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American women’s participation would have been 82 percent if they had access to the other countries’ policies, which include a right to part-time work.

For women to be able to take advantage of these arrangements without judgment, men need to use them freely, too. But that requires viewing men not solely as breadwinners, but as individuals who also have the same choices as women.

“Feminism is all about choices — well, choices for whom?” she asked. “Even feminism is putting pressure on men to live up to the ideal of work devotion. So long as that is the state of play, nothing is changing for men. And if nothing is changing for men, nothing is changing for women.”

The Senate on Wednesday approved the Every Student Succeeds Act, the bill that will reauthorize the nation’s 50-year-old omnibus education law and make the “pretty-much-universally despised” No Child Left Behind obsolete.

The legislation, which has already gotten the Obama administration’s tacit approval, is being touted by observers and policymakers from both the right and left as a product of rare bipartisan compromise.

The most conspicuous manifestation of that bipartisan give-and-take is what’s being highlighted by news outlets and pundits across the country: Schools will still be held accountable for student performance, but states can determine the nuances of how that will take place.

They’ll have to use “college-and-career ready” standards and intervene when those expectations aren’t met, but states will get to design their own standards and intervention protocol.

They’ll still be required to administer annual testing in certain grades, ensure at least 95 percent of students participate, and disaggregate data based on students’ race, income, and disability status, but they can use other factors on top of testing to assess student performance, and the details of how the testing happens and how the scores are interpreted are up to states.

The overthrow of No Child Left Behind, which has been up for reauthorization for years, is certainly cause for excitement. The George W. Bush-era law required schools to administer annual tests in certain grades in an effort to identify and elevate the achievement of underperforming youth.

It was also loathed for its one-size-fits-all approach to education reform, its promotion of teaching-to-the-test, and its harsh system of sanctions. Republicans grew to despise it for how much it allowed the Department of Education to micromanage states and school districts (especially when Obama rose into office).

And given how little power the Every Student Succeeds Act gives to the federal government, it may feel, particularly among those on the right, as if the nation’s schools are about to experience a major makeover—as if the next era of public education will mark a major, much-anticipated divergence from the status quo.

But in reality, schools may not see much on-the-ground change. Forty-two states and the District of Columbia already have waivers from No Child Left Behind’s “most troublesome and restrictive requirements”—flexibility granted several years ago by the Obama administration in exchange for states’ commitment to “setting their own higher, more honest standards for student success.”

This means that most of the country’s students have already been learning under a system that eschewed much of No Child Left Behind’s most obvious and onerous aspects—and looks a lot like the system envisioned in Every Student Succeeds.

States with waivers were essentially allowed to set their own goals for raising achievement, come up with their own strategies for turning around struggling schools, and design their own methods of measuring student progress.

In many ways, what most conservatives seem to be rejoicing about the Every Student Succeeds Act is that it’s replacing Obama’s waiver system. At a Senate Health, Education, Labor, and Pensions Committee hearing in early 2013, Alexander was quoted as saying: “This simple waiver authority has turned into a conditional waiver with the [Education] Secretary having more authority to make decisions that in my view should be made locally by state and local governments.”

Indeed, some of the most controversial elements being overturned or prohibited by the Every Student Succeeds Act were implemented not under No Child Left Behind but through the waiver system. It was through the waivers (and the Race to the Top grant program) that the Obama administration mandated test-score-based teacher evaluations. And it was through the waivers (and the Race to the Top grant program) that the administration all but required participating states to adopt the Common Core. (The Every Student Succeeds Act makes it clear that the federal government can’t mandate teacher evaluations or standards.)

Instead, the women still chasing the having-it-all dream are the group we’re calling the Scale Backers—13 women who dialed down high-powered careers to simultaneously be full-time mothers and workers. And in the process of downsizing, they became, ironically, the most stressed-out of our subjects, attempting to do everything well, but feeling like they excelled at none of it.

