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But the geneticists see no trace of the Danelaw, the Danish rule over northern England from the ninth to the 11th century, nor of the Norman Conquest of England in 1066. The numbers of invaders may have been too small to leave a demographic imprint

in the case of the Normans, who had previously emigrated from southern Denmark to Normandy, it is hard to distinguish their genes from those of the earlier Danish invaders.

The researchers found that the modern British population falls into 17 clusters altogether, based on genetic relatedness. Though very similar, the groups are genetically distinguishable, and even the main population cluster, that of southern and central England, is distinguishable from the populations of France, Germany and other European countries.

The people of the southern and central parts of England form a homogeneous population, but all around the Celtic periphery, in Cornwall, Wales and Scotland, lie small clusters of genetically different populations that have maintained their identity over the generations. This is a surprise, given that the Celtic peoples who ruled most of England until Caesar’s invasion in B.C. 55 were assumed to be fairly homogeneous.

Dr. Donnelly and his colleagues managed to sidestep this recent churning of the population history by seeking out elderly people who lived in rural areas and whose grandparents had been born locally. Because individual genomes are composed of random samples of the four grandparents’ DNA, the researchers were in effect looking two generations into the past and testing the population of the late 19th century.

They analyzed the DNA of their 2,000 subjects at 500,000 sites along the genome, and then organized them into the 17 genetic clusters. They also analyzed the genomes of 6,000 Europeans in the same way, and could thus identify the source populations in Europe from which each of the 17 British clusters was derived.

The migrations revealed in that way match the known historical record but also point to events that have not been recorded, such as a major migration from northern France that accounts for about one-third of the ancestry of the average person in Britain.

“History is written by the winners, and archaeology studies the burials of wealthy people,” Dr. Donnelly said. “But genetic evidence is interesting because it complements that by showing what is happening to the masses rather than the elite.”

the coronavirus more often infects clusters of family members, friends and work colleagues,

“You can contain clusters,” Dr. Heymann said. “You need to identify and stop discrete outbreaks, and then do rigorous contact tracing.”

The microphone should not even be at the White House, scientists said, so that briefings of historic importance do not dissolve into angry, politically charged exchanges with the press corps, as happened again on Friday.

Americans must be persuaded to stay home, they said, and a system put in place to isolate the infected and care for them outside the home

Travel restrictions should be extended, they said; productions of masks and ventilators must be accelerated, and testing problems must be resolved.

It was not at all clear that a nation so fundamentally committed to individual liberty and distrustful of government could learn to adapt to many of these measures, especially those that smack of state compulsion.

What follows are the recommendations offered by the experts interviewed by The Times.

they were united in the opinion that politicians must step aside and let scientists both lead the effort to contain the virus and explain to Americans what must be done.

medical experts should be at the microphone now to explain complex ideas like epidemic curves, social distancing and off-label use of drugs.

doing so takes intelligent, rapidly adaptive work by health officials, and near-total cooperation from the populace. Containment becomes realistic only when Americans realize that working together is the only way to protect themselves and their loved ones.

Above all, the experts said, briefings should focus on saving lives and making sure that average wage earners survive the coming hard times — not on the stock market, the tourism industry or the president’s health.

“At this point in the emergency, there’s little merit in spending time on what we should have done or who’s at fault,”

The next priority, experts said, is extreme social distancing.If it were possible to wave a magic wand and make all Americans freeze in place for 14 days while sitting six feet apart, epidemiologists say, the whole epidemic would sputter to a halt.

The virus would die out on every contaminated surface and, because almost everyone shows symptoms within two weeks, it would be evident who was infected. If we had enough tests for every American, even the completely asymptomatic cases could be found and isolated.

The crisis would be over.

Obviously, there is no magic wand, and no 300 million tests. But the goal of lockdowns and social distancing is to approximate such a total freeze.

