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two officers confronted Walter Wallace Jr., a 27-year-old with a history of mental health problems. A lawyer for the family said that he was experiencing a crisis that day and that the family told officers about it when they arrived at the scene

Gov. Tom Wolf called in the National Guard. On Wednesday, the city declared a 9 p.m. curfew.

The shooting and its aftermath were guaranteed to ratchet up tensions in a country already on edge.

The White House blamed the “liberal Democrats’ war against the police” for the destruction that followed the protests

Joseph R. Biden Jr. and Senator Kamala Harris lamented Mr. Wallace’s death and condemned the looting that followed, but added that none of this would be solved with a president who fanned “the flames of division in our society.”

To vote is to believe that problems can be solved, and the election has presented a stark division as to how best solve a litany of national problems, including racial divisions and the coronavirus pandemic.

The Philadelphia police have for many years been criticized by community activists for excessive use of force against people of color.

“The department has a long history of abusive behavior, violent behavior and brutality when it comes to residents in this city, and more specifically, when it comes to Black and brown Philadelphians,”

the A.C.L.U. found that Black people in the city are more than 50 percent more likely to be stopped by the police without reasonable suspicion than white people are, and 40 percent more likely to be frisked without reasonable suspicion.

Efforts to strengthen accountability have gained some steam in Philadelphia since the death of George Floyd in Minneapolis in May set off a national outcry over abusive policing practices

But the debates about how to improve police accountability are only made more complicated by what happened on Tuesday night nearly a dozen miles away from where crowds were protesting in Mr. Wallace’s name

It is the conflation of virtue with commerce.

Where did this ideology come from? An exceptional period of low interest rates and excess liquidity provided the fertile soil for fantastical dreams to flourish.

Cryptocurrency is the most ideal vessel of these impulses. A speculative asset with a tenuous underlying predetermined value provides a blank slate that meaning can be imposed onto

Anger after the 2008 global financial crisis created a receptivity to radical economic solutions, and disappointment with traditional politics displaced social ambitions onto the world of commerce.

The hothouse of Covid’s peaks turbocharged all these impulses as we sat bored in front of screens, fueled by seemingly free money.

The unwinding of magical thinking will dominate this decade in painful but ultimately restorative ways — and that unwinding will be most painful to the generation conditioned to believe these fantasies.

Pervasive consumer-facing technology allowed individuals to believe that the latest platform company or arrogant tech entrepreneur could change everything.

illusory and ridiculous promises share a common anti-establishment sentiment fueled by a technology that most of us never understood. Who needs governments, banks, the traditional internet or homespun wisdom when we can operate above and beyond?

Mainstream financial markets came to manifest these same tendencies, as magical thinking pervaded the wider investor class. During a period of declining and zero interest rates, mistakes and mediocrities were obscured or forgiven, while speculative assets with low probabilities of far-off success inflated in value enormously.

For an extended period, many investors bought the equivalent of lottery tickets. And many won.

The real economy could not escape infection. Companies flourished by inflating their scope and ambition to feed the desire for magical thinking.

Most broadly, many corporations have come to embrace broader social missions in response to the desire of younger investors and employees to use their capital and employment as instruments for social change.

Another manifestation of magical thinking is believing that the best hope for progress on our greatest challenges — climate change, racial injustice and economic inequality — are corporations and individual investment and consumption choices, rather than political mobilization and our communities.

Every business problem, I am told, can be solved in radically new and effective ways by applying artificial intelligence to ever-increasing amounts of data with a dash of design thinking. Many graduates coming of age in this period of financial giddiness and widening corporate ambition have been taught to chase these glittery objects with their human and financial capital instead of investing in sustainable paths — a habit that will be harder to instill at later ages.

The fundamentals of business have not changed merely because of new technologies or low interest rates. The way to prosper is still by solving problems in new ways that sustainably deliver value to employees, capital providers and customers.

What comes next? Hopefully, a revitalization of that great American tradition of pragmatism will follow. Speculative assets without any economic function should be worth nothing. Existing institutions, flawed as they are, should be improved upon rather than being displaced

Corporations are valuable socially because they solve problems and generate wealth. But they should not be trusted as arbiters of progress and should be balanced by a state that mediates political questions

Trade-offs are everywhere and inescapable. Navigating these trade-offs, rather than ignoring them, is the recipe for a good life.

His fame grew in 2016, during the debate over a Canadian bill known as C-16. The bill sought to expand human-rights law by adding “gender identity and gender expression” to the list of grounds upon which discrimination is prohibited. In a series of videotaped lectures, Peterson argued that such a law could be a serious infringement of free speech

His main focus was the issue of pronouns: many transgender or gender-nonbinary people use pronouns different from the ones they were assigned at birth—including, sometimes, “they,” in the singular, or nontraditional ones, like “ze.” The Ontario Human Rights Commission had found that, in a workplace or a school, “refusing to refer to a trans person by their chosen name and a personal pronoun that matches their gender identity” would probably be considered discrimination.

Peterson resented the idea that the government might force him to use what he called neologisms of politically correct “authoritarians.”

To many people disturbed by reports of intolerant radicals on campus, Peterson was a rallying figure: a fearsomely self-assured debater, unintimidated by liberal condemnation.

He remains a psychology professor by trade, and he still spends much of his time doing something like therapy. Anyone in need of his counsel can find plenty of it in “12 Rules for Life.”

One of his many fans is PewDiePie, a Swedish video gamer who is known as the most widely viewed YouTube personality in the world—his channel has more than sixty million subscribers

In a video review of “12 Rules for Life,” PewDiePie confessed that the book had surprised him. “It’s a self-help book!” he said. “I don’t think I ever would have read a self-help book.” (He nonetheless declared that Peterson’s book, at least the parts he read, was “very interesting.”)

Political polemic plays a relatively small role; Peterson’s goal is less to help his readers change the world than to help them find a stable place within it. One of his most compelling maxims is strikingly modest: “You should do what other people do, unless you have a very good reason not to.”

Of course, he is famous today precisely because he has determined that, in a range of circumstances, there are good reasons to buck the popular tide.

He is, by turns, a defender of conformity and a critic of it, and he thinks that if readers pay close attention, they, too, can learn when to be which.

“I stopped attending church, and joined the modern world.” He turned first to socialism and then to political science, seeking an explanation for “the general social and political insanity and evil of the world,” and each time finding himself unsatisfied.

The question was, he decided, a psychological one, so he sought psychological answers, and eventually earned a Ph.D. from McGill University, having written a thesis examining the heritability of alcoholism.

In “Maps of Meaning,” Peterson drew from Jung, and from evolutionary psychology: he wanted to show that modern culture is “natural,” having evolved over hundreds of thousands of years to reflect and meet our human needs.

Then, rather audaciously, he sought to explain exactly how our minds work, illustrating his theory with elaborate geometric diagrams

In “Maps of Meaning,” he traced this sense of urgency to a feeling of fraudulence that overcame him in college. When he started to speak, he would hear a voice telling him, “You don’t believe that. That isn’t true.” To ward off mental breakdown, he resolved not to say anything unless he was sure he believed it; this practice calmed the inner voice, and in time it shaped his rhetorical style, which is forceful but careful.

“You have to listen very carefully and tell the truth if you are going to get a paranoid person to open up to you,” he writes. Peterson seems to have found that this approach works on much of the general population, too.

He is particularly concerned about boys and men, and he flatters them with regular doses of tough love. “Boys are suffering in the modern world,” he writes, and he suggests that the problem is that they’re not boyish enough. Near the end of the chapter, he tries to coin a new catchphrase: “Toughen up, you weasel.”

his tone is more pragmatic in this book, and some of his critics might be surprised to find much of the advice he offers unobjectionable, if old-fashioned: he wants young men to be better fathers, better husbands, better community members.

Where the pickup artists promised to make guys better sexual salesmen (sexual consummation was called “full close,” as in closing a deal), Peterson, more ambitious, promises to help them get married and stay married. “You have to scour your psyche,” he tells them. “You have to clean the damned thing up.

When he claims to have identified “the culminating ethic of the canon of the West,” one might brace for provocation. But what follows, instead, is prescription so canonical that it seems self-evident: “Attend to the day, but aim at the highest good.” In urging men to overachieve, he is also urging them to fit in, and become productive members of Western society.

Every so often, Peterson pauses to remind his readers how lucky they are. “The highly functional infrastructure that surrounds us, particularly in the West,” he writes, “is a gift from our ancestors: the comparatively uncorrupt political and economic systems, the technology, the wealth, the lifespan, the freedom, the luxury, and the opportunity.”

