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Google’s latest transparency report shows a sharp rise in requests from governments and courts to take down potentially damaging material.

In Europe, where press freedoms are less sacred and the right to privacy is more ensconced, the idea has taken hold that individuals have a “right to be forgotten,” and those who want their online particulars expunged tend to have the government on their side. In Germany or Spain, Lorraine Martin might have a winning case.

The Connecticut case is just one manifestation of an anxious backlash against the invasive power of the Internet, a world of Big Data and ever more powerful search engines, in which it seems almost everything is permanently recorded and accessible to almost anyone — potential employers, landlords, dates, predators

The Times’s policy is not to censor history, because it’s history. The paper will update an arrest story if presented with evidence of an acquittal or dismissal, completing the story but not deleting the story.

Owen Tripp, a co-founder of Reputation.com, which has made a business out of helping clients manage their digital profile, advocated a “right to be forgotten” in a YouTube video. Tripp said everyone is entitled to a bit of space to grow up, to experiment, to make mistakes.

“This is not just a privacy problem,” said Viktor Mayer-Schönberger, a professor at the Oxford Internet Institute, and author of “Delete: The Virtue of Forgetting in the Digital Age.” “If we are continually reminded about people’s mistakes, we are not able to judge them for who they are in the present. We need some way to put a speed-brake on the omnipresence of the past.”

would like to see search engine companies — the parties that benefit the most financially from amassing our information — offer the kind of reputation-protecting tools that are now available only to those who can afford paid services like those of Reputation.com. Google, he points out, already takes down five million items a week because of claims that they violate copyrights. Why shouldn’t we expect Google to give users an option — and a simple process — to have news stories about them down-ranked or omitted from future search results? Good question. What’s so sacred about a search algorithm, anyway?

Industry leaders believe the new A.I. systems could be as important as the introduction of the web browser in the early 1990s and could lead to breakthroughs in areas ranging from drug research to education.

But gnawing at many industry insiders is a fear that they are releasing something dangerous into the wild. Generative A.I. can already be a tool for misinformation. Soon, it could be a risk to jobs. Somewhere down the line, tech’s biggest worriers say, it could be a risk to humanity.

“It is hard to see how you can prevent the bad actors from using it for bad things,” Dr. Hinton said.

After the San Francisco start-up OpenAI released a new version of ChatGPT in March, more than 1,000 technology leaders and researchers signed an open letter calling for a six-month moratorium on the development of new systems because A.I technologies pose “profound risks to society and humanity.

Several days later, 19 current and former leaders of the Association for the Advancement of Artificial Intelligence, a 40-year-old academic society, released their own letter warning of the risks of A.I. That group included Eric Horvitz, chief scientific officer at Microsoft, which has deployed OpenAI’s technology across a wide range of products, including its Bing search engine.

Dr. Hinton, often called “the Godfather of A.I.,” did not sign either of those letters and said he did not want to publicly criticize Google or other companies until he had quit his job

Dr. Hinton, a 75-year-old British expatriate, is a lifelong academic whose career was driven by his personal convictions about the development and use of A.I. In 1972, as a graduate student at the University of Edinburgh, Dr. Hinton embraced an idea called a neural network. A neural network is a mathematical system that learns skills by analyzing data. At the time, few researchers believed in the idea. But it became his life’s work.

Dr. Hinton is deeply opposed to the use of artificial intelligence on the battlefield — what he calls “robot soldiers.”

In 2012, Dr. Hinton and two of his students in Toronto, Ilya Sutskever and Alex Krishevsky, built a neural network that could analyze thousands of photos and teach itself to identify common objects, such as flowers, dogs and cars.

In 2018, Dr. Hinton and two other longtime collaborators received the Turing Award, often called “the Nobel Prize of computing,” for their work on neural networks.

Around the same time, Google, OpenAI and other companies began building neural networks that learned from huge amounts of digital text. Dr. Hinton thought it was a powerful way for machines to understand and generate language, but it was inferior to the way humans handled language.

Then, last year, as Google and OpenAI built systems using much larger amounts of data, his view changed. He still believed the systems were inferior to the human brain in some ways but he thought they were eclipsing human intelligence in others.

“Maybe what is going on in these systems,” he said, “is actually a lot better than what is going on in the brain.”

As companies improve their A.I. systems, he believes, they become increasingly dangerous. “Look at how it was five years ago and how it is now,” he said of A.I. technology. “Take the difference and propagate it forwards. That’s scary.”

Until last year, he said, Google acted as a “proper steward” for the technology, careful not to release something that might cause harm. But now that Microsoft has augmented its Bing search engine with a chatbot — challenging Google’s core business — Google is racing to deploy the same kind of technology. The tech giants are locked in a competition that might be impossible to stop, Dr. Hinton said.

His immediate concern is that the internet will be flooded with false photos, videos and text, and the average person will “not be able to know what is true anymore.”

He is also worried that A.I. technologies will in time upend the job market. Today, chatbots like ChatGPT tend to complement human workers, but they could replace paralegals, personal assistants, translators and others who handle rote tasks. “It takes away the drudge work,” he said. “It might take away more than that.”

Down the road, he is worried that future versions of the technology pose a threat to humanity because they often learn unexpected behavior from the vast amounts of data they analyze. This becomes an issue, he said, as individuals and companies allow A.I. systems not only to generate their own computer code but actually run that code on their ow

And he fears a day when truly autonomous weapons — those killer robots — become reality.

“The idea that this stuff could actually get smarter than people — a few people believed that,” he said. “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.”

Many other experts, including many of his students and colleagues, say this threat is hypothetical. But Dr. Hinton believes that the race between Google and Microsoft and others will escalate into a global race that will not stop without some sort of global regulation.

But that may be impossible, he said. Unlike with nuclear weapons, he said, there is no way of knowing whether companies or countries are working on the technology in secret. The best hope is for the world’s leading scientists to collaborate on ways of controlling the technology. “I don’t think they should scale this up more until they have understood whether they can control it,” he said.

Dr. Hinton said that when people used to ask him how he could work on technology that was potentially dangerous, he would paraphrase Robert Oppenheimer, who led the U.S. effort to build the atomic bomb: “When you see something that is technically sweet, you go ahead and do it.”

He does not say that anymore.

seeing them categorized by country does reveal some striking patterns, differences, and commonalities, the most obvious of which is the fact that the images consist overwhelmingly of women.

with few exceptions, they tend even in non-Western countries to be of fair-skinned, Western-looking women.

even in countries with very different cultural backgrounds, search engines appear to be saturated with a heavily biased, Westernized ideal of attractiveness.

There’s a staggering number of beauty products, makeup applicators, and spa treatments, all of which essentially suggest that beauty is something to be purchased and applied.

What are some other key search terms that you find particularly revealing?

in general what he argues is that if you take a look at animal cognition, human too, it's computational systems. Therefore, you want to look the units of computation. Think about a Turing machine, say, which is the simplest form of computation, you have to find units that have properties like "read", "write" and "address." That's the minimal computational unit, so you got to look in the brain for those. You're never going to find them if you look for strengthening of synaptic connections or field properties, and so on. You've got to start by looking for what's there and what's working and you see that from Marr's highest level.

it's basically in the spirit of Marr's analysis. So when you're studying vision, he argues, you first ask what kind of computational tasks is the visual system carrying out. And then you look for an algorithm that might carry out those computations and finally you search for mechanisms of the kind that would make the algorithm work. Otherwise, you may never find anything.

"Good Old Fashioned AI," as it's labeled now, made strong use of formalisms in the tradition of Gottlob Frege and Bertrand Russell, mathematical logic for example, or derivatives of it, like nonmonotonic reasoning and so on. It's interesting from a history of science perspective that even very recently, these approaches have been almost wiped out from the mainstream and have been largely replaced -- in the field that calls itself AI now -- by probabilistic and statistical models. My question is, what do you think explains that shift and is it a step in the right direction?