While our Opt Out group has left the primary earning to their spouses, and our High Achievers have hired the help they need to run their lives, the Scale Backers insist on having one (super-flexed) leg in every realm—leaving many of them hobbling through their days.

A January 2013 study by the American Sociological Association backed the idea that flexible work environments make for happier, healthier, more productive workers. But even our subjects with flexible or work-from-home jobs, while grateful for the arrangement, still seemed to operate from a baseline of frazzled.

Having it all has always been exhausting, but our interviewees are attempting it not because they’re aspiring to be CEO, but under the illusion of work-life balance.

Having it all today means answering emails from the playground, abruptly ending a conference call to deliver a forgotten lunch, and giving both work and your kids short shrift.

And yet, when asked what might make their lives easier, most of these subjects demurred, saying they wouldn’t change a thing. Every one of them described her life, complete with compromises and chaos, as a good life. Most seemed pleased at how their lives had turned out 25 years after college, despite sacrifices for both their career and their children. And many women admitted that part of what they liked about attempting to juggle it all is the sense of engineering their own destiny in every avenue.

True innovators are adept at taking very specific areas of knowledge (technologies, scientific discoveries, social phenomena, etc.) and constantly reframing them in broader social, cultural, or political contexts. Innovation thinkers also know that in order to find opportunities to act, to make a difference in the world, they must collaborate--and be damn good at it.

it's the specialization of subject matter, from English and History to math and Engineering, that impedes innovation, more than a lack in any particular subject area

it means focusing as much on teaching how to combine those fields of knowledge and think in flexible, integrative, and creative ways, as we do on the subject matter itself. 

The video report, which aired in several installments, credited the U.S. conservative group Judicial Watch with “keeping an eye” on Soros.

Judicial Watch also appears to have served as the foundation for the earlier Martí report on Soros, which was unknown to Soros’s organization until The Post shared a link to the story last week. The existence of the Martí article was first reported in El Nuevo Herald, a sister publication of the Miami Herald.

Silber, the spokeswoman for Soros’s organization, said that the apology ­USAGM offered for the one, previously known broadcast is no longer sufficient.

“We are deeply concerned to learn about additional content with the same nefarious, anti-Semitic undertones. USAGM needs to be much more forthcoming and Congress needs to conduct more vigorous oversight. In the end we want to know how this content got made, who made it, and who’s been held accountable so that it will not happen again.”

This was a necessary precondition for the Industrial Revolution. While factory technology would consolidate this development, the creation of factory technology was possible only because people’s relationship to work had already changed. A power loom would have served no purpose for networks of farmers making cloth at home.

The same goes for today’s digital revolution. While often described as a second machine age, our current historical moment is better understood as a second industrious revolution. It has been underway for at least 40 years, encompassing the collapse, since the 1970s, of the relatively secure wage-work economy of the postwar era — and the rise of post-industrialism and the service economy.

Over these four decades we have seen an increase in the use of day laborers, office temps, management consultants, contract assemblers, adjunct professors, Blackwater mercenaries and every other kind of worker filing an I.R.S. form 1099. These jobs span the income ranks, but they share what all work seems to have in common in the post-1970s economy: They are temporary and insecure

In the last 10 years, 94 percent of net new jobs have appeared outside of traditional employment. Already approximately one-third of workers, and half of young workers, participate in this alternative world of work, either as a primary or a supplementary source of income.

services like Uber and online freelance markets like TaskRabbit were created to take advantage of an already independent work force; they are not creating it. Their technology is solving the business and consumer problems of an already insecure work world. Uber is a symptom, not a cause.