In contrast to the halting steps taken here, China shut down Wuhan — the epicenter of the nation’s outbreak — and restricted movement in much of the country on Jan. 23, when the country had a mere 500 cases and 17 deaths.Its rapid action had an important effect: With the virus mostly isolated in one province, the rest of China was able to save Wuhan.

Even as many cities fought their own smaller outbreaks, they sent 40,000 medical workers into Wuhan, roughly doubling its medical force.

Stop transmission within cities

the weaker the freeze, the more people die in overburdened hospitals — and the longer it ultimately takes for the economy to restart.

People in lockdown adapt. In Wuhan, apartment complexes submit group orders for food, medicine, diapers and other essentials. Shipments are assembled at grocery warehouses or government pantries and dropped off. In Italy, trapped neighbors serenade one another.

Each day’s delay in stopping human contact, experts said, creates more hot spots, none of which can be identified until about a week later, when the people infected there start falling ill.

South Korea avoided locking down any city, but only by moving early and with extraordinary speed. In January, the country had four companies making tests, and as of March 9 had tested 210,000 citizens — the equivalent of testing 2.3 million Americans.

As of the same date, fewer than 9,000 Americans had been tested.

Fix the testing mess

Testing must be done in a coordinated and safe way, experts said. The seriously ill must go first, and the testers must be protected.In China, those seeking a test must describe their symptoms on a telemedicine website. If a nurse decides a test is warranted, they are directed to one of dozens of “fever clinics” set up far from all other patients.

Isolate the infected

As soon as possible, experts said, the United States must develop an alternative to the practice of isolating infected people at home, as it endangers families. In China, 75 to 80 percent of all transmission occurred in family clusters.

Cellphone videos from China show police officers knocking on doors and taking temperatures. In some, people who resist are dragged away by force. The city of Ningbo offered bounties of $1,400 to anyone who turned in a coronavirus sufferer.

In China, said Dr. Bruce Aylward, leader of the World Health Organization’s observer team there, people originally resisted leaving home or seeing their children go into isolation centers with no visiting rights — just as Americans no doubt would.

In China, they came to accept it.“They realized they were keeping their families safe,” he said. “Also, isolation is really lonely. It’s psychologically difficult. Here, they were all together with other people in the same boat. They supported each other.”

Find the fevers

Make masks ubiquitous

In China, having a fever means a mandatory trip to a fever clinic to check for coronavirus. In the Wuhan area, different cities took different approaches.

In most cities in affected Asian countries, it is commonplace before entering any bus, train or subway station, office building, theater or even a restaurant to get a temperature check. Washing your hands in chlorinated water is often also required.

The city of Qianjiang, by contrast, offered the same amount of money to any resident who came in voluntarily and tested positive

Voluntary approaches, like explaining to patients that they will be keeping family and friends safe, are more likely to work in the West, she added.

Trace the contacts

Finding and testing all the contacts of every positive case is essential, experts said. At the peak of its epidemic, Wuhan had 18,000 people tracking down individuals who had come in contact with the infected.

Dr. Borio suggested that young Americans could use their social networks to “do their own contact tracing.” Social media also is used in Asia, but in different ways

When he lectured at a Singapore university, Dr. Heymann said, dozens of students were in the room. But just before he began class, they were photographed to record where everyone sat.

Instead of a policy that advises the infected to remain at home, as the Centers for Disease and Prevention now does, experts said cities should establish facilities where the mildly and moderately ill can recuperate under the care and observation of nurses.

There is very little data showing that flat surgical masks protect healthy individuals from disease. Nonetheless, Asian countries generally make it mandatory that people wear them.

The Asian approach is less about data than it is about crowd psychology, experts explained.All experts agree that the sick must wear masks to keep in their coughs. But if a mask indicates that the wearer is sick, many people will be reluctant to wear one. If everyone is required to wear masks, the sick automatically have one on and there is no stigma attached.