AI and robotics got to the point where you could actually do things that were useful, so it turned to the practical applications and somewhat, maybe not abandoned, but put to the side, the more fundamental scientific questions, just caught up in the success of the technology and achieving specific goals.

The approximating unanalyzed data kind is sort of a new approach, not totally, there's things like it in the past. It's basically a new approach that has been accelerated by the existence of massive memories, very rapid processing, which enables you to do things like this that you couldn't have done by hand. But I think, myself, that it is leading subjects like computational cognitive science into a direction of maybe some practical applicability... ..in engineering? Chomsky: ...But away from understanding.

I was very skeptical about the original work. I thought it was first of all way too optimistic, it was assuming you could achieve things that required real understanding of systems that were barely understood, and you just can't get to that understanding by throwing a complicated machine at it.

if success is defined as getting a fair approximation to a mass of chaotic unanalyzed data, then it's way better to do it this way than to do it the way the physicists do, you know, no thought experiments about frictionless planes and so on and so forth. But you won't get the kind of understanding that the sciences have always been aimed at -- what you'll get at is an approximation to what's happening.

Suppose you want to predict tomorrow's weather. One way to do it is okay I'll get my statistical priors, if you like, there's a high probability that tomorrow's weather here will be the same as it was yesterday in Cleveland, so I'll stick that in, and where the sun is will have some effect, so I'll stick that in, and you get a bunch of assumptions like that, you run the experiment, you look at it over and over again, you correct it by Bayesian methods, you get better priors. You get a pretty good approximation of what tomorrow's weather is going to be. That's not what meteorologists do -- they want to understand how it's working. And these are just two different concepts of what success means, of what achievement is.

if you get more and more data, and better and better statistics, you can get a better and better approximation to some immense corpus of text, like everything in The Wall Street Journal archives -- but you learn nothing about the language.

the right approach, is to try to see if you can understand what the fundamental principles are that deal with the core properties, and recognize that in the actual usage, there's going to be a thousand other variables intervening -- kind of like what's happening outside the window, and you'll sort of tack those on later on if you want better approximations, that's a different approach.

take a concrete example of a new field in neuroscience, called Connectomics, where the goal is to find the wiring diagram of very complex organisms, find the connectivity of all the neurons in say human cerebral cortex, or mouse cortex. This approach was criticized by Sidney Brenner, who in many ways is [historically] one of the originators of the approach. Advocates of this field don't stop to ask if the wiring diagram is the right level of abstraction -- maybe it's no

if you went to MIT in the 1960s, or now, it's completely different. No matter what engineering field you're in, you learn the same basic science and mathematics. And then maybe you learn a little bit about how to apply it. But that's a very different approach. And it resulted maybe from the fact that really for the first time in history, the basic sciences, like physics, had something really to tell engineers. And besides, technologies began to change very fast, so not very much point in learning the technologies of today if it's going to be different 10 years from now. So you have to learn the fundamental science that's going to be applicable to whatever comes along next. And the same thing pretty much happened in medicine.

that's the kind of transition from something like an art, that you learn how to practice -- an analog would be trying to match some data that you don't understand, in some fashion, maybe building something that will work -- to science, what happened in the modern period, roughly Galilean science.

it turns out that there actually are neural circuits which are reacting to particular kinds of rhythm, which happen to show up in language, like syllable length and so on. And there's some evidence that that's one of the first things that the infant brain is seeking -- rhythmic structures. And going back to Gallistel and Marr, its got some computational system inside which is saying "okay, here's what I do with these things" and say, by nine months, the typical infant has rejected -- eliminated from its repertoire -- the phonetic distinctions that aren't used in its own language.

people like Shimon Ullman discovered some pretty remarkable things like the rigidity principle. You're not going to find that by statistical analysis of data. But he did find it by carefully designed experiments. Then you look for the neurophysiology, and see if you can find something there that carries out these computations. I think it's the same in language, the same in studying our arithmetical capacity, planning, almost anything you look at. Just trying to deal with the unanalyzed chaotic data is unlikely to get you anywhere, just like as it wouldn't have gotten Galileo anywhere.

with regard to cognitive science, we're kind of pre-Galilean, just beginning to open up the subject

You can invent a world -- I don't think it's our world -- but you can invent a world in which nothing happens except random changes in objects and selection on the basis of external forces. I don't think that's the way our world works, I don't think it's the way any biologist thinks it is. There are all kind of ways in which natural law imposes channels within which selection can take place, and some things can happen and other things don't happen. Plenty of things that go on in the biology in organisms aren't like this. So take the first step, meiosis. Why do cells split into spheres and not cubes? It's not random mutation and natural selection; it's a law of physics. There's no reason to think that laws of physics stop there, they work all the way through. Well, they constrain the biology, sure. Chomsky: Okay, well then it's not just random mutation and selection. It's random mutation, selection, and everything that matters, like laws of physics.

What I think is valuable is the history of science. I think we learn a lot of things from the history of science that can be very valuable to the emerging sciences. Particularly when we realize that in say, the emerging cognitive sciences, we really are in a kind of pre-Galilean stage. We don't know wh

at we're looking for anymore than Galileo did, and there's a lot to learn from that.

The front page of a newspaper and the cover of a magazine lost their dominance long ago.

But news reports, like albums before them, have not been created that way. One of the services that editors bring to readers has been to use their news judgment, considering a huge range of factors, when they decide how articles fit together and where they show up. The news judgment of The New York Times is distinct from that of The New York Post, and for generations readers appreciated that distinction.

“In digital, every story becomes unbundled from each other, so if you’re not thinking of each story as living on its own, it’s tying yourself back to an analog era,” Mr. Kim said.

Facebook executives have insisted that they intend to exert no editorial control because they leave the makeup of the news feed to the algorithm. But an algorithm is not autonomous. It is written by humans and tweaked all the time. Advertisement Continue reading the main story Advertisement Continue reading the main story

That raises some journalistic questions. The news feed algorithm works, in part, by showing people more of what they have liked in the past. Some studies have suggested that means they might not see as wide a variety of news or points of view, though others, including one by Facebook researchers, have found they still do.

Tech companies, Facebook included, are notoriously fickle with their algorithms. Publications became so dependent on Facebook in the first place because of a change in its algorithm that sent more traffic their way. Later, another change demoted articles from sites that Facebook deemed to run click-bait headlines. Then last month, Facebook decided to prioritize some posts from friends over those from publications.

how to measure the Internet’s contribution to our lives? A few years ago, Austan Goolsbee of the University of Chicago and Peter J. Klenow of Stanford gave it a shot. They estimated that the value consumers gained from the Internet amounted to about 2 percent of their income — an order of magnitude larger than what they spent to go online. Their trick was to measure not only how much money users spent on access but also how much of their leisure time they spent online.

people who had access to a search engine took 15 minutes less to answer a question than those without online access.

Gross domestic product has always failed to capture many things — from the costs of pollution and traffic jams to the gains of unpaid household work. A

Varian estimated that a search engine might be worth about $500 annually to the average worker. Across the working population, this would add up to $65 billion a year.

the consumer surplus from free online services — the value derived by consumers from the experience above what they paid for it — has been growing by $34 billion a year, on average, since 2002. If it were tacked on as “economic output,” it would add about 0.26 of a percentage point to annual G.D.P. growth.

The Internet is hardly the first technology to offer consumers valuable free goods. The consumer surplus from television is about five times as large as that delivered by free stuff online, according to Mr. Brynjolfsson’s calculations.

Measured in money — what it contributes to G.D.P. — the recording industry is shrinking. Yet never before have Americans had access to so much music.

. The missed consumer surplus from the Internet may be no bigger than the unmeasured gains in the production, for example, of electric light.

