Group items tagged

. In May, David Banks, the chancellor of New York City’s public schools, for many years a stronghold of “whole language” instruction, announced a sharp pivot toward phonics, a major victory for the “science of reading” movement and a blow to devotees of entrenched “balanced literacy” methods

As corporate management models and zealous state legislatures refashion the academy into a gated outpost of the gig economy, the humanities have lost their luster for undergraduates. According to reports in The New Yorker and elsewhere, fewer and fewer students are majoring in English, and many of those who do (along with their teachers) have turned away from canonical works of literature toward contemporary writing and pop culture. Is anyone reading “Paradise Lost” anymore? Are you?

While we binge and scroll and D.M., the robots, who are doing more and more of our writing, may also be taking over our reading.

There is so much to worry about. A quintessentially human activity is being outsourced to machines that don’t care about phonics or politics or beauty or truth. A precious domain of imaginative and intellectual freedom is menaced by crude authoritarian politics. Exposure to the wrong words is corrupting our children, who aren’t even learning how to decipher the right ones. Our attention spans have been chopped up and commodified, sold off piecemeal to platforms and algorithms. We’re too busy, too lazy, too preoccupied to lose ourselves in books.

the fact that the present situation has a history doesn’t mean that it isn’t rea

the reading crisis isn’t simply another culture-war combat zone. It reflects a deep ambivalence about reading itself, a crack in the foundations of modern consciousness.

Just what is reading, anyway? What is it for? Why is it something to argue and worry about? Reading isn’t synonymous with literacy, which is one of the necessary skills of contemporary existence. Nor is it identical with literature, which designates a body of written work endowed with a special if sometimes elusive prestige.

Is any other common human undertaking so riddled with contradiction? Reading is supposed to teach us who we are and help us forget ourselves, to enchant and disenchant, to make us more worldly, more introspective, more empathetic and more intelligent. It’s a private, even intimate act, swathed in silence and solitude, and at the same time a social undertaking. It’s democratic and elitist, soothing and challenging, something we do for its own sake and as a means to various cultural, material and moral ends.

Fun and fundamental: Together, those words express a familiar utilitarian, utopian promise — the faith that what we enjoy doing will turn out to be what we need to do, that our pleasures and our responsibilities will turn out to be one and the same. It’s not only good; it’s good for you.

Reading is, fundamentally, both a tool and a toy. It’s essential to social progress, democratic citizenship, good government and general enlightenment.

It’s also the most fantastically, sublimely, prodigiously useless pastime ever invented

Teachers, politicians, literary critics and other vested authorities labor mightily to separate the edifying wheat from the distracting chaff, to control, police, correct and corral the transgressive energies that propel the turning of pages.

His despair mirrors his earlier exhilaration and arises from the same source. “I envied my fellow-slaves for their stupidity. I have often wished myself a beast. I preferred the condition of the meanest reptile to my own. Any thing, no matter what, to get rid of thinking!”

Reading is a relatively novel addition to the human repertoire — less than 6,000 years old — and the idea that it might be available to everybody is a very recent innovation

Written language, associated with the rise of states and the spread of commerce, was useful for trade, helpful in the administration of government and integral to some religious practices. Writing was a medium for lawmaking, record-keeping and scripture, and reading was the province of priests, bureaucrats and functionaries.

For most of history, that is, universal literacy was a contradiction in terms. The Latin word literatus designated a member of the learned elite

Anyone could learn to do it, but the mechanisms of learning were denied to most people on the grounds of caste, occupation or gender.

According to Steven Roger Fischer’s lively and informative “A History of Reading” (2003), “Western Europe began the transition from an oral to a literate society in the early Middle Ages, starting with society’s top rungs — aristocracy and clergy — and finally including everyone else around 1,200 years later.”

. The print revolution catalyzed a global market that flourishes to this day: Books became commodities, and readers became consumers.

For Fischer, as for many authors of long-range synthetic macrohistories, the story of reading is a chronicle of progress, the almost mythic tale of a latent superpower unlocked for the benefit of mankind.

The crisis is what happens either when those efforts succeed or when they fail. Everyone likes reading, and everyone is afraid of it.

For one thing, the older, restrictive model of literacy as an elite prerogative proved to be tenacious

The novel, more than any other genre, catered to this market. Like every other development in modern popular culture, it provoked a measure of social unease. Novels, at best a source of harmless amusement and mild moral instruction, were at worst — from the pens of the wrong writers, or in the hands of the wrong readers — both invitations to vice and a vice unto themselves

More consequential — and more revealing of the destabilizing power of reading — was the fear of literacy among the laboring classes in Europe and America. “Reading, writing and arithmetic,” the Enlightenment political theorist Bernard Mandeville asserted, were “very pernicious to the poor” because education would breed restlessness and disconte

“It was unlawful, as well as unsafe, to teach a slave to read,” Frederick Douglass writes in his “Narrative of the Life” recalling the admonitions of one of his masters, whose wife had started teaching young Frederick his letters. If she persisted, the master explained, their chattel would “become unmanageable, and of no value to his master. As to himself, it could do him no good, but a great deal of harm. It would make him discontented and unhappy.”

