of the user- friendly feedback; in the man- made world, that feedback has to be designed. When you push a button, does the button actually affect the thing it's supposed to? There may be no greater design challenge for the twenty- first century than creating better, tighter feedback loops in places where they don't exist, be they in the environment, health care, or government. In a previous era, we used brands to create trust— when you saw a toothpaste stamped with Colgate, you knew it was the product of a big, stable company whose long- term success depended on good products. Today, we have feedback from people who've tried out something we might like; even if you don't know them, you put your faith in there being a lot of them. One of the most significant technologies of the twenty- first century, artificial intelligence, rests on feedback: Put simply, AI and machine learning are a collection of methods that allow algorithms to gauge how well they've performed, and then tweak their own parameters until they perform better. When feedback is tied not merely to the way machines work but instead to the things we value most— our social circles, our self- image— it can become the map by which we chart our lives. It can determine how the experiences around us feel. In an era when how a product feels to use is the measure of how much we'll use it, this is everything. Mental models are nothing more and nothing less than the intuitions we have about how something works— how its pieces and functions fit together. They're based on the things we've used before; you might describe the entire task of user experience as the challenge of fitting a new product to our mental models of how things should work. When we can't assume how a gadget works, we use feedback— in the form of trial and error— to form a hazy mental model of its logic. Feedback is what turns information into action. The secret to having a productive argument with your spouse is to listen to what she has to say, repeat what you just heard, then finally have your spouse confirm that's what she meant. 2. Industry The designers who create them assume that better product design can be wielded to solve almost any problem, even those on a societal scale. ... By understanding someone else's life— abashed, prideful, confused, curious— you could make their life better. By understanding how he or she thought, you could reach past the obvious problem and into the problem that they couldn't quite articulate, the one that they might not even think to solve. Dreyfuss turned the question from what to make and how to make it, into whom to make it for. ... He'd even ginned up a slogan: "Design is the silent salesman." ... Dreyfuss was perhaps the first American designer to articulate and then act on the idea that design wasn't just styling— it sprang from a knowledge about how things were made and what was possible. Dreyfuss described design as an act of translation between the companies that made things and the consumers who used them. ... the performance of men under duress bore no resemblance to that of those operating a demonstration model. They realized that as much as humans might learn, they would always be prone to err. But if you understood why these errors occurred, they could be designed out of existence. Seeing humans as they are, instead of as they're supposed to be, was one of the great, unappreciated intellectual shifts of the twentieth century. What both user- friendliness and behavioral economics shared was an overriding sense that our minds could never be perfected, and that our imperfections made us who we are. User- friendliness is simply the fit between the objects around us and the ways we behave. The truest material for making new things isn't aluminum or carbon fiber. It's behavior. 4. Trust The magic of a well- designed invention is that you seem to know how it will work even before you've used it. As his frequent collaborator Byron Reeves told The New York Times, "Everybody thought [computers] were tools, that they were hammers and screwdrivers and things to be looked at in an inanimate fashion. Cliff said, 'No, these things talk, they have relationships with you, and they make you feel good or bad.'" 11 Grice's maxims, which boil down to being truthful, saying no more than you need to, being relevant, and being clear. Grice's maxims also shed light on politeness. Being polite means following a conversation, not co- opting it and dragging it in other directions. It means knowing who you're talking with, and knowing what they know. It's not enough to make a dashboard just easy to use or easy to read. And while we don't need a dashboard with a full- blown personality, it'll have to have personality traits. It'll need to be calming, communicative, or helpful, as the situation demands. If you do have a smart speaker, it's probably the most expensive kitchen timer you've ever bought— and it remains only that, because whatever else it might do is difficult to discover and impossible to remember. Don Norman is probably most famous among designers for popularizing the idea of an affordance— physical details, designed in products, that tell us how they're to be used, such as the subtle curve of a door handle that tells you which way to pull, 5. Metaphor The design theorist Klaus Krippendorff writes, "Metaphors die in repeated use but leave behind the reality that they had languaged into being.") ... that metaphors provide us a web of inferences, which we use to explain the underlying logic of how something should work. ... If time is like money, then, just like money, it can be saved or invested wisely; it can be wasted or stolen or borrowed. 9 ... The right metaphor is like an instruction manual but better, because it teaches you how something should work without you ever having to be told. faithful mimicry of how the physical world behaved would make the digital world somehow easier to understand— and even magical, if that mimicry was nuanced enough. Amazon's 1- Click would easily be the single most consequential button ever invented, but for the Facebook Like button. ... That's how metaphors work: Once their underlying logic becomes manifest, we forget that they were ever there. No one remembers that before the steering wheel in a car, there were tillers, and that tillers made for a natural comparison when no one drove cars and far more people had piloted a boat. ... Designers still scour the world for metaphors that relate not just to how we understand a product, but how we feel when we use it. ... This is almost a universal practice in design, creating mood boards to summon how something should look and feel, and then trying to translate those into form- giving metaphors and words. ... personification is just one of the ways designers use metaphors to create beauty. Part II: Easy to Want 6. Empathy Design thinking, "user- centered design," and user experience are all forms of industrialized empathy. ... the best students didn't demonstrate creativity in solving a problem so much as in finding the problem. ... the discrepancy between how people were supposed to use things and how they actually used them." ... In addition to creating a culture in which the entire staff became students of human behavior, there were two more ingredients in IDEO's way of working: putting prototypes, no matter how primitive, in front of users as quickly as possible, and the idea that the design process didn't lie with any one "designer." To create a design that worked, you had to build it, watch it fail while people tried to use it, fix it, then watch it fail again until you finally had something. ... All these processes are subsumed in a larger, ubiquitous framework— observe, prototype, test, and repeat— that equates observation with creation. Influence spread only because IDEO created the vocabulary that others could use to sell the idea that "design" wasn't just prettiness. Rather, it was a process of industrialized empathy— one that could be marketed, explained, circulated, repeated, and then spread. Even if a disproportionate number of inventions begin with someone's personal sense of a problem, most inventions aren't perfected by their creator but rather by other people who finally understood a problem after someone else inspired them. 7. Humanity But Capital One also discovered that if Eno had some sense of humor and could talk to people about other things besides banking, people would use it more. ... balance. "You would be surprised how delighted people are when they can extend the conversation beyond a functional- use case," said Audra Koklys, Capital One's head ... "We actually designed character flaws because we found that's how people connect with characters." 8. Personalization Movies might seem like Disney's core business, but they are really marketing vehicles. Most of the company's billions come from turning movie hits into franchises: first with toys and TV shows, then with theme- park rides that imprint kids anew, powering sequels and selling more toys. Today, we are surrounded more and more by technology like this, meant to serve us without our ever having to ask or even to push a button. ... few years later, looking up at those tiles. 