Today’s smartphone app is an easy way to hire a temp, but is it really that much easier than picking up a phone was in 1950?

shortly after World War II, a Milwaukee man named Elmer Winter founded Manpower, the first major temp agency, to supply emergency secretaries. But by the end of the ’50s, Winter had concluded that the future growth of Manpower was in replacing entire work forces

persuading companies to abandon how they operated was easier said than done, even though Winter could readily demonstrate that it would be cheaper. Few companies took him up on his offer. Higher profits were possible, but not as important, in the lingering wake of the Great Depression, as the moral compact between employer and employee

Big corporations had always had their critics, but no one before the ’70s would have thought that smaller companies would be better run than large ones. Large companies had resources, economies of scale, professional managers, lots of options. Yet terms like “small” and “efficient” and “flexible” would come to seem like synonyms. And with the rise of the lean corporation, work forces became expendable and jobs more precarious.

for the vast majority of workers, the “freedom” of the gig economy is just the freedom to be afraid. It is the severing of obligations between businesses and employees. It is the collapse of the protections that the people of the United States, in our laws and our customs, once fought hard to enshrine.

We can’t turn back the clock, but neither is job insecurity inevitable. Just as the postwar period managed to make industrialization benefit industrial workers, we need to create new norms, institutions and policies that make digitization benefit today’s workers. Pundits have offered many paths forward — “portable” benefits, universal basic income, worker reclassification — but regardless of the option, the important thing to remember is that we do have a choice.

Insurance plans are not tied to employers. Services are funded through progressive taxation, so access is based on need, not ability to pay, and financial contributions are based on wealth, not health.

Contributions to sickness funds are centrally pooled and then allocated to individual insurers using a per-beneficiary formula that factors in differences in health risks.

Compared with the mostly fee-for-service, single-payer arrangements in Canada or the Medicare system, enrolling Americans in managed care plans paid on a per-patient basis would offer greater incentives to increase efficiency, improve quality of care and promote coordination of care.

The United States has the foundation for this kind of system. Its Social Security and Medicare systems use taxation to pay for social insurance policies, and the health care exchanges created by the Affordable Care Act provide marketplaces for insurance policies.

In Germany, for example, insurers can charge only small out-of-pocket fees limited to 2 percent or less of household income annually

Editors’ PicksYou Know the Lorena Bobbitt

Under a German-style plan, states could still be given flexibility in regulating nonprofit insurers to reflect regional priorities, similar to the flexibility offered to states in managing Medicaid and the A.C.A. exchanges.

Germany, Austria, the Netherlands and other countries with similar systems vastly underspend the United States.

Americans may be concerned that lower spending reflects rationing of care, but research has consistently found that not to be the case

Administrative and governance costs in multipayer systems are higher than in single-payer systems — 5 percent of health spending in Germany compared with 3 percent in Canada.

While recent polls indicate that a majority of Americans support so-called Medicare for all, approval diminishes when the plan is explained or clarified.

Americans have long valued choice and competition in their health care. The German model offers both: Patients choose private insurers that compete for enrollees, in the process driving innovation and improving quality.

Advocates and policymakers should pick carefully among these paths, choosing one that strikes a balance between what is possible and what is ideal for the United States health system

While the single-payer model serves Canada well, transitioning the United States to a multipayer model like Germany’s would require a far smaller leap. And that might encourage Americans to finally make the jump

Meanwhile, Facebook said that if COVID-19 numbers in Menlo Park, Calif., the home of its headquarters, continues to decline, up to 10% of its workforce can go back to the office on May 10. Similarly, offices in Fremont and Sunnyvale can open a little later — May 17 and May 24, respectively. And the San Francisco office is slated to open its doors on June 7. All three companies say they intend to abide by all local health protocols and safety guidelines that have been developed in coordination with experts.

Uber added, "Employees returning to the workplace need to take a virtual training, sign a COVID-19 Precautions & Acknowledgement form, and take a daily health screening (including temperature check) at home to qualify for return."

A sprawling study by Microsoft on the impact of forced work-from-home policies due to the coronavirus pandemic revealed that "flexible work is here to stay" and that employers who want to retain talented employees should accept the idea of hybrid work even after the current health crisis. The report, titled "The Next Great Disruption Is Hybrid Work — Are We Ready?" advises business leaders to accept that "the past year has fundamentally changed the nature of work.

