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The finding led to controversy over whether the skeleton was really a warrior, with scholars and pundits protesting what they called revisionist history.

But in 2016 archaeologists conducted a fresh examination of the grave. The two central figures, it turned out, were a male and a female; they were surrounded by other male-female pairs. Thomas Emerson, who conducted the study with colleagues from the Illinois State Archaeological Survey at the University of Illinois, alongside scientists from other institutions, said the Cahokia discovery demonstrated the existence of male and female nobility. “We don’t have a system in which males are these dominant figures and females are playing bit parts,” as he put it.

There was inequality, but it wasn’t absolute, and there were a lot of shifts over time. When it comes to female power, and gender roles, the past was as ambiguous as the present.

“We do know how to dial down the transmission of the virus by a lot with our behavior,” said Carl T. Bergstrom, an evolutionary biologist at the University of Washington in Seattle. “We’ve got a lot of agency there.”

The vaccine alone will not be enough to get ahead of the virus: It will take years to inoculate enough people to limit its evolution. In the meantime, social distancing, mask-wearing and hand-washing — coupled with aggressive testing, tracking and tracing — might buy some time and avert devastating spikes in hospitalizations and deaths along the way. These strategies could still turn the tide against the virus, experts said.

“It’s a race against time,” said Marion Koopmans, a Dutch virologist and a member of a World Health Organization working group on coronavirus adaptations.

experts had warned from the start that it would only be a matter of time before the virus became an even more formidable adversary.

The spread of the variant lashing Britain has left some countries vulnerable at a time when they seemed on the brink of scientific salvation.A case in point: Israel. The country, which had launched a remarkably successful vaccine rollout, tightened its lockdown on Friday after having discovered cases of the variant. About 8,000 new infections have been detected daily in recent days, and the rate of spread in ultra-Orthodox communities has increased drastically.

Yet in the course of the pandemic, governments have often proven reluctant or unable to galvanize support for those basic defenses. Many countries have all but given up on tracking and tracing. Mask-wearing remains politically charged in the United States, despite clear evidence of its efficacy.

“Every situation we have studied in depth, where a virus has jumped into a new species, it has become more contagious over time,” said Andrew Read, an evolutionary microbiologist at Penn State University. “It evolves because of natural selection to get better, and that’s what’s happening here.”

Experts say that countries should focus instead on ramping up vaccinations, particularly among essential workers who face a high risk with few resources to protect themselves. The longer the virus spreads among the unvaccinated, the more mutations it might collect that can undercut the vaccines’ effectiveness.

The variants that have emerged in South Africa and Brazil are a particular threat to immunization efforts, because both contain a mutation associated with a drop in the efficacy of vaccines. In one experiment, designed to identify the worst-case scenario, Dr. Bloom’s team analyzed 4,000 mutations, looking for those that would render vaccines useless. The mutation present in the variants from both Brazil and South Africa proved to have the biggest impact.

Still, every sample of serum in the study neutralized the virus, regardless of its mutations, Dr. Bloom said, adding that it would take a few more years before the vaccines need to be tweaked.“There should be plenty of time where we can be prospective, identify these mutations, and probably update the vaccines in time.”

Dr. Rambaut and colleagues released a paper on the variant discovered in Britain on Dec. 19 — the same day that British officials announced new measures. The variant had apparently been circulating undetected as early as September. Dr. Rambaut has since credited the South Africa team with the tip that led to the discovery of the variant surging in Britain.

Public health officials have formally recommended that type of swift genetic surveillance and information-sharing as one of the keys to staying on top of the ever-changing virus. But they have been calling for such routine surveillance for years, with mixed results.

Britain has one of the most aggressive surveillance regimens, analyzing up to 10 percent of samples that test positive for the virus. But few countries have such robust systems in place. The United States sequences less than 1 percent of its positive samples. And others cannot hope to afford the equipment or build such networks in time for this pandemic.

so that when they cough, sneeze, talk or scream, they likely expel more virus into the air. More virus in the air likely means you infect more people around you.

Less is known about the South African strain, but at least one of its mutations appears to alter the spike protein. So stricter steps, such as lockdowns and border controls, may be necessary to curtail spread, say experts who live in current hot spots.

In lieu of government action, it may be best to avoid other people as much as possible

"One of the big problems is people thinking as individuals and not as a global population,

The actions of one person have the potential for enormous impact on people they've never met.