6 The park couldn't have been built without an abiding faith in a user- friendly world where commerce was social progress, and better design meant a better life. Hyperpersonalization. As the gadgets around us become more and more capable, they'll need to become more polite, more socially aware. They'll need to adopt better etiquette, and to do that, they'll need to model our mores better. ... When technology gets laced into the fabric of everything, what we demand is that those technologies hew closer to our social mores and the expectations of polite society. 9. Peril Of course, almost no one had consciously thought to create a world of Skinner- inspired addictiveness. But that only makes the creation more profound. Designers evolved these solutions because, in a quest for what got users to come back more and more often, they stumbled upon what we cannot resist. But a mix of ambition, intuition, ingenuity, and greed rediscovered one of the intractable facets of our brain chemistry. The most enduring businesses in the world have always been built upon addiction— alcohol, tobacco, drugs. The trick of the user- friendly world is that not only are we addicted, the drug doesn't have to be bought. The drug lies in our own brains, hardwired there by evolution. Making things easier to use morphed into making them usable without a second thought. That ease eventually morphed into making products more irresistible, even outright addicting. For a brief period in Silicon Valley, that search for addictiveness seemed harmless— partly because addiction itself was usually framed as "engagement," a Silicon Valley byword for having users constantly coming back for more. ... Today, Skinner's blind focus on whatever goads an animal into action has been transformed, thanks to technology platforms, into a presumption that what users want can be reduced to what makes them click. ... omits motive in favor of impulse and action. Alan Cooper, the eminent user- experience designer who came up with the idea of user personas, "Facebook's most consequential impact may be in amplifying the universal tendency toward tribalism. Posts dividing the world into 'us' and 'them' rise naturally, tapping into users' desire to belong. ... Moreover, on the street, people might think awful things, but they're held in check by the rhythms and mores of the commons. Society, after all, is built to encourage some behaviors while tamping down others— to foster certain types of communities while holding others in check. That is society's most basic function. Facebook, by contrast, makes it easy to say awful things in public. Kosinski had shown that if you knew a person's Facebook likes, you knew their personality. ...And if you knew their personality, then you could readily tailor messages to them— based on what made them angry or scared or motivated or lonely. ... Kosinski's work had proved in startling fashion that Facebook's advertisers didn't have to rely on crude demographic targeting. Instead, with the mere rudiments of Facebook's data, they could target people based on their specific personalities: how a particular person reacted to messages of fear or hope or generosity or greed. ... Facebook may be harmful precisely because it allows people we don't know, with motives we cannot track, to predict exactly who we are. ... Yet in hiding great complexity behind alluringly simple buttons, we also lose the ability to control how things work, to take them apart, and to question the assumptions that guided their creation. ... The more seamless an experience is, the more opaque it becomes. Good user- experience design always hinges upon making an interface well ordered, with an intuitive logic that's easy to navigate, and making sure that interface engages you with feedback, letting you know whether you've done what you wanted. user- friendly products trap us in assumptions we can never break. ... when digital products have greater and greater reach, it means fewer and fewer people are making the decisions. ... The automation paradox suggests that as machines make things easier for us— as they take more friction from our daily life— they leave us less able to do things we once took for granted. ... As gadgets get easier to use, they become more mysterious; they make us more capable of doing what we want, while also making us more feeble in deciding whether what we seem to want is actually worth doing. ... Designers now have to confront the alarming possibility that user- friendliness helps us avoid consequences by abstracting away any downstream impacts. ... the user- experience designer Alan Cooper has called for something he calls "ancestor thinking" in design: a consideration not just of whether a product works, but what its implications are. ... a new way of working that privileges the future over the present, 10. Promise The ease of readapting user- friendly patterns is the single biggest reason that design now dwells in so many places we wouldn't expect. ... we know what usability means— it's feedback, mental models, ... creating the incentives and feedback loops ... social networking was the product of a generation of latchkey kids who grew up isolated in the suburbs; ... when a designer creates something new, she is giving form to a thought that allows other people to become more than they were. ... The next phase in user experience will be to change our founding metaphors so that we can express our higher needs, not just our immediate preferences. As the acclaimed Japanese industrial designer Naoto Fukasawa— an early IDEO employee— eloquently put it, the best designs "dissolve into behavior" so that they become invisible rather than stand out for their artistry. designers must accept the consequences of their work in the world, not just the intentions that went into designing them or the beauty of the result. ... we are highly trained tinkerers, with a robust set of prototyping skills that make up for our lack of formal credentials. What does it feel like to follow a user- centered design process, step- by- step? 1. Start with the User individual needs often diverge quickly once you start observing what people actually do. ... Once you identify an interesting group of people to learn from, you have to meet them on their own terms. ... why you should always meet users on their turf, and why you should also conduct research in a way that puts them in the lead. They should be the guide to their own world. ... Designers have developed a number of clever techniques to open up fresh windows into users' lives. For example, you might try asking someone to unpack their handbag, backpack, or satchel while narrating the reasons they choose to keep certain objects with them at all times— ... we use these techniques not to learn about the objects themselves (though that can be interesting) but to get at the deeper motivations behind people's choices, particularly their habitual ones. 2. Walk in the User's Shoes "The critical component is to not just notice what people are doing, but to really try to understand what's driving it," 3. Make the Invisible Visible ... feedback surrounds us every moment of every day, helping us to make sense of the user- friendly world. If feedback is well designed, we generally take it for granted. ... Feedback is the fundamental language of user- friendly design. But the big challenge with designing feedback is figuring out when and where to provide it. Many designers I know are fond of a technique called "Wizard of Oz," in which we use smoke and mirrors to simulate the behavior of a smart system to see if it makes sense to users long before our clients invest in building it. "Treat your competitors as your first prototypes." 4. Build on Existing Behavior behaviors we can observe in the world today— even if they might seem pretty unusual at first— rather than potential future behaviors dreamed up by marketing executives. ... Users tend to be surprised when you show interest in their work- arounds and mental assists, but they are invaluable sources of insight. The job of the designer is to surface these mental models so that products can be better tuned to user expectations and easier to integrate into our lives. ... ask the user to sketch the way something works from memory. ... ask the user to draw and label the various options and choices from memory so that you can get a deeper window into their understanding ... then ask the users to narrate the steps they go through to complete a simple task (something designers call a "think- aloud"). And I ask the users to narrate a series of actions, the ones they are accustomed to as well as ones they might not have tried, ... Exercises like these can reveal the limits of the mental model the user has constructed for how and why something works the way it | Brad Nunnally, David FarkasNotable QuotationsExpand to full screen