Also, experts emphasized, Americans should be taught to take seriously admonitions to stop shaking hands and hugging

Preserve vital services

Only the federal government can enforce interstate commerce laws to ensure that food, water, electricity, gas, phone lines and other basic needs keep flowing across state lines to cities and suburbs

“I sense that most people — and certainly those in business — get it. They would prefer to take the bitter medicine at once and contain outbreaks as they start rather than gamble with uncertainty.”

Produce ventilators and oxygen

The manufacturers, including a dozen in the United States, say there is no easy way to ramp up production quickly. But it is possible other manufacturers, including aerospace and automobile companies, could be enlisted to do so.

Canadian nurses are disseminating a 2006 paper describing how one ventilator can be modified to treat four patients simultaneously. Inventors have proposed combining C-PAP machines, which many apnea sufferers own, and oxygen tanks to improvise a ventilator.

One of the lessons of China, he noted, was that many Covid-19 patients who would normally have been intubated and on ventilators managed to survive with oxygen alone.

Retrofit hospitals

In Wuhan, the Chinese government famously built two new hospitals in two weeks. All other hospitals were divided: 48 were designated to handle 10,000 serious or critical coronavirus patients, while others were restricted to handling emergencies like heart attacks and births.

Wherever that was impractical, hospitals were divided into “clean” and “dirty” zones, and the medical teams did not cross over. Walls to isolate whole wards were built

Decide when to close schools

Recruit volunteers

China’s effort succeeded, experts said, in part because of hundreds of thousands of volunteers. The government declared a “people’s war” and rolled out a “Fight On, Wuhan! Fight On, China!” campaign.

Many people idled by the lockdowns stepped up to act as fever checkers, contact tracers, hospital construction workers, food deliverers, even babysitters for the children of first responders, or as crematory workers.

“In my experience, success is dependent on how much the public is informed and participates,” Admiral Ziemer said. “This truly is an ‘all hands on deck’ situation.”

Prioritize the treatments

Clinicians in China, Italy and France have thrown virtually everything they had in hospital pharmacies into the fight, and at least two possibilities have emerged that might save patients: the anti-malaria drugs chloroquine and hydroxychloroquine, and the antiviral remdesivir, which has no licensed use.

An alternative is to harvest protective antibodies from the blood of people who have survived the illness,

The purified blood serum — called immunoglobulin — could possibly be used in small amounts to protect emergency medical workers, too.

“Unfortunately, the first wave won’t benefit from this,” Dr. Hotez said. “We need to wait until we have enough survivors.”Find a vaccine

testing those candidate vaccines for safety and effectiveness takes time.

The roadblock, vaccine experts explained, is not bureaucratic. It is that the human immune system takes weeks to produce antibodies, and some dangerous side effects can take weeks to appear.

After extensive animal testing, vaccines are normally given to about 50 healthy human volunteers to see if they cause any unexpected side effects and to measure what dose produces enough antibodies to be considered protective.

If that goes well, the trial enrolls hundreds or thousands of volunteers in an area where the virus is circulating. Half get the vaccine, the rest do not — and the investigators wait. If the vaccinated half do not get the disease, the green light for production is finally given.

In the past, some experimental vaccines have produced serious side effects, like Guillain-Barre syndrome, which can paralyze and kill. A greater danger, experts said, is that some experimental vaccines, paradoxically, cause “immune enhancement,” meaning they make it more likely, not less, that recipients will get a disease. That would be a disaster.

One candidate coronavirus vaccine Dr. Hotez invented 10 years ago in the wake of SARS, he said, had to be abandoned when it appeared to make mice more likely to die from pneumonia when they were experimentally infected with the virus.

Reach out to other nations

The chemicals were traced back to an out-of-use chemical manufacturing facility, last held by the Olin Chemical Corporation in 1980, though now in the hands of the Environmental Protection Agency, which has a proposed a $48 million clean-up plan.

"Wilmington’s public drinking water is no longer contaminated with NDMA or TCE and currently poses no known risk to public health,"

years of painstakingly worded treaties, high-level visits and cutting-edge disease surveillance—all meant to encourage good-faith cooperation—have only bitten around the edges of the problem.