Yes and no, experts say. If you've been following the oft-repeated safety protocols to the letter, you might not have much adjusting to do.

Despite pandemic fatigue, this is not the time for slacking, the prevention specialists say.

When it's your turn, get your vaccine as soon as you can.

"It's important to really follow through and be as much of a stickler as you can for the recommendations,"

There is an upside. If we are actually able to adhere to strict protocols, we'd be rewarded with more than personal protection, Squires says. A virus mutates when its RNA is incorrectly copied in a host.

So who’s right: Alston or Kim?

They are in a more important sense interpretive, about whether progress has been fast or slow, and whether today’s global poverty counts are laudable or tragic.

This is a realm of yes-ands and no-buts, not direct refutations.

“By being able to rely so heavily on the World Bank's flagship figure, they can say, ‘Look, progress has been consistent. We’ve been doing great,’” Alston told me. “The implication of that is that the triumph of neoliberalism has brought with it very significant benefits for poor people. In reality, that’s just not the case.”

That poverty threshold represents “a staggeringly low standard of living, well below any reasonable conception of a life with dignity,” Alston argues—it is a catastrophic-destitution measure, not a poverty measure. He emphasizes the lack of progress made at the $3.20-a-day and $5.50-a-day poverty lines, too. Half the world lives on less than the latter figure.

Alston takes issue with the fact that the World Bank’s extreme-poverty line is an absolute measure, not a relative one: It sets a line and sees how many people cross it, country by country, rather than pegging the poverty threshold to median income, country by country

But “relative poverty is what really counts these days,” Alston told me, as it captures social exclusion, and the way that living on a few dollars a day is more challenging in middle-income countries like India and Kenya than in low-income countries like Afghanistan and Chad.

Alston’s most controversial, and most important, argument is that the focus on progress measured against the $1.90-a-day line—the prevalence of “everything’s getting better” arguments, made by Davos types like Bill Gates and Steven Pinker—has hampered progress toward true poverty eradication, and toward civil rights, social inclusion, and a basic standard of living for all.

Extreme poverty has declined rapidly, but the extreme-poverty line is very low: A person living below it spends no more than $1.90 a day, enough in many poor countries to cover some starch, a few fruits and vegetables, some cooking oil, a bit of protein, and that’s about it—with nothing left over for utilities, education, health care, transportation, or investment in wealth-generating assets, such as a cow or a motorbike

What if the headline story were that half the world still qualifies as desperately poor, and poverty head counts remain stubbornly high in dozens of countries? What if the story were not that we are succeeding, but that we are failing?

it would hold the world accountable for the fact that poverty is, always and everywhere, a choice.

Alston’s view, and a necessary one, is that the world cannot wait for economic expansion to lift people above the poverty line. It cannot count on trade compacts and infrastructure projects and the ticking of GDP growth rates from 2.3 to 3.2 percent to do it. It needs direct interventions by governments, as fast as possible, to eliminate inequality and build safety nets, even in the poorest places.

Scientists are also using different strains of another virus, adenovirus, as a vector or a missile to deliver genes that code for these same spike proteins and that also provoke an immune response. The vector has been engineered in the lab to be replication-defective; that is, the vector is able to deliver the spike gene into humans but once it’s done its job, the vector cannot replicate any further. At least three groups are testing these vectors.

The science is paying off. Novavax, a Maryland-based company working on this type of vaccine, recently reported the results of its Phase 1 trial. The levels of antibodies generated were stunning, about four times higher than those in individuals who are recovering from a COVID-19 infection.

Nine vaccine candidates have now entered Phase 3 human trials, the final step before regulatory approval. The fact that entirely different approaches to vaccine development are all yielding promising early results is highly encouraging.

Equally important is the unprecedented global collaboration among scientists around the world, as well as the high degree of cooperation between scientists and clinicians, biopharmaceutical companies, government, philanthropic funders, and regulators. They are all working together toward the common goal of developing as quickly as possible a safe and effective vaccine against COVID-19.