Page 8 Throughout this book you'll find exercises that connect the tasks of research with the act of being in the world. Preface Page 17 We will compare the rigor of research in academic settings with the streamlined and often-accelerated research found in product design. Page 17 Advances in manufacturing and mass production allowed improvements in efficiency and utility to be explored in a scalable and measurable manner. Page 20 who knows the challenges of a task better than those completing them? Page 22 GOMS: Goals, operators, methods, and selection rules measure the intent and process of a system. Conveniently, this approach can apply to both physical and digital spaces. Page 23 Goals asks the question "What do you want to do?" ... This pillar questions participants' frame of mind and clarifies what supporting information might be needed to be successful not just in the moment but also throughout a task. ... Operators seeks to understand "What tools do you have as a person to get the job done?" ... Methods looks at "How can the tools, or operators, be used to complete the task?" ... Selection rules, the final pillar of GOMS, measures the various options an interface or product offers to assist users in accomplishing their goals. Page 25 GOMS in practice: Keystroke-Level Modeling ... At its simplest, KLM is the mathematical study of a tool's efficiency. Page 38 Good research starts with good questions. You must start with an idea of what type of questions you want to ask. Page 39 It's hard to conduct research when you don't know what question needs to be answered. Every research effort starts with you needing to know why something happens, what people do in certain circumstances, and how they perform key tasks. Page 39 we must find people to talk to and phrase our questions effectively to get to the heart of the matter. Page 40 The following factors can lead to misinformed or poor research results. ... Leading questions ... Research participants want to be helpful and want to provide value to your team. Since they are primed to help, if you ask a question that implies the type of answer you want, they are more likely to give you that answer, even if it doesn't really apply to them. ... Shallow questions ... Yes/ no questions are harmful because they give participants an easy out. ... Personal bias ... The less "you" there is in the interview, the better the information that you collect will be. ... "Tell me about your experience with your accounting software" than "I know I always struggle with invoices; what challenges do you have with your software?" ... Knowing When to Break the Rules ... You can even use a participant's personal and unconscious bias to drive to a deeper conversation about how people might use a product. ... You can use leading questions to help build trust with a participant and to validate a previous comment they made that maybe wasn't totally clear. ... When you start a research session, sometimes participants aren't yet comfortable and they need to get used to talking with you and answering your questions. Shallow questions give participants that opportunity and can help ease them into the activity so you can get to the good stuff. Page 43 The Basic Structure of a Question The Setup: Every question starts with a purpose, or setup. This takes the form of what (description), why (explanation), how (process), when (situation), and where (context). It gives the participant an idea of the type and, more importantly, the length of response you expect out of them. Page 44 Area of Inquiry: The area of inquiry is what you want to learn about— for example, how your product impacts or influences someone's life. Page 44 Laddering: Some responses to your questions will have an automatic "Why?" behind them. Asking for a participant to go into more detail or to explain the rationale behind their response is known as laddering, and it's an aspect of a question that helps you get to deeper information and potentially impactful stories. Page 44 Segue to Next Question: The best research sessions are focused conversations between you and a participant. Page 45 Every question in your interview guide should tie back to why you're doing research in the first place. Page 45 Example: Learn about how people determine which photos to share with family and friends. Bad question: How do you ensure that you get good composition when you're out taking photos? Revised question: When you're out taking photos, how do you know a particular shot is worth sharing with people? Page 47 Figuring out what we want to learn is the easiest part of my day. The hard part is translating our big research questions into good interview questions. We're often trying to answer abstract questions (like "What's hard about interacting with the government?"), but abstract interview questions aren't fruitful. Page 50 The motto of a good researcher is "there is no such thing as user error." One thing you'll learn is that many users will blame themselves. These are moments of exploration because you can get to the source of why errors are made and what frustrates users when things don't work out for them. Page 50 How to Practice Asking Questions ...It's always a good idea to validate your interview guide internally. From the product owner to the engineering team, these are the folks who are putting their time and energy into building a product, and their feedback helps refine and shift your lines of questioning. Page 56 Quantitative Research by the Numbers Quantitative research is simply defined as the study of what can be measured and observed. Page 56 quantitative research means the results will be consistent and generally agreed on by all parties involved. Page 56 Bounce rates Time on task Conversion rates Order size (number of items or their value) Number of visitors to a site (physical or digital) Average size of group Page 58 While very informative, quantitative research doesn't tell us how to fix things, doesn't tell us why things happen, and doesn't share information that isn't asked for. That being said, quantitative research can act as a benchmark for future studies and for qualitative research. Page 59 Numbers do not consider context of use. ... Insight-driven research seeks to understand what the problem space is, why the problem exists, and where opportunities lie. ... For quantitative research, insight-driven research manifests as benchmarks, often referred to as key performance indicators (KPIs). ... Evaluative research, on the other hand, looks to measure how a design or solution stands up to the KPIs and benchmarks laid out. Page 61 Lastly, generative research methods offer opportunities to create and explore new designs through research. Often called data-driven design, generative research methods balance subjective design recommendations and trends with quantifiable, measurable gains and opportunities. Page 61 System analytics are probably the most common form of quantitative data. Often referred to as site analytics for web-based experiences, analytics provide passive access to a wide array of data points. Analytics are a great example of insight-driven research because of their low cost of entry and, assuming correct tagging on the backend, depth of information. Some of the most common pieces of data include user flows, demographics, and geography. Page 63 Unlike analytics, surveys straddle both evaluative and insight-driven methodologies by providing data around not only how a system is used but also how it might meet or fail to meet expectations. Page 65 Tree Jacking Tree jacking is an example of a generative research method, though it can also be used as an evaluative measure. It is a method of evaluating a system's navigation and terminology. A designer will enter a proposed taxonomy into the system and prompt customers to navigate the information. By clicking through the site map, the designer gathers data on users' expectations and understanding of terms by tracking their path through the tree structure. Page 71 different. Quantitative methods are best used when a large number of participants or customers may be accessed for the most statistically significant outcomes. Page 71 A good question for quantitative research might be "How many users abandon the checkout process when signing up for a product's service?" Page 72 When you are looking to understand a user's motivations or comprehension of a task, qualitative methods, covered in Chapter 4, offer more tangible results. Similarly, if you have access only to a small number of users, analytics may not be statistically significant. Page 77 design. As efficiencies in technology reach a predictable flow, designers seek to do more than streamline tasks. They ask what drives people to do the work they do and what makes it enjoyable. Enter qualitative research. Page 77 is subjective, notably the personal stories and challenges of our customers. Where quantitative research focuses on what can be measured, such as the time to complete a task, qualitative research looks at why customers are completing the task in the first place. Qualitative research seeks to understand customers' motivations and desires by focusing on comprehension and accessibility that might not be numerically measured, but can nevertheless impact usability and desirability of systems. Page 78 Qualitative research has roots in ethnography, anthropology, and psychology. The study of how humans behave is, at its core, qualitative research. Page 78 Projects may have as few as 5-10 participants or, for projects with a broad scope and range of user profiles, upward of 20-40 people. Page 81 For a landscape analysis, designers identify existing products or services that reflect a portion of the new product's functions or customer segmentation. ... identify broad gaps or opportunities Page 84 Contextual inquiry Contextual inquiries— also known by names like think-aloud studies