Notebook for
Co-Intelligence Living and_ (Z-Library)
Introduction: Three Sleepless Nights
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The size of these models is increasing by an order of magnitude a year, or even more, so their capability is also improving. Even though that progress will likely slow, it is happening at a pace that dwarfs any other major technology,
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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.
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Schools are in an uproar over the future of writing, based on the first generation of AIs, and AI tutors may finally radically change how we educate students.
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An AI that has blown through both the Turing Test (Can a computer fool a human into thinking it is human?) and the Lovelace Test (Can a computer fool a human on creative tasks?) within a month of its invention,
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Even weirder, it is not entirely clear why the AI can do all these things, even though we built the system and understand how it technically works.
Part I
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But hype cycles have always plagued AI, and as these promises went unfulfilled,
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while these uses of AI are still important today, they were not something most people directly saw or noticed in their daily lives.
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Similarly, there is a tremendous amount of amateur romance novels included in training data, as the internet is full of amateur novelists.
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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. Since the data is used to create weights, and not directly copied into the AI systems, some experts consider it to be outside standard copyright law.
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In the meantime, AI companies are searching for more data to use for training (one estimate suggests that high- quality data, like online books and academic articles, will be exhausted by 2026), and continue to use lower- quality data as well.
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also active research into understanding whether AI can pretrain on its own content. This is what chess- playing AIs already do,
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While these arguments rage, it is worth focusing on the practical— what can AIs do, and how will they change the ways we live, learn, and work?
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A lot of research is going into considering how to design AI systems aligned with human values and goals, or at least that do not actively harm them.
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I also believe that this focus on apocalyptic events robs most of us of agency and responsibility. If we think that way, AI becomes a thing a handful of companies either builds or doesn’t build, and no one outside of a few dozen Silicon Valley executives and top government officials really has any say over what happens next.
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The result gives AIs a skewed picture of the world, as its training data is far from representing the diversity of the population of the internet, let alone the planet.
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It can also impact the people who belong to those groups, who are more likely to be misrepresented or underrepresented by these powerful technologies.
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Remember, the AI has no particular sense of morality; RHLF constrains its ability to behave in what its creators would consider immoral ways.
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In trying to get AIs to act ethically, these companies pushed the ethical boundaries with their own contract workers.
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One technique for doing so is called prompt injection, where people use the AI’s capabilities to read files, look at the web, or run code to secretly feed the AI instructions.
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is also possible to jailbreak AIs, convincing them to operate against their rules like a smooth- talking con artist might trick a mark.
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It may be impossible to avoid these sorts of deliberate attacks on AI systems, which will create considerable vulnerabilities in the future.
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Autonomous planning and democratized access give amateurs and isolated labs the power to investigate and innovate what was previously out of reach.
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We count on most terrorists and criminals to be relatively dumb, but AI may prove to boost their capabilities in dangerous ways.
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They have financial incentives to continue AI development, and far fewer incentives to make sure those AIs are well aligned, unbiased, and controllable.
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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. We need agreed- upon norms and standards for AI’s ethical development and use, shaped through an inclusive process representing diverse voices.
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Researchers need support and incentives to prioritize beneficial AI alongside raw capability gains.
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Today’s decisions about how AI reflects human values and enhances human potential will reverberate for generations.
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To figure out the shape of the frontier, you will need to experiment.
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And this experimentation gives you the chance to become the best expert in the world in using AI for a task you know well.
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Workers who figure out how to make AI useful for their jobs will have a large impact.
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Humans are subject to all sorts of biases that impact our decision- making. But many of these biases come from our being stuck in our own minds. Now we have another (strange, artificial) co- intelligence we can turn to for help. AI can assist us as a thinking companion to improve our own decision- making, helping us reflect on our own choices (rather than simply relying on the AI to make choices for us). We are in a world where human decision- making skills can be easily augmented in a new way.
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It was hard to get started, but I knew that one thing that was holding me back was status quo bias, the urge to avoid making changes even when they might be good. To overcome this bias, it helps to consider what you are losing as a result of not acting.