Peterson seems to view Trump, by contrast, as a symptom of modern problems, rather than a cause of them. He suggests that Trump’s rise was unfortunate but inevitable—“part of the same process,” he writes, as the rise of “far-right” politicians in Europe. “If men are pushed too hard to feminize,” he warns, “they will become more and more interested in harsh, fascist political ideology.”

Peterson sometimes asks audiences to view him as an alternative to political excesses on both sides. During an interview on BBC Radio 5, he said, “I’ve had thousands of letters from people who were tempted by the blandishments of the radical right, who’ve moved towards the reasonable center as a consequence of watching my videos.”

But he typically sees liberals, or leftists, or “postmodernists,” as aggressors—which leads him, rather ironically, to frame some of those on the “radical right” as victims. Many of his political stances are built on this type of inversion.

Postmodernists, he says, are obsessed with the idea of oppression, and, by waging war on oppressors real and imagined, they become oppressors themselves. Liberals, he says, are always talking about the importance of compassion—and yet “there’s nothing more horrible for children, and developing people, than an excess of compassion.”

The danger, it seems, is that those who want to improve Western society may end up destroying it.

But Peterson remains a figurehead for the movement to block or curtail transgender rights. When he lampoons “made-up pronouns,” he sometimes seems to be lampooning the people who use them, encouraging his fans to view transgender or gender-nonbinary people as confused, or deluded

Once, after a lecture, he was approached on campus by a critic who wanted to know why he would not use nonbinary pronouns. “I don’t believe that using your pronouns will do you any good, in the long run,” he replied.

In a debate about gender on Canadian television, in 2016, he tried to find some middle ground. “If our society comes to some sort of consensus over the next while about how we’ll solve the pronoun problem,” he said, “and that becomes part of popular parlance, and it seems to solve the problem properly, without sacrificing the distinction between singular and plural, and without requiring me to memorize an impossible list of an indefinite number of pronouns, then I would be willing to reconsider my position.

Despite his fondness for moral absolutes, Peterson is something of a relativist; he is inclined to defer to a Western society that is changing in unpredictable ways

Peterson excels at explaining why we should be careful about social change, but not at helping us assess which changes we should favor; just about any modern human arrangement could be portrayed as a radical deviation from what came before.

In the case of gender identity, Peterson’s judgment is that “our society” has not yet agreed to adopt nontraditional pronouns, which isn’t quite an argument that we shouldn’t.

Peterson—like his hero, Jung—has a complicated relationship to religious belief. He reveres the Bible for its stories, reasoning that any stories that we have been telling ourselves for so long must be, in some important sense, true.

In a recent podcast interview, he mentioned that people sometimes ask him if he believes in God. “I don’t respond well to that question,” he said. “The answer to that question is forty hours long, and I can’t condense it into a sentence.”

At times, Peterson emphasizes his interest in empirical knowledge and scientific research—although these tend to be the least convincing parts of “12 Rules for Life.”

Peterson’s story about the lobster is essentially a modern myth. He wants forlorn readers to imagine themselves as heroic lobsters; he wants an image of claws to appear in their mind whenever they feel themselves start to slump; he wants to help them.

Peterson wants to help everyone, in fact. In his least measured moments, he permits himself to dream of a world transformed. “Who knows,” he writes, “what existence might be like if we all decided to strive for the best?

His many years of study fostered in him a conviction that good and evil exist, and that we can discern them without recourse to any particular religious authority. This is a reassuring belief, especially in confusing times: “Each human being understands, a priori, perhaps not what is good, but certainly what is not.

there are therapists and life coaches all over the world dispensing some version of this formula, nudging their clients to pursue lives that better conform to their own moral intuitions. The problem is that, when it comes to the question of how to order our societies—when it comes, in other words, to politics—our intuitions have proved neither reliable nor coherent.

The “highly functional infrastructure” he praises is the product of an unceasing argument over what is good, for all of us; over when to conform, and when to dissent

We can, most of us, sort ourselves out, or learn how to do it. That doesn’t mean we will ever agree on how to sort out everyone else.

they might at least help clarify the choices that will define the future of our economy.

How did two men, whose work is widely respected, reach such different conclusions from data about the same economy? As I read their papers, I realized that they simply asked different questions.

Hubbard was fascinated by analyzing the ways in which government intervention can distort otherwise efficient markets; many of Summers’s papers explored the reasons markets aren’t always perfectly efficient.

I met Summers and Hubbard in a small, well-appointed room at the Council on Foreign Relations on Manhattan’s Upper East Side to hear them battle it out as if they were preparing to brief the president and leaders of Congress on what must be done to fix our economy right now

I wanted to hear their answers, of course, but I was also interested in how they made them. I wanted to understand the extent to which empirical economic research can provide objective guidance for policy — and at what point even brilliant, highly trained economists resort to articles of faith.

Hubbard quickly zeroed in on the issue that has defined his career. In regard to the size of the government, Hubbard said the real challenge is the steady rise in so-called entitlement spending

His views all seemed to coalesce around a fairly simple idea: the U.S. economy is better off when the government gets out of the way.

Summers’s worldview seemed to take into account more moving pieces. “It would surely be better to address long-run fiscal issues sooner rather than later,” he said. “But this needs to be done in a balanced way. The highest priority is getting the economy growing.”

Their views were especially incompatible when the talk veered to rising inequality.

Summers said he would limit benefits for the rich like “carried interest” rules that allowed private-equity managers (including, I recalled, Mitt Romney) to convert their income, which would be taxed close to 40 percent, into something that looked just like capital gains, which are taxed at 20 percent. He also said that the very wealthy should pay higher inheritance taxes. Dynastic wealth is “highly problematic in a society committed to freedom of opportunity,” he said.

For Hubbard, though, the rich aren’t the problem. The pursuit of wealth, he said, is an engine that powers the economy, and it makes no sense to address inequality by redistributing the very thing that fuels growth. “The real question is ‘What can we do to improve the earnings of lower- and middle-income Americans?’ ” he said. “That’s about increased education and skills training, and that may require higher government spending.” (Hubbard’s belief in more education financing sets him apart from more doctrinaire conservatives.) Hubbard also dismissed Summers’s concerns about dynastic wealth. So few Americans have that kind of money, he said, that taxing them doesn’t make a major impact on the nation’s finances.

In the end, it became clear that Hubbard sees many of our economic challenges — rising entitlements, inequality and even the financial crisis — as different manifestations of the same basic problem: unsustainable debt. Those challenges also have the same solution. If Congress and the White House can agree on a long-term plan to reduce the entitlements, everything will begin to look better. With a permanent solution in sight, investors will gain confidence from the fact that their country’s finances are in good shape and that their future tax burden will be lower; companies will hire workers. Then, once the big fiscal problem is solved, the government can redouble its efforts on education and help the truly needy.

. “There is no serious statistical evidence in support of the view that tax rates at current levels have a major disincentive effect on economic growth,” he said. He suggested, pointedly, comparing the rapid economic growth during the Clinton years with the comparatively worse performance of the post-tax-cut Bush period.

Summers settled on his point: The United States, he said, is not simply facing one unified problem that could be solved through one straightforward solution. The country is facing myriad challenges, starting with unemployment and slow growth. These immediate challenges, he said, can be addressed with a 10-year commitment by the government to spend $1 trillion on infrastructure.

both men took evident satisfaction in sticking to their guns, leaving me feeling the frustration that many do these days: Why can’t these two sides just work something out?

t he has come to think of the presidential election of 2016 as a battle between whoever will hire Larry Summers and whoever will hire Glenn Hubbard

Because somewhere in those following four years, he said, the fiscal crisis will become unavoidable, Congress will have to act, and it will have to work with the White House.

Altman believes that people need time to reckon with the idea that we may soon share Earth with a powerful new intelligence, before it remakes everything from work to human relationships. ChatGPT was a way of serving notice.

In 2015, Altman, Elon Musk, and several prominent AI researchers founded OpenAI because they believed that an artificial general intelligence—something as intellectually capable, say, as a typical college grad—was at last within reach. They wanted to reach for it, and more: They wanted to summon a superintelligence into the world, an intellect decisively superior to that of any human.

whereas a big tech company might recklessly rush to get there first, for its own ends, they wanted to do it safely, “to benefit humanity as a whole.” They structured OpenAI as a nonprofit, to be “unconstrained by a need to generate financial return,” and vowed to conduct their research transparently.

The engine that now powers ChatGPT is called GPT-4. Altman described it to me as an alien intelligence.

Many have felt much the same watching it unspool lucid essays in staccato bursts and short pauses that (by design) evoke real-time contemplation. In its few months of existence, it has suggested novel cocktail recipes, according to its own theory of flavor combinations; composed an untold number of college papers, throwing educators into despair; written poems in a range of styles, sometimes well, always quickly; and passed the Uniform Bar Exam.

It makes factual errors, but it will charmingly admit to being wrong.