When surveyed, 73% of workers said they want flexible remote options. The study also found remote job postings on LinkedIn increased more than five times during the pandemic. But people are also working a lot more and having a hard time, the report says. Around the world, people are spending more than twice as much time in meetings and "over 40 billion more emails were delivered in February of this year compared with last." People are also crying with the coworkers a lot more. One is six report having cried with a colleague in the past year.

"The medical evidence is clear: access to medication-assisted treatment, including buprenorphine that can be prescribed in office-based settings, is the gold standard for treating individuals suffering from opioid use disorder,"

The administration's move comes at a time when the U.S. is again facing record levels of drug overdose deaths, according to the Centers for Disease Control and Prevention.

More than 83,000 people died of drug overdoses in the U.S. in the 12 months ending in June 2020, the CDC said.

"As emergency physicians, we see every day the devastating effects that the opioid crisis has had on the communities we serve—a crisis that has unfortunately only worsened during the COVID-19 pandemic,"

The American Medical Association also praised the move, saying it will allow earlier intervention by doctors treating patients suffering addiction.

"With this change, office-based physicians and physician-led teams working with patients to manage their other medical conditions can also treat them for their opioid use disorder,"

Harris also said allowing doctors to treat opioid addiction as they treat other medical conditions, without additional regulatory hurdles, will reduce the stigma that has often shaped the healthcare response to substance use disorders.

doctors who possess a Drug Enforcement Administration registration will still be limited to treating no more than 30 in-state patients with buprenorphine for addiction treatment at any one time.

The guidelines are not considered a new law or federal regulation, making it very easy for the President-elect Joe Biden administration to walk back this policy if so desired. Giroir told Stat News that he thinks that scenario is unlikely, saying, "I doubt it seriously."

Very little actual work or production is being relocated. What’s really changing are the addresses of those who own and control the capital

Until recently, high-tax cities had a fighting chance against their lower-tax rivals. That is why so many blue-state politicians have called for getting rid of the Trump-era caps and restoring the ability to deduct state and local taxes.

Some progressive economists have rightly countered that enabling the wealthiest Americans to write off their state and local tax payments is highly regressive, amounting to a tax break of $100 billion or more a year that flows mainly to the very rich

But eliminating those write-offs has created a race to the bottom that is already devastating the budgets of expensive coastal cities

Others recommend replacing the SALT deduction with a 15% credit for state and local taxes. Given the pressure from Democrats in impacted cities, this is something that the Biden-Harris administration may have to revisit. 

the effect of new remote technology on state and local taxes requires some serious scrutiny by all levels of government. As more Americans, especially the 1%, have flexibility about where they work, city and state governments will need to develop new revenue models that account for the locations of both the people and their businesses

When an advantaged class can live thousands of miles away from where they work and own assets, it deprives cities of a vital source of revenue.

A conventional war response is the least bad of the three because it avoids the higher risks of either the weaker or the stronger options.

Today, with the balance of forces reversed since the Cold War, the current Russian doctrine of “escalate to deescalate” mimics NATO’s Cold War “flexible response” concept.

NATO promoted the policy of flexible response rhetorically, but the idea was always shaky strategically. The actual contingency plans it generated never commanded consensus simply because initiating the use of nuclear weapons risked tit-for-tat exchanges that could culminate in an apocalyptic unlimited war.

the group could not reach agreement on specific follow-on options beyond an initial symbolic “demonstration shot” for psychological effect, for fear that Moscow could always match them or up the ante.

NATO policymakers should not bank on Moscow’s restraint. Putin has more at stake in the war than Ukraine’s nuclear-armed supporters outside the country do, and he could bet that in a pinch, Washington would be less willing to play Russian roulette than he is

As NATO confronts the possibility of Russia using nuclear weapons, the first question it needs to answer is whether that eventuality should constitute a real redline for the West

As dishonorable as submission sounds to hawks in advance, if the time actually comes, it will have the strong appeal to Americans, because it would avoid the ultimate risk of national suicide.