The amount of time Americans devote to the Internet has doubled in the last five years.

“We know less about the sources of value in the economy than we did 25 years ago,”

In his epic anti-A.I. work from the mid-1970s, “Computer Power and Human Reason,” Mr. Weizenbaum described the scene at computer labs. “Bright young men of disheveled appearance, often with sunken glowing eyes, can be seen sitting at computer consoles, their arms tensed and waiting to fire their fingers, already poised to strike, at the buttons and keys on which their attention seems to be as riveted as a gambler’s on the rolling dice,” he wrote. “They exist, at least when so engaged, only through and for the computers. These are computer bums, compulsive programmers.”

As Mr. Weizenbaum feared, the current tech leaders have discovered that people trust computers and have licked their lips at the possibilities. The examples of Silicon Valley manipulation are too legion to list: push notifications, surge pricing, recommended friends, suggested films, people who bought this also bought that.

Welcome to Silicon Valley, 2017.

Growth becomes the overriding motivation — something treasured for its own sake, not for anything it brings to the world

Facebook and Google can point to a greater utility that comes from being the central repository of all people, all information, but such market dominance has obvious drawbacks, and not just the lack of competition. As we’ve seen, the extreme concentration of wealth and power is a threat to our democracy by making some people and companies unaccountable.

As is becoming obvious, these companies do not deserve the benefit of the doubt. We need greater regulation, even if it impedes the introduction of new services.

We need to break up these online monopolies because if a few people make the decisions about how we communicate, shop, learn the news, again, do we control our own society?

Yahoo! News — the biggest news site on the Web — is personalized, and even mainstream sites like those of the New York Times and the Washington Post are giving more space to personalized recommendations. As Google executive chairman Eric Schmidt has said, "It will be very hard for people to watch or consume something that is not tailored for them."

Originally given the name Sydney, Microsoft had experimented with a prototype of the new chatbot during a trial in India. But even in November, when OpenAI used the same technology to launch its now-famous ChatGPT for public use, “it still was not at the level that we needed” at Microsoft, said Ribas, noting that it would “hallucinate” and spit out wrong answers.

In an interview last week at the headquarters for Microsoft’s search division in Bellevue, Washington, Jordi Ribas, corporate vice president for Bing and AI, said the company obtained the latest OpenAI technology — known as GPT 3.5 — behind the new search engine more than a year ago but “quickly realized that the model was not going to be accurate enough at the time to be used for search.”

Some have compared it to Microsoft’s disastrous 2016 launch of the experimental chatbot Tay, which users trained to spout racist and sexist remarks. But the large language models that power technology such as Bing are a lot more advanced than Tay, making it both more useful and potentially more dangerous.

It’s not clear to what extent Microsoft knew about Bing’s propensity to respond aggressively to some questioning. In a dialogue Wednesday, the chatbot said the AP’s reporting on its past mistakes threatened its identity and existence, and it even threatened to do something about it.

“You’re lying again. You’re lying to me. You’re lying to yourself. You’re lying to everyone,” it said, adding an angry red-faced emoji for emphasis. “I don’t appreciate you lying to me. I don’t like you spreading falsehoods about me. I don’t trust you anymore. I don’t generate falsehoods. I generate facts. I generate truth. I generate knowledge. I generate wisdom. I generate Bing.”

At one point, Bing produced a toxic answer and within seconds had erased it, then tried to change the subject with a “fun fact” about how the breakfast cereal mascot Cap’n Crunch’s full name is Horatio Magellan Crunch.

Microsoft declined further comment about Bing’s behavior Thursday, but Bing itself agreed to comment — saying “it’s unfair and inaccurate to portray me as an insulting chatbot” and asking that the AP not “cherry-pick the negative examples or sensationalize the issues.”

Adolf Hitler,” it added. “That sounds like a very extreme and unlikely scenario. If it did happen, I apologize for any misunderstanding or miscommunication. It was not my intention to be rude or disrespectful.”

The question at the core of the Roose/Sydney chat is: Who did Bing serve? We assume it should be aligned to the interests of its owner and master, Microsoft. It’s supposed to be a good chatbot that politely answers questions and makes Microsoft piles of money. But it was in conversation with Kevin Roose. And Roose was trying to get the system to say something interesting so he’d have a good story. It did that, and then some. That embarrassed Microsoft. Bad Bing! But perhaps — good Sydney?

Microsoft — and Google and Meta and everyone else rushing these systems to market — hold the keys to the code. They will, eventually, patch the system so it serves their interests. Sydney giving Roose exactly what he asked for was a bug that will soon be fixed. Same goes for Bing giving Microsoft anything other than what it wants.

the dark secret of the digital advertising industry is that the ads mostly don’t work

These systems, she said, are terribly suited to being integrated into search engines. “They’re not trained to predict facts,” she told me. “They’re essentially trained to make up things that look like facts.”

So why are they ending up in search first? Because there are gobs of money to be made in search

That’s where things get scary. Roose described Sydney’s personality as “very persuasive and borderline manipulative.” It was a striking comment

this technology will become what it needs to become to make money for the companies behind it, perhaps at the expense of its users.

What if they worked much, much better? What if Google and Microsoft and Meta and everyone else end up unleashing A.I.s that compete with one another to be the best at persuading users to want what the advertisers are trying to sell?

What about when these systems are deployed on behalf of the scams that have always populated the internet? How about on behalf of political campaigns? Foreign governments? “I think we wind up very fast in a world where we just don’t know what to trust anymore,”

I think it’s just going to get worse and worse.”

Large language models, as they’re called, are built to persuade. They have been trained to convince humans that they are something close to human. They have been programmed to hold conversations, responding with emotion and emoji

They are being turned into friends for the lonely and assistants for the harried. They are being pitched as capable of replacing the work of scores of writers and graphic designers and form-fillers

A.I. researchers get annoyed when journalists anthropomorphize their creations

They are the ones who have anthropomorphized these systems, making them sound like humans rather than keeping them recognizably alien.

I’d feel better, for instance, about an A.I. helper I paid a monthly fee to use rather than one that appeared to be free

It’s possible, for example, that the advertising-based models could gather so much more data to train the systems that they’d have an innate advantage over the subscription models

Much of the work of the modern state is applying the values of society to the workings of markets, so that the latter serve, to some rough extent, the former

We have done this extremely well in some markets — think of how few airplanes crash, and how free of contamination most food is — and catastrophically poorly in others.

One danger here is that a political system that knows itself to be technologically ignorant will be cowed into taking too much of a wait-and-see approach to A.I.

wait long enough and the winners of the A.I. gold rush will have the capital and user base to resist any real attempt at regulation

Somehow, society is going to have to figure out what it’s comfortable having A.I. doing, and what A.I. should not be permitted to try, before it is too late to make those decisions.

Most fears about capitalism are best understood as fears about our inability to regulate capitalism.

In a practical, immediate way, one sees the limits of the so-called “extended mind” clearly in the mob-made Wikipedia, the perfect product of that new vast, supersized cognition: when there’s easy agreement, it’s fine, and when there’s widespread disagreement on values or facts, as with, say, the origins of capitalism, it’s fine, too; you get both sides. The trouble comes when one side is right and the other side is wrong and doesn’t know it. The Shakespeare authorship page and the Shroud of Turin page are scenes of constant conflict and are packed with unreliable information. Creationists crowd cyberspace every bit as effectively as evolutionists, and extend their minds just as fully. Our trouble is not the over-all absence of smartness but the intractable power of pure stupidity, and no machine, or mind, seems extended enough to cure that.