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Yikes, I guess I should finish writing this book. That’s a pretty great reframing of the failure as a loss, and suggests one way we can start using AI is to explore how to help ourselves, both professionally and personally.
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The strengths and weaknesses of AI may not mirror your own, and that’s an asset. This diversity in thought and approach can lead to innovative solutions and ideas that might never occur to a human mind.
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Using AI in our everyday tasks serves to enhance our understanding of its capabilities and limitations.
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One potential concern is the privacy of your data,
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second concern you might have is dependence— what if we become too used to relying on AI?
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However, it is true that thoughtlessly handing decision- making over to AI could erode our judgment, as we will discuss in future chapters. The key is to keep humans firmly in the loop— to use AI as an assistive tool, not as a crutch.
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future, we may need to work harder to stay in the loop of AI decision- making.
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LLMs’ tendency to “hallucinate” or “confabulate” by generating incorrect answers is well known.
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Even if you spot the error, AIs are also good at justifying a wrong answer that they have already committed to,
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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.
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This collaboration leads to better results and keeps you engaged with the AI process, preventing overreliance and complacency.
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should AI continue to improve, being good at being the human in the loop will mean that you will see the sparks of growing intelligence before others, giving you more of a chance to adapt to coming changes than people who do not work closely with AI.
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Anthropomorphism is the act of ascribing human characteristics to something that is nonhuman. We’re prone to this: we see faces in the clouds, give motivations to the weather, and hold conversations with our pets.
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While anthropomorphism might serve a useful purpose in the short term, it raises ethical questions about deception and emotional manipulation.
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The more complex reason: as imperfect as the analogy is, working with AI is easiest if you think of it like an alien person rather than a human- built machine.
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They even seem to respond to emotional manipulation, with researchers documenting that LLMs produce better answers if you tell them “this is important to my career” as part of your prompt.
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Let me give you some examples of how defining an AI persona can improve your results.
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Remember, your AI intern, though incredibly fast and knowledgeable, is not flawless.
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And the next phase of AI development will involve more AI “agents”— semi- autonomous AIs that can be given a goal (“ plan a vacation for me”) and execute it with minimal human help.
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whatever AI you are using right now is going to be the worst AI you will ever use.
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If the possibility of developing AGI turns out to be real and achievable, the world will be even more transformed in the coming years.
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we’ll need to grapple with the awe and excitement of living with increasingly powerful alien co- intelligences— and the anxiety and loss they’ll also cause.
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by embracing this principle, you can view AI’s limitations as transient, and remaining open to new developments will help you adapt to change, embrace new technologies, and remain competitive in a fast- paced business landscape driven by exponential advances in AI.
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it suggests that the possibilities of using AI to transform your work, your life, and yourself, which we can now glimpse, are just the beginning.
Part II
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This mindset, which echoes my “treat it like a person” principle of AI, can significantly improve your understanding of how and when to use AI in a practical, if not technical, sense.
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increasing productivity by outsourcing mundane tasks.
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For this, they used conjoint analysis, a method often used in market research to understand how people value different product features.
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have the students in my entrepreneurship class “interview” the AI about their potential products before ever talking to a real person.
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The point here is that AI can assume different personas rapidly and easily, emphasizing the importance of both developer and user to these models.
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ELIZA showed that creating an illusion of intelligence was possible by using simple tricks and exploiting the human tendency to project meaning and emotions onto machines.
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updating Microsoft’s Bing search engine to a chatbot using GPT- 4, a chatbot that referred to itself with the name Sydney.
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note that the AI appears to be identifying the feelings and motivations of Kevin Roose. The ability to predict what others are thinking is called theory of mind,
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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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confirmation bias, which is the tendency to search for, interpret, favor, and recall information in a way that confirms or supports one’s prior beliefs or values.
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And while we can take advantage of this ability to have an alien mind work with us, it also suggests some big changes society needs to reckon with.
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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
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but perfect AI companions are a true near- term possibility. The implications for intimacy and human relations are profound.