That immediate appeal has to be balanced by the longer-term risks that would balloon from setting the epochal precedent that initiating a nuclear attack pays off

This dilemma underlines the obvious imperative of maximizing Moscow’s disincentives to go nuclear in the first place.

if it wants to deter Putin from the nuclear gambit in the first place—governments need to indicate as credibly as possible that Russian nuclear use would provoke NATO, not cow it.

If NATO decides it would strike back on Ukraine’s behalf, then more questions arise: whether to also fire nuclear weapons and, if so, how. The most prevalent notion is an eye-for-an-eye nuclear counterattack destroying Russian targets comparable to the ones the original Russian attack had hit.

it invites slow-motion exchanges in which neither side gives up and both ultimately end up devastated.

both the tit-for-tat and the disproportionate retaliatory options pose dauntingly high risks.

A less dangerous option would be to respond to a nuclear attack by launching an air campaign with conventional munitions alone against Russian military targets and mobilizing ground forces for potential deployment into the battle in Ukraine. This would be coupled with two strong public declarations. First, to dampen views of this low-level option as weak, NATO policymakers would emphasize that modern precision technology makes tactical nuclear weapons unnecessary for effectively striking targets that used to be considered vulnerable only to undiscriminating weapons of mass destruction

That would frame Russia’s resort to nuclear strikes as further evidence not only of its barbarism but of its military backwardness.

The second important message to emphasize would be that any subsequent Russian nuclear use would trigger American nuclear retaliation.

Such a strategy would appear weaker than retaliation in kind and would worsen the Russians’ desperation about losing rather than relieve it, thus leaving their original motive for escalation in place along with the possibility that they would double down and use even more nuclear weapons.

The main virtue of the conventional option is simply that it would not be as risky as either the weaker do-nothing or the stronger nuclear options.

If the challenge that is now only hypothetical actually arrives, entering a nuclearized war could easily strike Americans as an experiment they do not want to run. For that reason, there is a very real possibility that policymakers would wind up with the weakest option: rant about the unthinkable barbarity of the Russian action and implement whatever unused economic sanctions are still available but do nothing militarily.

So far, Moscow has been buoyed by the refusal of China, India, and other countries to fully join the economic sanctions campaign imposed by the West. These fence sitters, however, have a stake in maintaining the nuclear taboo. They might be persuaded to declare that their continued economic collaboration with Russia is contingent on it refraining from the use of nuclear weapons.

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.

Economic gains have still left 30.9 percent of Latvia’s population “severely materially deprived,” according to 2011 data released in December by Eurostat, the European Union’s statistics agency, second only to Bulgaria. Unemployment has fallen from more than 20 percent in early 2010, but was still 14.2 percent in the third quarter of 2012

“I’m always asking people here, ‘How can you put up with this?’ ” said Juris Calitis, a Latvian-born Anglican chaplain whose family fled Soviet occupation in the 1940s and who returned when the Soviet empire crumbled. “It is really shocking,” added Mr. Calitis, who runs a soup kitchen at his church in Riga’s old town. Latvians, he said, “should be shouting in the streets,” but “there is an acceptance of hard knocks.”

In contrast to much of Europe, Latvia today has no tradition of labor activism. “What can you achieve in the street? It is cold and snowing,” said Peteris Krigers, president of the Free Trade Union Confederation of Latvia. Organizing strikes, he said, is nearly impossible. “It is seen as shameful for people who earn any salary, no matter how small, to go on strike.”

Also largely absent are the leftist political forces that have opposed austerity elsewhere in Europe, or the rigid labor laws that protect job security and wage levels. In the second half of 2010, after less than 18 months of painful austerity, Latvia’s economy began to grow again.

Since 2008, Latvia has lost more than 5 percent of its population, mostly young people, to emigration. The recent exodus peaked in 2010, when 42,263 people moved abroad, a huge number in a country of just two million now, according to Mihails Hazans, a professor at the University of Latvia.