My optimism doesn’t stop with these early results, although they are key. I’m also encouraged because at least five very different approaches (I’ve walked through only three above) are being explored to make a vaccine. As we say in Canada, if you want to win, you have to take many shots on goal.

the encouraging news is that all of the vaccine candidates that have entered trials in humans so far are safe and have elicited high levels of antibodies against COVID-19. Some have also been shown to activate the cellular arm of our immune system, another crucial component of our defenses against foreign pathogens.

the mandate that the approval process be above any political considerations and solely based on data from the clinical trials. Anything else risks losing the public’s confidence in a vaccine or, in a worst-case scenario, might result in a vaccine that is less effective than those that might be approved later, or the widespread administration of a vaccine that turns out to have serious adverse side effects. That would be a public-health tragedy.

The world will need billions of doses and many billions of dollars to produce and disseminate the vaccine. My main concern in this whole process is that governments will not spend enough on manufacturing the vaccine to administer it to every adult on the planet

Ensuring equitable access to a vaccine is imperative, and not just a generous gesture by wealthy nations. It’s also in their best interests. If the virus is anywhere, it’s everywhere.

The United States, the wealthiest nation in the world and historically the first among nations in its generosity and leadership, has yet to contribute to the various multilateral initiatives established to purchase vaccines for the developing world. To date, 75 industrialized nations have agreed to finance vaccine purchases for 90 lower-income countries. But the U.S. is not yet one of them.

The cost of manufacturing enough doses to vaccinate every adult on the planet will be in the hundreds of billions of dollars. But compared with the trillions of dollars that governments are now spending to assist individuals who have lost their jobs and to prop up their economies, $100–200 billion is a bargain and an insurance policy that developed countries cannot afford not to buy.

If people everywhere—regardless of their gender, citizenship, ethnicity, skin color, or ability to pay—have equal and timely access to a safe and effective vaccine against COVID-19, the world will come out of this pandemic stronger than it went in

Both companies said they expected to apply within weeks to the F.D.A. for emergency authorization to begin vaccinating the public. In addition to the evidence for effectiveness, the companies must also submit two months of safety data on at least half of the participants.

An additional concern is that both vaccines must be stored and transported at low temperatures — minus 4 degrees Fahrenheit for Moderna, and minus 94 Fahrenheit for Pfizer — which could complicate their distribution, particularly to low-income areas in hot climates.

Researchers say the positive results from Pfizer and Moderna bode well for other vaccines, because all of the candidates being tested aim at the same target — the so-called spike protein on the coronavirus that it uses to invade human cells.

The drugmaker Moderna announced on Monday that its coronavirus vaccine was 94.5 percent effective, joining Pfizer as a front-runner in the global race to contain a raging pandemic that has killed 1.2 million people worldwide.

the Trump administration’s program to accelerate development of vaccines and treatments for Covid-19, said that if any early vaccine candidates received permission for emergency use, immunization could begin sometime in December.

Both companies plan to apply within weeks to the Food and Drug Administration for emergency authorization to begin vaccinating the public

But a vaccine that would be widely available to the public is still months away, while the need for one is becoming increasingly urgent

Covid-19 is killing more than 1,100 Americans a day, and the last million cases occurred in just six days.

This vaccine presents the opportunity of using doctors’ offices, clinics and pharmacies as vaccination sites,” he said, adding that he would not be surprised, should both vaccines become available, if vaccination sites requested Moderna’s.

Moderna said it would have 20 million doses ready by the end of 2020

Moderna is the second company to report preliminary results from a large trial testing a vaccine. But there are still months to go before it will be widely available to the public.

The company announced on Oct. 22 that it had completed enrollment of its 30,000-person study, and that 25,650 participants had already received two shots.

Moderna has received a commitment of $955 million from the U.S. government’s Biomedical Advanced Research and Development Authority for research and development of its vaccine, and the United States has committed up to $1.525 billion to buy 100 million doses.

other experts on the committee said all available evidence indicated the vaccine is safe and effective for nursing home residents and older Americans generally.

As people age, bodily defenses against pathogens weaken, and the response to vaccines also falters.

“We have a clear and present danger of Covid, and we have social isolation,” Dr. Farrell said. “We know that that’s an independent risk factor for mortality, even stronger than individual chronic diseases.”

“For many, the immune response can sometimes be diminished or dampened or delayed,”

“When you’ve come to near-death experiences twice, volunteering for a vaccine trial — it wasn’t a great sense of worry or apprehension for me,”

Some people worry, incorrectly, that the vaccine may somehow give them Covid-19. In fact, the vaccine carries instructions to make only a single protein from the virus.