and ride-along studies— are easily the most common version of discovery and exploration. Page 86 Contextual inquiries can be conducted with as few as 5 or as many as 50 participants, depending on the project scope. Page 86 This is a major distinction from quantitative research, where large data pools are the only way to guarantee good data. Contextual inquiries instead rely on trends and the researcher's experience and ability to make judgment calls about what is important. Page 86 as you start to hear the same information again and again, you have conducted enough research. Page 88 Participatory Design Participatory design takes on many shapes and flavors. This may be as simple as a workshop brainstorming ideas and opportunities, or a more formal sketching exercise. Participants may be asked to sketch actual interfaces or adapt their mental model in visual ways. Page 93 Qualitative methods prove effective when there is a small, identifiable population of customers. Page 93 One of the major hurdles qualitative research has is that it is a "soft science." Because it's not based in numbers like its sibling, quantitative research, many business stakeholders don't want to rely on qualitative research alone. To address this, invite stakeholders to observe and participate in qualitative research so they might experience the "aha" moments directly. Page 93 Quantitative and qualitative research methods are equals, not opposites. You cannot have one without the other. The most successful projects balance the two and inform our product designs. Page 94 Personas are fictional customers you create to represent various user types. These may include the call center representative, the tech native, or the Luddite. Traditional market segments are typically focused on the numbers (age, gender, geography) of customers. Similarly, classical personas may be generated from a handful of contextual inquiries with no hard data grounding them. Page 94 Data-driven personas balance this. By combining the analytical data about who's using a system with the data on users' wants, needs, and motivations Page 95 Data-Driven Customer Journeys Customer journeys are often created to illustrate the touchpoints a customer has throughout a process. This may be inclusive of an entire ecosystem, from researching cars online to entering an auto dealership to purchasing the car and making payments. It may also be more specific, such as onboarding for a new piece of technology. Page 101 Qualitative methods are great for small sample sizes, especially when you can travel to participants in their environment. But with products spanning global markets, travel costs and time may become cost-prohibitive. While many qualitative methods can be used remotely, location is one key factor in determining a research approach. Page 103 While a good foundation is important for choosing methods, the best research initiatives borrow from both qualitative and quantitative research methods. | Jaime Levy Notable QuotationsExpand to full screen
UX design and UX strategy are two different things. When you are doing design, you are creating something. When you are doing strategy, you are coming up with a game plan before creating something. UX strategy is the process that should be started first, before the design or development of a digital product begins. It's the vision of a solution that needs to be validated with real potential customers to prove that it's desired in the marketplace. Although UX design encompasses numerous details such as visual design, content messaging, and how easy it is for a user to accomplish a task, UX strategy is the "Big Picture." It is the high-level plan to achieve one or more business goals under conditions of uncertainty.Page: 7 Customers have to see the value in the new way before they'll consider abandoning the old.Page: 8 STELLAR UX STRATEGY IS A MEANS TO ACHIEVING DISRUPTION IN the marketplace through mental-model innovation. Page: 11 In the digital world, strategy usually begins in the discovery phase. This is when teams dig deep into research to reveal key information about the product they want to build.Page: 12 My formula is this: UX Strategy = Business Strategy + Value Innovation + Validated User Research + Killer UX Design. It's not enough to understand your marketplace if you don't talk directly to your customers. It's not enough to validate that your product works if you're not creating something unique.Page: 15 A competitive advantage is essential to the company's long-term existence. The two most common ways to achieve a competitive advantage: cost leadership and differentiation. When prices hit rock bottom? Then, the battle needs to be about what makes the product better.Page: 16 We need to recognize that building a business strategy isn't about formulating and executing a perfect plan. Instead, it's about being able to research what's out there, analyze the opportunities, run structured experiments, fail, learn, and iterate until we devise something of value that people truly want.Page: 21 In 1984, Michael Lanning first coined the term "value proposition" to explain how a firm proposes to deliver a valuable customer experience.Page: 22 When producing digital products, we must continuously research, redesign, and remarket to keep up with the rapidly evolving online marketplace, customer values, and value chains that are required to keep our products in production.Page: 23 User research is how you verify that you're on the right track with your value proposition.Page: 27 Validation is the process of confirming that a specific customer segment finds value in your product. Without validation, you are simply assuming that customers will find use for your product. Page: 28 Eric Ries popularized the term Minimum Viable Product (MVP). It simply means learning if potential customers want your product by building just the core features of your value proposition. Page: 28 The "user experience" (UX) is how a human feels when using the interface of a digital product while attempting to accomplish a task or goal. The common problem that many product makers don't realize is how much their UX decisions are tied to customer acquisition. Page: 30 Experiments need to be focused on how successful the value proposition can be communicated to the customer from the moment the customer opens the landing page. They learn everything about the existing market space to identify UX opportunities that can be exploited. They talk directly to potential users or existing power users of the product to discover and validate its primary utility with respect to the problem that must be solved. Page: 31 You first need to figure out what problem you're going to solve and what kind of customer needs it solved the most.Page: 37 a value proposition takes the form of a statement and is usually the first sentence out of the mouth, as it was for my movie producer client. Think of it as an elevator pitch — when you distill something into a discrete, easy-to-remember, compelling, and repeatable phrase. Its primary purpose is to communicate the benefits that the customer can expect from your offering.Snapchat is the fastest way to share messages, photos, videos, texts, and drawings with friends for a limited amount of time. Page: 40 Step 1: Define your primary customer segment. Step 2: Identify your customer segment's (biggest) problem. Step 3: Create provisional personas based on your assumptions. Step 4: Conduct customer discovery to validate or invalidate your solution's initial value proposition. Step 5: Reassess your value proposition based on what you learned! Page: 42 Your primary customer's pain point must be severe, because there is a lot of risk involved in trying to change how people do something in a familiar way to an unfamiliar one in an uncontested market space. Page: 43 Write out the customer and problem hypothesis in a statement. Which, if proven true, would validate an important need for the value proposition: Don't build your product's UX around a value proposition unless you have tangible evidence that people will want the product! Page: 45 Don't believe your own hype. Instead, approach every new product or project like an experiment. Personas can be a helpful tool in giving stakeholders and the product team an empathetic sense of what the end user's needs, goals, and motivations are. In this way, they can make a product more "user-friendly." Page: 46 By the third edition of Cooper's book About Face in 2007, he added a new section called "When Rigorous Personas Aren't Possible: Provisional Personas." Thus, you can think of a provisional persona as a "back-up" or "low budget" persona, which is better than having no persona at all. The provisional personas will collect and present the assumptions you are making about your primary customer segment. You want to focus your personas on what you assume is important to customers and how they are currently dealing with the problem. Page: 48 Name and snapshot/sketch Description Behaviors Needs and goals Customer discovery is a process used to discover, test, and validate whether a specific product solves a known problem for an identifiable group of users; it is essentially conducting user research. "Get out of the building" and get customer validation. Page 51 During customer discovery, the goal of the