Alf Vanags, director of the Baltic International Center for Economic Policy Studies here, is skeptical. “The idea of a Latvian ‘success story’ is ridiculous,” he said. “Latvia is not a model for anybody.”

A better and more equitable way out of Latvia’s troubles, he believes, would have been a devaluation of the currency, an option closed to Greece and 16 other countries that use the euro. Latvia kept its currency pegged to the euro, putting itself in much the same straitjacket as euro zone nations.

“You can only do this in a country that is willing to take serious pain for some time and has a dramatic flexibility in the labor market,” he said. “The lesson of what Latvia has done is that there is no lesson.”

This flexibility has convinced many social conservatives that Mr Trump cannot be trusted to appoint a Supreme Court justice who would oppose abortion rights.

advocated deporting nearly 11 million undocumented workers

Almost all Republicans oppose abortion. In recent years, Republican-controlled state legislatures have supported a wave of regulations that have limited access to abortions - new laws that have been met with legal challenges.

Although the group is one of the leading abortion providers in the US, the health care organisation also provides cancer screenings, contraception and screening and treatment for sexual transmitted diseases.

Both President Ronald Reagan and President George HW Bush extended amnesty to millions of undocumented workers while in office. Mainstream Republican figures such as Florida Senator Marco Rubio initially favoured similar immigration reforms that would have provided a "path to citizenship",

any Republicans in the Congress currently support the Trans-Pacific Partnership, a pending trade agreement between the US and many Pacific Rim countries.

Most Republicans oppose tariffs, saying they would spark a trade war that would damage the economy.

Republicans have long supported a muscular foreign policy and have not shied away from supporting the use of military force aboard. While generally opposed to government spending,

While Mr Trump has supported strengthening the military, he says he would do so by extracting concessions from allies.

A key faction of the Republican Party is made of fiscal conservatives who view the federal deficit as a major long-term problem for the country.

have long supported changes to Social Security and Medicare that would turn those programmes over to the private market.

Mr Trump has said he would not make cuts or changes to those programmes and instead he would bolster their funding sources by strengthening the US economy and reallocating some foreign aid to the coffers of Social Security and Medicare.

Politics unfortunately abounds in shams that must be treated reverentially for every politician who would succeed. If you are the sort of man whose stomach revolts against treating shams reverentially, you will be well advised to stay out of politics altogether and set up as a prophet; your prophecies may perhaps sow good seed for some future harvest. But as a politician you would be impotent. For at any given time the bulk of your countrymen believe firmly and devoutly, not only in various things that are worthy of belief, but also in illusions of one kind and another; and they will never submit to have their affairs managed for them by one who appears not to share in their credulity.

Trump’s taught Republican politicians that they’ve neglected the interests and values of their core supporters. He’s demonstrated that much of their party ideology is obsolete, and that their language no longer moves their voters. He’s proven that their party is less culturally conservative than they believed, less hostile to social insurance than they imagined, and more worried about the economic and social costs of mass migration than they realized. Those are valuable lessons that need to be absorbed and pondered.

Their task ahead, in the Biblical phrase, is to pluck the brands from the fire—rescue as much of their party as can be rescued—while simultaneously minimizing the damage to party and country by the nominee their rank-and-file has imposed on them. They need to maneuver so that Trump’s defeat is as solitary as possible, and so that he cannot shift the blame for the failure he has earned onto the heads of others

Bad choices over the past decade by Republican political leaders opened the way to Donald Trump, yes.  For a decade, Republican voters have signaled they wanted to protect Medicare, cut immigration, fight fewer wars, and nominate no more Bushes. Their party leaders interpreted those signals as demands to cut Medicare, increase immigration, put boots on the ground in Syria, and nominate another Bush.

He’s also demonstrated that he himself is a dangerous person

To save themselves and their country, Republican politicians will have to rediscover the politician’s arts of deftness, flexibility, and self-preservation—while stealthily hastening Trump toward the defeat that almost certainly awaits him in November.