Every time she gets a flu shot, Ms. Ebrani said, she feels unwell for three days, with headaches and a deep exhaustion. But she gets that vaccine anyway, because she feels healthy the rest of the year and because her doctor has told her she should.

Pfizer’s vaccine also contains nine other ingredients. Four of them are lipids with impossibly complex chemical names: (4-hydroxybutyl)azanediyl)bis(hexane-6,1-diyl)bis (ALC-3015); (2- hexyldecanoate),2-[(polyethylene glycol)-2000]-N,N-ditetradecylacetamide (ALC-0159); 1,2-distearoyl-snglycero-3-phosphocholine (DPSC); and cholesterol.

The vaccine also contains four salts: potassium chloride, monobasic potassium phosphate, basic sodium phosphate dihydrate and sodium chloride.

The virus kept spreading. By early April officials were logging an average of around 15,000 new cases a day. A spike in deaths followed. In Hong Kong alone, nearly 9,000 people have died since mid-February. To be clear, that’s a fraction of the infections and deaths that countries with fewer restrictions tallied during the worst of their own COVID surges. What’s so worrying in China is the trend—and the potential for cases, and deaths, to keep going up and up.

“It could be that we are seeing the resurgences in China, including the emergence and spread of new sub-strains, primarily because the population there never achieved high levels of natural immunity,”

You can’t build up natural antibodies across a large population if no one is ever exposed to the virus. That’s the downside of total lockdowns

The antibodies in recovered COVID patients lend strong immunity that, combined with vaccinations across large groups of people, can help blunt the impact of a new lineage. Michael for one said he believes natural immunity is stronger and longer-lasting than immunity resulting from even the best messenger-RNA vaccines.

“They also used inactivated viruses in their Sinovac and Sinopharm vaccines, which I had expected to be more robust than mRNA vaccines in terms of producing a more diversified immune response that could counter new mutants, et cetera,” Michael said, “but apparently it would seem that this response has waned, making people susceptible again to new strains.”

even if they are reasonably effective, the vaccines are unevenly distributed in China. The government’s attacks on foreign jabs has had the effect of encouraging anti-vax attitudes, especially among older Chinese who might be less media-savvy than their younger counterparts.

just half of the most vulnerable age group–over-80s–is fully vaccinated. That plus the lack of natural immunity has left millions of Chinese exposed to aggressive lineages that can punch right through lockdowns.

. “Any place can be a source of new variants, but those places with low levels of population immunity and unchecked spread of the virus are the most likely,”

Each individual infection, unchecked by antibodies, tends to produce two mutations every two weeks,

“What if we had 50 million people pull slot-machine levers simultaneously at the same time?” Moshiri asked. “We would expect at least one person would hit the jackpot pretty quickly. Now, replace the slot machine with ‘clinically-meaningful SARS-CoV-2 mutation,’ and that’s the situation we're in.”

All that is to say, the longer COVID rates remain high in the world’s most-populous country, the greater the chance that the next major lineage will be Chinese.

New lineages are inevitable from one country or another, of course. The trick is to slow the rate of mutation so that fresh vaccine formulations, therapies and public-health policies can at least keep pace with major changes in the virus.

billion people with uneven rates of vaccination by potentially low-quality jabs and very little natural immunity to back up the shots.

Uma Valeti, the company’s CEO, said Upside has proven it can safely produce a delicious product. The company said that it has helped pioneer an industry and that it is making progress on growing larger quantities of meat, while bringing down its cost.

According to former employees, Upside has struggled to produce large quantities of meat. They said the company often scrambled to make enough for lab analysis and tastings. Upside for years worked to grow whole cuts of meat, which proved difficult in its bioreactors. It battled contamination in its labs. Traces of rodent DNA once tainted a chicken cell line, according to former employees, and confirmed by company executives.  

Today, the company is growing its marquee filet not in large bioreactors at its pilot plant but in two-liter plastic bottles akin to those used to grow cells for decades by pharmaceutical companies. 

Industry champions said they are confident that steady scientific progress will help reduce production costs for cultivated meat, while climate change and global population growth will intensify the need for it.