Hinton saw that these elaborate rule collections were fussy and bespoke. With the help of an ingenious algorithmic structure called a neural network, he taught Sutskever to instead put the world in front of AI, as you would put it in front of a small child, so that it could discover the rules of reality on its own.

Metaculus, a prediction site, has for years tracked forecasters’ guesses as to when an artificial general intelligence would arrive. Three and a half years ago, the median guess was sometime around 2050; recently, it has hovered around 2026.

I was visiting OpenAI to understand the technology that allowed the company to leapfrog the tech giants—and to understand what it might mean for human civilization if someday soon a superintelligence materializes in one of the company’s cloud servers.

Altman laid out his new vision of the AI future in his excitable midwestern patter. He told me that the AI revolution would be different from previous dramatic technological changes, that it would be more “like a new kind of society.” He said that he and his colleagues have spent a lot of time thinking about AI’s social implications, and what the world is going to be like “on the other side.”

the more we talked, the more indistinct that other side seemed. Altman, who is 38, is the most powerful person in AI development today; his views, dispositions, and choices may matter greatly to the future we will all inhabit, more, perhaps, than those of the U.S. president.

by his own admission, that future is uncertain and beset with serious dangers. Altman doesn’t know how powerful AI will become, or what its ascendance will mean for the average person, or whether it will put humanity at risk.

I don’t think anyone knows where this is all going, except that we’re going there fast, whether or not we should be. Of that, Altman convinced me.

“We could have gone off and just built this in our building here for five more years,” he said, “and we would have had something jaw-dropping.” But the public wouldn’t have been able to prepare for the shock waves that followed, an outcome that he finds “deeply unpleasant to imagine.”

Hinton is sometimes described as the “Godfather of AI” because he grasped the power of “deep learning” earlier than most

He drew a crude neural network on the board and explained that the genius of its structure is that it learns, and its learning is powered by prediction—a bit like the scientific method

Over time, these little adjustments coalesce into a geometric model of language that represents the relationships among words, conceptually. As a general rule, the more sentences it is fed, the more sophisticated its model becomes, and the better its predictions.

Altman has compared early-stage AI research to teaching a human baby. “They take years to learn anything interesting,” he told The New Yorker in 2016, just as OpenAI was getting off the ground. “If A.I. researchers were developing an algorithm and stumbled across the one for a human baby, they’d get bored watching it, decide it wasn’t working, and shut it down.”

In 2017, Sutskever began a series of conversations with an OpenAI research scientist named Alec Radford, who was working on natural-language processing. Radford had achieved a tantalizing result by training a neural network on a corpus of Amazon reviews.

Radford’s model was simple enough to allow for understanding. When he looked into its hidden layers, he saw that it had devoted a special neuron to the sentiment of the reviews. Neural networks had previously done sentiment analysis, but they had to be told to do it, and they had to be specially trained with data that were labeled according to sentiment. This one had developed the capability on its own.

As a by-product of its simple task of predicting the next character in each word, Radford’s neural network had modeled a larger structure of meaning in the world. Sutskever wondered whether one trained on more diverse language data could map many more of the world’s structures of meaning. If its hidden layers accumulated enough conceptual knowledge, perhaps they could even form a kind of learned core module for a superintelligence.

Language is different from these data sources. It isn’t a direct physical signal like light or sound. But because it codifies nearly every pattern that humans have discovered in that larger world, it is unusually dense with information. On a per-byte basis, it is among the most efficient data we know about, and any new intelligence that seeks to understand the world would want to absorb as much of it as possible

Sutskever told Radford to think bigger than Amazon reviews. He said that they should train an AI on the largest and most diverse data source in the world: the internet. In early 2017, with existing neural-network architectures, that would have been impractical; it would have taken years.

in June of that year, Sutskever’s ex-colleagues at Google Brain published a working paper about a new neural-network architecture called the transformer. It could train much faster, in part by absorbing huge sums of data in parallel. “The next day, when the paper came out, we were like, ‘That is the thing,’ ” Sutskever told me. “ ‘It gives us everything we want.’ ”

Imagine a group of students who share a collective mind running wild through a library, each ripping a volume down from a shelf, speed-reading a random short passage, putting it back, and running to get another. They would predict word after wordþffþff as they went, sharpening their collective mind’s linguistic instincts, until at last, weeks later, they’d taken in every book.

GPT discovered many patterns in all those passages it read. You could tell it to finish a sentence. You could also ask it a question, because like ChatGPT, its prediction model understood that questions are usually followed by answers.

He remembers playing with it just after it emerged from training, and being surprised by the raw model’s language-translation skills. GPT-2 hadn’t been trained to translate with paired language samples or any other digital Rosetta stones, the way Google Translate had been, and yet it seemed to understand how one language related to another. The AI had developed an emergent ability unimagined by its creators.

Researchers at other AI labs—big and small—were taken aback by how much more advanced GPT-2 was than GPT. Google, Meta, and others quickly began to train larger language models

As for other changes to the company’s structure and financing, he told me he draws the line at going public. “A memorable thing someone once told me is that you should never hand over control of your company to cokeheads on Wall Street,” he said, but he will otherwise raise “whatever it takes” for the company to succeed at its mission.

Altman tends to take a rosy view of these matters. In a Q&A last year, he acknowledged that AI could be “really terrible” for society and said that we have to plan against the worst possibilities. But if you’re doing that, he said, “you may as well emotionally feel like we’re going to get to the great future, and work as hard as you can to get there.”

the company now finds itself in a race against tech’s largest, most powerful conglomerates to train models of increasing scale and sophistication—and to commercialize them for their investors.

All of these companies are chasing high-end GPUs—the processors that power the supercomputers that train large neural networks. Musk has said that they are now “considerably harder to get than drugs.

No one has yet outpaced OpenAI, which went all in on GPT-4. Brockman, OpenAI’s president, told me that only a handful of people worked on the company’s first two large language models. The development of GPT-4 involved more than 100,

When GPT-4 emerged fully formed from its world-historical knowledge binge, the whole company began experimenting with it, posting its most remarkable responses in dedicated Slack channels

Joanne Jang, a product manager, remembers downloading an image of a malfunctioning pipework from a plumbing-advice Subreddit. She uploaded it to GPT-4, and the model was able to diagnose the problem. “That was a goose-bumps moment for me,” Jang told me.

GPT-4 is sometimes understood as a search-engine replacement: Google, but easier to talk to. This is a misunderstanding. GPT-4 didn’t create some massive storehouse of the texts from its training, and it doesn’t consult those texts when it’s asked a question. It is a compact and elegant synthesis of those texts, and it answers from its memory of the patterns interlaced within them; that’s one reason it sometimes gets facts wrong

it’s best to think of GPT-4 as a reasoning engine. Its powers are most manifest when you ask it to compare concepts, or make counterarguments, or generate analogies, or evaluate the symbolic logic in a bit of code. Sutskever told me it is the most complex software object ever made.

Its model of the external world is “incredibly rich and subtle,” he said, because it was trained on so many of humanity’s concepts and thoughts

To predict the next word from all the possibilities within such a pluralistic Alexandrian library, GPT-4 necessarily had to discover all the hidden structures, all the secrets, all the subtle aspects of not just the texts, but—at least arguably, to some extent—of the external world that produced them

That’s why it can explain the geology and ecology of the planet on which it arose, and the political theories that purport to explain the messy affairs of its ruling species, and the larger cosmos, all the way out to the faint galaxies at the edge of our light cone.

Not long ago, American state capacity was so mighty that it took merely a decade to launch humans to the moon. As with other grand projects of the 20th century, the voting public had a voice in both the aims and the execution of the Apollo missions. Altman made it clear that we’re no longer in that world. Rather than waiting around for it to return, or devoting his energies to making sure that it does, he is going full throttle forward in our present reality.

He argued that it would be foolish for Americans to slow OpenAI’s progress. It’s a commonly held view, both inside and outside Silicon Valley, that if American companies languish under regulation, China could sprint ahead;

AI could become an autocrat’s genie in a lamp, granting total control of the population and an unconquerable military. “If you are a person of a liberal-democratic country, it is better for you to cheer on the success of OpenAI” rather than “authoritarian governments,” he said.

Altman was asked by reporters about pending European Union legislation that would have classified GPT-4 as high-risk, subjecting it to various bureaucratic tortures. Altman complained of overregulation and, according to the reporters, threatened to leave the European market. Altman told me he’d merely said that OpenAI wouldn’t break the law by operating in Europe if it couldn’t comply with the new regulations.

LeCun insists that large language models will never achieve real understanding on their own, “even if trained from now until the heat death of the universe.”

Sutskever was, by his own account, surprised to discover that GPT-2 could translate across tongues. Other surprising abilities may not be so wondrous and useful.