That’s a big job and a hard job, all the harder because they cannot acknowledge what they are doing. They will seem to help Trump win, while actually working to ensure he fail

What Walter Lippman said of presidents is really true of all politicians: They are not “working through noble institutions to dear ends … but trying to grind out a few crude results from a decadent political machine.”

The harms they stop are more important than the good they cannot achieve. What they’re called upon to do is to practice statesmanship without fine phrases; to protect the republic without receiving any credit for it

What’s less discussed is the polarization of culture, and the new echo chambers within which we hear about and experience today’s cultural hits

There’s just about nothing as popular today as old sitcoms were; the only bits of shared culture that come close are periodic sporting events, viral videos, memes and occasional paroxysms of political outrage (see Meryl Streep’s Golden Globes speech and the aftermath).

we’re returning to the cultural era that predated radio and TV, an era in which entertainment was fragmented and bespoke, and satisfying a niche was a greater economic imperative than entertaining the mainstream.

“We’re back to normal, in a way, because before there was broadcasting, there wasn’t much of a shared culture,

“For most of the history of civilization, there was nothing like TV. It was a really odd moment in history to have so many people watching the same thing at the same time.”

Because it featured little choice, TV offered something else: the raw material for a shared culture. Television was the thing just about everyone else was watching at the same time as you. In its enforced similitude, it became a kind of social glue, stitching together a new national identity across a vast, growing and otherwise diverse nation.

As the broadcast era morphed into one of cable and then streaming, TV was transformed from a wasteland into a bubbling sea of creativity. But it has become a sea in which everyone swims in smaller schools.

Only around 12 percent of television households, or about 14 million to 15 million people, regularly tuned into “NCIS” and “The Big Bang Theory,” the two most popular network shows of the 2015-16 season, according to Nielsen. Before 2000, those ratings would not even have qualified them as Top 10 shows

HBO’s “Game of Thrones” is the biggest prestige drama on cable, but its record-breaking finale drew only around nine million viewers

Netflix’s biggest original drama last year, “Stranger Things,” was seen by about 14 million adults in the month after it first aired. “Fuller House,” Netflix’s reboot of the broadcast sitcom “Full House,” attracted an audience of nearly 16 million. (These numbers are for the entire season, not for single episodes.)

For perspective, during much of the 1980s, a broadcast show that attracted 14 million to 16 million would have been in danger of cancellation.

As people pull back from broadcast and cable TV and jump deeper into streaming, we’re bound to see more shows with smaller audiences.

It’s possible we’re not at the end of the story. Some youngsters might argue that the internet has produced its own kind of culture, one that will become a fount of shared references for years to come. What if “Chewbacca Mom” and the blue and black/white and gold dress that broke the internet one day become part of our library of globally recognized references

Shaming them for making “small change” is a way to reduce numbers of protesters, not to increase them. Shaming can also create a legacy of political inactivity: Turning kids off from involvement now could encourage decades of disengagement.

“Flash activism,” the label I prefer for online protest forms such as online petition, can be effective at influencing targets in specific circumstances

Numbers matter. Whether you are a high school coach, Bank of America, the Obama administration or a local council member, an overwhelming flood of signatures, emails and phone calls can be quite persuasive

Online protest is easy, nearly cost-free in democratic nations, and can help drive positive social change. In addition, flash activism can help build stronger movements in the future. If current activists view online support as an asset, rather than with resentment because it is different from “traditional” methods, they can mobilize vast numbers of people.

People who participate in one online action may join future efforts, or even broaden their involvement in activism. For example, kids who engage in politics online often do other political activities as well.

Critics often worry that valuing flash activism will “water down” the meaning of activism. But that misses the point and is counterproductive. The goal of activism is social change, not nostalgia or activism for activism’s sake. Most people who participate in flash activism would not have done more – rather, they would have done nothing at all.

