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Ambiguities are often reconciled with other observed data, such as that we know by other means that the planet is too small to cause the scale of distortion seen.

“The two previous theories of degeneracy deal with cases where the background star appears to pass close to the foreground star or the foreground planet. The AI algorithm showed us hundreds of examples from not only these two cases, but also situations where the star doesn’t pass close to either the star or planet and cannot be explained by either previous theory,” said Zhang in a Berkeley news release.

But without the systematic and confident calculations of the AI, it’s likely the simplified, less correct theory would have persisted for many more years.

As a result — and after some convincing, since a grad student questioning established doctrine is tolerated but perhaps not encouraged — they ended up proposing a new, “unified” theory of how degeneracy in these observations can be explained, of which the two known theories were simply the most common cases.

“People were seeing these microlensing events, which actually were exhibiting this new degeneracy but just didn’t realize it. It was really just the machine learning looking at thousands of events where it became impossible to miss,” said Scott Gaudi

But Zhang seemed convinced that the AI had clocked something that human observers had systematically overlooked.

Just as people learned to trust calculators and later computers, we are learning to trust some AI models to output an interesting truth clear of preconceptions and assumptions — that is, if we haven’t just coded our own preconceptions and assumptions into them.

She showed that accounts of so-called “aces” — fighters said to have heroically held off much more powerful enemies with a single tank or plane — were based on propaganda.

In making these repairs, Coffman faced resistance from a group of editors who were mainly military buffs and wanted to write about battlefield valor without too much scrutiny. She, however, kept coming back to facts and sources — how do we know what we think we know? — and an insistence that Wikipedia not be swept up in mythology.

In a speech last year, Putin strolled through 1,000 years of battles and alliances to justify his claim of the “historical unity of Russians and Ukrainians.”

Just look at the rhetoric around Russian’s invasion of Ukraine, which Vladimir Putin has described as a battle to “de-Nazify” the leadership of Ukraine.

Since the Russian invasion, the English Wikipedia articles about the historical figures and topics Putin invoked have been racking up pop-star numbers.

When it comes to allegations about Nazi collaboration by prominent Ukrainian nationalists like Bandera, Wikipedia has pulled no punches. Even as Putin has emphasized these Nazi ties as a reason for his invasion, Wikipedia has resisted attempts to water down this history.

When Putin’s foreign minister, Sergei Lavrov, was asked how Ukraine could be in need of de-Nazification if its president, Volodymyr Zelensky, is Jewish, Lavrov replied: “I could be wrong, but Hitler also had Jewish blood. [That Zelensky is Jewish] means absolutely nothing. Wise Jewish people say that the most ardent anti-Semites are usually Jews.” It was an appalling answer that, according to the Israeli government, Putin apologized for.

In an unusual step, the English Wikipedia article brings up this particular falsehood to explicitly refute it.

The Wikipedia project comes with a stubborn confidence that facts can guide us through the darkness. In Wikipedia’s 20-year history, this belief has never been asked to do more.

Time and again, we learned there was never a grand plan or big ideas—just weapons-grade ego, incompetence, thin skin, and prejudice against those who don’t revere him.

Despite all the incredible, damning reporting coming out of Twitter and all of Musk’s very public mistakes, many people still refuse to believe—even if they detest him—that he is simply incompetent.

What is amazing about the current moment is that, despite how ridiculous it all feels, a fundamental tenet of reality and logic appears to be holding true: If you don’t know what you’re doing or don’t really care, you’ll run the thing you’re in charge of into the ground, and people will notice.

And so the moment feels too dumb and too on the nose to be real and yet also very real—kind of like all of reality in 2022.

I don’t really know where any of this will lead, but one interesting possibility is that Musk gets increasingly reactionary and trollish in his politics and stewardship of Twitter.

Leaving the politics aside, from a basic customer-service standpoint this is generally an ill-advised way for the owner of a company to treat an elected official when that elected official wishes to know why your service has failed them. The reason it is ill-advised is because then the elected official could tweet something like what Senator Markey tweeted on Sunday: “One of your companies is under an FTC consent decree. Auto safety watchdog NHTSA is investigating another for killing people. And you’re spending your time picking fights online. Fix your companies. Or Congress will.”