interview is to talk to real people.Page: 52 good screener questions must help you quickly weed out the wrong people. They should seem nonintrusive to the participant, but you know they are deal-breaker questions. It might be helpful to work backward. What are the exact answers you must hear from someone to qualify them for this mini-experiment? Page: 54 Phase 1: The screener questions Phase 2: The interview Customer discovery is about listening and not selling. You end with your money-shot questions, which are when you actually pitch your hypothetical value proposition. Again, you want to listen and not sell.Page: 56 Step 5: Reassess your value proposition based on what you learned! (And continue to iterate until you have product/market fit.)Page: 59 Value propositions of products evolve with a great understanding of customers' needs. Page: 62 Conducting Competitive Research NOW THAT YOU HAVE A STRONG POSITIVE SIGNAL THAT YOU'RE ONTO something, you need to ask, "Why hasn't this solution been built yet?" Page: 63 Chances are you actually are entering an existing market. Therefore, you want to study how all the current digital solutions address the needs of your target customers.Page: 66 First, you look for direct competitors — products that compete head-to-head with your value proposition.Page: 72 Competitive Analysis Matrix template To beat your competitors, you need to know exactly what they are doing. You want to know the unknowns. In many cases, the only way to learn this is through your competitor's experience and/or sales funnel by becoming a user yourself. Page: 76 Create one global username and password for use on all the products in your audit. If you are creating profiles on social networks, do not use your personal or work email address! Instead, create secondary accounts on Gmail or Yahoo first. Then, use these fake email accounts to create fake profiles. Page: 77 The purpose of the site is why it exists; it's basically the high-level description of the product or the value proposition. Think about how the competitor might explain it to a user or investor. "About" or "About Us" section The value proposition is often written here by the competitor. Page: 78 Year founded Funding rounds Revenue streams Monthly traffic Check out Compete.com, Quantcast, and Alexa. You can pilfer free data from these sites and many others. By looking at multiple data points, you can triangulate an average of the traffic data, which is a lot better than not having any traffic data at all.Page: 81 Primary categories If the site is selling products (like Honda.com) or offering content (like Oprah.com), you need to understand how everything is categorized. The site probably (hopefully) already does this, so check out the site's global navigation menu. Social networks: You need to determine what social media strategy each competitor is trying to harness well.Page: 82 Content types: what type of content is on the competitor's site, as depicted in Figure 4-21. Page: 83 Personalization features: Does it display the user's name? Does it remember and display the last item the user looked at? Does it let the user make a list of favorite items? Community/UGC features User-generated content (UGC) or crowd-sourced content is content that is created by the users.Page: 84 Competitive advantage: distinct features that a product provides that are not found in its competitors. Figure out the top three differentiators of each product and list them in this cell. Ask yourself questions such as which features were successful because the product was first-to-market? Can those features be easily replicated? Which is better, the filtering or the large database. Heuristic evaluation Heuristic is a fancy word that means experimentation and trial-by-error, using the product to see how it works for you, personally.Page: 85 Customer reviews General/miscellaneous notes Questions/notes to team or self One last note: often product teams and stakeholders don't keep an eye on the market after they've moved on from the research process. The competitive landscape is always shifting; consequently, competitive research will never be final.Page: 87 When analyzing things, you are essentially trying to convert a lot of information into smaller actionable bites.Page: 92 A data point is a discrete unit of information. Any single fact or observation is a data point. In our analysis, data points can help us illuminate whether something is a failure or success.Page: 95 your goal is to make it easier to identify which factors give other products their competitive advantage.Page: 97 When you benchmark the indirect competitors, you're analyzing how these digital products offer alternative ways to solve a problem.Page: 99 Often, you will notice common patterns being repeated across many sites within a vertical market. You might wonder why they're all broken in the same way. You might realize that they are all overlooking an especially useful capability that could be the secret sauce you use in your value innovation. Are most of the losers missing on content, traffic, stickiness, personalization, a bad browse or search experience? Determine the causes. You can say which competitor is number one, number two, and who's doing something impressive even if they're farther behind in the marketplace race than others.Page: 99 Abductive reasoning is a form of logical inference that goes from observation to a hypothesis. It accounts for the reliable data (observation) and seeks to explain relevant evidence.[38] It shapes your rationale for making recommendations, and making recommendations is the purpose of the Competitive Analysis Findings Brief. Page: 101 This document will be what your client takes away from your research.Page: 102 Which competitors are closest to delivering a similar value proposition (that is, an online shopping site featuring high-end brands)? Are their products failing? Why? Or are their products succeeding so well that there's no room for your product? Which competitors directly appeal to your customer segment (wealthy men)? Of those competitors, how do you think customers discover them (perhaps paid advertising)? What products offer the best user experiences and business models? Who is doing something unique? What is working well for them? What do they have that your users like? What opportunities exist for it? What gaps could it fill? Page: 102 In short, your goal is to determine if there's room to win, and that's when you need to address the opportunities based on the research.Page: 103 need to show incredible value before you move into a formal design phase.Page: 117 the patterns aren't about building replicas of existing products. Instead, you want to build on existing design conventions and take those capabilities to the next level.Page: 123 there is nothing illegal about poaching features and interaction patterns that are the general approach to solving a common type of problem. Page: 124 "A Minimum Viable Product (MVP) is just enough product to validate your key experiences (value proposition)." The key experience is the feature set that defines your value innovation. It must exist in order for your product to have a competitive advantage. It defines the experience that sets your product apart from all others. To get your idea juices flowing on the key experience, ask yourself these questions: What will make your provisional personas (hypothesized customers) love this product? What moment or part of the user's journey makes this product unique? Based on your competitive research and analysis, what scenario or feature resolves a big shortfall? What kind of workarounds are your potential customers currently doing to accomplish their goals?Page: 125 What is an example of the most important thing the customer can do with your product that they can't do with other competitors? What is the pain point that you are trying to solve that is not currently being solved by competitors? How would your solution be presented to the customers on a screen? Is it an interactive interface or a displayed result? Express the benefit that users will see. Finally, what would customers do next after they saw this screen? Would they realize the value proposition? Again, express the benefit as a scenario that the user will see.Page: 127 The storyboarding process has been around since German filmmaker Lotte Reiniger. The goal of a storyboard is to tell the story of your key experience(s) visually. Page: 135 Step 1: Create your list of panels. Keep in mind that you do not want to demonstrate all the features of the product only showing the most "valuable" Step 2: Decide on your visual format (digital montages versus sketching on paper). Do not waste time wireframing a storyboard. Step 3: Lay out your storyboard on a canvas, add captions below each panel.Page: 136 you need to jump from your storyboard to an MVP or prototype of your product. You'll then take it into small, structured, lean experiments to learn as soon as possible if your team's latest assumptions are on the right track and to force you to confront the reality of what it would actually take to make your business model work in the real world.Page: 144 The goal is to discover whether your hypothesis is right or wrong based on measurable results. After the experiment, you should be able to evaluate your results and