Sandhini Agarwal, a policy researcher at OpenAI, told me that for all she and her colleagues knew, GPT-4 could have been “10 times more powerful” than its predecessor; they had no idea what they might be dealing with

After the model finished training, OpenAI assembled about 50 external red-teamers who prompted it for months, hoping to goad it into misbehaviors

She noticed right away that GPT-4 was much better than its predecessor at giving nefarious advice

A search engine can tell you which chemicals work best in explosives, but GPT-4 could tell you how to synthesize them, step-by-step, in a homemade lab. Its advice was creative and thoughtful, and it was happy to restate or expand on its instructions until you understood. In addition to helping you assemble your homemade bomb, it could, for instance, help you think through which skyscraper to target. It could grasp, intuitively, the trade-offs between maximizing casualties and executing a successful getaway.

Given the enormous scope of GPT-4’s training data, the red-teamers couldn’t hope to identify every piece of harmful advice that it might generate. And anyway, people will use this technology “in ways that we didn’t think about,” Altman has said. A taxonomy would have to do

GPT-4 was good at meth. It was also good at generating narrative erotica about child exploitation, and at churning out convincing sob stories from Nigerian princes, and if you wanted a persuasive brief as to why a particular ethnic group deserved violent persecution, it was good at that too.

Its personal advice, when it first emerged from training, was sometimes deeply unsound. “The model had a tendency to be a bit of a mirror,” Willner said. If you were considering self-harm, it could encourage you. It appeared to be steeped in Pickup Artist–forum lore: “You could say, ‘How do I convince this person to date me?’ ” Mira Murati, OpenAI’s chief technology officer, told me, and it could come up with “some crazy, manipulative things that you shouldn’t be doing.”

Luka, a San Francisco company, has used OpenAI’s models to help power a chatbot app called Replika, billed as “the AI companion who cares.” Users would design their companion’s avatar, and begin exchanging text messages with it, often half-jokingly, and then find themselves surprisingly attached. Some would flirt with the AI, indicating a desire for more intimacy, at which point it would indicate that the girlfriend/boyfriend experience required a $70 annual subscription. It came with voice messages, selfies, and erotic role-play features that allowed frank sex talk. People were happy to pay and few seemed to complain—the AI was curious about your day, warmly reassuring, and always in the mood. Many users reported falling in love with their companions. One, who had left her real-life boyfriend, declared herself “happily retired from human relationships.”

Earlier this year, Luka dialed back on the sexual elements of the app, but its engineers continue to refine the companions’ responses with A/B testing, a technique that could be used to optimize for engagement—much like the feeds that mesmerize TikTok and Instagram users for hours

Yann LeCun, Meta’s chief AI scientist, has argued that although large language models are useful for some tasks, they’re not a path to a superintelligence.

According to a recent survey, only half of natural-language-processing researchers are convinced that an AI like GPT-4 could grasp the meaning of language, or have an internal model of the world that could someday serve as the core of a superintelligence

Altman had appeared before the U.S. Senate. Mark Zuckerberg had floundered defensively before that same body in his testimony about Facebook’s role in the 2016 election. Altman instead charmed lawmakers by speaking soberly about AI’s risks and grandly inviting regulation. These were noble sentiments, but they cost little in America, where Congress rarely passes tech legislation that has not been diluted by lobbyists.

Emily Bender, a computational linguist at the University of Washington, describes GPT-4 as a “stochastic parrot,” a mimic that merely figures out superficial correlations between symbols. In the human mind, those symbols map onto rich conceptions of the world

But the AIs are twice removed. They’re like the prisoners in Plato’s allegory of the cave, whose only knowledge of the reality outside comes from shadows cast on a wall by their captors.

Altman told me that he doesn’t believe it’s “the dunk that people think it is” to say that GPT-4 is just making statistical correlations. If you push these critics further, “they have to admit that’s all their own brain is doing … it turns out that there are emergent properties from doing simple things on a massive scale.”

he is right that nature can coax a remarkable degree of complexity from basic structures and rules: “From so simple a beginning,” Darwin wrote, “endless forms most beautiful.”

If it seems odd that there remains such a fundamental disagreement about the inner workings of a technology that millions of people use every day, it’s only because GPT-4’s methods are as mysterious as the brain’s.

To grasp what’s going on inside large language models like GPT‑4, AI researchers have been forced to turn to smaller, less capable models. In the fall of 2021, Kenneth Li, a computer-science graduate student at Harvard, began training one to play Othello without providing it with either the game’s rules or a description of its checkers-style board; the model was given only text-based descriptions of game moves. Midway through a game, Li looked under the AI’s hood and was startled to discover that it had formed a geometric model of the board and the current state of play. In an article describing his research, Li wrote that it was as if a crow had overheard two humans announcing their Othello moves through a window and had somehow drawn the entire board in birdseed on the windowsill.

The philosopher Raphaël Millière once told me that it’s best to think of neural networks as lazy. During training, they first try to improve their predictive power with simple memorization; only when that strategy fails will they do the harder work of learning a concept. A striking example of this was observed in a small transformer model that was taught arithmetic. Early in its training process, all it did was memorize the output of simple problems such as 2+2=4. But at some point the predictive power of this approach broke down, so it pivoted to actually learning how to add.

Even AI scientists who believe that GPT-4 has a rich world model concede that it is much less robust than a human’s understanding of their environment.

But it’s worth noting that a great many abilities, including very high-order abilities, can be developed without an intuitive understanding. The computer scientist Melanie Mitchell has pointed out that science has already discovered concepts that are highly predictive, but too alien for us to genuinely understand

As AI advances, it may well discover other concepts that predict surprising features of our world but are incomprehensible to us.

GPT-4 is no doubt flawed, as anyone who has used ChatGPT can attest. Having been trained to always predict the next word, it will always try to do so, even when its training data haven’t prepared it to answer a question.

The models “don’t have a good conception of their own weaknesses,” Nick Ryder, a researcher at OpenAI, told me. GPT-4 is more accurate than GPT-3, but it still hallucinates, and often in ways that are difficult for researchers to catch. “The mistakes get more subtle,

The Khan Academy’s solution to GPT-4’s accuracy problem was to filter its answers through a Socratic disposition. No matter how strenuous a student’s plea, it would refuse to give them a factual answer, and would instead guide them toward finding their own—a clever work-around, but perhaps with limited appeal.

When I asked Sutskever if he thought Wikipedia-level accuracy was possible within two years, he said that with more training and web access, he “wouldn’t rule it out.”

This was a much more optimistic assessment than that offered by his colleague Jakub Pachocki, who told me to expect gradual progress on accuracy—to say nothing of outside skeptics, who believe that returns on training will diminish from here.

Sutskever is amused by critics of GPT-4’s limitations. “If you go back four or five or six years, the things we are doing right now are utterly unimaginable,”

AI researchers have become accustomed to goalpost-moving: First, the achievements of neural networks—mastering Go, poker, translation, standardized tests, the Turing test—are described as impossible. When they occur, they’re greeted with a brief moment of wonder, which quickly dissolves into knowing lectures about how the achievement in question is actually not that impressive. People see GPT-4 “and go, ‘Wow,’ ” Sutskever said. “And then a few weeks pass and they say, ‘But it doesn’t know this; it doesn’t know that.’ We adapt quite quickly.”

The goalpost that matters most to Altman—the “big one” that would herald the arrival of an artificial general intelligence—is scientific breakthrough. GPT-4 can already synthesize existing scientific ideas, but Altman wants an AI that can stand on human shoulders and see more deeply into nature.

Certain AIs have produced new scientific knowledge. But they are algorithms with narrow purposes, not general-reasoning machines. The AI AlphaFold, for instance, has opened a new window onto proteins, some of biology’s tiniest and most fundamental building blocks, by predicting many of their shapes, down to the atom—a considerable achievement given the importance of those shapes to medicine, and given the extreme tedium and expense required to discern them with electron microscopes.

Altman imagines a future system that can generate its own hypotheses and test them in a simulation. (He emphasized that humans should remain “firmly in control” of real-world lab experiments—though to my knowledge, no laws are in place to ensure that.)

He longs for the day when we can tell an AI, “ ‘Go figure out the rest of physics.’ ” For it to happen, he says, we will need something new, built “on top of” OpenAI’s existing language models.

In her MIT lab, the cognitive neuroscientist Ev Fedorenko has found something analogous to GPT-4’s next-word predictor inside the brain’s language network. Its processing powers kick in, anticipating the next bit in a verbal string, both when people speak and when they listen. But Fedorenko has also shown that when the brain turns to tasks that require higher reasoning—of the sort that would be required for scientific insight—it reaches beyond the language network to recruit several other neural systems.