“As I read and contemplated the subject, behold! that very discontentment which Master Hugh had predicted would follow my learning to read had already come, to torment and sting my soul to unutterable anguish. As I writhed under it, I would at times feel that learning to read had been a curse rather than a blessing.”

“If extraordinary human faculties and powers do lie dormant until a social innovation calls them into life,” he writes, “perhaps this might help to explain humanity’s constant advancement.” “Reading,” he concludes, “had become our union card to humanity.”

Douglass’s literary genius resides in the way he uses close attention to his own situation to arrive at the essence of things — to crack the moral nut of slavery and, in this case, to peel back the epistemological husk of freedom.

He has freed his mind, but the rest has not followed. In time it would, but freedom itself brings him uncertainty and terror, an understanding of his own humanity that is embattled and incomplete.

Here, the autobiographical touches on the mythic, specifically on the myth of Prometheus, whose theft of fire — a curse as well as a blessing bestowed on a bumbling, desperate species — is a primal metaphor for reading.

A school, however benevolently conceived and humanely administered, is a place of authority, where the energies of the young are regulated, their imaginations pruned and trained into conformity. As such, it will inevitably provoke resistance, rebellion and outright refusal on the part of its wards

Schools exist to stifle freedom, and also to inculcate it, a dialectic that is the essence of true education. Reading, more than any other discipline, is the engine of this process, precisely because it escapes the control of those in charge.

Apostles of reading like to quote Franz Kafka’s aphorism that “a book must be the ax for the frozen sea within us.” By itself, the violence of the metaphor is tempered by its therapeutic implication.

Kafka’s previous sentence: “What we need are books that hit us like the most painful misfortune, like the death of someone we loved more than we love ourselves, that make us feel as though we had been banished to the woods, far from any human presence, like a suicide.”

Are those the books you want in your child’s classroom? To read in this way is to go against the grain, to feel oneself at odds, alienated, alone. Schools exist to suppress those feelings, to blunt the ax and gently thaw the sea

Chaotic reading is something else. It isn’t bad so much as unjustified, useless, unreasonable, ungoverned. Defenses of this kind of reading, which are sometimes the memoirs of a certain kind of reader, favor words like promiscuous, voracious, indiscriminate and compulsive.

Roland Barthes distinguished between two kinds of literary work:

Text of pleasure: the text that contents, fills, grants euphoria: the text that comes from culture and does not break with it, is linked to a comfortable practice of reading. Text of bliss: the text that imposes a state of loss, the text that discomforts (perhaps to the point of a certain boredom), unsettles the reader’s historical, cultural, psychological assumptions, the consistency of his tastes, values, memories, brings to a crisis his relation with language.

he is really describing modalities of reading. To a member of the slaveholding Southern gentry, “The Columbian Orator” is a text of pleasure, a book that may challenge and surprise him in places, but that does not undermine his sense of the world or his place in it. For Frederick Douglass, it is a text of bliss, “bringing to crisis” (as Barthes would put it) his relation not only to language but to himself.

If you’ll forgive a Dungeons and Dragons reference, it might help to think of these types of reading as lawful and chaotic.

Lawful reading rests on the certainty that reading is good for us, and that it will make us better people. We read to see ourselves represented, to learn about others, to find comfort and enjoyment and instruction. Reading is fun! It’s good and good for you.

That is important work, but it’s equally critical for that work to be subverted, for the full destructive potential of reading to lie in reach of innocent hands.

Bibliophilia is lawful. Bibliomania is chaotic.

The point is not to choose between them: This is a lawful publication staffed by chaotic readers. In that way, it resembles a great many English departments, bookstores, households and classrooms. Here, the crisis never ends. Or rather, it will end when we stop reading. Which is why we can’t.

As AI writes more and more of what we read, vast, unvetted pools of online data may not be grounded in reality, warns Margaret Mitchell, chief ethics scientist at the AI start-up Hugging Face

“The main issue is losing track of what truth is,” she said. “Without grounding, the system can make stuff up. And if it’s that same made-up thing all over the world, how do you trace it back to what reality is?”

a raft of online publishers have been using automated writing tools based on ChatGPT’s predecessors, GPT-2 and GPT-3, for years. That experience shows that a world in which AI creations mingle freely and sometimes imperceptibly with human work isn’t speculative; it’s flourishing in plain sight on Amazon product pages and in Google search results.

“If you have a connection to the internet, you have consumed AI-generated content,” said Jonathan Greenglass, a New York-based tech investor focused on e-commerce. “It’s already here.

“In the last two years, we’ve seen this go from being a novelty to being pretty much an essential part of the workflow,”

the news credibility rating company NewsGuard identified 49 news websites across seven languages that appeared to be mostly or entirely AI-generated.

The sites sport names like Biz Breaking News, Market News Reports, and bestbudgetUSA.com; some employ fake author profiles and publish hundreds of articles a day, the company said. Some of the news stories are fabricated, but many are simply AI-crafted summaries of real stories trending on other outlets.

Ingenio, the San Francisco-based online publisher behind sites such as horoscope.com and astrology.com, is among those embracing automated content. While its flagship horoscopes are still human-written, the company has used OpenAI’s GPT language models to launch new sites such as sunsigns.com, which focuses on celebrities’ birth signs, and dreamdiary.com, which interprets highly specific dreams.