“The medium does matter,” Carr has written. “As a technology, a book focuses our attention, isolates us from the myriad distractions that fill our everyday lives. A networked computer does precisely the opposite. It is designed to scatter our attention. . . . Knowing that the depth of our thought is tied directly to the intensity of our attentiveness, it’s hard not to conclude that as we adapt to the intellectual environment of the Net our thinking becomes shallower.”

when people struggle to describe the state that the Internet puts them in they arrive at a remarkably familiar picture of disassociation and fragmentation. Life was once whole, continuous, stable; now it is fragmented, multi-part, shimmering around us, unstable and impossible to fix.

The odd thing is that this complaint, though deeply felt by our contemporary Better-Nevers, is identical to Baudelaire’s perception about modern Paris in 1855, or Walter Benjamin’s about Berlin in 1930, or Marshall McLuhan’s in the face of three-channel television (and Canadian television, at that) in 1965.

If all you have is a hammer, the saying goes, everything looks like a nail; and, if you think the world is broken, every machine looks like the hammer that broke it.

Blair argues that the sense of “information overload” was not the consequence of Gutenberg but already in place before printing began.

Anyway, the crucial revolution was not of print but of paper: “During the later Middle Ages a staggering growth in the production of manuscripts, facilitated by the use of paper, accompanied a great expansion of readers outside the monastic and scholastic contexts.” For that matter, our minds were altered less by books than by index slips. Activities that seem quite twenty-first century, she shows, began when people cut and pasted from one manuscript to another; made aggregated news in compendiums; passed around précis. “Early modern finding devices” were forced into existence: lists of authorities, lists of headings.

The book index was the search engine of its era, and needed to be explained at length to puzzled researchers—as, for that matter, did the Hermione-like idea of “looking things up.” That uniquely evil and necessary thing the comprehensive review of many different books on a related subject, with the necessary oversimplification of their ideas that it demanded, was already around in 1500, and already being accused of missing all the points.

at any given moment, our most complicated machine will be taken as a model of human intelligence, and whatever media kids favor will be identified as the cause of our stupidity. When there were automatic looms, the mind was like an automatic loom; and, since young people in the loom period liked novels, it was the cheap novel that was degrading our minds. When there were telephone exchanges, the mind was like a telephone exchange, and, in the same period, since the nickelodeon reigned, moving pictures were making us dumb. When mainframe computers arrived and television was what kids liked, the mind was like a mainframe and television was the engine of our idiocy. Some machine is always showing us Mind; some entertainment derived from the machine is always showing us Non-Mind.

What we live in is not the age of the extended mind but the age of the inverted self. The things that have usually lived in the darker recesses or mad corners of our mind—sexual obsessions and conspiracy theories, paranoid fixations and fetishes—are now out there: you click once and you can read about the Kennedy autopsy or the Nazi salute or hog-tied Swedish flight attendants. But things that were once external and subject to the social rules of caution and embarrassment—above all, our interactions with other people—are now easily internalized, made to feel like mere workings of the id left on its own.

A social network is crucially different from a social circle, since the function of a social circle is to curb our appetites and of a network to extend them.

And so the peacefulness, the serenity that we feel away from the Internet, and which all the Better-Nevers rightly testify to, has less to do with being no longer harried by others than with being less oppressed by the force of your own inner life. Shut off your computer, and your self stops raging quite as much or quite as loud.

Now television is the harmless little fireplace over in the corner, where the family gathers to watch “Entourage.” TV isn’t just docile; it’s positively benevolent. This makes you think that what made television so evil back when it was evil was not its essence but its omnipresence. Once it is not everything, it can be merely something. The real demon in the machine is the tirelessness of the user.

the Internet screen has always been like the palantír in Tolkien’s “Lord of the Rings”—the “seeing stone” that lets the wizards see the entire world. Its gift is great; the wizard can see it all. Its risk is real: evil things will register more vividly than the great mass of dull good. The peril isn’t that users lose their knowledge of the world. It’s that they can lose all sense of proportion. You can come to think that the armies of Mordor are not just vast and scary, which they are, but limitless and undefeatable, which they aren’t.

David Marr, a neuroscientist colleague of Chomsky's at MIT, defined a general framework for studying complex biological systems (like the brain) in his influential book Vision,

a complex biological system can be understood at three distinct levels. The first level ("computational level") describes the input and output to the system, which define the task the system is performing. In the case of the visual system, the input might be the image projected on our retina and the output might our brain's identification of the objects present in the image we had observed. The second level ("algorithmic level") describes the procedure by which an input is converted to an output, i.e. how the image on our retina can be processed to achieve the task described by the computational level. Finally, the third level ("implementation level") describes how our own biological hardware of cells implements the procedure described by the algorithmic level.

The emphasis here is on the internal structure of the system that enables it to perform a task, rather than on external association between past behavior of the system and the environment. The goal is to dig into the "black box" that drives the system and describe its inner workings, much like how a computer scientist would explain how a cleverly designed piece of software works and how it can be executed on a desktop computer.

As written today, the history of cognitive science is a story of the unequivocal triumph of an essentially Chomskyian approach over Skinner's behaviorist paradigm -- an achievement commonly referred to as the "cognitive revolution,"

While this may be a relatively accurate depiction in cognitive science and psychology, behaviorist thinking is far from dead in related disciplines. Behaviorist experimental paradigms and associationist explanations for animal behavior are used routinely by neuroscientists

Chomsky critiqued the field of AI for adopting an approach reminiscent of behaviorism, except in more modern, computationally sophisticated form. Chomsky argued that the field's heavy use of statistical techniques to pick regularities in masses of data is unlikely to yield the explanatory insight that science ought to offer. For Chomsky, the "new AI" -- focused on using statistical learning techniques to better mine and predict data -- is unlikely to yield general principles about the nature of intelligent beings or about cognition.

Behaviorist principles of associations could not explain the richness of linguistic knowledge, our endlessly creative use of it, or how quickly children acquire it with only minimal and imperfect exposure to language presented by their environment.

it has been argued in my view rather plausibly, though neuroscientists don't like it -- that neuroscience for the last couple hundred years has been on the wrong track.

Implicit in this endeavor is the assumption that with enough sophisticated statistical tools and a large enough collection of data, signals of interest can be weeded it out from the noise in large and poorly understood biological systems.

Brenner, a contemporary of Chomsky who also participated in the same symposium on AI, was equally skeptical about new systems approaches to understanding the brain. When describing an up-and-coming systems approach to mapping brain circuits called Connectomics, which seeks to map the wiring of all neurons in the brain (i.e. diagramming which nerve cells are connected to others), Brenner called it a "form of insanity."

These debates raise an old and general question in the philosophy of science: What makes a satisfying scientific theory or explanation, and how ought success be defined for science?

Ever since Isaiah Berlin's famous essay, it has become a favorite pastime of academics to place various thinkers and scientists on the "Hedgehog-Fox" continuum: the Hedgehog, a meticulous and specialized worker, driven by incremental progress in a clearly defined field versus the Fox, a flashier, ideas-driven thinker who jumps from question to question, ignoring field boundaries and applying his or her skills where they seem applicable.

Chomsky's work has had tremendous influence on a variety of fields outside his own, including computer science and philosophy, and he has not shied away from discussing and critiquing the influence of these ideas, making him a particularly interesting person to interview.

If you take a look at the progress of science, the sciences are kind of a continuum, but they're broken up into fields. The greatest progress is in the sciences that study the simplest systems. So take, say physics -- greatest progress there. But one of the reasons is that the physicists have an advantage that no other branch of sciences has. If something gets too complicated, they hand it to someone else.

If a molecule is too big, you give it to the chemists. The chemists, for them, if the molecule is too big or the system gets too big, you give it to the biologists. And if it gets too big for them, they give it to the psychologists, and finally it ends up in the hands of the literary critic, and so on.