“It can’t demand entry into a country because they think something bad is happening.”

Nearly 200 countries were counting on an agency whose budget—roughly $2.4 billion in 2020—is less than a sixth of the Maryland Department of Health’s. Its donors, largely Western governments, earmark most of that money for causes other than pandemic preparedness.

In 2018 and 2019, about 8% of the WHO’s budget went to activities related to pandemic preparedness

It took those experts more than four months to agree that widespread mask-wearing helps, and that people who are talking, shouting or singing can expel the virus through tiny particles that linger in the air. In that time, about half a million people died.

To write its recommendations, the WHO solicits outside experts, which can be a slow process.

the agency’s bureaucratic structure, diplomatic protocol and funding were no match for a pandemic as widespread and fast-moving as Covid-19.

As months rolled on, it became clear that governments were reluctant to allow the U.N. to scold, shame or investigate them.

In particular, The Wall Street Journal found:

* China appears to have violated international law requiring governments to swiftly inform the WHO and keep it in the loop about an alarming infectious-disease cluster

—there are no clear consequences for violations

* The WHO lost a critical week waiting for an advisory panel to recommend a global public-health emergency, because some of its members were overly hopeful that the new disease wasn’t easily transmissible from one person to another.

* The institution overestimated how prepared some wealthy countries were, while focusing on developing countries, where much of its ordinary assistance is directed

Public-health leaders say the WHO plays a critical role in global health, leading responses to epidemics and setting health policies and standards for the world. It coordinates a multinational effort every year to pick the exact strains that go into the seasonal flu vaccine, and has provided public guidance and advice on Covid-19 when many governments were silent.

The world’s public-health agency was born weak, created in 1948 over U.S. and U.K. reluctance. For decades, it was legally barred from responding to diseases that it learned about from the news. Countries were required to report outbreaks of only four diseases to the WHO: yellow fever, plague, cholera and smallpox, which was eradicated in 1980.

SARS convinced governments to retool the WHO. The next year, delegates arrived in the Geneva palace where the League of Nations once met to resolve a centuries-old paradox: Countries don’t report outbreaks, because they fear—correctly—their neighbors will respond by blocking travel and trade.

Nearly three times that amount was budgeted for eradicating polio, a top priority for the WHO’s two largest contributors: the U.S. and the Bill & Melinda Gates Foundation.

“Everybody pushed back. No sovereign country wants to have this.”

China wanted an exemption from immediately reporting SARS outbreaks. The U.S. argued it couldn’t compel its 50 states to cooperate with the treaty. Iran blocked American proposals to make the WHO focus on bioterrorism. Cuba had an hourslong list of objections.

Around 3:15 a.m. on the last day, exhausted delegates ran out of time. The treaty they approved, called the International Health Regulations, imagined that each country would quickly and honestly report, then contain, any alarming outbreaks

In return, the treaty discouraged restrictions on travel and trade. There would be no consequences for reporting an outbreak—yet no way to punish a country for hiding one.

The treaty’s key chokepoint: Before declaring a “public health emergency of international concern,” or PHEIC, the WHO’s director-general would consult a multinational emergency committee and give the country in question a chance to argue against such a declaration.

Delegates agreed this could give some future virus a head start but decided it was more important to discourage the WHO from making any unilateral announcements that could hurt their economies.

Over the next few years, emergency committees struggled over how to determine whether an outbreak was a PHEIC. It took months to declare emergencies for two deadly Ebola epidemics

On Jan. 3, representatives of China’s National Health Commission arrived at the WHO office in Beijing. The NHC acknowledged a cluster of pneumonia cases, but didn’t confirm that the new pathogen was a coronavirus, a fact Chinese officials already knew.

That same day, the NHC issued an internal notice ordering laboratories to hand over or destroy testing samples and forbade anyone from publishing unauthorized research on the virus.