Ingenio used to pay humans to write birth sign articles on a handful of highly searched celebrities like Michael Jordan and Ariana Grande, said Josh Jaffe, president of its media division. But delegating the writing to AI allows sunsigns.com to cheaply crank out countless articles on not-exactly-A-listers

In the past, Jaffe said, “We published a celebrity profile a month. Now we can do 10,000 a month.”

It isn’t just text. Google users have recently posted examples of the search engine surfacing AI-generated images. For instance, a search for the American artist Edward Hopper turned up an AI image in the style of Hopper, rather than his actual art, as the first result.

Jaffe said he isn’t particularly worried that AI content will overwhelm the web. “It takes time for this content to rank well” on Google, he said — meaning that it appears on the first page of search results for a given query, which is critical to attracting readers. And it works best when it appears on established websites that already have a sizable audience: “Just publishing this content doesn’t mean you have a viable business.”

Google clarified in February that it allows AI-generated content in search results, as long as the AI isn’t being used to manipulate a site’s search rankings. The company said its algorithms focus on “the quality of content, rather than how content is produced.”

Reputations are at risk if the use of AI backfires. CNET, a popular tech news site, took flack in January when fellow tech site Futurism reported that CNET had been using AI to create articles or add to existing ones without clear disclosures. CNET subsequently investigated and found that many of its 77 AI-drafted stories contained errors.

Jaffe said his company discloses its use of AI to readers, and he promoted the strategy at a recent conference for the publishing industry. “There’s nothing to be ashamed of,” he said. “We’re actually doing people a favor by leveraging generative AI tools” to create niche content that wouldn’t exist otherwise.

BuzzFeed, which pioneered a media model built around reaching readers directly on social platforms like Facebook, announced in January it planned to make “AI inspired content” part of its “core business,” such as using AI to craft quizzes that tailor themselves to each reader. BuzzFeed announced last month that it is laying off 15 percent of its staff and shutting down its news division, BuzzFeed News.

it’s finding traction in the murkier worlds of online clickbait and affiliate marketing, where success is less about reputation and more about gaming the big tech platforms’ algorithms.

That business is driven by a simple equation: how much it costs to create an article vs. how much revenue it can bring in. The main goal is to attract as many clicks as possible, then serve the readers ads worth just fractions of a cent on each visit — the classic form of clickbait

In the past, such sites often outsourced their writing to businesses known as “content mills,” which harness freelancers to generate passable copy for minimal pay. Now, some are bypassing content mills and opting for AI instead.

“Previously it would cost you, let’s say, $250 to write a decent review of five grills,” Semrush’s Levin said. “Now it can all be done by AI, so the cost went down from $250 to $10.”

The problem, Levin said, is that the wide availability of tools like ChatGPT means more people are producing similarly cheap content, and they’re all competing for the same slots in Google search results or Amazon’s on-site product reviews

So they all have to crank out more and more article pages, each tuned to rank highly for specific search queries, in hopes that a fraction will break through. The result is a deluge of AI-written websites, many of which are never seen by human eyes.

But CNET’s parent company, Red Ventures, is forging ahead with plans for more AI-generated content, which has also been spotted on Bankrate.com, its popular hub for financial advice. Meanwhile, CNET in March laid off a number of employees, a move it said was unrelated to its growing use of AI.

The rise of AI is already hurting the business of Textbroker, a leading content platform based in Germany and Las Vegas, said Jochen Mebus, the company’s chief revenue officer. While Textbroker prides itself on supplying credible, human-written copy on a huge range of topics, “People are trying automated content right now, and so that has slowed down our growth,”

Mebus said the company is prepared to lose some clients who are just looking to make a “fast dollar” on generic AI-written content. But it’s hoping to retain those who want the assurance of a human touch, while it also trains some of its writers to become more productive by employing AI tools themselves.

He said a recent survey of the company’s customers found that 30 to 40 percent still want exclusively “manual” content, while a similar-size chunk is looking for content that might be AI-generated but human-edited to check for tone, errors and plagiarism.

Levin said Semrush’s clients have also generally found that AI is better used as a writing assistant than a sole author. “We’ve seen people who even try to fully automate the content creation process,” he said. “I don’t think they’ve had really good results with that. At this stage, you need to have a human in the loop.”

For Cowell, whose book title appears to have inspired an AI-written copycat, the experience has dampened his enthusiasm for writing.“My concern is less that I’m losing sales to fake books, and more that this low-quality, low-priced, low-effort writing is going to have a chilling effect on humans considering writing niche technical books in the future,”

It doesn’t help, he added, knowing that “any text I write will inevitably be fed into an AI system that will generate even more competition.”

Amazon removed the impostor book, along with numerous others by the same publisher, after The Post contacted the company for comment.

AI-written books aren’t against Amazon’s rules, per se, and some authors have been open about using ChatGPT to write books sold on the site.

“Amazon is constantly evaluating emerging technologies and innovating to provide a trustworthy shopping experience for our customers,”

NHTSA said it has an “active investigation” of Autopilot. “NHTSA generally does not comment on matters related to open investigations,” NHTSA spokeswoman Veronica Morales said in a statement. In 2021, the agency adopted a rule requiring carmakers such as Tesla to report crashes involving their driver-assistance systems.Beyond the data collection, though, there are few clear legal limitations on how this type of advanced driver-assistance technology should operate and what capabilities it should have.