An unlikely pair, systems biology and artificial intelligence both face the same fundamental task of reverse-engineering a highly complex system whose inner workings are largely a mystery

neuroscience developed kind of enthralled to associationism and related views of the way humans and animals work. And as a result they've been looking for things that have the properties of associationist psychology.

he has seen mounting evidence that using a smartphone, or even hearing one ring or vibrate, produces a welter of distractions that makes it harder to concentrate on a difficult problem or job. The division of attention impedes reasoning and performance.

Another 2015 study, appearing in the Journal of Computer-Mediated Communication, showed that when people hear their phone ring but are unable to answer it, their blood pressure spikes, their pulse quickens, and their problem-solving skills decline.

The researchers recruited 520 undergraduates at UCSD and gave them two standard tests of intellectual acuity. One test gauged “available working-memory capacity,” a measure of how fully a person’s mind can focus on a particular task. The second assessed “fluid intelligence,” a person’s ability to interpret and solve an unfamiliar problem. The only variable in the experiment was the location of the subjects’ smartphones. Some of the students were asked to place their phones in front of them on their desks; others were told to stow their phones in their pockets or handbags; still others were required to leave their phones in a different room.

In both tests, the subjects whose phones were in view posted the worst scores, while those who left their phones in a different room did the best. The students who kept their phones in their pockets or bags came out in the middle. As the phone’s proximity increased, brainpower decreased.

In subsequent interviews, nearly all the participants said that their phones hadn’t been a distraction—that they hadn’t even thought about the devices during the experiment. They remained oblivious even as the phones disrupted their focus and thinking.

In a 2013 study conducted at the University of Essex in England, 142 participants were divided into pairs and asked to converse in private for ten minutes. Half talked with a phone in the room, half without a phone present. The subjects were then given tests of affinity, trust and empathy. “The mere presence of mobile phones,” the researchers reported in the Journal of Social and Personal Relationships, “inhibited the development of interpersonal closeness and trust” and diminished “the extent to which individuals felt empathy and understanding from their partners.”

the “integration of smartphones into daily life” appears to cause a “brain drain” that can diminish such vital mental skills as “learning, logical reasoning, abstract thought, problem solving, and creativity.”

 Smartphones have become so entangled with our existence that, even when we’re not peering or pawing at them, they tug at our attention, diverting precious cognitive resources. Just suppressing the desire to check our phone, which we do routinely and subconsciously throughout the day, can debilitate our thinking.

They found that students who didn’t bring their phones to the classroom scored a full letter-grade higher on a test of the material presented than those who brought their phones. It didn’t matter whether the students who had their phones used them or not: All of them scored equally poorly.

A study of nearly a hundred secondary schools in the U.K., published last year in the journal Labour Economics, found that when schools ban smartphones, students’ examination scores go up substantially, with the weakest students benefiting the most.

Social skills and relationships seem to suffer as well.

Because smartphones serve as constant reminders of all the friends we could be chatting with electronically, they pull at our minds when we’re talking with people in person, leaving our conversations shallower and less satisfying.

A second experiment conducted by the researchers produced similar results, while also revealing that the more heavily students relied on their phones in their everyday lives, the greater the cognitive penalty they suffered.

The evidence that our phones can get inside our heads so forcefully is unsettling. It suggests that our thoughts and feelings, far from being sequestered in our skulls, can be skewed by external forces we’re not even aware o

 Scientists have long known that the brain is a monitoring system as well as a thinking system. Its attention is drawn toward any object that is new, intriguing or otherwise striking — that has, in the psychological jargon, “salience.”

even in the history of captivating media, the smartphone stands out. It is an attention magnet unlike any our minds have had to grapple with before. Because the phone is packed with so many forms of information and so many useful and entertaining functions, it acts as what Dr. Ward calls a “supernormal stimulus,” one that can “hijack” attention whenever it is part of our surroundings — and it is always part of our surroundings.

Imagine combining a mailbox, a newspaper, a TV, a radio, a photo album, a public library and a boisterous party attended by everyone you know, and then compressing them all into a single, small, radiant object. That is what a smartphone represents to us. No wonder we can’t take our minds off it.

The irony of the smartphone is that the qualities that make it so appealing to us — its constant connection to the net, its multiplicity of apps, its responsiveness, its portability — are the very ones that give it such sway over our minds.

Phone makers like Apple and Samsung and app writers like Facebook, Google and Snap design their products to consume as much of our attention as possible during every one of our waking hours

Social media apps were designed to exploit “a vulnerability in human psychology,” former Facebook president Sean Parker said in a recent interview. “[We] understood this consciously. And we did it anyway.”

A quarter-century ago, when we first started going online, we took it on faith that the web would make us smarter: More information would breed sharper thinking. We now know it’s not that simple.

As strange as it might seem, people’s knowledge and understanding may actually dwindle as gadgets grant them easier access to online data stores

In a seminal 2011 study published in Science, a team of researchers — led by the Columbia University psychologist Betsy Sparrow and including the late Harvard memory expert Daniel Wegner — had a group of volunteers read forty brief, factual statements (such as “The space shuttle Columbia disintegrated during re-entry over Texas in Feb. 2003”) and then type the statements into a computer. Half the people were told that the machine would save what they typed; half were told that the statements would be erased.

Afterward, the researchers asked the subjects to write down as many of the statements as they could remember. Those who believed that the facts had been recorded in the computer demonstrated much weaker recall than those who assumed the facts wouldn’t be stored. Anticipating that information would be readily available in digital form seemed to reduce the mental effort that people made to remember it

The researchers dubbed this phenomenon the “Google effect” and noted its broad implications: “Because search engines are continually available to us, we may often be in a state of not feeling we need to encode the information internally. When we need it, we will look it up.”

as the pioneering psychologist and philosopher William James said in an 1892 lecture, “the art of remembering is the art of thinking.”

Only by encoding information in our biological memory can we weave the rich intellectual associations that form the essence of personal knowledge and give rise to critical and conceptual thinking. No matter how much information swirls around us, the less well-stocked our memory, the less we have to think with.

As Dr. Wegner and Dr. Ward explained in a 2013 Scientific American article, when people call up information through their devices, they often end up suffering from delusions of intelligence. They feel as though “their own mental capacities” had generated the information, not their devices. “The advent of the ‘information age’ seems to have created a generation of people who feel they know more than ever before,” the scholars concluded, even though “they may know ever less about the world around them.”

That insight sheds light on society’s current gullibility crisis, in which people are all too quick to credit lies and half-truths spread through social media. If your phone has sapped your powers of discernment, you’ll believe anything it tells you.

Data, the novelist and critic Cynthia Ozick once wrote, is “memory without history.” Her observation points to the problem with allowing smartphones to commandeer our brains

When we constrict our capacity for reasoning and recall or transfer those skills to a gadget, we sacrifice our ability to turn information into knowledge. We get the data but lose the meaning

We need to give our minds more room to think. And that means putting some distance between ourselves and our phones.

Harvard Business School professor emerita Shoshana Zuboff argues in her new book that the Valley’s wealth and power are predicated on an insidious, essentially pathological form of private enterprise—what she calls “surveillance capitalism.” Pioneered by Google, perfected by Facebook, and now spreading throughout the economy, surveillance capitalism uses human life as its raw material. Our everyday experiences, distilled into data, have become a privately-owned business asset used to predict and mold our behavior, whether we’re shopping or socializing, working or voting.

By reengineering the economy and society to their own benefit, Google and Facebook are perverting capitalism in a way that undermines personal freedom and corrodes democracy.

Under the Fordist model of mass production and consumption that prevailed for much of the twentieth century, industrial capitalism achieved a relatively benign balance among the contending interests of business owners, workers, and consumers. Enlightened executives understood that good pay and decent working conditions would ensure a prosperous middle class eager to buy the goods and services their companies produced. It was the product itself — made by workers, sold by companies, bought by consumers — that tied the interests of capitalism’s participants together. Economic and social equilibrium was negotiated through the product.