accept or reject your original hypothesis. No matter what type, though, experiments are all about testing a variable in order to falsify a hypothesis. Any item, factor, or condition that you can control or change.Page: 148 do a lot less but do it well.Page: 152 You are simply de-risking your product by simulating as many of the frontend key experiences as possible without the interface backend to see what goes wrong and what goes right.Page: 155 MVP. But, what if you don't have an actual website yet? What if you just have a storyboard and an idea? That's when a prototype comes in handy. The goal of a prototype is to avoid coding and designing until you have true validation A prototype is anything that serves to familiarize the user with the ultimate experience you are trying to create. These can be low-fidelity paper prototypes or high-fidelity mockups. In the tech industry today, prototyping is a big deal. Digital teams create highly detailed ones in programs such as Axure or OmniGraffle. These prototypes can be useful for tactical usability testing and conveying functionality to the development teams. Conversely, they can easily become resource overkill just making something "clickable" to convey a strategic concept, a prototype that you don't learn from is a waste of time. Page: 159 Step 1 Write a simple list or an outline of the screens you are going to show.Page: 160 Step 2 Start creating mashing up the images that tell the story.Page: 161 Step 3 Paste all the screenshots into the presentation tool.Page: 170 The users need to feel like they are using the interface even if it isn't interactive.Page: 170 Most experiments fail, so just think through the outcomes and focus on the worthwhile takeaways. Sometimes, the results will not be black and white. Thus, you will need to interpret them by having collaborative team-debriefing discussions following each experiment.Page: 174 The purpose of conducting user research is to understand the needs and goals of your target audience in order to inform the value proposition of the product.Page: 180 techniques. The two recent ones that I like are UX for Lean Startups by Laura Klein[56] and The User Experience Team of One by Leah Buley.[57]Page: 180 UX for Lean Startups by Laura Klein[56] and The User Experience Team of One by Leah Buley. User research usually involves usability testing and/or ethnographic research. Usability testing focuses on whether your products work by discovering how people use the product in real time.Page: 180 Traditionally, usability testing is conducted in special usability labs with two-way mirrors or on the premises of large corporations. Nowadays, it can be conducted remotely by using online services (such as Usertesting.com) that provide quick, reasonably priced screencast videos that record how people use your product or prototype while speaking their thoughts. Page: 181 In contrast, ethnographic research — the study of people in their natural environment Page: 181 For clients with little time or no budgets, traditional user research such as ethnographic studies would take too much time, and usability testing just isn't relevant to help determine if your value proposition is on target or your key user experiences provide value innovation. That's where guerrilla user research comes in — it's cost-effective and the mobile tactics should help you to validate the following quickly: Are you targeting the correct customer segment? Are you solving a common pain point the customer has? Is the solution you are proposing (demonstrated in the prototype of key experiences) something they would seriously consider using? Would they pay for the product, and, if not, what are the other potential revenue models? Does the business model work? Page: 182 The planning phase is the most complicated of all three phases because it involves everything from finalizing your solution prototype to scheduling the participants. Everything must be thought through, timed, and rehearsed.Page: 184 The five steps that I will teach you to ensure a successful planning phase include the following: Step 1: Determining the objectives of the research study. Define which aspects of the value proposition and UX are being examined. Step 2: Preparing the questions to be asked that will get us validating. Then, rehearse the entire interview along with giving the prototype demonstration. Step 3: Scouting out the venue(s) and mapping out logistics. Step 4: Advertising for participants. Step 5: Screening the participants and scheduling time slots.Page: 184 The interview phase can be the most nerve-wracking and exhilarating of all three phases, because you must prep the location, coordinate the sessions, and conduct the interviews.Page: 185 Prepping the venue Participant payments, café etiquette, and tipping Conducting the interviews Taking succinct notes Page: 185 Analysis phase Page: 185 Interviewing Users by Steve Portigal.Page: 200 A journey map looks like a flowchart and shows all touch points. It's a visual representation of the user's journey and interaction through the UX of your product. It is generally created from output following a collaborative brainstorming session with product stakeholders. Sometimes these maps are quite complex to decipher, especially if you were not a participant during the session. If you haven't seen one of these maps, do a keyword search for them on Google Images.Page: 211 The rows in the Funnel Matrix contain the stages of user engagement for your online product. These stage labels should be directly correlated to the business model of your product, Page: 213 Suspect stage A suspect is a user who might possibly require your product or service. Page: 215 Lead stage A lead is a potential sales contact, an individual, or an organization that expresses an interest in your product Page: 215 Prospect stage A prospect is anyone who has a need and strongly wants this need met through the purchase or consumption of your particular product. He is now in the negotiation phase toward becoming either a paid customer or engaged user (to be defined in the next stage).Page: 215 Customer stage A customer is a person or organization that is valuable to your business model.Page: 216 Repeat User stage Repeat users are customers who "regularly" use your product or service.Page: 216 Reference User stage A reference user is somebody who refers others to the site based purely on her initial or continued experience. She brings other suspects to your product by spreading the word.Page: 216 The Horizontal Axis User's Process The User's Process (see Figure 9-8) describes the types of activities that users will be engaging in at each stage as they experience your product.Page: 216 Desired Action The Desired Action for the Funnel Matrix (see Figure 9-9) is what you hope users do in response to the "process" they just experienced. Page: 218 Business Task Now take off your UX strategy hat and put on your marketing cap. It's time to look at the Funnel Matrix from the standpoint of your business goals, as illustrated in Figure 9-10.Page: 219 The basic concept is to build marketing organically into the product,Page: 219 What goes in metrics columns are those quantifiable "things" you plan to measure at each stage that specifically tie a user's action to a level of engagement.Page: 220 Lean Startup guru Eric Ries states that the real metrics are "actionable metrics that either confirm or refute a previously stated hypothesis (stated in customer discovery)." This means that you want to measure things that demonstrate your product is actually working and that the users are engaged. However, investors and stakeholders like totals and percentages, so a common trap is to look at page views or other vanity metrics that don't represent anything more than your ability to buy or drive traffic to your landing pages.Page: 221 Required Functionality: Each feature needs to be something that will make the product better and easier to use rather than more complicated.Page: 222 By now the funnel matrix exercise might feel like a science experiment. That's because it is. You are conducting a methodical procedure with the goal of validating or falsifying your hypothesis — your product's unique value proposition to your perceived customers. Page: 224 A landing page is a web page that is not the home page of your product. Landing pages are designed to specifically elicit one key action from users. They are also sometimes called lead capture pages, squeeze pages, and destination pages. Page: 226 | Clifford GeertzNotable QuotationsExpand to full screen
Works and Lives (PDF)
Geertz was a great writer and, I think, an important Anthropologist. He mentions Kenneth Burke in the Acknowledgements, which is reason enough to read this book. But you can learn a lot about human-subject story telling from him.
| Todd Rogers, Jessica Lasky-Fink
Notable QuotationsExpand to full screen
Introduction
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Simple thing that anyone can do but that most of us don't do particularly well.
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We know why certain types of writing draw a reader's focus while others tend to get lost in the fog of distraction and competition for attention.
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... account for how busy people read.