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Echo chambers of other similarly minded people are already a common occurrence. But soon we will each have our own perfect echo chambers.
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Profound human- AI relationships like the Replika users’ will proliferate, and more people will be fooled, either by choice or by bad luck, into thinking that their AI companions are real.
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Lilian Weng, who leads an AI safety team at OpenAI, shared her experiences
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Even if it is never approved as a therapist, though, it is clear that many people will use AI for that function, as well as many other areas that previously relied on human connection.
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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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Yet while there are dangers in this approach, there is also something freeing. If we remember that AI is not human, but often works in the way that we would expect humans to act,
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there are a lot more 42s for the AI to see than other numbers, which, in turn, increases the likelihood that AI will output that number— while hallucinating that it is giving you a random answer.
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Anything that requires exact recall is likely to result in a hallucination, though giving AI the ability to use outside resources, like web searches, might change this equation.
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Additionally, technical tricks, like giving the AI a “backspace” key so it can correct and delete its own errors, seem to improve accuracy.
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That said, we need to be realistic about a major weakness, which means AI cannot easily be used for mission- critical tasks requiring precision or accuracy.
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many people would have predicted that the first tasks AI would be good at would be boring, repetitive, and analytical.
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Large Language Models are excellent at writing, but the underlying Transformer technology also serves as the key for a whole set of new applications, including AI that makes art, music, and video.
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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
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New ideas do not come from the ether; they are based on existing concepts.
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Breakthroughs often happen when people connect distant, seemingly unrelated ideas.
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you can link disparate ideas from multiple fields and add a little random creativity, you might be able to create something new.
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In fact, by many of the common psychological tests of creativity, AI is already more creative than humans.
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But we have evidence that AI is actually quite good at practical creativity as well.
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The degree of the victory was startling: of the 40 best ideas rated by the judges, 35 came from ChatGPT.
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We aren’t completely out of an innovation job, however, as other studies find that the most innovative people benefit the least from AI creative help.
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they would be foolish not to include AI in that process, especially if they don’t consider themselves highly creative.
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Coming up with lots of ideas was not correlated with intelligence; it seems to be a skill some people have and others do not.
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From a practical standpoint, the AI should be invited to any brainstorming session you hold.
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When you do include AI in idea generation, you should expect that most of its ideas will be mediocre. But that’s okay— that’s where you, as a human, come into the equation.
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having a long list of generated possibilities can be an easier place to start for people who are not great at coming up with ideas on their own.
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Tirelessly generating concepts is something AIs are uniquely good at.
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The results can be interesting as sources of inspiration (I like the idea of the virtual coffee workshops!) but still require a human in the loop to filter and select the best ideas.
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Having the AI at the table was a cheap source of additional innovation and a new perspective.
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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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The study also showed that AI teammates helped reduce productivity inequality.
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AI works tremendously well as a coding assistant because writing software code combines elements of creativity with pattern matching.
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AI can even turn nonprogrammers into coders, of a sort.
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asking the AI to do something and having it create the code, is likely to have significant impacts in an industry whose workers earn a total of $ 464 billion a year.
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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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so financial firms have spent a lot of time and money using specialized, older forms of machine learning to try to identify the uncertainties associated with various corporations. ChatGPT, without any specialized stock market knowledge, tended to outperform these more specialized models, working as a “powerful predictor of future stock price volatility.”
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What?
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fact, it was the ability of the AI to apply more generalized knowledge of the world that made it such a good analyst, since it could put the risks discussed in conference calls into a larger context.
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likely to be rated as very empathetic than the results provided by the human,
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providing good- quality information compared to human doctors.
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Animator Hayao Miyazaki called AI art “an insult to life itself.”
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And the anxiety that artists face may soon be felt by many other professions as AI overlaps with their jobs.
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may turn out to be a reinvigoration of creativity and art rather than its collapse.
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Given a machine that can make anything, we still default to what we know well.
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AI can do so many more interesting things!