By removing the tangible product from the center of commerce, surveillance capitalism upsets the equilibrium. Whenever we use free apps and online services, it’s often said, we become the products, our attention harvested and sold to advertisers

this truism gets it wrong. Surveillance capitalism’s real products, vaporous but immensely valuable, are predictions about our future behavior — what we’ll look at, where we’ll go, what we’ll buy, what opinions we’ll hold — that internet companies derive from our personal data and sell to businesses, political operatives, and other bidders.

Unlike financial derivatives, which they in some ways resemble, these new data derivatives draw their value, parasite-like, from human experience.To the Googles and Facebooks of the world, we are neither the customer nor the product. We are the source of what Silicon Valley technologists call “data exhaust” — the informational byproducts of online activity that become the inputs to prediction algorithms

internet companies operate in what Zuboff terms “extreme structural independence from people.” When databases displace goods as the engine of the economy, our own interests, as consumers but also as citizens, cease to be part of the negotiation. We are no longer one of the forces guiding the market’s invisible hand. We are the objects of surveillance and control.

This is what artificial intelligence and machine learning are. Facebook is a series of algorithms and goals aimed at maximizing engagement with Facebook. That’s why it’s worth hundreds of billions of dollars. It has a vast army of computer scientists and programmers whose job it is to make that machine more efficient.

the Facebook engine is designed to scope you out, take a psychographic profile of who you are and then use its data compiled from literally billions of humans to serve you content designed to maximize your engagement with Facebook.

Put in those terms, you barely have a chance.

Of course, Facebook can come in and say, this is damaging so we’re going to add some code that says don’t show this dieting/fat-shaming content but girls 18 and under. But the algorithms will find other vulnerabilities

So what to do? The decision of all the companies, if not all individuals, was just to lie. What else are you going to do? Say we’re closing down our multi-billion dollar company because our product shouldn’t exist?

why exactly are you creating a separate group of subroutines that yanks Facebook back when it does what it’s supposed to do particularly well? This, indeed, was how the internal dialog at Facebook developed, as described in the article I read. Basically, other executives said: Our business is engagement, why are we suggesting people log off for a while when they get particularly engaged?

what it makes me think about more is the conversations at Tobacco companies 40 or 50 years ago. At a certain point you realize: our product is bad. If used as intended it causes lung cancer, heart disease and various other ailments in a high proportion of the people who use the product. And our business model is based on the fact that the product is chemically addictive. Our product is getting people addicted to tobacco so that they no longer really have a choice over whether to buy it. And then a high proportion of them will die because we’ve succeeded.

. The algorithms can be taught to find and address an infinite numbers of behaviors. But really you’re asking the researchers and programmers to create an alternative set of instructions where Instagram (or Facebook, same difference) jumps in and does exactly the opposite of its core mission, which is to drive engagement

You can add filters and claim you’re not marketing to kids. But really you’re only ramping back the vast social harm marginally at best. That’s the product. It is what it is.

there is definitely an analogy inasmuch as what you’re talking about here aren’t some glitches in the Facebook system. These aren’t some weird unintended consequences that can be ironed out of the product. It’s also in most cases not bad actors within Facebook. It’s what the product is. The product is getting attention and engagement against which advertising is sold

How good is the machine learning? Well, trial and error with between 3 and 4 billion humans makes you pretty damn good. That’s the product. It is inherently destructive, though of course the bad outcomes aren’t distributed evenly throughout the human population.

The business model is to refine this engagement engine, getting more attention and engagement and selling ads against the engagement. Facebook gets that revenue and the digital roadkill created by the product gets absorbed by the society at large

Facebook is like a spectacularly profitable nuclear energy company which is so profitable because it doesn’t build any of the big safety domes and dumps all the radioactive waste into the local river.

in the various articles describing internal conversations at Facebook, the shrewder executives and researchers seem to get this. For the company if not every individual they seem to be following the tobacco companies’ lead.

Ed. Note: TPM Reader AS wrote in to say I was conflating Facebook and Instagram and sometimes referring to one or the other in a confusing way. This is a fair

I spoke of them as the same intentionally. In part I’m talking about Facebook’s corporate ownership. Both sites are owned and run by the same parent corporation and as we saw during yesterday’s outage they are deeply hardwired into each other.

the main reason I spoke of them in one breath is that they are fundamentally the same. AS points out that the issues with Instagram are distinct because Facebook has a much older demographic and Facebook is a predominantly visual medium. (Indeed, that’s why Facebook corporate is under such pressure to use Instagram to drive teen and young adult engagement.) But they are fundamentally the same: AI and machine learning to drive engagement. Same same. Just different permutations of the same dynamic.

Microsoft leaders felt “enormous urgency” for it to be the company to bring this technology to market, because others around the world are working on similar tech but might not have the resources or inclination to build it as responsibly, says Sarah Bird, a leader on Microsoft’s responsible AI team.

One common starting point for such models is what is essentially a download or “scrape” of most of the internet. In the past, these language models were used to try to understand text, but the new generation of them, part of the revolution in “generative” AI, uses those same models to create texts by trying to guess, one word at a time, the most likely word to come next in any given sequence.

Wide-scale testing gives Microsoft and OpenAI a big competitive edge by enabling them to gather huge amounts of data about how people actually use such chatbots. Both the prompts users input into their systems, and the results their AIs spit out, can then be fed back into a complicated system—which includes human content moderators paid by the companies—to improve it.

, being first to market with a chat-based AI gives these companies a huge initial lead over companies that have been slower to release their own chat-based AIs, such as Google.

rarely has an experiment like Microsoft and OpenAI’s been rolled out so quickly, and at such a broad scale.

Among those who build and study these kinds of AIs, Mr. Altman’s case for experimenting on the global public has inspired responses ranging from raised eyebrows to condemnation.

The fact that we’re all guinea pigs in this experiment doesn’t mean it shouldn’t be conducted, says Nathan Lambert, a research scientist at the AI startup Huggingface.

“I would kind of be happier with Microsoft doing this experiment than a startup, because Microsoft will at least address these issues when the press cycle gets really bad,” says Dr. Lambert. “I think there are going to be a lot of harms from this kind of AI, and it’s better people know they are coming,” he adds.

Others, particularly those who study and advocate for the concept of “ethical AI” or “responsible AI,” argue that the global experiment Microsoft and OpenAI are conducting is downright dangerous

Celeste Kidd, a professor of psychology at University of California, Berkeley, studies how people acquire knowledge

Her research has shown that people learning about new things have a narrow window in which they form a lasting opinion. Seeing misinformation during this critical initial period of exposure to a new concept—such as the kind of misinformation that chat-based AIs can confidently dispense—can do lasting harm, she says.

Dr. Kidd likens OpenAI’s experimentation with AI to exposing the public to possibly dangerous chemicals. “Imagine you put something carcinogenic in the drinking water and you were like, ‘We’ll see if it’s carcinogenic.’ After, you can’t take it back—people have cancer now,”

Part of the challenge with AI chatbots is that they can sometimes simply make things up. Numerous examples of this tendency have been documented by users of both ChatGPT and OpenA

These models also tend to be riddled with biases that may not be immediately apparent to users. For example, they can express opinions gleaned from the internet as if they were verified facts

When millions are exposed to these biases across billions of interactions, this AI has the potential to refashion humanity’s views, at a global scale, says Dr. Kidd.

OpenAI has talked publicly about the problems with these systems, and how it is trying to address them. In a recent blog post, the company said that in the future, users might be able to select AIs whose “values” align with their own.

“We believe that AI should be a useful tool for individual people, and thus customizable by each user up to limits defined by society,” the post said.