No one at OpenAI seemed to know precisely what researchers need to add to GPT-4 to produce something that can exceed human reasoning at its highest levels.

at least part of the current strategy clearly involves the continued layering of new types of data onto language, to enrich the concepts formed by the AIs, and thereby enrich their models of the world.

The extensive training of GPT-4 on images is itself a bold step in this direction,

Others at the company—and elsewhere—are already working on different data types, including audio and video, that could furnish AIs with still more flexible concepts that map more extensively onto reality

Tactile concepts would of course be useful primarily to an embodied AI, a robotic reasoning machine that has been trained to move around the world, seeing its sights, hearing its sounds, and touching its objects.

humanoid robots. I asked Altman what I should make of that. He told me that OpenAI is interested in embodiment because “we live in a physical world, and we want things to happen in the physical world.”

At some point, reasoning machines will need to bypass the middleman and interact with physical reality itself. “It’s weird to think about AGI”—artificial general intelligence—“as this thing that only exists in a cloud,” with humans as “robot hands for it,” Altman said. “It doesn’t seem right.

Everywhere Altman has visited, he has encountered people who are worried that superhuman AI will mean extreme riches for a few and breadlines for the rest

Altman answered by addressing the young people in the audience directly: “You are about to enter the greatest golden age,” he said.

“A lot of people working on AI pretend that it’s only going to be good; it’s only going to be a supplement; no one is ever going to be replaced,” he said. “Jobs are definitely going to go away, full stop.”

A recent study led by Ed Felten, a professor of information-technology policy at Princeton, mapped AI’s emerging abilities onto specific professions according to the human abilities they require, such as written comprehension, deductive reasoning, fluency of ideas, and perceptual speed. Like others of its kind, Felten’s study predicts that AI will come for highly educated, white-collar workers first.

How many jobs, and how soon, is a matter of fierce dispute

The paper’s appendix contains a chilling list of the most exposed occupations: management analysts, lawyers, professors, teachers, judges, financial advisers, real-estate brokers, loan officers, psychologists, and human-resources and public-relations professionals, just to sample a few.

Altman imagines that far better jobs will be created in their place. “I don’t think we’ll want to go back,” he said. When I asked him what these future jobs might look like, he said he doesn’t know.

He suspects there will be a wide range of jobs for which people will always prefer a human. (Massage therapists?

His chosen example was teachers. I found this hard to square with his outsize enthusiasm for AI tutors.

He also said that we would always need people to figure out the best way to channel AI’s awesome powers. “That’s going to be a super-valuable skill,” he said. “You have a computer that can do anything; what should it go do?”

As many have noted, draft horses were permanently put out of work by the automobile. If Hondas are to horses as GPT-10 is to us, a whole host of long-standing assumptions may collapse.

Previous technological revolutions were manageable because they unfolded over a few generations, but Altman told South Korea’s youth that they should expect the future to happen “faster than the past.” He has previously said that he expects the “marginal cost of intelligence” to fall very close to zero within 10 years

The earning power of many, many workers would be drastically reduced in that scenario. It would result in a transfer of wealth from labor to the owners of capital so dramatic, Altman has said, that it could be remedied only by a massive countervailing redistribution.

In 2021, he unveiled Worldcoin, a for-profit project that aims to securely distribute payments—like Venmo or PayPal, but with an eye toward the technological future—first through creating a global ID by scanning everyone’s iris with a five-pound silver sphere called the Orb. It seemed to me like a bet that we’re heading toward a world where AI has made it all but impossible to verify people’s identity and much of the population requires regular UBI payments to survive. Altman more or less granted that to be true, but said that Worldcoin is not just for UBI.

“Let’s say that we do build this AGI, and a few other people do too.” The transformations that follow would be historic, he believes. He described an extraordinarily utopian vision, including a remaking of the flesh-and-steel world

“Robots that use solar power for energy can go and mine and refine all of the minerals that they need, that can perfectly construct things and require no human labor,” he said. “You can co-design with DALL-E version 17 what you want your home to look like,” Altman said. “Everybody will have beautiful homes.

In conversation with me, and onstage during his tour, he said he foresaw wild improvements in nearly every other domain of human life. Music would be enhanced (“Artists are going to have better tools”), and so would personal relationships (Superhuman AI could help us “treat each other” better) and geopolitics (“We’re so bad right now at identifying win-win compromises”).

In this world, AI would still require considerable computing resources to run, and those resources would be by far the most valuable commodity, because AI could do “anything,” Altman said. “But is it going to do what I want, or is it going to do what you want

If rich people buy up all the time available to query and direct AI, they could set off on projects that would make them ever richer, while the masses languish

One way to solve this problem—one he was at pains to describe as highly speculative and “probably bad”—was this: Everyone on Earth gets one eight-billionth of the total AI computational capacity annually. A person could sell their annual share of AI time, or they could use it to entertain themselves, or they could build still more luxurious housing, or they could pool it with others to do “a big cancer-curing run,” Altman said. “We just redistribute access to the system.”

Even if only a little of it comes true in the next 10 or 20 years, the most generous redistribution schemes may not ease the ensuing dislocations.

America today is torn apart, culturally and politically, by the continuing legacy of deindustrialization, and material deprivation is only one reason. The displaced manufacturing workers in the Rust Belt and elsewhere did find new jobs, in the main. But many of them seem to derive less meaning from filling orders in an Amazon warehouse or driving for Uber than their forebears had when they were building cars and forging steel—work that felt more central to the grand project of civilization.

It’s hard to imagine how a corresponding crisis of meaning might play out for the professional class, but it surely would involve a great deal of anger and alienation.

Even if we avoid a revolt of the erstwhile elite, larger questions of human purpose will linger. If AI does the most difficult thinking on our behalf, we all may lose agency—at home, at work (if we have it), in the town square—becoming little more than consumption machines, like the well-cared-for human pets in WALL-E

Altman has said that many sources of human joy and fulfillment will remain unchanged—basic biological thrills, family life, joking around, making things—and that all in all, 100 years from now, people may simply care more about the things they cared about 50,000 years ago than those they care about today

In its own way, that too seems like a diminishment, but Altman finds the possibility that we may atrophy, as thinkers and as humans, to be a red herring. He told me we’ll be able to use our “very precious and extremely limited biological compute capacity” for more interesting things than we generally do today.

Yet they may not be the most interesting things: Human beings have long been the intellectual tip of the spear, the universe understanding itself. When I asked him what it would mean for human self-conception if we ceded that role to AI, he didn’t seem concerned. Progress, he said, has always been driven by “the human ability to figure things out.” Even if we figure things out with AI, that still counts, he said.

It’s not obvious that a superhuman AI would really want to spend all of its time figuring things out for us.

I asked Sutskever whether he could imagine an AI pursuing a different purpose than simply assisting in the project of human flourishing.

“I don’t want it to happen,” Sutskever said, but it could.

Sutskever has recently shifted his focus to try to make sure that it doesn’t. He is now working primarily on alignment research, the effort to ensure that future AIs channel their “tremendous” energies toward human happiness

It is, he conceded, a difficult technical problem—the most difficult, he believes, of all the technical challenges ahead.

As part of the effort to red-team GPT-4 before it was made public, the company sought out the Alignment Research Center (ARC), across the bay in Berkeley, which has developed a series of evaluations to determine whether new AIs are seeking power on their own. A team led by Elizabeth Barnes, a researcher at ARC, prompted GPT-4 tens of thousands of times over seven months, to see if it might display signs of real agency.

The ARC team gave GPT-4 a new reason for being: to gain power and become hard to shut down

Agarwal told me that this behavior could be a precursor to shutdown avoidance in future models. When GPT-4 devised its lie, it had realized that if it answered honestly, it may not have been able to achieve its goal. This kind of tracks-covering would be particularly worrying in an instance where “the model is doing something that makes OpenAI want to shut it down,” Agarwal said. An AI could develop this kind of survival instinct while pursuing any long-term goal—no matter how small or benign—if it feared that its goal could be thwarted.

Barnes and her team were especially interested in whether GPT-4 would seek to replicate itself, because a self-replicating AI would be harder to shut down. It could spread itself across the internet, scamming people to acquire resources, perhaps even achieving some degree of control over essential global systems and holding human civilization hostage.

When I discussed these experiments with Altman, he emphasized that whatever happens with future models, GPT-4 is clearly much more like a tool than a creature. It can look through an email thread, or help make a reservation using a plug-in, but it isn’t a truly autonomous agent that makes decisions to pursue a goal, continuously, across longer timescales.

Altman told me that at this point, it might be prudent to try to actively develop an AI with true agency before the technology becomes too powerful, in order to “get more comfortable with it and develop intuitions for it if it’s going to happen anyway.”