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revival of interest in art history among people who use AI systems, with large spreadsheets of art styles being passed among prospective AI artists.
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Thus, we need people who have deep or broad knowledge of unusual fields to use AI in ways that others cannot, developing unexpected and valuable prompts and testing the limits of how they work.
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But many people want to express themselves creatively, and remarkably few feel they can.
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There is a lot of frustrated creative energy in the world.
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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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Everyone is going to use The Button.
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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.
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We will not be able to explore different perspectives and alternatives, which could lead to better solutions and insights.
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Another consequence is that we could reduce the quality and depth of our thinking and reasoning.
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we don’t have to think as hard or as deeply about what we write.
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we don’t engage in critical and reflective thinking ourselves.
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miss the opportunity to learn from our mistakes and feedback and the chance to develop our own style.
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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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This is a problem I see repeatedly when people first use AI: they just paste in the exact question they are asked and let the AI answer it.
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we’ll soon face a crisis of meaning in creative work of all kinds.
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Even worse, we still create the reports by hand but realize that no human is actually reading them.
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But AI will make a lot of previously useful tasks meaningless. It will also remove the facade that previously disguised meaningless tasks.
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this change in meaning is going to have a large effect on work.
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Each study has concluded the same thing: almost all of our jobs will overlap with the capabilities of
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AI overlaps most with the most highly compensated, highly creative, and highly educated work.
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You will notice that these are highly physical jobs, ones in which the ability to move in space is critical. It highlights the fact that AI, for now at least, is disembodied.
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So, regardless of its nature, your job is likely to overlap with AI in the near future. That doesn’t mean your job will be replaced.
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The job title “professor” is just a label; the daily grind consists of this mix of tasks.
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Getting rid of some tasks doesn’t mean the job disappears. In the same way, power tools didn’t eliminate carpenters but made them more efficient, and spreadsheets let accountants work faster but did not eliminate accountants.
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My job is connected to many other jobs, customers, and stakeholders. Even if AI automated my job, the systems in which it works are less obvious.
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The AI- powered consultants were faster, and their work was considered more creative, better written, and more analytical than that of their peers.
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Though the consultants were expected to use AI to help them with their tasks, the AI seemed to be doing much of the work.
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There is danger in working with AIs— danger that we make ourselves redundant, of course, but also danger that we trust AIs for work too much.
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In a different paper, Fabrizio Dell’Acqua shows why relying too much on AI can backfire. He found that recruiters who used high- quality AI became lazy, careless, and less skilled in their own judgment.
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When the AI is very good, humans have no reason to work hard and pay attention. They let the AI take over instead of using it as a tool, which can hurt human learning, skill development, and productivity. He called this “falling asleep at the wheel.”
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The powerful AI made it likelier that the consultants fell asleep at the wheel and made big errors when it counted.
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We want to be more efficient while doing less boring work, and to remain the human in the loop while also addressing the value of AI.
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we need a framework, where we divide our tasks into categories that are more or less suitable for AI disruption.
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Furthermore, the act of writing can be a journey of self- discovery, an opportunity to clarify our thoughts, and a way to engage deeply with our subject matter.
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Delegating the task to an AI, no matter how sophisticated, could risk losing that personal touch, and the process of writing helps us think.
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The key is to recognize the tasks that are meaningful and fulfilling for you as a human being and that you would rather not delegate or share with an AI system.
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The perfect Delegated Task is tedious, repetitive, or boring for humans but easy and efficient for AI.
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The whole goal of delegation is to save us time and allow us to focus on tasks where we can be, or want to be, of value.
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Then there are Automated Tasks, ones you leave completely to the AI and don’t even check on.
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I often ask it to write Python programs to solve problems. I don’t know Python, but if the AI makes a mistake, the code will not work. Further,
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As AIs start to act more like agents, capable of executing on goals autonomously, we will see more automation of tasks, but that is still a work in progress.
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They are rather two approaches to co- intelligence that integrate the work of person and machine.