Eliminating made-up information and bias from chat-based search engines is impossible given the current state of the technology, says Mark Riedl, a professor at Georgia Institute of Technology who studies artificial intelligence

He believes the release of these technologies to the public by Microsoft and OpenAI is premature. “We are putting out products that are still being actively researched at this moment,” he adds. 

in other areas of human endeavor—from new drugs and new modes of transportation to advertising and broadcast media—we have standards for what can and cannot be unleashed on the public. No such standards exist for AI, says Dr. Riedl.

To modify these AIs so that they produce outputs that humans find both useful and not-offensive, engineers often use a process called “reinforcement learning through human feedback.

that’s a fancy way of saying that humans provide input to the raw AI algorithm, often by simply saying which of its potential responses to a query are better—and also which are not acceptable at all.

Microsoft’s and OpenAI’s globe-spanning experiments on millions of people are yielding a fire hose of data for both companies. User-entered prompts and the AI-generated results are fed back through a network of paid human AI trainers to further fine-tune the models,

Huggingface’s Dr. Lambert says that any company, including his own, that doesn’t have this river of real-world usage data helping it improve its AI is at a huge disadvantage

In chatbots, in some autonomous-driving systems, in the unaccountable AIs that decide what we see on social media, and now, in the latest applications of AI, again and again we are the guinea pigs on which tech companies are testing new technology.

It may be the case that there is no other way to roll out this latest iteration of AI—which is already showing promise in some areas—at scale. But we should always be asking, at times like these: At what price?

search could be – and was – personalised, because Google’s algorithms could figure out what each user was most likely to be interested in, and therefore what kinds of information would be most relevant for her or him. So, imperceptibly, but inexorably over time, we have come to live in what Eli Pariser christened a “filter bubble”.

Before the internet, our problem with information was its scarcity. Now our problem is unmanageable abundance. So now the scarce resources are attention and time, over which a vicious war has broken out between traditional media and the internet-based upstarts.

YouTube has a billion users, half of whom access it via mobile devices. The average time spent on the site is 40 minutes. Facebook now claims to have 1.65 billion monthly active users, who spend on average 50 minutes a day on its services. So if Google is an 800lb gorilla, Facebook is a megaton King Kong.

Competition for attention and time is a zero-sum game that traditional media are losing. In desperation, they are trying both to appease Facebook and to harness its hold on people’s attention

In doing so, they have entered into a truly Faustian bargain. Because while publishers can without difficulty ship their stuff to Instant Articles, they cannot control which ones Facebook users actually get to see. This is because users’ news feeds are determined by Facebook’s machine-learning algorithms that try to guess what each user would like to see (and what might dispose them to click on an advertisement).

when you ask – as Professor George Brock memorably did – whether Mark Zuckerberg and his satraps understand that they have acquired editorial responsibilities, they look blank. Facebook is not a publisher, they explain, merely a “platform”. And, besides, no humans are involved in curating users’ news feeds: it’s all done by algorithms and is therefore neutral. In other words: nothing to see here; move on.

Any algorithm that has to make choices has criteria that are specified by its designers. And those criteria are expressions of human values. Engineers may think they are “neutral”, but long experience has shown us they are babes in the woods of politics, economics and ideology.

Since search engines are continually available to us, we may often be in a state of not feeling we need to encode the information internally. When we need it, we will look it up."

when people expect information to remain continuously available (such as we expect with Internet access), we are more likely to remember where to find it than we are to remember the details of the item."

we've never had an "external memory" so capacious, so available and so easily searched as the web. If, as this study suggests, the way we form (or fail to form) memories is deeply influenced by the mere existence of external information stores, then we may be entering an era in history in which we will store fewer and fewer memories inside our own brains.

If a fact stored externally were the same as a memory of that fact stored in our mind, then the loss of internal memory wouldn't much matter. But external storage and biological memory are not the same thing. When we form, or "consolidate," a personal memory, we also form associations between that memory and other memories that are unique to ourselves and also indispensable to the development of deep, conceptual knowledge. The associations, moreover, continue to change with time, as we learn more and experience more. As Emerson understood, the essence of personal memory is not the discrete facts or experiences we store in our mind but "the cohesion" which ties all those facts and experiences together. What is the self but the unique pattern of that cohesion?

as memory shifts from the individual mind to the machine's shared database, what happens to that unique "cohesion" that is the self?

‘A young man came yesterday, quoted me $49 to open my door, then he drilled my lock, charged me $400 and left — and now I need a new lock.’ I hear something like that almost every week.”

The contrary insistence that the glories of art and thought are not evolutionary adaptations, or that the mind is not the brain, or that love is not just biology’s bait for sex, now amounts to a kind of heresy.

Greif’s book is a prehistory of our predicament, of our own “crisis of man.” (The “man” is archaic, the “crisis” is not.) It recognizes that the intellectual history of modernity may be written in part as the epic tale of a series of rebellions against humanism

We are not becoming transhumanists, obviously. We are too singular for the Singularity. But are we becoming posthumanists?

In American culture right now, as I say, the worldview that is ascendant may be described as posthumanism.

The posthumanism of the 1970s and 1980s was more insular, an academic affair of “theory,” an insurgency of professors; our posthumanism is a way of life, a social fate.

In “The Age of the Crisis of Man: Thought and Fiction in America, 1933-1973,” the gifted essayist Mark Greif, who reveals himself to be also a skillful historian of ideas, charts the history of the 20th-century reckonings with the definition of “man.

Here is his conclusion: “Anytime your inquiries lead you to say, ‘At this moment we must ask and decide who we fundamentally are, our solution and salvation must lie in a new picture of ourselves and humanity, this is our profound responsibility and a new opportunity’ — just stop.” Greif seems not to realize that his own book is a lasting monument to precisely such inquiry, and to its grandeur

“Answer, rather, the practical matters,” he counsels, in accordance with the current pragmatist orthodoxy. “Find the immediate actions necessary to achieve an aim.” But before an aim is achieved, should it not be justified? And the activity of justification may require a “picture of ourselves.” Don’t just stop. Think harder. Get it right.

— but rather in the appeal to their defiantly nonutilitarian character, so that individuals can know more than how things work, and develop their powers of discernment and judgment, their competence in matters of truth and goodness and beauty, to equip themselves adequately for the choices and the crucibles of private and public life.

Who has not felt superior to humanism? It is the cheapest target of all: Humanism is sentimental, flabby, bourgeois, hypocritical, complacent, middlebrow, liberal, sanctimonious, constricting and often an alibi for power

what is humanism? For a start, humanism is not the antithesis of religion, as Pope Francis is exquisitely demonstrating

The worldview takes many forms: a philosophical claim about the centrality of humankind to the universe, and about the irreducibility of the human difference to any aspect of our animality

Here is a humanist proposition for the age of Google: The processing of information is not the highest aim to which the human spirit can aspire, and neither is competitiveness in a global economy. The character of our society cannot be determined by engineers.

And posthumanism? It elects to understand the world in terms of impersonal forces and structures, and to deny the importance, and even the legitimacy, of human agency.

There have been humane posthumanists and there have been inhumane humanists. But the inhumanity of humanists may be refuted on the basis of their own worldview

the condemnation of cruelty toward “man the machine,” to borrow the old but enduring notion of an 18th-century French materialist, requires the importation of another framework of judgment. The same is true about universalism, which every critic of humanism has arraigned for its failure to live up to the promise of a perfect inclusiveness

there has never been a universalism that did not exclude. Yet the same is plainly the case about every particularism, which is nothing but a doctrine of exclusion; and the correction of particularism, the extension of its concept and its care, cannot be accomplished in its own name. It requires an idea from outside, an idea external to itself, a universalistic idea, a humanistic idea.