China’s failure to notify the WHO of the cluster of illnesses is a violation of the International Health Regulations

China also flouted the IHR by not disclosing all key information it had to the WHO

The WHO said it’s up to member states to decide whether a country has complied with international health law, and that the coming review will address those issues.

While Chinese scientists had sequenced the genome and posted it publicly, the government was less forthcoming about how patients might be catching the virus.

WHO scientists pored over data they did get, and consulted with experts from national health agencies, including the CDC, which has 33 staff detailed to the WHO.

Then a 61-year-old woman was hospitalized in Thailand on Jan. 13.

The next day, Dr. van Kerkhove told reporters: “It’s certainly possible that there is limited human-to-human transmission.” MERS and SARS, both coronaviruses, were transmissible among people in close quarters. Epidemiological investigations were under way, she said.

On Jan. 22, a committee of 15 scientists haggled for hours over Chinese data and a handful of cases in other countries. Clearly, the virus was spreading between people in China, though there was no evidence of that in other countries. The question now: Was it mainly spreading from very sick people in hospitals and homes—or more widely?

The committee met over two days, but was split. They mostly agreed on one point: The information from China “was a little too imprecise to very clearly state that it was time” to recommend an emergency declaration,

On Jan. 28, Dr. Tedros and the WHO team arrived for their meeting with Mr. Xi

Leaning across three wooden coffee tables, Dr. Tedros pressed for cooperation. In the absence of information, countries might react out of fear and restrict travel to China, he repeated several times throughout the trip. Mr. Xi agreed to allow a WHO-led international team of experts to visit. It took until mid-February to make arrangements and get the team there.

China also agreed to provide more data, and Dr. Tedros departed, leaving Dr. Briand behind with a list of mysteries to solve. How contagious was the virus? How much were children or pregnant women at risk? How were cases linked? This was vital information needed to assess the global risk, Dr. Briand said

Back in Geneva, Dr. Tedros reconvened the emergency committee. By now it was clear there was human-to-human transmission in other countries. When it met on Jan. 30, the committee got the information the WHO had been seeking. This time the committee recommended and Dr. Tedros declared a global public-health emergency.

President Trump and New York Gov. Andrew Cuomo both assured constituents their health systems would perform well. The U.K.’s chief medical officer described the WHO’s advice as largely directed at poor and middle-income countries. As for keeping borders open, by then many governments had already closed them to visitors from China.

The WHO shifted focus to the developing world, where it believed Covid-19 would exact the heaviest toll. To its surprise, cases shot up just across the border, in northern Italy.

the WHO’s health emergencies unit should report to the director-general and not member states, and its budget should be protected so it doesn’t have to compete with other programs for money.

If there were one thing the WHO might have done differently, it would be to offer wealthier countries the type of assistance with public-health interventions that the WHO provides the developing world

the WHO’s warning system of declaring a global public-health emergency needs to change. Some want to see a warning system more like a traffic light—with color-coded alarms for outbreaks, based on how worried the public should be

Emergency committees need clearer criteria for declaring a global public-health emergency and should publicly explain their thinking

The WHO should have more powers to intervene in countries to head off a health crisis

Lessons learned

Implementing many of those ideas would require herding diplomats back for another monthslong slog of treaty revisions. If and when such talks begin, new governments will likely be in place, and political priorities will float elsewher

“Unfortunately, I’m very cynical about this,” he said. “We are living through cycles of panic and neglect. We’ve been through all of this before.”

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.

“The evolution of cooperation required out-group hatred. Which is really sad,” said Nicholas Christakis, a Yale sociologist and author of “Blueprint: The Evolutionary Origins of a Good Society.”

No researcher argues that human nature is the sole, or even the primary, cause of today’s polarization. But savvy political operatives can exploit, leverage and encourage it. And those operatives are learning from their triumphs in divide-and-conquer politics.

“We wouldn’t have civilizations if we didn’t create groups. We are designed to form groups, and the only way to define a group is there has to be someone who’s not in it,” Mason said.