Upside’s pilot plant isn’t yet operating at the 50,000-pound annual capacity the company announced when it opened in 2021, according to company executives, much less its future target of 400,000 pounds. Production can accelerate once Upside receives USDA clearance, company executives said.

“Roller bottles aren’t scalable. Too small, too labor-intensive,”

“It turned out that tissue, or creating this whole-cut texture, was really challenging,” said Amy Chen, Upside’s chief operating officer

Upside also wrestled with problems common to other cultivated-meat makers, including a battle against bacteria, according to former employees.Growing meat requires meticulous sterilization because small quantities of bacteria can quickly overtake a bioreactor, ruining a batch.

The company said contamination can slow production, but doesn’t affect final cultivated products, unlike conventional meat. The company said that autoclaves sometimes require maintenance and that meat grown for consumers won’t be produced in the older building

Some industry officials think companies can surmount contamination problems, but that other hurdles will still abound, including those tied to growing the finicky cells and the high cost of supplies.  

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.

One of the first questions experts asked me was whether my family was vaccinated. Yes, I said: My husband and I are vaccinated and boosted, and our kids are vaccinated but not yet boosted. This is a relevant question because, if you’re exposed to the virus that causes Covid-19, “your immune system kicks into action a lot faster if you’re vaccinated versus not vaccinated,”

First, the swift immune reaction slows the rate of viral reproduction and spread. “This is what the vaccines are there for — to educate your immune system so that it gets a jump on the invaders before they are able to replicate out of control,”

Because the virus doesn’t replicate as quickly in vaccinated people, they may be less likely to test positive for Covid-19 after coronavirus exposure, because their immune system “keeps the viral load below the level of detection,”

It’s possible, then, that my husband and son did catch Covid-19, but their vaccinated immune systems fended off the infection so well that they never had enough viral proteins in their nose or throat to test positive. And their continual negative tests probably meant that they were never that contagious

If my husband and son never tested positive, why did they feel sick? Even if a vaccinated person doesn’t have much virus in their body, they can still have powerful Covid symptoms

That’s because many illness symptoms — fever, malaise, runny nose, fatigue — are actually caused by the immune system’s response to the virus, rather than the virus itself

And as for why I felt fine, Dr. Morrison said that perhaps my immune system fought off the incoming virus so quickly that I didn’t even have a chance to feel sick. “It sounds to me like you were definitely exposed,”

maybe I had high levels of vaccine antibodies or immune cells called T cells that were able to kill the invading virus before it had a chance to alert the parts of my immune system that would incite symptoms.

All this said, nobody really knows what happened to me, my son or my husband. When it comes to understanding how Covid-19 affects the body, “there are so many open questions,”

people can have different experiences for many different reasons. For instance, Dr. Andino said, it’s possible that the virus was replicating in parts of my husband’s or my son’s body that the tests didn’t reach

Research suggests that the coronavirus can replicate in the pancreas, heart, brain, kidneys and other organs, although vaccination may reduce the chance that the virus spreads outside the respiratory system.

Dr. Andino said that he and his colleagues have been conducting studies in which they follow and repeatedly test entire households after one person in the home tests positive for Covid-19. “What we see is exactly what you described — that some people in the household don’t test positive,”

The U.S. logs fewer than 1,000 annual deaths out of millions of documented cases

this is more a nauseating nuisance than a public-health crisis. In most people, norovirus triggers, at most, a few miserable days of GI distress that can include vomiting, diarrhea, and fevers, then resolves on its own; the keys are to stay hydrated and avoid spreading it to anyone vulnerabl

norovirus is the most common cause of foodborne illness in the United States.)

direct contact with those substances, or the food or water they contaminate, may not even be necessary: Sometimes people vomit with such force that the virus gets aerosolized; toilets, especially lidless ones, can send out plumes of infection

Still, fighting norovirus isn’t easy, as plenty of parents can attest. The pathogen, which prompts the body to expel infectious material from both ends of the digestive tract, is seriously gross and frustratingly hardy. Even the old COVID standby, a spritz of hand sanitizer, doesn’t work against it—the virus is encased in a tough protein shell that makes it insensitive to alcohol.

At an extreme, a single gram of feces—roughly the heft of a jelly bean—could contain as many as 5.5 billion infectious doses, enough to send the entire population of Eurasia sprinting for the toilet.

norovirus mainly targets the gut, and spreads especially well when people swallow viral particles that have been released in someone else’s vomit or stool.

the virus is far more deadly in parts of the world with limited access to sanitation and potable water.

