Daily Chord
5 Brain-Training Games For Kids And Adults
While the benefits are clear, not all brain-training games are created equal. When looking at different options, try those which utilize multiple skill sets, instead of just one. "The best brain games connect various parts of the brain simultaneously," says David Hunter, M.D., assistant professor of neurology at the McGovern Medical School at UTHealth Houston.
Dr. Pilitis adds that these games should be adjusted to accommodate a player's age and cognitive abilities. Many popular brain-training apps and websites include features which auto-adjust the game difficulty, and users should look for games with this function.
To jumpstart your brain-training, here are five brain-healthy games that meet that criteria. All games offer a free version, though some have paid offers.
SudokuOne of the most popular—and effective—brain-training games is sudoku, notes Dr. Hunter. In this Japanese puzzle game, players enter numbers into a 9×9 grid, careful not to repeat any numbers in individual rows, columns or 3×3 sections. Players must use the numbers one through nine at least one time in each section, column and row.
Unlike other brain-training games, sudoku improves multiple skills at once. "Most brain-training games focus on one cognitive skill, but sudoku focuses on multiple," explains Dr. Hunter. "When you play sudoku, you are improving your attention, problem-solving skills and computational abilities."
Sudoku can be played on paper or virtually. There are multiple websites and apps that allow users to play sudoku for free, with Easybrain offering a popular option on the web, Google Play store and Apple App store.
LumosityLumosity offers users customized brain-training games on its website and app. Each day, it provides users with a new set of games that utilizes skills like memory, problem-solving, language, math and speed. As you use the app, Lumosity customizes the set of daily games to improve any weaknesses and cultivate a player's cognitive strengths.
The website features over 40 custom-made games. Some games include Trouble Brewing, where players have to manage multiple coffee orders and cope with divided attention, and Pirate Passage, where players improve their planning skills as they find a route to buried treasure.
Lumosity offers a free subscription with access to limited games. Players can also opt into a paid subscription at any time, which provides more games and tools to monitor progress. The paid subscription is $11.99 a month or $59.99 for a year.
ElevateElevate is an app with games to improve a variety of cognitive skills. Users can play games that can help improve their concentration, as well as their skills in math, reading, writing, speaking and vocabulary.
Each day, users choose between three to five games to play, with the app featuring over 40 games total. As users play more often, the games get more difficult, so users can continue to grow their skill set. Users can also track their progress against their past performance and other players.
After a free seven-day trial of all features, app users can choose between a free plan with limited games and a paid version with access to all games. The only paid option is an annual subscription which varies in price, depending on your location. The subscription renews automatically each year, unless auto-renew is turned off.
BriangleTo train your brain in an online community setting, consider Briangle. This free website includes puzzles, trivia sets and other games to exercise your cognitive abilities. The company claims to have the largest collection of brain-training games in the world, with over 15,000 games on its website.
Unlike other brain-training platforms, Briangle also includes a community feature where players can chat with each other and compete against other players. "For some people, the added benefit of a sense of community and social support means they're more likely to stick to the game," says Dr. Pilitisis.
Users can play games as a guest or create an account. Registration is free, with no paid options. Anyone over the age of 13 (or under the age of 13 with parental consent) can sign up for an account.
DuolingoDuolingo has two brain-training games for users: its widely-known language learning app and its newer Duolingo Math. Both apps provide micro-lessons that help users develop real-world skills through gamified daily modules.
Whether users choose the original language learning app or the math one, Duolingo boasts that its apps don't only train the brain, but provide users with a valuable skill: math or the ability to communicate in another language.
Both Duolingo Math and Duolingo Language Lesson have a free version. At the time of publication, Duolingo Math only has a free option while Duolingo Language Lesson offers a paid Super Duolingo option, which is available for $6.99 a month or $79.99 a year, depending on promotions running at time of purchase.
Does Sam Altman Know What He's Creating?
On a Monday morning in April, Sam Altman sat inside OpenAI's San Francisco headquarters, telling me about a dangerous artificial intelligence that his company had built but would never release. His employees, he later said, often lose sleep worrying about the AIs they might one day release without fully appreciating their dangers. With his heel perched on the edge of his swivel chair, he looked relaxed. The powerful AI that his company had released in November had captured the world's imagination like nothing in tech's recent history. There was grousing in some quarters about the things ChatGPT could not yet do well, and in others about the future it may portend, but Altman wasn't sweating it; this was, for him, a moment of triumph.