“Tesla has decided to take these much greater risks with the technology because they have this sense that it’s like, ‘Well, you can figure it out. You can determine for yourself what’s safe’ — without recognizing that other road users don’t have that same choice,” former NHTSA administrator Steven Cliff said in an interview.“If you’re a pedestrian, [if] you’re another vehicle on the road,” he added, “do you know that you’re unwittingly an object of an experiment that’s happening?”

Banner researched Tesla for years before buying a Model 3 in 2018, his wife, Kim, told federal investigators. Around the time of his purchase, Tesla’s website featured a video showing a Tesla navigating the curvy roads and intersections of California while a driver sits in the front seat, hands hovering beneath the wheel.The video, recorded in 2016, is still on the site today.“The person in the driver’s seat is only there for legal reasons,” the video says. “He is not doing anything. The car is driving itself.”

Musk made a similar assertion about a more sophisticated form of Autopilot called Full Self-Driving on an earnings call in July. “Now, I know I’m the boy who cried FSD,” he said. “But man, I think we’ll be better than human by the end of this year.”

While the video concerned Full Self-Driving, which operates on surface streets, the plaintiffs in the Banner case argue Tesla’s “marketing does not always distinguish between these systems.”

Not only is the marketing misleading, plaintiffs in several cases argue, the company gives drivers a long leash when deciding when and how to use the technology. Though Autopilot is supposed to be enabled in limited situations, it sometimes works on roads it’s not designed for. It also allows drivers to go short periods without touching the wheel and to set cruising speeds well above posted speed limits.

Identifying semi-trucks is a particular deficiency that engineers have struggled to solve since Banner’s death, according to a former Autopilot employee who spoke on the condition of anonymity for fear of retribution.

Tesla complicated the matter in 2021 when it eliminated radar sensors from its cars, The Post previously reported, making vehicles such as semi-trucks appear two-dimensional and harder to parse.

“If a system turns on, then at least some users will conclude it must be intended to work there,” Koopman said. “Because they think if it wasn’t intended to work there, it wouldn’t turn on.”Andrew Maynard, a professor of advanced technology transitions at Arizona State University, said customers probably just trust the technology.“Most people just don’t have the time or ability to fully understand the intricacies of it, so at the end they trust the company to protect them,” he said.

Inflammatory language has also been used by journalists, retired generals, celebrities, and social media influencers, according to experts who track the statements. Calls for Gaza to be “flattened,” “erased” or “destroyed” had been mentioned about 18,000 times since Oct. 7 in Hebrew posts on X,

The cumulative effect, experts say, has been to normalize public discussion of ideas that would have been considered off limits before Oct. 7: talk of “erasing” the people of Gaza, ethnic cleansing, and the nuclear annihilation of the territory.

Itamar Ben-Gvir, a right-wing settler who went from fringe figure to minister of national security in Mr. Netanyahu’s cabinet, has a long history of making incendiary remarks about Palestinians. He said in a recent TV interview that anyone who supports Hamas should be “eliminated.”

The idea of a nuclear strike on Gaza was raised last week by another right-wing minister, Amichay Eliyahu, who told a Hebrew radio station that there was no such thing as noncombatants in Gaza. Mr. Netanyahu suspended Mr. Eliyahu, saying that his comments were “disconnected from reality.”

Mr. Netanyahu says that the Israeli military is trying to prevent harm to civilians. But with the death toll rising to more than 11,000, according to the Gaza health ministry, those claims are being met with skepticism, even in the United States,

Such reassurances are also belied by the language Mr. Netanyahu uses with audiences in Israel. His reference to Amalek came in a speech delivered in Hebrew on Oct. 28 as Israel was launching the ground invasion. While some Jewish scholars argue that the scripture’s message is metaphoric not literal, his words resonated widely, as video of his speech was shared on social media, often by critics

“These are not just one-off statements, made in the heat of the moment,”

“When ministers make statements like that,” Mr. Sfard added, “it opens the door for everyone else.”

“Erase Gaza. Don’t leave a single person there,” Mr. Golan said in an interview with Channel 14 on Oct. 15.

“I don’t call them human animals because that would be insulting to animals,” Ms. Netanyahu said during a radio interview on Oct. 10, referring to Hamas

In the West Bank last week, several academics and officials cited Mr. Eliyahu’s remark about dropping an atomic bomb on Gaza as evidence of Israel’s intention to clear the enclave of all Palestinians — a campaign they call a latter-day nakba.

On Saturday, the Israeli agriculture minister, Avi Dichter, said that the military campaign in Gaza was explicitly designed to force the mass displacement of Palestinians. “We are now rolling out the Gaza nakba,” he said in a television interview. “Gaza nakba 2023.”

The rise in incendiary statements comes against a backdrop of rising violence in the West Bank. Since Oct. 7, according to the United Nations, Israeli soldiers have killed 150 Palestinians, including 44 children, in clashes.

the use of inflammatory language by Israeli leaders is not surprising, and even understandable, given the brutality of the Hamas attacks, which inflicted collective and individual trauma on Israelis.