If the spittle finding holds for humans, then talking, singing, and laughing in close proximity could be risky too.

Once emitted into the environment, norovirus particles can persist on surfaces for days—making frequent hand-washing and surface disinfection key measures to prevent spread

Handshakes and shared meals tend to get dicey during outbreaks, along with frequently touched items such as utensils, door handles, and phones.

One 2012 study pointed to a woven plastic grocery bag as the source of a small outbreak among a group of teenage soccer players; the bag had just been sitting in a bathroom used by one of the girls when she fell sick the night before.

Once a norovirus transmission chain begins, it can be very difficult to break. The virus can spread before symptoms start, and then for more than a week after they resolve

Once the virus arrives, the entire family is almost sure to be infected. Baldridge, who has two young children, told me that her household has weathered at least four bouts of norovirus in the past several years.

Roughly 20 percent of European populations, for instance, are genetically resistant to common norovirus strains. “So you can hope,” Frenck told me. For the rest of us, it comes down to hygiene

Altan-Bonnet recommends diligent hand-washing, plus masking to ward off droplet-borne virus. Sick people should isolate themselves if they can. “And keep your saliva to yourself,” she told me.

The family fastidiously scrubbed their hands with hot water and soap, donned disposable gloves when touching shared surfaces, and took advantage of the virus’s susceptibility to harsh chemicals and heat. When her son threw up on the floor, Cameron sprayed it down with bleach; when he vomited on his quilt, she blasted it twice in the washing machine on the sanitizing setting, then put it through the dryer at a super high temp

After three years of COVID, the world has gotten used to thinking about infections in terms of airways. “We need to recalibrate,” Bhumbra told me, “and remember that other things exist.”

The key issue is not “human-competitive” intelligence (as the open letter puts it); it’s what happens after AI gets to smarter-than-human intelligence. Key thresholds there may not be obvious, we definitely can’t calculate in advance what happens when, and it currently seems imaginable that a research lab would cross critical lines without noticing.

Many researchers steeped in these issues, including myself, expect that the most likely result of building a superhumanly smart AI, under anything remotely like the current circumstances, is that literally everyone on Earth will die. Not as in “maybe possibly some remote chance,” but as in “that is the obvious thing that would happen.”

It’s not that you can’t, in principle, survive creating something much smarter than you; it’s that it would require precision and preparation and new scientific insights, and probably not having AI systems composed of giant inscrutable arrays of fractional numbers.

Absent that caring, we get “the AI does not love you, nor does it hate you, and you are made of atoms it can use for something else.”

Without that precision and preparation, the most likely outcome is AI that does not do what we want, and does not care for us nor for sentient life in general. That kind of caring is something that could in principle be imbued into an AI but we are not ready and do not currently know how.

The likely result of humanity facing down an opposed superhuman intelligence is a total loss

To visualize a hostile superhuman AI, don’t imagine a lifeless book-smart thinker dwelling inside the internet and sending ill-intentioned emails. Visualize an entire alien civilization, thinking at millions of times human speeds, initially confined to computers—in a world of creatures that are, from its perspective, very stupid and very slow. A sufficiently intelligent AI won’t stay confined to computers for long. In today’s world you can email DNA strings to laboratories that will produce proteins on demand, allowing an AI initially confined to the internet to build artificial life forms or bootstrap straight to postbiological molecular manufacturing.

There’s no proposed plan for how we could do any such thing and survive. OpenAI’s openly declared intention is to make some future AI do our AI alignment homework. Just hearing that this is the plan ought to be enough to get any sensible person to panic. The other leading AI lab, DeepMind, has no plan at all.

An aside: None of this danger depends on whether or not AIs are or can be conscious; it’s intrinsic to the notion of powerful cognitive systems that optimize hard and calculate outputs that meet sufficiently complicated outcome criteria.

I didn’t also mention that we have no idea how to determine whether AI systems are aware of themselves—since we have no idea how to decode anything that goes on in the giant inscrutable arrays—and therefore we may at some point inadvertently create digital minds which are truly conscious and ought to have rights and shouldn’t be owned.