Scholars and advocates alike should stop asking if flash activism matters. We should also stop assuming that offline protest always succeeds. Instead, we should seek out the best ways to achieve specific goals. Sometimes the answer will be an online petition, sometimes it will be civil disobedience and sometimes it will be both – or something else entirely.

The real key for grassroots social change is to engage as many people as possible. That will require flexibility on how engagement occurs. If people want larger and more effective social movements, they should be working to find ways to include everyone who will do anything, not upholding an artificial standard of who is a “real activist” and who is not.

One such assumption, that voters follow the signals sent by party elites and officeholders, is the basis of the famous “party decides” thesis in political science, which was invoked early and often to explain why Trump couldn’t possibly end up as the Republican nominee.

While his progress has undercut that thesis, it hasn’t been fully disproved, since the “party decides” conceit doesn’t tell us about what happens when the party simply can’t decide. Whether you look at endorsements or fund-raising or any other metric, that’s what happened this time

before the Trump experience it was reasonable to assume that there was a consistent logic to primary voting — that regardless of what party elites decided, a kind of “wisdom of crowds” thesis could suffice to explain why major political parties don’t nominate people like, well, Donald Trump.

They are engaged partisans, a more distinct group. They often have to be registered with their party, they have to care enough to vote on a random Tuesday in February, March or April, in some cases they have to set aside the time to show up and caucus. By definition, they tend to be more interested in both policy and politics than the average citizen

On the evidence of past campaigns, this engagement inclines them (in the aggregate) to balance ideology and electability when they vote

there was also a fair amount of political-science evidence that the Republicans really were a more ideological party than the Democrats, less inclined to view compromise in favorable terms, more inclined to regard politics through a philosophical rather than an interest-group service lens.

Yes, Trump has adopted conservative positions on various issues, but he’s done so in a transparently cynical fashion, constantly signaling that he doesn’t really believe in or understand the stance that he’s taking, constantly suggesting a willingness to bargain any principle away

Until Donald Trump blew this model up

Except for immigration hawks, practically every ideological faction in the party regards Trump with mistrust, disgust, suspicion, fear. Pro-lifers, foreign-policy hawks, the Club for Growth, libertarians — nobody thinks Trump is really on their side. And yet he’s winning anyway.

perhaps Trumpism can be understood as a coup by the G.O.P.’s ideologically flexible minority against the conservative movement’s litmus tests; indeed to some extent that’s clearly what’s been happening.

he’s untrustworthy and unelectable — a combination that you’d normally expect engaged partisans to consider and reject. And yet he’s winning anyway.

But here the model isn’t completely broken, because a majority of Republican voters don’t actually believe that Trump faces long odds,

Instead, since last fall Republican voters have consistently told pollsters that they think Trump is the candidate most likely to win in November. So the party’s voters are choosing electability — as they see it — over ideology; they’re just in the grip of a strong delusion about Trump’s actual chances against Hillary Clinton.

The reason for this delusion might be the key unresolved question of Trump’s strange ascent. Is it the fruit of Trump’s unparalleled media domination — does he seem more electable than all his rivals because he’s always on TV

Is it a case of his victor’s image carrying all before it — if you win enough primary contests, even with 35 percent of the vote, people assume that your winning streak can be extended into November? Is this just how a personality cult rooted in identity politics works — people believe in the Great Leader’s capacity to crush their tribe’s enemies and disregard all contrary evidence?

Or is it somehow the pundits’ doing? Did the misplaced certainty that Trump couldn’t win the nomination create an impression that all projections are bunk, that he’ll always prove his doubters wrong?

the Republican Party is dominated by doctrines formerly on the political fringe. Friedman called for monetary flexibility; today, much of the G.O.P. is fanatically devoted to the gold standard. N. Gregory Mankiw of Harvard University, a Romney economic adviser, once dismissed those claiming that tax cuts pay for themselves as “charlatans and cranks”; today, that notion is very close to being official Republican doctrine.