Experiments have revealed that “children as young as two will prefer other children randomly assigned to the same T-shirt color,”

enmity and derision can arise independently of any rational reason for it.

Mason and Christakis point to a famous-among-academics experiment from 1954. Social psychologist Muzafer Sherif took 22 Boy Scouts and separated them into two groups camping at Robbers Cave State Park in Oklahoma. Only after a week did they learn that there was another group at the far end of the campground.

What they did next fascinated the research team. Each group developed irrational contempt for the other. The boys in the other group were seen not just as rivals, but as fundamentally flawed human beings. Only when the two groups were asked to work together to solve a common problem did they warm up to one another.

And because many more districts are now deeply red or blue, rather than a mix of constituencies, House members have fewer reasons to adopt moderate positions.

“Homo sapiens is a social species; group affiliation is essential to our sense of self. Individuals instinctively think of themselves as representing broad socioeconomic and cultural categories rather than as distinctive packages of traits,”

Here’s where psychology gives way to political science. The American political system may cultivate “out-group” hatred, as academics put it. One of the scarce resources in this country is political power at the highest levels of government. The country has no parliamentary system in which multiple parties form governing coalitions.

Add to this fact the redistricting that ensures there are fewer truly competitive congressional races. The two parties have inexorably moved further apart ideologically, and leaders are more likely to be punished — “primaried” — if they reach across the aisle.

Shanto Iyengar, a Stanford political psychologist who coined the term “affective polarization,” explained in a 2018 paper why people typically identify with a group.

Human nature hasn’t changed, but technology has. The fragmentation of the media has made it easier to gather information in an echo chamber, Iyengar said. He calls this “sorting.” Not only do people cluster around specific beliefs or ideas, they physically cluster, moving to neighborhoods where residents are likely to look like them and think like them.

Partisan clustering has increased even within households. In 1965, Iyengar said, only about 60 percent of married couples had the same party registration. Today, the figure is greater than 85 percent

Asked in the summer of 2022 if they agree or disagree that members of the other party “lack the traits to be considered fully human — they behave like animals,” about 30 percent in both parties agreed, Mason’s research shows.

Research shows that affective polarization is intensifying across the political spectrum. Recent survey data revealed that more than half of Republicans and Democrats view the other party as “a threat,” and nearly as many agree with the description of the other party as “evil,” Mason said.

A recent paper published in the journal Science argued that the three core ingredients of political sectarianism are “othering, aversion, and moralization.” Trump has mastered that recipe. He activates emotional responses in his followers by telling them that they are threatened.

. On Thursday, the Financial Times reported that productivity “is set to fall in the U.S. for the first time in more than three decades.”

States with more workers in routine-intensive tasks, like administrative duties, actually saw smaller declines in labor market fluidity.

Young people are more likely to switch jobs and move around.

If young people are tumbleweeds, adults are like trees: They grow roots, and they tend to stay put. So, as a country ages, it should become less dynamic.

The fraction of workers required to hold a government-issued license has sextupled since the 1950s, from less than 5 percent to almost 30 percent today. It’s harder to switch into an industry, especially one in a new state, that’s larded with licensing.

Geographic mobility was very high in the U.S. in the 19th century. This was initially due to the settling of the western frontier. But even after the “closing" of the frontier in 1890, mobility remained high for decades, according to the economists Jason Long and Joseph Ferrie.

In every major city, there are many stores, health-care facilities, and insurance offices. By and large, less educated workers might be less willing to move between states because they assume every area has generally the same type of work.

somebody moving from a small farm to Washington, D.C., would have to visit the capital to understand its culture, job mix, pretty falls, and humid summers. But today’s potential movers are more informed and therefore more strategic:

Between 1880 and 1980, people generally moved from poor states to rich states, seeking the best jobs. “The creation of a single automobile plant—Ford’s River Rouge complex, completed in 1928—boosted Michigan’s population by creating more than 100,000 workers,” as Tim Noah reported. Migration promoted geographical equality.