I refrained from signing because I think the letter is understating the seriousness of the situation and asking for too little to solve it.

the thing about trying this with superhuman intelligence is that if you get that wrong on the first try, you do not get to learn from your mistakes, because you are dead. Humanity does not learn from the mistake and dust itself off and try again, as in other challenges we’ve overcome in our history, because we are all gone.

If we held anything in the nascent field of Artificial General Intelligence to the lesser standards of engineering rigor that apply to a bridge meant to carry a couple of thousand cars, the entire field would be shut down tomorrow.

We are not on course to be prepared in any reasonable time window. There is no plan. Progress in AI capabilities is running vastly, vastly ahead of progress in AI alignment or even progress in understanding what the hell is going on inside those systems

Many researchers working on these systems think that we’re plunging toward a catastrophe, with more of them daring to say it in private than in public; but they think that they can’t unilaterally stop the forward plunge, that others will go on even if they personally quit their jobs.

This is a stupid state of affairs, and an undignified way for Earth to die, and the rest of humanity ought to step in at this point and help the industry solve its collective action problem.

When the insider conversation is about the grief of seeing your daughter lose her first tooth, and thinking she’s not going to get a chance to grow up, I believe we are past the point of playing political chess about a six-month moratorium.

The moratorium on new large training runs needs to be indefinite and worldwide. There can be no exceptions, including for governments or militaries. If the policy starts with the U.S., then China needs to see that the U.S. is not seeking an advantage but rather trying to prevent a horrifically dangerous technology which can have no true owner and which will kill everyone in the U.S. and in China and on Earth

Here’s what would actually need to be done:

Shut down all the large GPU clusters (the large computer farms where the most powerful AIs are refined). Shut down all the large training runs

Put a ceiling on how much computing power anyone is allowed to use in training an AI system, and move it downward over the coming years to compensate for more efficient training algorithm

Track all GPUs sold. If intelligence says that a country outside the agreement is building a GPU cluster, be less scared of a shooting conflict between nations than of the moratorium being violated; be willing to destroy a rogue datacenter by airstrike.

Frame nothing as a conflict between national interests, have it clear that anyone talking of arms races is a fool

Make it explicit in international diplomacy that preventing AI extinction scenarios is considered a priority above preventing a full nuclear exchange, and that allied nuclear countries are willing to run some risk of nuclear exchange if that’s what it takes to reduce the risk of large AI training runs.

when your policy ask is that large, the only way it goes through is if policymakers realize that if they conduct business as usual, and do what’s politically easy, that means their own kids are going to die too.

Google has been worried about A.I.-powered competitors since OpenAI, a San Francisco start-up that is working with Microsoft, demonstrated a chatbot called ChatGPT in November. About two weeks later, Google created a task force in its search division to start building A.I. products,

Modernizing its search engine has become an obsession at Google, and the planned changes could put new A.I. technology in phones and homes all over the world.

Magi would keep ads in the mix of search results. Search queries that could lead to a financial transaction, such as buying shoes or booking a flight, for example, would still feature ads on their results pages.

Google has been doing A.I. research for years. Its DeepMind lab in London is considered one of the best A.I. research centers in the world, and the company has been a pioneer with A.I. projects, such as self-driving cars and the so-called large language models that are used in the development of chatbots. In recent years, Google has used large language models to improve the quality of its search results, but held off on fully adopting A.I. because it has been prone to generating false and biased statements.

Now the priority is winning control of the industry’s next big thing. Last month, Google released its own chatbot, Bard, but the technology received mixed reviews.

The system would learn what users want to know based on what they’re searching when they begin using it. And it would offer lists of preselected options for objects to buy, information to research and other information. It would also be more conversational — a bit like chatting with a helpful person.

The Samsung threat represented the first potential crack in Google’s seemingly impregnable search business, which was worth $162 billion last year.

Last week, Google invited some employees to test Magi’s features, and it has encouraged them to ask the search engine follow-up questions to judge its ability to hold a conversation. Google is expected to release the tools to the public next month and add more features in the fall, according to the planning document.

The company plans to initially release the features to a maximum of one million people. That number should progressively increase to 30 million by the end of the year. The features will be available exclusively in the United States.

Google has also explored efforts to let people use Google Earth’s mapping technology with help from A.I. and search for music through a conversation with a chatbot

A tool called GIFI would use A.I. to generate images in Google Image results.