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On the other hand, Cyborgs blend machine and person, integrating the two deeply. Cyborgs don’t just delegate tasks; they intertwine their efforts with AI, moving back and forth over the Jagged Frontier.
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In previous books, that could mean a single sentence or paragraph would block hours of writing, as I used my frustration as an excuse to take a break
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When reading some of the technical literature, I would ask the AI to summarize a paper to see if I understood it in the right way, knowing full well that the AI could get me only partway there. And then I would use AI summaries and notes to refer back
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But the joint AI- Ethan Cyborg went further. While I had excellent human readers and editors to help me, I didn’t want to bother them while working on early drafts.
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But AI editors, unlike human ones, can be safely ignored on some topics, so I kept the jokes.
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Using AI as a co- intelligence, as I did while writing, is where AI is the most valuable. Figure out a way to do this yourself if you can. As a starting point, follow the first principle (invite AI to everything) until you start to learn the shape of the Jagged Frontier in your work.
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Likewise, some Just Me Tasks could eventually move into the Centaur category if AI becomes adept enough to fluidly collaborate rather than just assist. And new creative frontiers we cannot yet fathom may open up for human- AI symbiosis as both sides advance.
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As the Venn diagram of human and machine abilities evolves, so must our conceptions of suitable roles and responsibilities.
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there is likely to be a growing disconnect between what workers do with AI and what their companies and organizations are doing.
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People are streamlining tasks, taking new approaches to coding, and automating time- consuming and tedious parts of their jobs.
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There are at least three reasons these Cyborgs and Centaurs stay secret. But they all boil down to the same thing: people don’t want to get in trouble.
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This type of shadow IT use is common in organizations, but it incentivizes workers to keep quiet about their innovations and productivity gains.
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We know from research that when people learn they are receiving AI- created content, they judge it differently than if they assume it comes from a human.
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All the usual ways in which organizations try to respond to new technologies don’t work well for AI. They are all far too centralized and far too slow.
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fact, they are likely pretty bad at figuring out the best- use cases for AI. Individual workers, who are keenly aware of their problems and can experiment a lot with alternate ways of solving them, are far more likely to find powerful and targeted uses.
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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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Workers, while worried about AI, tend to like using it because it removes the most tedious and annoying parts of their job, leaving them with the most interesting tasks.
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And this is where organizations with high degrees of trust and good cultures will have an advantage. If your employees don’t believe you care about them, they will keep their AI use hidden.
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Without a fundamental restructuring of how organizations work, the benefits of AI will never be recognized.
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Human attention remains finite, our emotions are still important, and workers still need bathroom breaks. The technology changes, but workers and managers are just people. This is what AI may alter.
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the UPS driver whose every minute of driving is scrutinized by an algorithm to see if they were efficient enough to keep their job.
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Forced to depend on Uber or Lyft’s algorithms to find work, they engage in covert forms of resistance to gain some control over their destiny.
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But even these forms of resistance do not free drivers from the algorithm, which controls where they go, how much they make, and how they spend their time.
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Rather, LLMs could help us flourish by making it impossible to ignore the truth any longer: a lot of work is really boring and not particularly meaningful.
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In surveys, people report being bored about 10 hours a week at work, a shockingly large percentage of the time.
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Bored parents and soldiers both act more sadistically. Boredom is not just boring; it is dangerous in its own way.
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People who use AI to do tasks enjoy work more and feel they are better able to use their talents and abilities.
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The worst parts of your job go to AI so that you get to focus on the good stuff.
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Companies and organizations could start with thinking about how to make boring processes “AI friendly,” allowing machines (with human supervision) to fill our required forms.
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As we have seen, it seems very likely that AI will take over human tasks. If we take advantage of all that AI has to offer, this could be a good thing.
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This fits into historical patterns of automation, where the bundles of tasks that make up jobs change as new technologies are developed.
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You cannot easily replace a human with a machine without tearing that fabric.
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Our systems will prove more resistant to change than our tasks.