Smaller counties used to lead the nation in the growth in new businesses even through the early 1990s. But this decade, small counties have lost businesses, while venture capital, the lifeblood of high-growth startups, clustered in a handful of metros.

Land-use policies prevent more middle-class families from living in productive areas, because housing becomes too expensive. Meanwhile, the rich can afford to cluster in a handful of metros where entrepreneurship is a norm, while business dynamism falls in the rest of the country.

It has recruited talent from Microsoft, Google, Amazon.com and Myspace and is using a powerful, multiplatform approach to predicting consumer behavior that could raise its standing among investors and clients.

Acxiom has its own classification system, PersonicX, which assigns consumers to one of 70 detailed socioeconomic clusters and markets to them accordingly. In this situation, it pegs Mr. Hughes as a “savvy single” — meaning he’s in a cluster of mobile, upper-middle-class people who do their banking online, attend pro sports events, are sensitive to prices — and respond to free-shipping offers.

Analysts say companies design these sophisticated ecosystems to prompt consumers to volunteer enough personal data — like their names, e-mail addresses and mobile numbers — so that marketers can offer them customized appeals any time, anywhere.

Acxiom maintains its own database on about 190 million individuals and 126 million households in the United States. Separately, it manages customer databases for or works with 47 of the Fortune 100 companies. It also worked with the government after the September 2001 terrorist attacks

This year, Advertising Age ranked Epsilon, another database marketing firm, as the biggest advertising agency in the United States, with Acxiom second.

race coding may be incorrect. And even if a data broker has correct information, a person may not want to be marketed to based on race.

if marketing algorithms judge certain people as not worthy of receiving promotions for higher education or health services, they could have a serious impact.

“Over time, that can really turn into a mountain of pathways not offered, not seen and not known about,”

Unlike consumer reporting agencies that sell sensitive financial information about people for credit or employment purposes, database marketers aren’t required by law to show consumers their own reports and allow them to correct errors.

ACXIOM’S Consumer Data Products Catalog offers hundreds of details — called “elements” — that corporate clients can buy about individuals or households, to augment their own marketing databases.

the catalog also offers delicate information that has set off alarm bells among some privacy advocates, who worry about the potential for misuse by third parties that could take aim at vulnerable groups. Such information includes consumers’ interests — derived, the catalog says, “from actual purchases and self-reported surveys” — like “Christian families,” “Dieting/Weight Loss,” “Gaming-Casino,” “Money Seekers” and “Smoking/Tobacco.” Acxiom also sells data about an individual’s race, ethnicity and country of origin. “Our Race model,” the catalog says, “provides information on the major racial category: Caucasians, Hispanics, African-Americans, or Asians.” Competing companies sell similar data.

“At the same time, this is ethnic profiling,” he says. “The people on this list, they are being sold based on their ethnic stereotypes. There is a very strong citizen’s right to have a veto over the commodification of their profile.”

it’s as if the ore of our data-driven lives were being mined, refined and sold to the highest bidder, usually without our knowledge — by companies that most people rarely even know exist.

In its system, a store clerk need only “capture the shopper’s name from a check or third-party credit card at the point of sale and then ask for the shopper’s ZIP code or telephone number.” With that data Acxiom can identify shoppers within a 10 percent margin of error, it says, enabling stores to reward their best customers with special offers. Other companies offer similar services. “This is a direct way of circumventing people’s concerns about privacy,” says Mr. Chester of the Center for Digital Democracy.

“At the beginning, when we had relatively few cases, when it came to finding them and isolating them, we did quite well in Germany,”

outh Korea, which has been praised as a model for responding to the outbreak, has a death rate of 1.2 percent.

Germany’s high diagnostic capacity had “secured us an extreme lead . . . in the detection of the epidemic.”

But he cautioned against complacency, and said the death rate would increase: “We are no exception.”

As community spread became prevalent, infection chains in some cases became impossible to trace. Some doctors said they had no choice but to send people seeking tests back home to call jammed coronavirus hotlines because they lacked protective equipment to conduct them safely.