“People in this situation look for very, very clear answers,” Professor Halperin said. “You don’t have the mental luxury of complexity. You want to see a world of good guys and bad guys.”

“Leaders understand that,” he added, “and it leads them to use this kind of language, because this kind of language has an audience.”

Casting the threat posed by Hamas in stark terms, Professor Halperin said, also helps the government ask people to make sacrifices for the war effort: the compulsory mobilization of 360,000 reservists, the evacuation of 126,000 people from border areas in the north and south, and the shock to the economy.

It will also make Israelis more inured to the civilian death toll in Gaza, which has isolated Israel around the world, he added. A civilian death toll of 10,000 or 20,000, he said, could seem to “the average Israeli that it’s not such a big deal.”

In the long run, Mr. Sfard said, such language dooms the chance of ending the conflict with the Palestinians, erodes Israel’s democracy and breeds a younger generation that is “easily using the language in their discussion with their friends.”

“Once a certain rhetoric becomes legitimized, turning the wheel back requires a lot of education,” he said. “There is an old Jewish proverb: ‘A hundred wise men will struggle a long time to take out a stone that one stupid person dropped into the well.’”

According to some reports, he never expected to win in 2016. But even then, in the run-up to the election, Trump’s opponents were already calling him a fascist. I counseled against such usage at the time, because Trump, as a person and as a public figure, is just so obviously ridiculous; fascists, by contrast, are dangerously serious people, and in many circumstances, their leaders have been unnervingly tough and courageous. Trump—whiny, childish, unmanly—hardly fits that bill. (A rare benefit of his disordered character is that his defensiveness and pettiness likely continue to limit the size of his personality cult.)

Unfortunately, the overuse of fascist (among other charges) quickly wore out the part of the public’s eardrums that could process such words.

ere I want to caution my fellow citizens. Trump, whether from intention or stupidity or fear, has identified himself as a fascist under almost any reasonable definition of the word.

He is also constrained by circumstance: The country is not in disarray, or at war, or in an economic collapse

many schools have encouraged that mind-set, casting themselves as stewards of students’ welfare, guarantors of their safety, places of refuge, precincts of healing.

“The campus protests of the late 1960s sought in part to dismantle the in loco parentis role that colleges and universities had held in American life. But the past two decades have been shaped by a reversal of that, as institutions have sought to reconstruct this role in response to what students and parents paying enormous sums for their education have seemed to want.”

For us professors, the surrogate-parent paradigm means regular emails and other reminders from administrators that we should be taking our students’ temperatures, watching for glimmers of distress, intervening proactively and fashioning accommodations, especially if there has been some potentially discomfiting global, national or local news event

where does reasonable consideration end and unreasonable coddling begin? And what do validation and comfort have to do with learning?

Arguably, everything. If you’re not mentally healthy, you’ll be harder pressed to do the reading, writing and critical thinking at the core of college work

Also, college students aren’t full-fledged grown-ups. They do need guidance, and they benefit from it.

But are we responsibly preparing them for the world after college — and for the independence, toughness, resourcefulness and resilience it will almost surely demand of them — when we too easily dole out A’s, too readily grant extensions, too gingerly deliver critiques, and too quickly wonder and sound alarms about any disturbance in the atmosphere?

We keep witnessing episodes of students taking their colleges to task in ways that smack of entitlement and fragility and are out of bounds

Hamline’s president, Fayneese Miller, defended that sequence of events by saying that to not weigh academic freedom against a “debt to the traditions, beliefs and views of students” is a “privileged reaction.”

That’s a troubling assertion, as Tom Nichols wrote in The Atlantic: “If you don’t want your traditions, beliefs or views challenged, then don’t come to a university, at least not to study anything in the humanities or the social sciences.”

The school is a merchant, a kind of department store, or so a student could easily assume, based on the come-ons from affluent colleges competing with one another for applicants. They peddle tantalizing dining options, themed living arrangements, diverse amusements. They assign students the role of discerning customers. And the customer is always right.

But in an educational environment, that credo is all wrong, because learning means occasionally being provoked, frequently being unsettled and regularly being yanked outside of your comfort zone,

most students can handle that dislocation — if they’re properly prepped for it, if they’re made to understand its benefits. We shortchange them when we sell them short

If we don’t draw some lines now, legions of citizens could be manipulated, disenfranchised or lose faith in the whole system — opening doors to foreign adversaries who want to do the same. AI might break us in 2024.

“The ability of AI to interfere with our elections, to spread misinformation that’s extremely believable is one of the things that’s preoccupying us,” Schumer said, after watching me so easily create a deepfake of him. “Lots of people in the Congress are examining this.”

Of course, fibbing politicians are nothing new, but examples keep multiplying of how AI supercharges misinformation in ways we haven’t seen before. Two examples: The presidential campaign of Florida Gov. Ron DeSantis (R) shared an AI-generated image of former president Donald Trump embracing Anthony S. Fauci. That hug never happened. In Chicago’s mayoral primary, someone used AI to clone the voice of candidate Paul Vallas in a fake news report, making it look like he approved of police brutality.

But what will happen when a shocking image or audio clip goes viral in a battleground state shortly before an election? What kind of chaos will ensue when someone uses a bot to send out individually tailored lies to millions of different voters?