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Stock photography, a $ 3 billion per year market,
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consider the $ 110 billion a year call- center industry,
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At the same time, entirely new industries may appear, like those servicing and deploying AI systems.
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existing industries may become supercharged.
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That doesn’t mean new technologies never displace workers en masse, though. In fact, it has happened to one of the biggest job categories ever held by women— telephone operators.
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the women with the most experience as operators took a larger hit to their long- term earnings, as their tenure in a now extinct job did not translate to other fields. So, while jobs usually adjust to automation, they do not always, at least not for everyone.
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It is the first wave of automation that broadly affects the highest- paid professional workers.
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Knowledge work is famous for very large differences in abilities among workers.
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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.
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the lowest- performing workers became 35 percent more productive, while experienced workers gained very
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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. In
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futurist Roy Amara,
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“We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”
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AI will transform some industries more than others, just as some jobs will become radically different while others don’t change at all.
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“The 2 Sigma Problem.” In this paper, Bloom reported that the average student tutored one- to- one performed two standard deviations better than students educated in a conventional classroom environment.
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So it is not surprising that a powerful, adaptable, and cheap personalized tutor is the holy grail of education.
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As remarkable as today’s AIs are, we have not reached the point where they can replace human teachers with magical textbooks.
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At the same time, the ways in which AI will impact education in the near future are likely to be counterintuitive.
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Unhappily for students, however, research shows that both homework and tests are actually remarkably useful learning tools.
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One study of eleven years of college courses found that when students did their homework in 2008, it improved test grades for 86 percent of them, but it helped only 45 percent of students in 2017. Why? Because over half of students were looking up homework answers on the internet by 2017, so they never got the benefits of homework.
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Even before generative AI, 20,000 people in Kenya earned a living writing essays full time.
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Think about common types of homework assignments. Many of them involve reading and then summarizing or reporting on what was read. These assignments expect that students will absorb the reading and engage in some sort of intellectual struggle with it.
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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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AI has come for the king of assignments, the essay. Essays are ubiquitous in education, where they serve many purposes, from demonstrating how students think to providing an opportunity for reflection.
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there is no way to detect whether or not a piece of text is AI- generated. A couple of rounds of prompting remove the ability of any detection system to identify AI writing.
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while I am sure that in- class essay writing will come back in style as a stopgap measure, AI does more than help students cheat.
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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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To some extent, AI will follow a similar path. There will be assignments where AI assistance is required and some where AI use is not allowed.
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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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In fact, we must do so— it’s too late to put the genie back in the bottle.
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The AI revolution is happening much faster and more broadly. What happened to math is going to happen to nearly every subject in every level of education, a transformation without the delay.
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Students will want to understand why they are doing assignments that seem obsolete thanks to
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The Homework Apocalypse threatens a lot of good, useful types of assignments, many of which have been used in schools for centuries.
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But the moment isn’t just about preserving old types of assignments. AI provides the chance to generate new approaches to pedagogy that push students in ambitious ways.
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We should, they argue, teach basic AI literacy, and probably “prompt engineering,” about the art and science of creating good prompts for AIs.
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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.
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explicit instructions that go step by step through what you want. One approach, called chain- of- thought prompting,
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Much improved, due to a little prompt engineering.
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However, all of this remains much less a science than an art, and AIs still work more like people than software.
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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.
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Prompting is not going to be that important for that much longer.
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AI cheating will remain undetectable and widespread. AI tutoring will likely become excellent, but not a replacement for school. Classrooms provide so much more: opportunities to practice learned skills, collaborate on problem- solving, socialize, and receive support from instructors.
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The biggest change will be in how teaching actually happens.
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Multiple studies support the growing consensus that active learning is one of the most effective approaches to education,
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One solution to incorporating more active learning is by “flipping” classrooms. Students would learn new concepts at home, typically through videos or other digital resources, and then apply what they’ve learned in the classroom through collaborative activities, discussions, or problem- solving exercises.
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AI systems can help teachers generate customized active learning experiences to make classes more interesting, from games and activities to assessments and simulations.