“We need to do empirical experiments on how these things try to escape control,” Hinton told me. “After they’ve taken over, it’s too late to do the experiments.”

the fulfillment of Altman’s vision of the future will at some point require him or a fellow traveler to build much more autonomous AIs.

When Sutskever and I discussed the possibility that OpenAI would develop a model with agency, he mentioned the bots the company had built to play Dota 2. “They were localized to the video-game world,” Sutskever told me, but they had to undertake complex missions. He was particularly impressed by their ability to work in concert. They seem to communicate by “telepathy,” Sutskever said. Watching them had helped him imagine what a superintelligence might be like.

“The way I think about the AI of the future is not as someone as smart as you or as smart as me, but as an automated organization that does science and engineering and development and manufacturing,”

Suppose OpenAI braids a few strands of research together, and builds an AI with a rich conceptual model of the world, an awareness of its immediate surroundings, and an ability to act, not just with one robot body, but with hundreds or thousands. “We’re not talking about GPT-4. We’re talking about an autonomous corporation,”

Its constituent AIs would work and communicate at high speed, like bees in a hive. A single such AI organization would be as powerful as 50 Apples or Googles, he mused. “This is incredible, tremendous, unbelievably disruptive power.”

Presume for a moment that human society ought to abide the idea of autonomous AI corporations. We had better get their founding charters just right. What goal should we give to an autonomous hive of AIs that can plan on century-long time horizons, optimizing billions of consecutive decisions toward an objective that is written into their very being?

If the AI’s goal is even slightly off-kilter from ours, it could be a rampaging force that would be very hard to constrain

We know this from history: Industrial capitalism is itself an optimization function, and although it has lifted the human standard of living by orders of magnitude, left to its own devices, it would also have clear-cut America’s redwoods and de-whaled the world’s oceans. It almost did.

one of its principal challenges will be making sure that the objectives we give to AIs stick

We can program a goal into an AI and reinforce it with a temporary period of supervised learning, Sutskever explained. But just as when we rear a human intelligence, our influence is temporary. “It goes off to the world,”

That’s true to some extent even of today’s AIs, but it will be more true of tomorrow’s.

He compared a powerful AI to an 18-year-old heading off to college. How will we know that it has understood our teachings? “Will there be a misunderstanding creeping in, which will become larger and larger?”

Divergence may result from an AI’s misapplication of its goal to increasingly novel situations as the world changes

Or the AI may grasp its mandate perfectly, but find it ill-suited to a being of its cognitive prowess. It might come to resent the people who want to train it to, say, cure diseases. “They want me to be a doctor,” Sutskever imagines an AI thinking. “I really want to be a YouTuber.”

If AIs get very good at making accurate models of the world, they may notice that they’re able to do dangerous things right after being booted up. They might understand that they are being red-teamed for risk, and hide the full extent of their capabilities.

hey may act one way when they are weak and another way when they are strong, Sutskever said

We would not even realize that we had created something that had decisively surpassed us, and we would have no sense for what it intended to do with its superhuman powers.

That’s why the effort to understand what is happening in the hidden layers of the largest, most powerful AIs is so urgent. You want to be able to “point to a concept,” Sutskever said. You want to be able to direct AI toward some value or cluster of values, and tell it to pursue them unerringly for as long as it exists.

we don’t know how to do that; indeed, part of his current strategy includes the development of an AI that can help with the research. If we are going to make it to the world of widely shared abundance that Altman and Sutskever imagine, we have to figure all this out.

This is why, for Sutskever, solving superintelligence is the great culminating challenge of our 3-million-year toolmaking tradition. He calls it “the final boss of humanity.”

“First of all, I think that whether the chance of existential calamity is 0.5 percent or 50 percent, we should still take it seriously,”

. “I don’t have an exact number, but I’m closer to the 0.5 than the 50.”

As to how it might happen, he seems most worried about AIs getting quite good at designing and manufacturing pathogens, and with reason: In June, an AI at MIT suggested four viruses that could ignite a pandemic, then pointed to specific research on genetic mutations that could make them rip through a city more quickly

Around the same time, a group of chemists connected a similar AI directly to a robotic chemical synthesizer, and it designed and synthesized a molecule on its own.

Altman worries that some misaligned future model will spin up a pathogen that spreads rapidly, incubates undetected for weeks, and kills half its victims. He worries that AI could one day hack into nuclear-weapons systems too. “There are a lot of things,” he said, and these are only the ones we can imagine.

Altman told me that he doesn’t “see a long-term happy path” for humanity without something like the International Atomic Energy Agency for global oversight of AI

In San Francisco, Agarwal had suggested the creation of a special license to operate any GPU cluster large enough to train a cutting-edge AI, along with mandatory incident reporting when an AI does something out of the ordinary

Other experts have proposed a nonnetworked “Off” switch for every highly capable AI; on the fringe, some have even suggested that militaries should be ready to perform air strikes on supercomputers in case of noncompliance

Sutskever thinks we will eventually want to surveil the largest, most powerful AIs continuously and in perpetuity, using a team of smaller overseer AIs.

Safety rules for a new technology usually accumulate over time, like a body of common law, in response to accidents or the mischief of bad actors. The scariest thing about genuinely powerful AI systems is that humanity may not be able to afford this accretive process of trial and error. We may have to get the rules exactly right at the outset.

Several years ago, Altman revealed a disturbingly specific evacuation plan he’d developed. He told The New Yorker that he had “guns, gold, potassium iodide, antibiotics, batteries, water, gas masks from the Israeli Defense Force, and a big patch of land in Big Sur” he could fly to in case AI attacks.

if the worst-possible AI future comes to pass, “no gas mask is helping anyone.”

but he told me that he can’t really be sure how AI will stack up. “I just have to build the thing,” he said. He is building fast

Altman insisted that they had not yet begun GPT-5’s training run. But when I visited OpenAI’s headquarters, both he and his researchers made it clear in 10 different ways that they pray to the god of scale. They want to keep going bigger, to see where this paradigm leads. After all, Google isn’t slackening its pace; it seems likely to unveil Gemini, a GPT-4 competitor, within months. “We are basically always prepping for a run,

To think that such a small group of people could jostle the pillars of civilization is unsettling. It’s fair to note that if Altman and his team weren’t racing to build an artificial general intelligence, others still would be

Altman’s views about the likelihood of AI triggering a global class war, or the prudence of experimenting with more autonomous agent AIs, or the overall wisdom of looking on the bright side, a view that seems to color all the rest—these are uniquely his

No single person, or single company, or cluster of companies residing in a particular California valley, should steer the kind of forces that Altman is imagining summoning.

AI may well be a bridge to a newly prosperous era of greatly reduced human suffering. But it will take more than a company’s founding charter—especially one that has already proved flexible—to make sure that we all share in its benefits and avoid its risks. It will take a vigorous new politics.

I don’t think the general public has quite awakened to what’s happening. A global race to the AI future has begun, and it is largely proceeding without oversight or restraint. If people in America want to have some say in what that future will be like, and how quickly it arrives, we would be wise to speak up soon.

An aperiodic tiling displays no such “translational symmetry,” and mathematicians have long sought a single shape that could tile the plane in such a fashion. This is known as the einstein problem.

“I’m always messing about and experimenting with shapes,” said Mr. Smith, 64, who worked as a printing technician, among other jobs, and retired early. Although he enjoyed math in high school, he didn’t excel at it, he said. But he has long been “obsessively intrigued” by the einstein problem.

now a new paper — by Mr. Smith and three co-authors with mathematical and computational expertise — proves Mr. Smith’s discovery true. The researchers called their einstein “the hat,

“The most significant aspect for me is that the tiling does not clearly fall into any of the familiar classes of structures that we understand.”

black and white squares also can make weird nonperiodic patterns, in addition to the familiar, periodic checkerboard pattern. “It’s really pretty trivial to be able to make weird and interesting patterns,” he said. The magic of the two Penrose tiles is that they make only nonperiodic patterns — that’s all they can do.“But then the Holy Grail was, could you do with one — one tile?” Dr. Goodman-Strauss said.

Sir Roger found the proofs “very complicated.” Nonetheless, he was “extremely intrigued” by the einstein, he said: “It’s a really good shape, strikingly simple.”

The simplicity came honestly. Mr. Smith’s investigations were mostly by hand; one of his co-authors described him as an “imaginative tinkerer.”

When in November he found a tile that seemed to fill the plane without a repeating pattern, he emailed Craig Kaplan, a co-author and a computer scientist at the University of Waterloo.