A wide 85 percent of U.S. citizens said they were “very” or “somewhat” concerned about the spread of misleading AI video and audio, in an August survey by YouGov. And 78 percent were concerned about AI contributing to the spread of political propaganda.

We can’t put the genie back in the bottle. AI is already embedded in tech tool campaigns that all of us use every day. AI creates our Facebook feeds and picks what ads we see. AI built into our phone cameras brightens faces and smooths skin.

What’s more, there are many political uses for AI that are unobjectionable, and even empowering for candidates with fewer resources. Politicians can use AI to manage the grunt work of sorting through databases and responding to constituents. Republican presidential candidate Asa Hutchinson has an AI chatbot trained to answer questions like him. (I’m not sure politician bots are very helpful, but fine, give it a try.)

Clarke’s solution, included in a bill she introduced on political ads: Candidates should disclose when they use AI to create communications. You know the “I approve this message” notice? Now add, “I used AI to make this message.”

But labels aren’t enough. If AI disclosures become commonplace, we may become blind to them, like so much other fine print.

The bigger ask: We want candidates and their supporting parties and committees not to use AI to deceive us.

So what’s the difference between a dangerous deepfake and an AI facetune that makes an octogenarian candidate look a little less octogenarian?

“The core definition is showing a candidate doing or saying something they didn’t do or say,”

Sure, give Biden or Trump a facetune, or even show them shaking hands with Abraham Lincoln. But don’t use AI to show your competitor hugging an enemy or fake their voice commenting on current issues.

The pledge also includes not using AI to suppress voting, such as using an authoritative voice or image to tell people a polling place has been closed. That is already illegal in many states, but it’s still concerning how believable AI might make these efforts seem.

Don’t deepfake yourself. Making yourself or your favorite candidate appear more knowledgeable, experienced or culturally capable is also a form of deception.

(Pressed on the ethics of his use of AI, Adams just proved my point that we desperately need some ground rules. “These are part of the broader conversations that the philosophical people will have to sit down and figure out, ‘Is this ethically right or wrong?’ I’ve got one thing: I’ve got to run the city,” he said.)

The golden rule in my pledge — don’t use AI to be materially deceptive — is similar to the one in an AI regulation proposed by a bipartisan group of lawmakers

Such proposals have faced resistance in Washington on First Amendment grounds. The free speech of politicians is important. It’s not against the law for politicians to lie, whether they’re using AI or not. An effort to get the Federal Election Commission to count AI deepfakes as “fraudulent misrepresentation” under its existing authority has faced similar pushback.

But a pledge like the one I outline here isn’t a law restraining speech. It’s asking politicians to take a principled stand on their own use of AI

Schumer said he thinks my pledge is just a start of what’s needed. “Maybe most candidates will make that pledge. But the ones that won’t will drive us to a lower common denominator, and that’s true throughout AI,” he said. “If we don’t have government-imposed guardrails, the lowest common denominator will prevail.”

even after the 7 October massacre had stirred memories of the bleakest chapters of the Jewish past – and prompted a surge in antisemitism across the world – Jews were being told exactly how they can and cannot speak about their pain. We’re not to mention the Holocaust, one scholar advised, because that would be “weaponising” it. Historical context about the Nakba, the 1948 dispossession of the Palestinians, is – rightly – deemed essential. But mention the Nazi murder of 6 million Jews – the event that finally secured near-universal agreement among the Jewish people, and the United Nations in 1947, that Jews needed a state of their own – and you’ve broken the rules. Because it’s impossible that both sides might have suffered historic pain.

Instead, a shift is under way that has been starkly revealed during these past three weeks. It squeezes the Israel-Palestine conflict into a “decolonisation” frame it doesn’t quite fit, with all Israelis – not just those in the occupied West Bank – defined as the footsoldiers of “settler colonialism”, no different from, say, the French in Algeria

They have been framed as the modern world’s ultimate evildoer: the coloniser.

That matters because, in this conception, justice can only be done once the colonisers are gone

What’s more, such a framing brands all Israelis – not just West Bank settlers – as guilty of the sin of colonialism. Perhaps that explains why those letter writers could not full-throatedly condemn the 7 October killing of innocent Israeli civilians. Because they do not see any Israeli, even a child, as wholly innocent.

the late Israeli novelist and peace activist Amos Oz was never wiser than when he described the Israel/Palestine conflict as something infinitely more tragic: a clash of right v right. Two peoples with deep wounds, howling with grief, fated to share the same small piece of land.

Something similar might be said about “The Maniac,” Benjamín Labatut’s new novel, whose designated Prometheus is the Hungarian-born polymath John von Neumann, a pioneer of A.I. as well as an originator of game theory.

both narratives are grounded in fact, using the lives and ideas of real people as fodder for allegory and attempting to write a new mythology of the modern world.

on Neumann and Oppenheimer were close contemporaries, born a year apart to prosperous, assimilated Jewish families in Budapest and New York. Von Neumann, conversant in theoretical physics, mathematics and analytic philosophy, worked for Oppenheimer at Los Alamos during the Manhattan Project. He spent most of his career at the Institute for Advanced Study, where Oppenheimer served as director after the war.