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Effective writing is not the same as beautiful writing.
... Often, beautiful writing is intentionally demanding and multilayered.
... Effective writing is not so subjective.
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We too often believe our readers will find our messages as important to them as they are to us,
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Much of the formal writing we learned in school is irrelevant or counterproductive for real- world practical writing.
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Our principles derive from the sciences of psychology and human behavior, blended with a social understanding that most people have limited time and attention.
Part One: Engaging the Reader
Get Inside Your Reader's Head
To be an effective writer, we need to remember that our readers experience the scarcity of time every bit as acutely as we do. Their distractions influence both what they read and how they read
...
Not only do we have limited time, we also have limited attention.
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We cannot notice or process everything in front of us. We can exhaust our focus over time, often in less time than we think. We struggle focusing on multiple things at one time, but we still try.
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For readers, selective attention also guides what they visually notice and focus on when interacting with any kind of writing.[
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elements that have a strong visual contrast with their surroundings.
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trying. Our brains have evolved to automatically notice things that stand out from their surroundings.
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Shortcut #2: Our selective attention can be intentionally and purposefully directed.
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When we look for something specific, our attention system helps us efficiently and quickly find it.
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In the process of noticing some elements, we miss others -- Brain research has revealed that when you notice and examine one item in a visual scene, the brain actively suppresses noticing other items that are also present.
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The brain's ability to ignore irrelevant information
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Once we get distracted, it is hard to refocus.
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Writing effectively for busy people requires keeping in mind just how easily they (and we, all of us) can get worn down and distracted.
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it's not even possible to be thinking about two tasks at the exact same time.
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The bottom line is that your mind works most effectively when it has a clear anchor point: one thing it is noticing, one thing it is focusing on, one task that it needs to initiate in response.
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Writing that respects those limitations is more likely to get through to a busy brain—and to the reader who possesses it.
Think Like a Busy Reader
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Every time a reader encounters a written communication—even something as short as an email, a text, a Slack message, or a social media post—they go through a four-stage process:
...
First, they must decide whether to engage with it at all. Second, if they decide to engage, they then must decide when to engage. Sometimes the decision to engage leads to a decision to engage later. Third, once they do engage, they must decide how much time and attention to allocate to reading the message. Fourth, if they read something that requires a response, they must decide whether to respond or react.
... Our job as effective writers is to navigate each of the four critical rounds in that brief but daunting process.
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Busy readers routinely decide how valuable a message is without actually reading it! ...
One common rule of thumb is that, when faced with a lot of options, we pick the first one that seems good enough (sometimes called "satisficing"),
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They also consider the costs involved: How much time and effort will be required to engage?
... They are more likely to engage with messages that are short or that appear easy to navigate because they seem like they will require less time, attention, and effort to read. ...
Most of us prefer doing enjoyable, pleasant, easy, and gratifying things now and push off less pleasant, more difficult things until later.
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The tendency to privilege the present over the future is hardwired into us.
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Busy readers are likely to prioritize messages they think can be dealt with easily and quickly, because they seem more enjoyable (or at least less awful).
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Busy readers aim to extract as much value as possible from a communication with as little time and attention as possible.
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They may closely read one section, skim another, and jump around in yet another, searching for specific information that they consider relevant.
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reading for utility is an efficient strategy for extracting as much information as possible while expending as little time and attention as possible.
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Skimming often involves skipping words, phrases, and even paragraphs. It also often involves jumping forward in anticipation, and jumping backward to review
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Readers often expect the first sentence of a paragraph to orient them to what the rest of the paragraph is about. Guided by that expectation, they may spend more time reading those opening sentences and use them to decide what (if anything) to read more closely.[13]
... Readers skip over large swaths of text, mainly landing on anchor points: headings, first sentences of paragraphs, images, and formatting that visually contrasts with the rest of the text.
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Before you can start to write for busy readers, though, you must be very clear about why you are writing: To communicate effectively, you need to know your goals.
Know Your Goals
If your reader is going to spend just five seconds on your message, what is the most important information you want them to come away with?
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Anything that disrupts the flow of words distracts the reader.
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Taking the time to clean up your words and present them readably is a first step toward engaging your reader.
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"What is the most important information I want my readers to understand?" and "How do I make it easier for my readers to understand it?"
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A reader overlooks information that we consider important and fails to act the way we want, that is not the reader's fault.
Part Two: Six Principles of Effective Writing
First Principle: Less Is More
More writing leads readers to be less likely to read anything.
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Although concise writing saves time and effort on the part of the reader, it requires more time and effort from the writer.
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patents: We tend to add ideas rather than subtract or remove them in the editing process.[4]
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Readers often interpret the length of a message as an indication of how difficult and time-consuming it will be to respond to, which is another reason why they might choose not to engage with a wordy communication.
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Most readers, but especially those who are pressed for time, are likely to be put off by messages and requests that they expect will be difficult to deal with.[7]
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A wordy message will be dealt with less quickly than a concise message.
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Readers' attention is more likely to drift when reading longer messages.[8]
... Writing concisely requires a ruthless willingness to cut unnecessary words, sentences, paragraphs, and ideas.
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Nancy Gibbs, former editor in chief of Time magazine, would tell her staff that every word has to earn its place in a sentence, every sentence has to earn its place in a paragraph, and every idea has to earn its place in a text.[10]
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The bottom line is that effective writing needs to be appropriate to the context of the communication. We can provide the guidelines, but you have to make the informed decisions about how to balance your desire to include more words, ideas, and requests with the many constraints facing a busy reader.
Rule 1: Use Fewer Words
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Replace this . . . (Wordy) . . . with this (Concise)
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Sometimes it is worth losing a little precision and meaning to save readers' time.
... Strategic omission.
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Like cutting words, cutting ideas often requires discarding less important but still relevant information to emphasize the more important information.
Rule 3: Make Fewer Requests
Second Principle: Make Reading Easy
acquiesce versus agree:
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Shorter words are generally more readable than longer words and common words are generally more readable than uncommon words.
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Tweets using the most common words received about 75% more retweets than tweets using the least common words.[
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In many contexts, using words like "sophisticated" instead of "fancy" can come across as pretentious or exclusionary.
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Even in academia there has been a shift toward more readable writing. The association for marketing professionals and scholars, the American Marketing Association, instructs would-be authors that its journal "is designed to be read, not deciphered."[
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When you are balancing the trade-offs between readability and using longer, less common but (potentially) more precise words, ask yourself two questions. First, how valuable are the subtle differences in word meaning for conveying the essence of the sentence? Second, is the additional meaning conveyed by the harder-to-read word worth the costs of fewer readers engaging and understanding it and the increased effort required by those who do?
Rule 2: Write Straightforward Sentences
Write so that readers can understand the meaning of a sentence after a single read-through.
Rule 3: Write Shorter Sentences
Page 90 Writing in a style that is easy to read is not necessarily easy to write.
6. Third Principle: Design for Easy Navigation > Page 95
designing the written content to be easy to navigate.