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View MoreIn small doses, Altman's large blue eyes emit a beam of earnest intellectual attention, and he seems to understand that, in large doses, their intensity might unsettle. In this case, he was willing to chance it: He wanted me to know that whatever AI's ultimate risks turn out to be, he has zero regrets about letting ChatGPT loose into the world. To the contrary, he believes it was a great public service.
"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." 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. And 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. There would be no retreat to a top-secret lab in the New Mexico desert.
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For years, the public didn't hear much about OpenAI. When Altman became CEO in 2019, reportedly after a power struggle with Musk, it was barely a story. OpenAI published papers, including one that same year about a new AI. That got the full attention of the Silicon Valley tech community, but the technology's potential was not apparent to the general public until last year, when people began to play with ChatGPT.
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. Altman can still remember where he was the first time he saw GPT-4 write complex computer code, an ability for which it was not explicitly designed. "It was like, 'Here we are,' " he said.
Within nine weeks of ChatGPT's release, it had reached an estimated 100 million monthly users, according to a UBS study, likely making it, at the time, the most rapidly adopted consumer product in history. Its success roused tech's accelerationist id: Big investors and huge companies in the U.S. And China quickly diverted tens of billions of dollars into R&D modeled on OpenAI's approach. 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. Ever since the computing revolution's earliest hours, AI has been mythologized as a technology destined to bring about a profound rupture. Our culture has generated an entire imaginarium of AIs that end history in one way or another. Some are godlike beings that wipe away every tear, healing the sick and repairing our relationship with the Earth, before they usher in an eternity of frictionless abundance and beauty. Others reduce all but an elite few of us to gig serfs, or drive us to extinction.
Altman has entertained the most far-out scenarios. "When I was a younger adult," he said, "I had this fear, anxiety … and, to be honest, 2 percent of excitement mixed in, too, that we were going to create this thing" that "was going to far surpass us," and "it was going to go off, colonize the universe, and humans were going to be left to the solar system."
"As a nature reserve?" I asked.
"Exactly," he said. "And that now strikes me as so naive."
Across several conversations in the United States and Asia, 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."
But 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. But 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 hold that against him, exactly—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.
OpenAI's headquarters are in a four-story former factory in the Mission District, beneath the fog-wreathed Sutro Tower. Enter its lobby from the street, and the first wall you encounter is covered by a mandala, a spiritual representation of the universe, fashioned from circuits, copper wire, and other materials of computation. To the left, a secure door leads into an open-plan maze of handsome blond woods, elegant tile work, and other hallmarks of billionaire chic. Plants are ubiquitous, including hanging ferns and an impressive collection of extra-large bonsai, each the size of a crouched gorilla. The office was packed every day that I was there, and unsurprisingly, I didn't see anyone who looked older than 50. Apart from a two-story library complete with sliding ladder, the space didn't look much like a research laboratory, because the thing being built exists only in the cloud, at least for now. It looked more like the world's most expensive West Elm.
One morning I met with Ilya Sutskever, OpenAI's chief scientist. Sutskever, who is 37, has the affect of a mystic, sometimes to a fault: Last year he caused a small brouhaha by claiming that GPT-4 may be "slightly conscious." He first made his name as a star student of Geoffrey Hinton, the University of Toronto professor emeritus who resigned from Google this spring so that he could speak more freely about AI's danger to humanity.
Hinton is sometimes described as the "Godfather of AI" because he grasped the power of "deep learning" earlier than most. In the 1980s, shortly after Hinton completed his Ph.D., the field's progress had all but come to a halt. Senior researchers were still coding top-down AI systems: AIs would be programmed with an exhaustive set of interlocking rules—about language, or the principles of geology or of medical diagnosis—in the hope that someday this approach would add up to human-level cognition. 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.
Altman has compared early-stage AI research to teaching a human baby. But during OpenAI's first few years, no one knew whether they were training a baby or pursuing a spectacularly expensive dead end.Sutskever described a neural network to me as beautiful and brainlike. At one point, he rose from the table where we were sitting, approached a whiteboard, and uncapped a red marker. 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
Imagine a neural network that has been programmed to predict the next word in a text. It will be preloaded with a gigantic number of possible words. But before it's trained, it won't yet have any experience in distinguishing among them, and so its predictions will be shoddy. If it is fed the sentence "The day after Wednesday is …" its initial output might be "purple." A neural network learns because its training data include the correct predictions, which means it can grade its own outputs. When it sees the gulf between its answer, "purple," and the correct answer, "Thursday," it adjusts the connections among words in its hidden layers accordingly. 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.
That's not to say that the path from the first neural networks to GPT-4's glimmers of humanlike intelligence was easy. 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." The first few years at OpenAI were a slog, in part because no one there knew whether they were training a baby or pursuing a spectacularly expensive dead end.