Tivoli Tutor, would teach users a new language through open-ended A.I. text conversations.

Yet another product, Searchalong, would let users ask a chatbot questions while surfing the web through Google’s Chrome browser. People might ask the chatbot for activities near an Airbnb rental, for example, and the A.I. would scan the page and the rest of the internet for a response.

“If we are the leading search engine and this is a new attribute, a new feature, a new characteristic of search engines, we want to make sure that we’re in this race as well,”

Dr. Monteiro and his colleagues developed a food classification system called Nova, named after the Portuguese and Latin words for “new.” It has since been adopted by researchers across the world.

Unprocessed or minimally processed foods, like fresh or frozen fruits and vegetables, beans, lentils, meat, poultry, fish, eggs, milk, plain yogurt, rice, pasta, corn meal, flour, coffee, tea and herbs and spices.

Processed culinary ingredients, such as cooking oils, butter, sugar, honey, vinegar and salt.

If you look at the ingredient list and you see things that you wouldn’t use in home cooking, then that’s probably an ultraprocessed food,”

his group includes freshly baked bread, most cheeses and canned vegetables, beans and fish. These foods may contain preservatives that extend shelf life.

Ultraprocessed foods made using industrial methods and ingredients you wouldn’t typically find in grocery stores — like high-fructose corn syrup, hydrogenated oils and concentrated proteins like soy isolate.

They often contain additives like flavorings, colorings or emulsifiers to make them appear more attractive and palatable.

Think sodas and energy drinks, chips, candies, flavored yogurts, margarine, chicken nuggets, hot dogs, sausages, lunch meats, boxed macaroni and cheese, infant formulas and most packaged breads, plant milks, meat substitutes and breakfast cereals.

Processed foods made by combining foods from Category 1 with the ingredients of Category 2 and preserving or modifying them with relatively simple methods like canning, bottling, fermentation and baking

That has led to debate among nutrition experts about whether it’s useful for describing the healthfulness of a food, partly since many UPFs — like whole grain breads, flavored yogurts and infant formulas — can provide valuable nutrients

Most research linking UPFs to poor health is based on observational studies, in which researchers ask people about their diets and then track their health over many years.

Why might UPFs be harmful?

In a large review of studies that was published in 2024, scientists reported that consuming UPFs was associated with 32 health problems, with the most convincing evidence for heart disease-related deaths, Type 2 diabetes and common mental health issues like anxiety and depression.

Such studies are valuable, because they can look at large groups of people — the 2024 review included results from nearly 10 million — over the many years it can take for chronic health conditions to develop

She added that the consistency of the link between UPFs and health issues increased her confidence that there was a real problem with the foods.

But the observational studies also have limitations,

It’s true that there is a correlation between these foods and chronic diseases, she said, but that doesn’t mean that UPFs directly cause poor health.

Dr. O’Connor questioned whether it’s helpful to group such “starkly different” foods — like Twinkies and breakfast cereals — into one category. Certain types of ultraprocessed foods, like sodas and processed meats, are more clearly harmful than others

UPFs like flavored yogurts and whole grain breads, on the other hand, have been associated with a reduced risk of developing Type 2 diabetes.

Clinical trials are needed to test if UPFs directly cause health problems, Dr. O’Connor said. Only one such study, which was small and had some limitations, has been done, s

In that study, published in 2019, 20 adults with a range of body sizes lived in a research hospital at the National Institutes of Health for four weeks. For two weeks, they ate mainly unprocessed or minimally processed foods, and for another two weeks, they ate mainly UPFs. The diets had similar amounts of calories and nutrients, and the participants could eat as much as they wanted at each meal.

During their two weeks on the ultraprocessed diet, participants gained an average of two pounds and consumed about 500 calories more per day than they did on the unprocessed diet

During their time on the unprocessed diet, they lost about two pounds.

That finding might help explain the link between UPFs, obesity and other metabolic conditions

The Nova system notably doesn’t classify foods based on nutrients like fat, fiber, vitamins or minerals. It’s “agnostic to nutrition,”

There are many “strong opinions” about why ultraprocessed foods are unhealthy, Dr. Hall said. “But there’s actually not a lot of rigorous science” on what those mechanisms are

Because UPFs are often cheap, convenient and accessible, they’re probably displacing healthier foods from our diets