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they should immediately be able to grasp its purpose, main points, and structure.
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stop thinking of your message as a set of words and think of it instead as a type of map.
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But letters, words, sentences, and paragraphs are intensely visual; they are literally graphic elements on the page or screen.
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Rule 1: Make Key Information Immediately Visible
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Sometimes writers bury the lede intentionally to spur curiosity and intrigue.
... But practical communications aren't relaxed literary voyages, and they shouldn't be written like them.
... "What do I want my readers to take away from this?"
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put the most important information in the places where busy readers most likely expect to find
... US Army: bottom line up front (BLUF).
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Abstracts, executive summaries, and TL;DR headlines similarly function as "key information" locations for busy readers.
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One of the most visually clear ways to signal to readers that ideas are distinct is to list each one with a bullet point.
Rule 3: Place Related Ideas Together
Rule 4: Order Ideas by Priority
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The first item in a list usually gets the most attention from the reader.
... In certain contexts, the last position in an ordered list can also be influential
... Studies of jury trials have found that the final evidence presented to the jurors can be the most heavily weighted and remembered.
Rule 6: Consider Using Visuals
Fourth Principle: Use Enough Formatting but No More
Page 126 Formatting serves two main purposes. First, it conveys meaning over and above the meaning of the words themselves. Second, formatting helps capture readers' attention by making certain words stand out against the others.
Rule 1: Match Formatting to Readers' Expectations
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Because importance and emphasis are not the same, and because readers may interpret italics and font colors as conveying either of those meanings, writers who want to use these tools need to be careful to manage their ambiguity.
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Writers can announce their style up front ... A majority of survey respondents also interpreted all caps as signaling importance, but a sizable fraction (25%) volunteered that they regarded all caps as conveying anger instead.
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Some state laws explicitly mandate that all caps must be used to highlight key sections in specific types of agreements,
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Bullets are extremely useful formatting tools, though they, too, suffer from mixed interpretations.
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readers look to the sentence preceding a bulleted list to determine whether the list itself is worth reading.
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The varied ways that readers interpret bullets mean that they need to be used carefully.
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Using bullet points to list low-priority items risks misdirecting the reader away from what truly matters.
... Busy readers should never need to stop and question what you mean by the bolded (or italicized, highlighted, underlined, etc.) text.
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Because these formatting types are so effective, they can have an important unintended consequence: They will easily draw readers' attention away from everything else.
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The key message here is that highlighting, bolding, and underlining involve trade-offs: They increase the likelihood that readers read the formatted words, but they can decrease the reading of everything else.
Rule 3: Limit Your Formatting
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avoid formatting multiple items when you particularly want your reader to focus on just one.
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find the focal point hidden within that mess.
Fifth Principle: Tell Readers Why They Should Care
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Most of us are not very good at imagining the world from someone else's perspective. In a whimsical but illustrative study, Stanford researcher Elizabeth Louise Newton divided test subjects into two groups, tappers and listeners. The tappers tapped out the rhythm of familiar songs such as "Happy Birthday" and "The Star-Spangled Banner"; the listeners tried to guess the songs being tapped. Then came the true test. Tappers were asked to imagine being listeners and to predict what fraction of the listeners would correctly identify the songs. Tappers predicted a success rate of 50%. In reality, listeners got it right just 2.5% of the time![1] Tappers were terrible at getting into the mindset of the listeners, and had no idea how terrible they were at it.
Rule 1: Emphasize What Readers Value ("So what?")
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A good shorthand for writers who are working on practical communications is: "So what?" Try to picture the recipient of your message and consider what would make that person care about what you are saying. An additional factor to consider is not only why the reader should care but why the reader should care now—that is, the timeliness of the message.
Rule 2: Emphasize Which Readers Should Care ("Why me?")
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Being explicit about your intended audience is especially pertinent in mass communications that are difficult to target to specific populations.
9. Sixth Principle: Make Responding Easy > Page 151
Sixth Principle: Make Responding Easy
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Not only do you want your readers to read and understand your message, you also want them to perform a concrete action.
Rule 1: Simplify the Steps Required to Act
Rule 2: Organize Key Information Needed for Action
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As an effective writer, part of your job is to ensure your readers have all the necessary information in one accessible location. If readers have to seek out the information needed to act, they will be more likely to put it off and eventually forget the request entirely.
Rule 3: Minimize the Amount of Attention Required
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Writers commonly offer their readers too many choices.
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Minimizing the amount of attention required to act can have important pragmatic consequences.
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Just as readers may be deterred from acting if they have to search for the necessary information about what to do, readers may also be deterred if they don't understand the steps required—how to do it.
Part Three: Putting the Principles to Work
Tools, Tips, and FAQs
Writers should aim to use the fewest number of words, ideas, and requests necessary to achieve their goals, and no fewer.
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The longer the message, the more challenging it can be to stay focused on why you are writing and what outcome you hope to achieve. Keeping your writing goals clear and top of mind can help you decide what information stays and what goes.
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Using introductory text to tell readers what the rest of the text is about is called "signposting."
... Although it typically adds words, it can be helpful for making longer messages or messages with multiple pieces of information easier to navigate.
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When you edit for conciseness, you should also review your language to make sure it matches the needs and expectations of your readers. Knowing your audience is the best way to ensure that your language matches their needs and expectations.
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Send messages when your readers are most likely to have time and motivation to read and respond.
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Ultimately, understanding your specific readers is the best way to know when the "right" time is to send communications.
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Social media writing should adhere to the same principles as other forms of practical writing.
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One of the strengths of digital communications is that it makes it easy to connect readers to other online sources.
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But if hyperlinks are not the most important information in a message, they can crowd out other information, much as other types of formatting can.
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Linking the fewest words possible while also ensuring that the hyperlinked words convey some meaning can help everyone, but especially the visually impaired and others who rely on audio reading tools.
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Humor and sarcasm are risky because people can easily misunderstand them in their written form.
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Emojis can lead to similarly unintended and unanticipated confusion, especially across varied age groups.[
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It remains to be seen whether emojis continue to evolve to take on serious connotations and meanings.
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For now, though, writers should be cautious and clear when using emojis in important writing, given their wide range of possible interpretations.
Our Words, Our Selves
Before composing any message, writers have to decide on their overall style and tone. Often there are context-specific norms you can turn to for guidance.
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some research has found that readers are more likely to respond to government communications written in relatively formal language, in part because formality acts as a signal of credibility in the public-sector context. ...
As a general rule, a formal communication style works better when that is what readers expect.
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Striking the right balance between precision and personality is especially consequential for writers who are women, racial and ethnic minorities, or of lower social or professional status. Power, status, race, gender, and other stereotyped identities can affect how readers expect people to write, and especially the warmth they are expected to convey.
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In certain settings, a writer's goal is not to be read and understood but rather the exact opposite. Some writers aim to obfuscate, obscure, and hide information they must disclose but would rather not. |