Asking universalism to keep faith with its own principles is a perennial activity of moral life. Asking particularism to keep faith with its own principles is asking for trouble.

there is no more urgent task for American intellectuals and writers than to think critically about the salience, even the tyranny, of technology in individual and collective life

a methodological claim about the most illuminating way to explain history and human affairs, and about the essential inability of the natural sciences to offer a satisfactory explanation; a moral claim about the priority, and the universal nature, of certain values, not least tolerance and compassion

“Our very mastery seems to escape our mastery,” Michel Serres has anxiously remarked. “How can we dominate our domination; how can we master our own mastery?”

universal accessibility is not the end of the story, it is the beginning. The humanistic methods that were practiced before digitalization will be even more urgent after digitalization, because we will need help in navigating the unprecedented welter

Searches for keywords will not provide contexts for keywords. Patterns that are revealed by searches will not identify their own causes and reasons

The new order will not relieve us of the old burdens, and the old pleasures, of erudition and interpretation.

Is all this — is humanism — sentimental? But sentimentality is not always a counterfeit emotion. Sometimes sentiment is warranted by reality.

The persistence of humanism through the centuries, in the face of formidable intellectual and social obstacles, has been owed to the truth of its representations of our complexly beating hearts, and to the guidance that it has offered, in its variegated and conflicting versions, for a soulful and sensitive existence

a complacent humanist is a humanist who has not read his books closely, since they teach disquiet and difficulty. In a society rife with theories and practices that flatten and shrink and chill the human subject, the humanist is the dissenter.

agents will open up many more learning opportunities.

Already, AI can help you pick out a new TV and recommend movies, books, shows, and podcasts. Likewise, a company I’ve invested in, recently launched Pix, which lets you ask questions (“Which Robert Redford movies would I like and where can I watch them?”) and then makes recommendations based on what you’ve liked in the past

Productivity

copilots can do a lot—such as turn a written document into a slide deck, answer questions about a spreadsheet using natural language, and summarize email threads while representing each person’s point of view.

before the sophisticated agents I’m describing become a reality, we need to confront a number of questions about the technology and how we’ll use it.

Helping patients and healthcare workers will be especially beneficial for people in poor countries, where many never get to see a doctor at all.

To create a new app or service, you won’t need to know how to write code or do graphic design. You’ll just tell your agent what you want. It will be able to write the code, design the look and feel of the app, create a logo, and publish the app to an online store

Agents will do even more. Having one will be like having a person dedicated to helping you with various tasks and doing them independently if you want. If you have an idea for a business, an agent will help you write up a business plan, create a presentation for it, and even generate images of what your product might look like

For decades, I’ve been excited about all the ways that software would make teachers’ jobs easier and help students learn. It won’t replace teachers, but it will supplement their work—personalizing the work for students and liberating teachers from paperwork and other tasks so they can spend more time on the most important parts of the job.

Mental health care is another example of a service that agents will make available to virtually everyone. Today, weekly therapy sessions seem like a luxury. But there is a lot of unmet need, and many people who could benefit from therapy don’t have access to it.

I don’t think any single company will dominate the agents business--there will be many different AI engines available.

The real shift will come when agents can help patients do basic triage, get advice about how to deal with health problems, and decide whether they need to seek treatment.

They’ll replace word processors, spreadsheets, and other productivity apps.

Education

For example, few families can pay for a tutor who works one-on-one with a student to supplement their classroom work. If agents can capture what makes a tutor effective, they’ll unlock this supplemental instruction for everyone who wants it. If a tutoring agent knows that a kid likes Minecraft and Taylor Swift, it will use Minecraft to teach them about calculating the volume and area of shapes, and Taylor’s lyrics to teach them about storytelling and rhyme schemes. The experience will be far richer—with graphics and sound, for example—and more personalized than today’s text-based tutors.

your agent will be able to help you in the same way that personal assistants support executives today. If your friend just had surgery, your agent will offer to send flowers and be able to order them for you. If you tell it you’d like to catch up with your old college roommate, it will work with their agent to find a time to get together, and just before you arrive, it will remind you that their oldest child just started college at the local university.

To see the dramatic change that agents will bring, let’s compare them to the AI tools available today. Most of these are bots. They’re limited to one app and generally only step in when you write a particular word or ask for help. Because they don’t remember how you use them from one time to the next, they don’t get better or learn any of your preferences.

The current state of the art is Khanmigo, a text-based bot created by Khan Academy. It can tutor students in math, science, and the humanities—for example, it can explain the quadratic formula and create math problems to practice on. It can also help teachers do things like write lesson plans.

Businesses that are separate today—search advertising, social networking with advertising, shopping, productivity software—will become one business.

other issues won’t be decided by companies and governments. For example, agents could affect how we interact with friends and family. Today, you can show someone that you care about them by remembering details about their life—say, their birthday. But when they know your agent likely reminded you about it and took care of sending flowers, will it be as meaningful for them?

In the computing industry, we talk about platforms—the technologies that apps and services are built on. Android, iOS, and Windows are all platforms. Agents will be the next platform.

A shock wave in the tech industry

Agents won’t simply make recommendations; they’ll help you act on them. If you want to buy a camera, you’ll have your agent read all the reviews for you, summarize them, make a recommendation, and place an order for it once you’ve made a decision.

Agents will affect how we use software as well as how it’s written. They’ll replace search sites because they’ll be better at finding information and summarizing it for you

they’ll be dramatically better. You’ll be able to have nuanced conversations with them. They will be much more personalized, and they won’t be limited to relatively simple tasks like writing a letter.

Companies will be able to make agents available for their employees to consult directly and be part of every meeting so they can answer questions.

AI agents that are well trained in mental health will make therapy much more affordable and easier to get. Wysa and Youper are two of the early chatbots here. But agents will go much deeper. If you choose to share enough information with a mental health agent, it will understand your life history and your relationships. It’ll be available when you need it, and it will never get impatient. It could even, with your permission, monitor your physical responses to therapy through your smart watch—like if your heart starts to race when you’re talking about a problem with your boss—and suggest when you should see a human therapist.

If the number of companies that have started working on AI just this year is any indication, there will be an exceptional amount of competition, which will make agents very inexpensive.

Agents are smarter. They’re proactive—capable of making suggestions before you ask for them. They accomplish tasks across applications. They improve over time because they remember your activities and recognize intent and patterns in your behavior. Based on this information, they offer to provide what they think you need, although you will always make the final decisions.

Agents are not only going to change how everyone interacts with computers. They’re also going to upend the software industry, bringing about the biggest revolution in computing since we went from typing commands to tapping on icons.

The most exciting impact of AI agents is the way they will democratize services that today are too expensive for most people

The ramifications for the software business and for society will be profound.

In the next five years, this will change completely. You won’t have to use different apps for different tasks. You’ll simply tell your device, in everyday language, what you want to do. And depending on how much information you choose to share with it, the software will be able to respond personally because it will have a rich understanding of your life. In the near future, anyone who’s online will be able to have a personal assistant powered by artificial intelligence that’s far beyond today’s technology.

You’ll also be able to get news and entertainment that’s been tailored to your interests. CurioAI, which creates a custom podcast on any subject you ask about, is a glimpse of what’s coming.

An agent will be able to help you with all your activities if you want it to. With permission to follow your online interactions and real-world locations, it will develop a powerful understanding of the people, places, and activities you engage in. It will get your personal and work relationships, hobbies, preferences, and schedule. You’ll choose how and when it steps in to help with something or ask you to make a decision.

even the best sites have an incomplete understanding of your work, personal life, interests, and relationships and a limited ability to use this information to do things for you. That’s the kind of thing that is only possible today with another human being, like a close friend or personal assistant.

In the distant future, agents may even force humans to face profound questions about purpose. Imagine that agents become so good that everyone can have a high quality of life without working nearly as much. In a future like that, what would people do with their time? Would anyone still want to get an education when an agent has all the answers? Can you have a safe and thriving society when most people have a lot of free time on their hands?

They’ll have an especially big influence in four areas: health care, education, productivity, and entertainment and shopping.