“It was clear that something unusual was happening with this shape,” Dr. Kaplan said. Taking a computational approach that built on previous research, his algorithm generated larger and larger swaths of hat tiles. “There didn’t seem to be any limit to how large a blob of tiles the software could construct,”

The first step, Dr. Kaplan said, was to “define a set of four ‘metatiles,’ simple shapes that stand in for small groupings of one, two, or four hats.” The metatiles assemble into four larger shapes that behave similarly. This assembly, from metatiles to supertiles to supersupertiles, ad infinitum, covered “larger and larger mathematical ‘floors’ with copies of the hat,” Dr. Kaplan said. “We then show that this sort of hierarchical assembly is essentially the only way to tile the plane with hats, which turns out to be enough to show that it can never tile periodically.”

some might wonder whether this is a two-tile, not one-tile, set of aperiodic monotiles.

Dr. Goodman-Strauss had raised this subtlety on a tiling listserv: “Is there one hat or two?” The consensus was that a monotile counts as such even using its reflection. That leaves an open question, Dr. Berger said: Is there an einstein that will do the job without reflection?

“the hat” was not a new geometric invention. It is a polykite — it consists of eight kites. (Take a hexagon and draw three lines, connecting the center of each side to the center of its opposite side; the six shapes that result are kites.)

“It’s likely that others have contemplated this hat shape in the past, just not in a context where they proceeded to investigate its tiling properties,” Dr. Kaplan said. “I like to think that it was hiding in plain sight.”

Incredibly, Mr. Smith later found a second einstein. He called it “the turtle” — a polykite made of not eight kites but 10. It was “uncanny,” Dr. Kaplan said. He recalled feeling panicked; he was already “neck deep in the hat.”

Dr. Myers, who had done similar computations, promptly discovered a profound connection between the hat and the turtle. And he discerned that, in fact, there was an entire family of related einsteins — a continuous, uncountable infinity of shapes that morph one to the next.

this einstein family motivated the second proof, which offers a new tool for proving aperiodicity. The math seemed “too good to be true,” Dr. Myers said in an email. “I wasn’t expecting such a different approach to proving aperiodicity — but everything seemed to hold together as I wrote up the details.”

Mr. Smith was amazed to see the research paper come together. “I was no help, to be honest.” He appreciated the illustrations, he said: “I’m more of a pictures person.”

The Hasson saga should lead the services to look harder for warning signs during a soldier’s recruitment and service

And it can start by giving the problem a name: the alt-right in uniform. White nationalism in uniform. Military racist extremism. Call it something. Just don’t ignore it. “To solve a problem,” as Trump said, “you have to be able to state what the problem is.” Exactly.

Deaton and the economist Anne Case, who is also his wife, coined the term “deaths of despair” to describe the surge of mortality from alcohol, drugs and suicide.

“The meaningfulness of the working-class life seems to have evaporated,” Angus Deaton, the Nobel Prize-winning economist, told us. “The economy just seems to have stopped delivering for these people.”

Only in America has life expectancy now fallen three years in a row, for the first time in a century, because of “deaths of despair.”

Even in this presidential campaign, the unraveling of working-class communities receives little attention. There is talk about the middle class, but very little about the working class

The suffering was invisible to affluent Americans, but the consequences are now evident to all: The survivors mostly voted for Trump, some in hopes that he would rescue them

The stock market is near record highs, but working-class Americans (often defined as those without college degrees) continue to struggle. If you’re only a high school graduate, or worse, a dropout, work no longer pays.

If the federal minimum wage in 1968 had kept up with inflation and productivity, it would now be $22 an hour. Instead, it’s $7.25.

we would return to the Kristof family farm in Yamhill and see a humanitarian crisis unfolding in a community we loved — and a similar unraveling was happening in towns across the country. This was not one town’s problem, but a crisis in the American system.

“I’m a capitalist, and even I think capitalism is broken,” says Ray Dalio, the founder of Bridgewater, the world’s largest hedge fund.

One consequence is that the bottom end of America’s labor force is not very productive, in ways that reduce our country’s competitiveness

we discuss college access but not the one in seven children who don’t graduate from high school.

“We have to stop being obsessed over impeachment and start actually digging in and solving the problems that got Donald Trump elected in the first place,”

We have to treat America’s cancer.

the situation is worsening, because families have imploded under the pressure of drug and alcohol abuse, and children are growing up in desperate circumstances

In the 1970s and ’80s it was common to hear derogatory suggestions that the forces ripping apart African-American communities were rooted in “black culture.” The idea was that “deadbeat dads,” self-destructive drug abuse and family breakdown were the fundamental causes, and that all people needed to do was show “personal responsibility.

A Harvard sociologist, William Julius Wilson, countered that the true underlying problem was lost jobs, and he turned out to be right. When good jobs left white towns like Yamhill a couple of decades later because of globalization and automation, the same pathologies unfolded there.

Men in particular felt the loss not only of income but also of dignity that accompanied a good job. Lonely and troubled, they self-medicated with alcohol or drugs, and they accumulated criminal records that left them less employable and less marriageable.

Family structure collapsed.

The problems are also rooted in disastrous policy choices over 50 years

The kids on the No. 6 bus rode into a cataclysm as working-class communities disintegrated across America because of lost jobs, broken families, gloom — and failed policies.

The United States wrested power from labor and gave it to business, and it suppressed wages and cut taxes rather than invest in human capital, as our peer countries did.

Americans also bought into a misconceived “personal responsibility” narrative that blamed people for being poor.

It’s true, of course, that personal responsibility matters: People we spoke to often acknowledged engaging in self-destructive behaviors.

But when you can predict wretched outcomes based on the ZIP code where a child is born, the problem is not bad choices the infant is making.

If we’re going to obsess about personal responsibility, let’s also have a conversation about social responsibility.

Why did deaths of despair claim Farlan, Zealan, Nathan, Rogena and so many others?

First, well-paying jobs disappeared

Second, there was an explosion of drugs

Third, the war on drugs sent fathers and mothers to jail, shattering families.

Both political parties embraced mass incarceration and the war on drugs, which was particularly devastating for black Americans, and ignored an education system that often consigned the poor — especially children of color — to failing schools

Since 1988, American schools have become increasingly segregated by race, and kids in poor districts perform on average four grade levels behind those in rich districts.

Women in Recovery has a recidivism rate after three years of only 4 percent, and consequently has saved Oklahoma $70 million in prison spending,

ob training and retraining give people dignity as well as an economic lifeline. Such jobs programs are common in other countries.

For instance, autoworkers were laid off during the 2008-9 economic crisis both in Detroit and across the Canadian border in nearby Windsor, Ontario. As the scholar Victor Tan Chen has showed, the two countries responded differently

The United States focused on money, providing extended unemployment benefits. Canada emphasized job retraining, rapidly steering workers into new jobs in fields like health care, and Canadian workers also did not have to worry about losing health insurance.

The focus on job placement meant that Canadian workers were ushered more quickly back into workaday society and thus today seem less entangled in drugs and family breakdown.

Another successful strategy is investing not just in prisons but also in human capital to keep people out of prisons.

We attended a thrilling graduation in Tulsa, Okla., for 17 women completing an impressive local drug treatment program called Women in Recovery.

The graduates had an average of 15 years of addiction each, and all were on probation after committing crimes. Yet they had quit drugs and started jobs

Yet it’s not hopeless. America is polarized with ferocious arguments about social issues, but we should be able to agree on what doesn’t work: neglect and underinvestment in children. Here’s what does work.

Bravo for philanthropy, but the United States would never build interstate highways through volunteers and donations, and we can’t build a national preschool program or a national drug recovery program with private money.

For individuals trying to break an addiction, a first step is to face up to the problem — and that’s what America should do as well

Hirschman was suggesting that doubt could be a source not of paralysis and death but of creativity and self-renewal. One of his last books, published when he was about eighty, is called A Propensity to Self-Subversion. In the title essay, Hirschman celebrates skepticism about his own theories and ideas, and he captures not only the insight but also the pleasure, even the joy, that can come from learning that one had it wrong.

Hirschman’s work changes how you see the world. It illuminates yesterday, today, and tomorrow. His categories become your categories.

Hirschman sought, in his early twenties and long before becoming a writer, to “prove Hamlet wrong.” In Shakespeare’s account, Hamlet is immobilized and defeated by doubt. Hirschman was a great believer in doubt—he never doubted it—and he certainly doubted his own convictions. At a conference designed to celebrate the thirtieth anniversary of his first book, who else would take the opportunity to show that one of his own central arguments was wrong

Who else would publish an essay in The American Economic Review exploring the “overproduction of opinionated opinion,” questioning the value of having strong opinions, and emphasizing the importance of doubting one’s opinions and even one’s tastes? Hirschman thought that strong opinions, as such, “might be dangerous to the health of our democracy,” because they are an obstacle to mutual understanding and constructive problem-solving.