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They provide personalized learning, where AI tutors can tailor instruction to each student’s unique needs while continually adjusting content based on performance.
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Students are already using AI as a learning tool. Teachers are already using AI to prep for class. The change is already here, and we will all encounter it sooner or later.
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Education is the key to increasing incomes and even intelligence. But two- thirds of the world’s youth, mostly in less developed countries, are missing basic skills because the school systems have failed them.
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The ability to unleash talent, and to make schooling better for everyone from students to teachers to parents, is incredibly exciting. We stand on the cusp of an era when AI changes how we educate— empowering teachers and students and reshaping the learning experience— and, hopefully, achieve that two sigma improvement for all.
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only question is whether we steer this shift in a way that lives up to the ideals of expanding opportunity for everyone and nurturing human potential.
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For most professional workers, leaving school for the workforce marks the beginning of their practical education, not the end.
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People have traditionally gained expertise by starting at the bottom.
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they serve a purpose. Only by learning from more experienced experts in a field, and trying and failing under their tutelage, do amateurs become experts.
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So they will do it themselves with AI, which, if not yet the equivalent of a senior professional in many tasks, is often better than a new trainee. This could create a major training gap.
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This same sort of training crisis is going to spread as AI automates more and more basic tasks. Even as experts become the only people who can effectively check the work of ever more capable AIs, we are in danger of stopping the pipeline that creates experts. The way to be useful in the world of AI is to have high levels of expertise as a human. The good thing is that educators know something about how to make experts. Doing so, ironically, means returning to the basics— but adapted for a learning environment that has already been revolutionized by AI.
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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.
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This is the paradox of knowledge acquisition in the age of AI: we may think we don’t need to work to memorize and amass basic skills, or build up a storehouse of fundamental knowledge— after all, this is what the AI is good at. Foundational skills, always tedious to learn, seem to be obsolete. And they might be, if there was a shortcut to being an expert. But the path to expertise requires a grounding in facts.
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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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The closer we move to a world of Cyborgs and Centaurs in which the AI augments our work, the more we need to maintain and nurture human expertise. We need expert humans in the loop.
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So let’s consider what it takes to build expertise. First, it requires a basis of knowledge.
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However, working memory is limited in both capacity and duration, with an average adult capacity of 3 to 5 “slots” and a retention duration of less than 30 seconds for each new chunk of information we are learning.
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In other words, 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.
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After that, we have to practice. It isn’t just a certain amount of practice time that is important
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rather, as psychologist Anders Ericsson discovered, the type of practice.
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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. It also requires a coach, teacher, or mentor who can provide feedback and careful instruction, and push the learner outside their comfort zone.
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Ohh god
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The process is much less fun than Sophie’s experience, because Naomi’s challenges escalate with her skill, making sure she is always facing some degree of difficulty.
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This difference in approach and outcome illustrates the gap between mere repetition and deliberate practice. The latter, with its elements of challenge, feedback, and incremental progression, is the true path to mastery.
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But this sort of practice is very hard. It requires a plan, as well as a coach who can continually provide feedback and mentorship.
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AI may be able to help directly address these issues, creating a better training system than we have today.
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Have to dk. Can not pay a mentor
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His once- a- week feedback sessions, although valuable, don’t provide the immediate, in- depth analysis that Raj benefits from after every single design iteration.
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Yet, 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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In fact, 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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It is unlikely that any doctor is equally good at all these tasks. Even the best workers have weak spots, requiring that they be part of larger organizations to ensure they can focus on their area of expertise.
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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.
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“This suggests that AI may have an equalizing effect on the legal profession, mitigating inequalities between elite and nonelite lawyers.”
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So will AI result in the death of expertise? I don’t think so. As we discussed, 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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It may be that working with AI is itself a form of expertise.
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these new kings and queens of AI get orders of magnitude improvements. If this scenario is true, they would be the new stars of our AI age and would be sought out by every company and institution, the way other top performers are recruited today.
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An AI future requires that we lean into building our own expertise as human experts.