"Nothing was working, and Google had everything: all the talent, all the people, all the money," Altman told me. The founders had put up millions of dollars to start the company, and failure seemed like a real possibility. Greg Brockman, the 35-year-old president, told me that in 2017, he was so discouraged that he started lifting weights as a compensatory measure. He wasn't sure that OpenAI was going to survive the year, he said, and he wanted "to have something to show for my time."
Neural networks were already doing intelligent things, but it wasn't clear which of them might lead to general intelligence. Just after OpenAI was founded, an AI called AlphaGo had stunned the world by beating Lee Se-dol at Go, a game substantially more complicated than chess. Lee, the vanquished world champion, described AlphaGo's moves as "beautiful" and "creative." Another top player said that they could never have been conceived by a human. OpenAI tried training an AI on Dota 2, a more complicated game still, involving multifront fantastical warfare in a three-dimensional patchwork of forests, fields, and forts. It eventually beat the best human players, but its intelligence never translated to other settings. Sutskever and his colleagues were like disappointed parents who had allowed their kids to play video games for thousands of hours against their better judgment.
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.
The inner workings of ChatGPT—all of those mysterious things that happen in GPT-4's hidden layers—are too complex for any human to understand, at least with current tools. Tracking what's happening across the model—almost certainly composed of billions of neurons—is, today, hopeless. But 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.
It's worth pausing to understand why language is such a special information source. Suppose you are a fresh intelligence that pops into existence here on Earth. Surrounding you is the planet's atmosphere, the sun and Milky Way, and hundreds of billions of other galaxies, each one sloughing off light waves, sound vibrations, and all manner of other information. 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. But 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.' "
One year later, in June 2018, OpenAI released GPT, a transformer model trained on more than 7,000 books. GPT didn't start with a basic book like See Spot Run and work its way up to Proust. It didn't even read books straight through. It absorbed random chunks of them simultaneously. 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 word 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. Still, it was janky, more proof of concept than harbinger of a superintelligence. Four months later, Google released BERT, a suppler language model that got better press. But by then, OpenAI was already training a new model on a data set of more than 8 million webpages, each of which had cleared a minimum threshold of upvotes on Reddit—not the strictest filter, but perhaps better than no filter at all.
Sutskever wasn't sure how powerful GPT-2 would be after ingesting a body of text that would take a human reader centuries to absorb. 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. Altman, a St. Louis native, Stanford dropout, and serial entrepreneur, had previously led Silicon Valley's preeminent start-up accelerator, Y Combinator; he'd seen plenty of young companies with a good idea get crushed by incumbents. To raise capital, OpenAI added a for-profit arm, which now comprises more than 99 percent of the organization's head count. (Musk, who had by then left the company's board, has compared this move to turning a rainforest-conservation organization into a lumber outfit.) Microsoft invested $1 billion soon after, and has reportedly invested another $12 billion since. OpenAI said that initial investors' returns would be capped at 100 times the value of the original investment—with any overages going to education or other initiatives intended to benefit humanity—but the company would not confirm Microsoft's cap.
Altman and OpenAI's other leaders seemed confident that the restructuring would not interfere with the company's mission, and indeed would only accelerate its completion. 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."
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.
Whether or not OpenAI ever feels the pressure of a quarterly earnings report, 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. Earlier this year, Musk founded an AI lab of his own—xAI—to compete with OpenAI. ("Elon is a super-sharp dude," Altman said diplomatically when I asked him about the company. "I assume he'll do a good job there.") Meanwhile, Amazon is revamping Alexa using much larger language models than it has in the past.
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." Even with GPUs scarce, in recent years the scale of the largest AI training runs has doubled about every six months.
As their creators so often remind us, the largest AI models have a record of popping out of training with unanticipated abilities.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, and the AI was trained on a data set of unprecedented size, which included not just text but images too.
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. Brockman told me that he wanted to spend every waking moment with the model. "Every day it's sitting idle is a day lost for humanity," he said, with no hint of sarcasm. 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. Altman has said that 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. All of those training data, however voluminous, are "just there, inert," he said. The training process is what "refines it and transmutes it, and brings it to life." 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.
I saw Altman again in June, in the packed ballroom of a slim golden high-rise that towers over Seoul. He was nearing the end of a grueling public-relations tour through Europe, the Middle East, Asia, and Australia, with lone stops in Africa and South America. I was tagging along for part of his closing swing through East Asia. The trip had so far been a heady experience, but he was starting to wear down. He'd said its original purpose was for him to meet OpenAI users. It had since become a diplomatic mission. He'd talked with more than 10 heads of state and government, who had questions about what would become of their countries' economies, cultures, and politics.