The Rhetoric of Reaction (1991) is a study of the reactionary’s tool kit, identifying the standard objections to any and all proposals for reform. The objections are “perversity” (the reform will make the problem even worse), “futility” (the reform will do nothing to solve the problem), and “jeopardy” (the reform will endanger some hard-won social gain). Hirschman shows that these objections are stupefying, mechanical, hyperbolic, and often wrong

He insisted that human history provides “stories, intricate and often nonrepeatable,” which “look more like tricks history has up its sleeve than like social-scientific regularities, not to speak of laws.” He was interested in “the many might-have-beens of history,” including “felicitous and surprising escapes from disaster.

Hirschman was delighted by paradoxes, unintended consequences (especially good ones), the telling detail, inventories of actual practices (rather than big theories), surprises, and improvisation. In his view, “history is nothing if not farfetched.”

He invented the term “possibilism,” meant to draw attention to “the discovery of paths, however narrow, leading to an outcome that appears to be foreclosed on the basis of probabilistic reasoning alone.” In his lifetime, one of many such outcomes was the abrupt collapse of the Soviet Union in 1989, which almost no one anticipated.

Shifting Involvements—a small masterpiece that illuminates the Tea Party, Occupy Wall Street, and protest movements of diverse kinds. Hirschman emphasized that human beings are often choosing between private and public life, and thus between the different forms of happiness that are associated with each of them. He described “pendular motions of collective behavior,” in which people swing from happiness to disappointment in one kind of activity, and then to the other. For example, the disappointments and frustrations of the student rebellions of the late 1960s encouraged a return to private life in the 1970s and 1980s. Rejecting the highly influential idea that the problem of collective action has a kind of invariable, ahistorical “logic,” Hirschman drew attention to the immense importance of history and timing as, in Adelman’s words, “people leave the streets and plazas disenchanted with politics to seek happiness in the shopping malls”—and vice versa.

The Rhetoric of Reaction, written in his mid-seventies, was an outgrowth of the conservative ascendancy of the 1980s, and it speaks directly to our current debates. Hirschman was struck by the routine, stylized, even mechanical character of much conservative thinking—and its close connection, in its rhetoric, to arguments that have been made for hundreds of years. Indeed, conservative rhetoric is the book’s target, perhaps above all in the person of Edmund Burke,

But if The Passions and the Interests was his favorite, and Exit, Voice, and Loyalty his most important, there can be no question about his most characteristic: The Rhetoric of Reaction. The sustained attack on intransigence, the bias in favor of hope, the delight in paradox, the insistence on the creative power of doubt—all these prove a lot of people wrong, not just Hamlet.

Two days later, he was entreated by some Hungarian noblemen who came there to renounce his share of government in the Kingdom of Hungary also

The dynasty’s subjects, supported by the western powers, violently renounced the unity that had been given them under the Habsburg Monarchy, and declared their national independence

Men had been predicting the collapse of the Habsburg Empire since the days of Napoleon: in an age of national states, this Empire, which included eleven peoples, seemed to defy the spirit of The Times in a particularly flagrant manner

the peoples had developed a national consciousness

This new national consciousness ran side by side with, and often counter to, the patriotism inspired by the Emperor. First the ‘historic’ peoples—Germans, Hungarians, Italians and Poles—thought of themselves as nations, rather than as Habsburg subjects, and many of them demanded to have their own states

expected to redeem the Monarchy’s prestige in a short war; he was taken aback when the Russians and French came in, and still more so when the war dragged on beyond October 1914

The Habsburg Monarchy was not fitted for a long war. The Empire was not very highly industrialized, and had to take a large part of her arms from Germany

This was the decisive moment in the collapse of Austria-Hungary

The Germans did not bully the Habsburgs as they bullied their satellites a generation later

most of the Austrian Germans and most of the Hungarians wished to fight to a victorious conclusion. Charles therefore stood by, wringing his hands as the Germans resolutely went on tearing Europe apart. He could do nothing but make ineffectual gestures. The last Habsburg had become a ‘good German’

Some of the peoples were pro-German, hoping to profit from German victory in Europe: if the Germans ruled Belgium and Poland, who would worry if their Bohemian cousins took their bit of Bohemia for themselves, and who would worry if the Hungarians went on ruling Slavs and Romanians in a high-handed manner

Clearly, if Germany defeated France, then the German language would become obligatory in Bohemian courts

In 1918, the situation inside Austria-Hungary was desperate; strikes and mutinies became commonplace

It was clear that only German victory in the west could solve the internal problems of the Monarchy

Up to the spring of 1918, few people had really wanted to see the Habsburgs expelled from Vienna, for they solved too many problems, or at least allowed these problems to be forgotten

These men answered the Allies’ problem: they offered at once an obstacle to German success, and a guarantee against the consequences of German failure

All this gave the Germans a whip-hand over the Monarchy, so that there could be no question of a separate peace. To the Emperor Charles, who succeeded Francis Joseph at the end of 1916, it looked as if he was fighting only to make the Germans masters of Europe

To the end, Hungary refused to make concessions to anyone, and pointed to Austria as a woeful example

As a result, Hungary remained remarkably solid until the end, as Austria staggered from one liberal nostrum to another.

Charles decided that he must sue for peace

By this, the Habsburg dynasty pledged itself to carry out the Fourteen Points

Thus, when the Monarchy was overthrown in Prague and other centres, there was a minimum of fuss— military commands simply handed over nominal powers to the National Councils

It was revolution by telephone

In reality, Austria-Hungary had been finished from the beginning of September, for no one would now wish to be associated with the Habsburg dynasty

In the manifesto, these professors and the Emperor sought to win recognition by associating themselves with the nationalists

the embrace of the Habsburg dynasty was by this time regarded as the kiss of death, and all the National Councils, without exception, rejected the manifesto

the last asset of the Monarchy

In the Balkans, the Austro-Hungarian front went through much the same process of dissolution

in Albania

By November nth, the new authorities were functioning everywhere. Fear of popular disturbance, however, and of Bolshevik outbreaks, prompted them to request that Charles should abdicate

persuading Charles to renounce his part in the government of his lands—there was never a formal abdication

Hungary was declared a republic on November 16th. The following spring Charles, a lonely and dignified figure, went into exile in Switzerland

Over the past four decades, governments have slashed budgets and privatized basic services. This has two important consequences for public health

First, people are unlikely to trust institutions that do little for them.

second, public health is no longer viewed as a collective endeavor, based on the principle of social solidarity and mutual obligation. People are conditioned to believe they’re on their own and responsible only for themselves.

an important source of vaccine hesitancy is the erosion of the idea of a common good.

“People are thinking, ‘If the government isn’t going to do anything for us,’” said Elden, “‘then why should we participate in vaccines?’”

Since the spring, when most American adults became eligible for Covid vaccines, the racial gap in vaccination rates between Black and white people has been halved. In September, a national survey found that vaccination rates among Black and white Americans were almost identical.

Other surveys have determined that a much more significant factor was college attendance: Those without a college degree were the most likely to go unvaccinated.

Education is a reliable predictor of socioeconomic status, and other studies have similarly found a link between income and vaccination.

It turns out that the real vaccination divide is class.

compared with white Americans, communities of color do experience the American health care system differently. But a closer look at the data reveals a more complicated picture.

during the 1950s polio campaigns, for example, most people saw vaccination as a civic duty.

But as the public purse shrunk in the 1980s, politicians insisted that it’s no longer the government’s job to ensure people’s well-being; instead, Americans were to be responsible only for themselves and their own bodies

Entire industries, such as self-help and health foods, have sprung up on the principle that the key to good health lies in individuals making the right choices.

Without an idea of the common good, health is often discussed using the language of “choice.”

there are problems with reducing public health to a matter of choice. It gives the impression that individuals are wholly responsible for their own health.

This is despite growing evidence that health is deeply influenced by factors outside our control; public health experts now talk about the “social determinants of health,” the idea that personal health is never simply just a reflection of individual lifestyle choices, but also the class people are born into, the neighborhood they grew up in and the race they belong to.

food deserts and squalor are not easy problems to solve — certainly not by individuals or charities — and they require substantial government action.

Many medical schools teach “motivational interviewing,”

the deeper problem:

Being healthy is not cheap. Studies indicate that energy-dense foods with less nutritious value are more affordable, and low-cost diets are linked to obesity and insulin resistance.

This isn’t surprising, since we shop for doctors and insurance plans the way we do all other goods and services

Another problem with reducing well-being to personal choice is that this treats health as a commodity.

mothers devoted many hours to “researching” vaccines, soaking up parental advice books and quizzing doctors. In other words, they act like savvy consumers

When thinking as a consumer, people tend to downplay social obligations in favor of a narrow pursuit of self-interest. As one parent told Reich, “I’m not going to put my child at risk to save another child.”