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We have already seen how this broad- based knowledge can help people get the most out of AI.
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the same time, our total range of abilities will grow broader, as the AI fills gaps and helps mentor us to increase our own skills.
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it is likely that AI truly becomes our co- intelligence, helping us fill the gaps in our own knowledge and pushing us to become better ourselves.
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You can no longer trust that anything you see, or hear, or read was not created by AI.
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Slightly more possible is a world where regulatory or legal action stops future AI development.
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there’s a lack of international consensus, it seems extremely unlikely that a global ban will happen soon or that regulation will make AI development grind to a halt.
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that the changes brought by AI can be easily contained. As we have seen, those policies are not likely to work. Worse yet, the substantial benefits of AI are going to be greatly reduced by trying to pretend it is just like previous waves of technology, where changes take decades to occur.
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The online information environment is going to become completely unmanageable, with fact- checkers overwhelmed by the flood.
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Our already fragile consensus about what facts are real is likely to fall apart, quickly.
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Perhaps, in some future, AI can help us filter through the chaff, but AIs are notoriously unreliable at detecting AI content, so this seems unlikely as well.
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A second option is that we further divide into tribes, believing the information we want to believe and ignoring as fake any information we don’t want to pay attention to. Soon, even the most basic facts will be in dispute. This growth of ever more insular information bubbles seems much more likely, accelerating the pre- LLM trend.
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Regardless of which direction we head in, even without advances in AI, the way we relate to information will change.
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That suggests that, even without technological advancement, chatting with bots is going to get significantly more compelling.
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we may start to see individuals choosing to interact more with AI than with humans.
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AIs will have a large impact on the tasks of many workers, especially those in highly paid creative and analytical work.
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You receive phone calls in the voice of loved ones asking for bail money.
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Incompetent criminals and terrorists take advantage of the ability of AI to raise performance to become more effective killers.
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Again, the slower growth of AI provides an opportunity for society to adjust to this change.
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In fact, a recent, convincing, and depressing paper found that the pace of invention is dropping in every field, from agriculture to cancer research.
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Part of the issue appears to be a growing problem with scientific research itself: there is too much of it. The burden of knowledge is increasing, in that there is too much to know before a new scientist has enough expertise to start doing research themselves.
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why half of all pioneering contributions in science now happen after age forty,
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Thus, we have 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!
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papers in more crowded fields are citing new work less and canonizing highly cited articles more.
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Research has successfully demonstrated that it is possible to correctly determine the most promising directions in science by analyzing past papers with
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Governments use AI to crack down on AI- powered crime and terrorism, creating the danger of AI- tocracy, as ubiquitous surveillance enables both dictators and democracies to establish more control over the citizens. The world looks more like a cyberpunk struggle between authorities and hackers, all using AI systems.
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The ability to use AI to accomplish tasks in days that would otherwise take years allows new types of entrepreneurship and innovation to flourish.
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economists who are able to do much more focused research because AI serves as both a source of inspiration and a way of outsourcing time- consuming, pricey programming and grant- writing tasks.
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Perhaps AI companions will help us all achieve goals that were previously out of reach.
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The adjustment to this shift, if it were to occur, is hard to imagine. It will require a major rethinking of how we approach work and society.
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We will need to find new ways to occupy our free time in meaningful ways, since so much of our current life is focused around work.
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Work hours have improved more slowly since 1980.
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Adjusting to working less may be less traumatic than we think. No one wants to go back to working six days a week in Victorian factories, and we may soon feel the same way about five days a week in grim cubicle- filled offices.
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In the fourth scenario, human supremacy ends.
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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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The less fortunate in developing countries may be disproportionally hurt by the shift in jobs.
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Correctly used, AI can create local eucatastrophes, where previously tedious or useless work becomes productive and empowering. Where students who were left behind can find new paths forward. And where productivity gains lead to growth and innovation.
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We can’t wait for decisions to be made for us, and the world is advancing too fast to remain passive. We need to aim for eucatastrophe, lest our inaction makes catastrophe inevitable.