More than most intellectual bastions, the institute is a house of theory. The Promethean mad scientists of the 19th century were creatures of the laboratory, tinkering away at their infernal machines and homemade monsters. Their 20th-century counterparts were more likely to be found at the chalkboard, scratching out our future in charts, equations and lines of code.

MANIAC. The name was an acronym for “Mathematical Analyzer, Numerical Integrator and Computer,” which doesn’t sound like much of a threat. But von Neumann saw no limit to its potential. “If you tell me precisely what it is a machine cannot do,” he declared, “then I can always make a machine which will do just that.” MANIAC didn’t just represent a powerful new kind of machine, but “a new type of life.”

the intellectual drama of “Oppenheimer” — as distinct from the dramas of his personal life and his political fate — is about how abstraction becomes reality. The atomic bomb may be, for the soldiers and politicians, a powerful strategic tool in war and diplomacy. For the scientists, it’s something else: a proof of concept, a concrete manifestation of quantum theory.

Oppenheimer wasn’t a principal author of that theory. Those scientists, among them Niels Bohr, Erwin Schrödinger and Werner Heisenberg, were characters in Labatut’s previous novel, “When We Cease to Understand the World.” That book provides harrowing illumination of a zone where scientific insight becomes indistinguishable from madness or, perhaps, divine inspiration. The basic truths of the new science seem to explode all common sense: A particle is also a wave; one thing can be in many places at once; “scientific method and its object could no longer be prised apart.”

. Oppenheimer’s designation as Prometheus is precise. He snatched a spark of quantum insight from those divinities and handed it to Harry S. Truman and the U.S. Army Air Forces.

Labatut’s account of von Neumann is, if anything, more unsettling than “Oppenheimer.” We had decades to get used to the specter of nuclear annihilation, and since the end of the Cold War it has been overshadowed by other terrors. A.I., on the other hand, seems newly sprung from science fiction, and especially terrifying because we can’t quite grasp what it will become.

Von Neumann, who died in 1957, did not teach machines to play Go. But when asked “what it would take for a computer, or some other mechanical entity, to begin to think and behave like a human being,” he replied that “it would have to play, like a child.”

More than 200 years after the Shelleys, Prometheus is having another moment, one closer in spirit to Mary’s terrifying ambivalence than to Percy’s fulsome gratitude. As technological optimism curdles in the face of cyber-capitalist villainy, climate disaster and what even some of its proponents warn is the existential threat of A.I., that ancient fire looks less like an ember of divine ingenuity than the start of a conflagration. Prometheus is what we call our capacity for self-destruction.

If Oppenheimer took hold of the sacred fire of atomic power, von Neumann’s theft was bolder and perhaps more insidious: He stole a piece of the human essence. He’s not only a modern Prometheus; he’s a second Frankenstein, creator of an all but human, potentially more than human monster.

“Technological power as such is always an ambivalent achievement,” Labatut’s von Neumann writes toward the end of his life, “and science is neutral all through, providing only means of control applicable to any purpose, and indifferent to all. It is not the particularly perverse destructiveness of one specific invention that creates danger. The danger is intrinsic. For progress there is no cure.”

I had gone to graduate school in Lebanon, then moved back there in an attempt to better understand how Hezbollah had evolved into Israel’s most capable foe. My research revealed as much about Israeli missteps and weaknesses as it did about Hezbollah’s strengths.

If Israel is going to have any strategic success against Hamas, it needs to do three things differently from conflicts past.

Hezbollah took everything Israel could throw at it for a month and was still standing.

As noted earlier, Israel has an unfortunate tendency to lay out maximalist goals—very often for domestic consumption—that it then fails to meet

In 2006, for example, Israel’s then–prime minister, Ehud Olmert, told the country he was going to destroy Hezbollah, return the bodies of two Israeli prisoners, and end the rocket attacks on Israel.

Israel did none of the three. And although Lebanon was devastated, and Hezbollah’s leader, Hassan Nasrallah, publicly apologized for the raid that started the conflict, most observers had little doubt about who had won the conflict.

Strategic Humility

As Eliot Cohen has pointed out, the other side also has maximalist goals. Hamas and Hezbollah want nothing less than the destruction of Israel. But they are in no rush.

Nasrallah addressed the Arabic-speaking world for the first time since the start of this conflict on Friday. Significantly, he declared that although fighting still rages, Hamas became the conflict’s winner as soon as Israel claimed that it would destroy the militant group, which he confidently predicted it would not.

Hezbollah clearly does not want to enter this conflict in any meaningful way. It knows that the pressure will grow to do so if Israel has any real success in Gaza, but for the moment, it doubts that Israel will accomplish any such thing.

that Israel will destroy Hamas. That just isn’t going to happen, especially because no one has any idea who, or what, should replace Hamas in Gaza. So tell the world what will happen—and how it will make Israel and the region safer.

Communications Discipline

One of the things that struck me was the almost profane way in which Israeli military spokespeople would often speak, to international audiences no less, about non-Israeli civilians

“Now we are at the stage in which we are firing into the villages in order to cause damage to property … The aim is to create a situation in which the residents will leave the villages and go north.”