With cases growing exponentially, health authorities have moved on to ordering social distancing and banning mass events. Federal states have restricted gatherings outside the home to a maximum of two people.

Initially, at least, the country’s health authorities tracked infection clusters meticulously. When an individual tested positive, they used contact tracing to find other people with whom they had been in touch and then tested and quarantined them, which broke infection chains

“In South Korea, there’s been a longer duration of infections, so we can’t really compare,” he said. “We have lots of newly positive cases. They didn’t have the chance to die yet.”

Busse said Germany is more comparable to Norway. The Scandinavian country is at a similar point in its outbreak, it’s also worked to test and contain cases, and it also has a death rate of 0.4 percent.

a period of exponential growth might be slowing, but cautioned that it was too early to say, given fluctuations in data. He said the picture should be clearer by Wednesday.

many initial cases had clear links to overseas travel.

the German outbreak started with large numbers of young people — “people who came back from holidays.”

Lauterbach said he expected Germany’s death rate to rise as more vulnerable segments of society are infected.

Because testing is not universal, and many people with the coronavirus might never be diagnosed, a true death rate is impossible to ascertain. But experts say the rate is likely to be significantly higher in countries such as Spain and Italy, where care is collapsing.

With more intensive care beds and ventilators than most other European nations and early measures to prevent the spread of the virus, Lauterbach said, he didn’t see Germany turning into Italy or Spain. Still, he has been advocating for wider restrictions.

“I think with all things considered, Germany will do reasonably well in this first round of a long fight,”

Early testing, detection, preventionNews that China had reported its first case of the coronavirus was enough reason for South Korean leaders and medical staff to brace themselves for the worst.

Acting fast was the most important decision South Korea made,"

Sites like Corona Map generate real-time updates about where current patients are located and inform proactive Koreans focused on protecting themselves.

By early February, the first test had been approved

"Among Shincheonji members, there were many 20- and 30-year-olds who were infected. Many of them may have never even known they were carrying the virus and recovered easily while silently infecting those around them," Hwang said. "Early testing is why Korea hasn't reached its breaking point yet.

Under South Korea's single-payer health care system, getting tested costs $134. But with a doctor's referral or for those who've made contact with an infected person, testing is free. Even undocumented foreigners are urged to get tested and won't face threats due to their status.

South Korean leaders have amped up efficiency for overwhelmed hospitals by digitally monitoring lower-risk patients under quarantine, as well as keeping close tabs on visiting travelers who are required to enter their symptoms into an app.

Active collaboration among central and regional government officials and medical staff took place before cases began piling up, enabling South Korea's current testing capacity of 20,000 people a day at 633 sites, including drive-thru centers and even phone booths.

That people are willing to forgo privacy rights and allow the publication of sensitive information underlines the willingness to pay the digital cost of state surveillance in the name of public safety,

78.5 percent of respondents agreed that they would sacrifice the protection of their privacy rights to help prevent a national epidemic.

97.6 percent responded that they at least sometimes wear a mask when they are outside, 63.6 percent of whom said they always wear one.

"Wearing masks or self-monitoring alone isn't foolproof to people in Korea, but taking part in these practices as a group is believed to have an impact,"

"This says that your individual choices may not have immediate benefit to you as an individual but will benefit the herd — that it doesn't work unless everybody is in the game."

Despite its apparently swift recovery from the coronavirus, South Korea may only be entering the beginning stages of what experts suspect may be a long ride ahead

bout 80 percent of COVID-19 cases can be categorized as mass infections. A call center in southwestern Seoul was at the center of a local outbreak this month that generated more than 156 infections. About 90 cases were traced to a Zumba class.

local infection clusters are emerging every day in churches, hospitals and other mundane spaces."

outh Korea has already started new testing on all arrivals from Europe, according to local news reports, preparing for a "second wave" of imported clusters. Even those who test negative are required to self-quarantine for 14 days