The event in Seoul was billed as a "fireside chat," but more than 5,000 people had registered. After these talks, Altman is often mobbed by selfie seekers, and his security team keeps a close eye. Working on AI attracts "weirder fans and haters than normal," he said. On one stop, he was approached by a man who was convinced that Altman was an alien, sent from the future to make sure that the transition to a world with AI goes well.
Altman did not visit China on his tour, apart from a video appearance at an AI conference in Beijing. ChatGPT is currently unavailable in China, and Altman's colleague Ryan Lowe told me that the company was not yet sure what it would do if the government requested a version of the app that refused to discuss, say, the Tiananmen Square massacre. When I asked Altman if he was leaning one way or another, he didn't answer. "It's not been in my top-10 list of compliance issues to think about," he said.
Until that point, he and I had spoken of China only in veiled terms, as a civilizational competitor. We had agreed that if artificial general intelligence is as transformative as Altman predicts, a serious geopolitical advantage will accrue to the countries that create it first, as advantage had accrued to the Anglo-American inventors of the steamship. I asked him if that was an argument for AI nationalism. "In a properly functioning world, I think this should be a project of governments," Altman said.
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.
Prior to the European leg of his trip, 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. In Europe, things are different. When Altman arrived at a public event in London, protesters awaited. He tried to engage them after the event—a listening tour!—but was ultimately unpersuasive: One told a reporter that he left the conversation feeling more nervous about AI's dangers.
That same day, 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. (This is perhaps a distinction without a difference.) In a tersely worded tweet after Time magazine and Reuters published his comments, he reassured Europe that OpenAI had no plans to leave.
It is a good thing that a large, essential part of the global economy is intent on regulating state-of-the-art AIs, because as their creators so often remind us, the largest models have a record of popping out of training with unanticipated abilities. 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. "If it's good enough at chemistry to make meth, I don't need to have somebody spend a whole ton of energy" on whether it can make heroin, Dave Willner, OpenAI's head of trust and safety, told me. 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."
Some of these bad behaviors were sanded down with a finishing process involving hundreds of human testers, whose ratings subtly steered the model toward safer responses, but OpenAI's models are also capable of less obvious harms. The Federal Trade Commission recently opened an investigation into whether ChatGPT's misstatements about real people constitute reputational damage, among other things. (Altman said on Twitter that he is confident OpenAI's technology is safe, but promised to cooperate with the FTC.)
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."
I asked Agarwal whether this was dystopian behavior or a new frontier in human connection. She was ambivalent, as was Altman. "I don't judge people who want a relationship with an AI," he told me, "but I don't want one." 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. Whatever they're doing, it casts a spell. I was reminded of a haunting scene in Her, the 2013 film in which a lonely Joaquin Phoenix falls in love with his AI assistant, voiced by Scarlett Johansson. He is walking across a bridge talking and giggling with her through an AirPods-like device, and he glances up to see that everyone around him is also immersed in conversation, presumably with their own AI. A mass desocialization event is under way.
No one yet knows how quickly and to what extent GPT-4's successors will manifest new abilities as they gorge on more and more of the internet's text. 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. 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."
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." Altman's claim about the brain is hard to evaluate, given that we don't have anything close to a complete theory of how it works. But 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. It will sometimes perform thousands of indecipherable technical operations just to answer a single question. 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.
"If you go back four or five or six years," Sutskever told me, "the things we are doing right now are utterly unimaginable."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. This is especially true in the quantum realm, where humans can reliably calculate future states of physical systems—enabling, among other things, the entirety of the computing revolution—without anyone grasping the nature of the underlying reality. 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. I once asked it how Japanese culture had produced the world's first novel, despite the relatively late development of a Japanese writing system, around the fifth or sixth century. It gave me a fascinating, accurate answer about the ancient tradition of long-form oral storytelling in Japan, and the culture's heavy emphasis on craft. But when I asked it for citations, it just made up plausible titles by plausible authors, and did so with an uncanny confidence. 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," Joanne Jang told me.
OpenAI had to address this problem when it partnered with the Khan Academy, an online, nonprofit educational venture, to build a tutor
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," he told me. The state of the art in text generation then was Smart Reply, the Gmail module that suggests "Okay, thanks!" and other short responses. "That was a big application" for Google, he said, grinning. 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 is betting that future general-reasoning machines will be able to move beyond these narrow scientific discoveries to generate novel insights. I asked Altman, if he were to train a model on a corpus of scientific and naturalis
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