The callousness with which Israeli spokespeople too often describe the human suffering on the other side of the conflict, the blunt way in which they described what many Americans would consider war crimes, never fails to offend international audiences not predisposed to have sympathy with Israeli war aims.

much like right-wing American politicians, who sometimes use inflammatory rhetoric about real or perceived U.S. enemies, Israeli officials often resort to language about adversaries and military operations that can be exceptionally difficult for their allies to defend on the international stage:

One minister casually muses about using nuclear weapons on Gaza; another claims that the Palestinians are a fictional people. One can safely assume that people will continue accusing the Israeli government of including genocidal maniacs when they can point to officials in that government talking like, well, genocidal maniacs.

Israel needs to develop a clear communications plan for its conflicts and to sharply police the kind of language that doesn’t go over as well in Johannesburg or Jordan as it does in Jerusalem.

Focus on Iran

Few people have any interest in a regional war. The economic consequences alone would be dire. But had I been in Israel’s position on October 8, I might have been sorely tempted to largely ignore Gaza—where even the best-trained military would struggle to dislodge Hamas without killing tens of thousands of innocent civilians—and focus my efforts much farther east

Israel nevertheless needs to find a way to change Iran’s strategic calculus. Otherwise, Hamas and Hezbollah will only grow stronger.

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.

“These are technologies that are supposed to be so democratized and universal, but they’re so heavily influenced by one person,”

“Everything they do is [framed as] a step toward much larger greatness and the transformation of society. But these are just cults of personality. They sell a product.”

during

“These are private-sector people making money off something that serves a public function. And when they take a turn because of very personal, very individual decisions, where a handful of people are shaping the trajectory of these companies, maybe even the existence of these companies, that’s something new we all have to deal with.”

where the other firms sold phones and search engines, Musk and Altman championed their work as a public mission for protecting mankind, with a for-profit business attached. It is notable that as private companies, they don’t have to report to federal regulators or to shareholders, who can vote down proposals or push back against their work.

The corporate storytelling that pushes technology as a force for public harmony has proved to be one of Silicon Valley’s great marketing tools, said Margaret O’Mara, a professor at the University of Washington who studies the history of technology. But it’s also obscured the dangers of centralizing power and subjecting it to leaders’ personal whim

“Silicon Valley has for years adopted this messaging and mood that it’s all about radical transparency and openness — remember Google’s ‘Don’t be evil’ motto? — and this idea of a kinder, gentle capitalism that’s going to change the world for the better,” she said.

“Then you have these moments of reckoning and remember: It’s capitalism. Some tech billionaires lost, and some other ones are winning,”

Jeff Hauser, the head of the left-leaning advocacy group Revolving Door Project, said in a statement Wednesday that Summers’s role on the board was a sign OpenAI was “unserious” about its oversight, and that it “should accelerate concerns that AI will be bad for all but the richest and most opportunistic amongst us.”

Ro Khanna, who represents parts of Silicon Valley, said in an interview that the OpenAI turmoil underscores concerns that “a few people, no matter how talented, no matter how knowledgeable, can’t be making the rules for a society on a technology that is going to have such profound consequences.”

“We’ve seen a parade of these big tech leaders come to D.C.,” Khanna said. “I think highly of them, but they’re not the ones who should be leading the conversation on the regulatory framework, what safeguards we need.”

Musk on Tuesday posted a message, under a picture of him holding a katana, saying, “There is a large graveyard filled with my enemies. I do not wish to add to it, but will if given no choice.”

The videos promoted narratives that Chinese technology was superior to America’s, that the United States was doomed to economic collapse, and that China and Russia were responsible geopolitical players. Some of the clips fawned over Chinese companies like Huawei and denigrated American companies like Apple.

Content from at least 30 channels in the network drew nearly 120 million views and 730,000 subscribers since last year, along with occasional ads from Western companies

Disinformation — such as the false claim that some Southeast Asian nations had adopted the Chinese yuan as their own currency — was common. The videos were often able to quickly react to current events

he coordinated campaign might be “one of the most successful influence operations related to China ever witnessed on social media.”

Historically, its influence operations have focused on defending the Communist Party government and its policies on issues like the persecution of Uyghurs or the fate of Taiwan

Efforts to push pro-China messaging have proliferated in recent years, but have featured largely low-quality content that attracted limited engagement or failed to sustain meaningful audiences

“This campaign actually leverages artificial intelligence, which gives it the ability to create persuasive threat content at scale at a very limited cost compared to previous campaigns we’ve seen,”

YouTube said in a statement that its teams work around the clock to protect its community, adding that “we have invested heavily in robust systems to proactively detect coordinated influence operations.” The company said it welcomed research efforts and that it had shut down several of the channels mentioned in the report for violating the platform’s policies.

China began targeting the United States more directly amid the mass pro-democracy protests in Hong Kong in 2019 and continuing with the Covid-19 pandemic, echoing longstanding Russian efforts to discredit American leadership and influence at home and aboard.

Over the summer, researchers at Microsoft and other companies unearthed evidence of inauthentic accounts that China employed to falsely accuse the United States of using energy weapons to ignite the deadly wildfires in Hawaii in August.

Meta announced last month that it removed 4,789 Facebook accounts from China that were impersonating Americans to debate political issues, warning that the campaign appeared to be laying the groundwork for interference in the 2024 presidential elections.

It was the fifth network with ties to China that Meta had detected this year, the most of any other country.