It seems clear that Musk, like any dedicated social-media poster, thrives on validation, so it makes sense that, as he continues to dismantle his own mystique as an innovator, he might look for adoration elsewhere

Recent history has shown that, for a specific audience, owning the libs frees a person from having to care about competency or outcome of their actions. Just anger the right people and you’re good, even if you’re terrible at your job. This won’t help Twitter’s financial situation, which seems bleak, but it’s … something!

Bankman-Fried, the archetype, appealed to people for all kinds of reasons. His narrative as a philanthropist, and a smart rationalist, and a stone-cold weirdo was something people wanted to buy into because, generally, people love weirdos who don’t conform to systems and then find clever ways to work around them and become wildly successful as a result.

Bankman-Fried was a way that a lot of people could access and maybe obliquely understand what was going on in crypto. They may not have understood what FTX did, but they could grasp a nerd trying to leverage a system in order to do good in the world and advance progressive politics. In that sense, Bankman-Fried is easy to root for and exciting to cover. His origin story and narrative become more important than the particulars of what he may or may not be doing.

the past few weeks have been yet another reminder that the smug-nerd-genius narrative may sell magazines, and it certainly raises venture funding, but the visionary founder is, first and foremost, a marketing product, not a reality. It’s a myth that perpetuates itself. Once branded a visionary, the founder can use the narrative to raise money and generate a formidable net worth, and then the financial success becomes its own résumé. But none of it is real.

Adversarial journalism ideally questions and probes power. If it is trained on technology companies and their founders, it is because they either wield that power or have the potential to do so. It is, perhaps unintuitively, a form of respect for their influence and potential to disrupt. But that’s not what these founders want.

even if all tech coverage had been totally flawless, Silicon Valley would have rejected adversarial tech journalism because most of its players do not actually want the responsibility that comes with their potential power. They want only to embody the myth and reap the benefits. They want the narrative, which is focused on origins, ambitions, ethos, and marketing, and less on the externalities and outcomes.

Looking at Musk and Bankman-Fried, it would appear that the tech visionaries mostly get their way. For all the complaints of awful, negative coverage and biased reporting, people still want to cheer for and give money to the “‘smug nerds building castles in the sky.’” Though they vary wildly right now in magnitude, their wounds are self-inflicted—and, perhaps, the result of believing their own hype.

That’s because, almost always, the smug-nerd-genius narrative is a trap. It’s one that people fall into because they need to believe that somebody out there is so brilliant, they can see the future, or that they have some greater, more holistic understanding of the world (or that such an understanding is possible)

It’s not unlike a conspiracy theory in that way. The smug-nerd-genius narrative helps take the complexity of the world and make it more manageable.

Putting your faith in a space billionaire or a crypto wunderkind isn’t just sad fanboydom; it is also a way for people to outsource their brain to somebody else who, they believe, can see what they can’t

the smug nerd genius is exceedingly rare, and, even when they’re not outed as a fraud or a dilettante, they can be assholes or flawed like anyone else. There aren’t shortcuts for making sense of the world, and anyone who is selling themselves that way or buying into that narrative about them should read to us as a giant red flag.

In the modern tech world, the value of a humanistic education shows up in evidence of its absence. Sam Bankman-Fried, the disgraced founder of the crypto exchange FTX who recently lost his $16 billion fortune in a few days, is a famously proud illiterate. “I would never read a book,” he once told an interviewer. “I don’t want to say no book is ever worth reading, but I actually do believe something pretty close to that.”

Elon Musk and Twitter are another excellent case in point. It’s painful and extraordinary to watch the ham-fisted way a brilliant engineering mind like Musk deals with even relatively simple literary concepts such as parody and satire. He obviously has never thought about them before.

The extraordinary ignorance on questions of society and history displayed by the men and women reshaping society and history has been the defining feature of the social-media era. Apparently, Mark Zuckerberg has read a great deal about Caesar Augustus, but I wish he’d read about the regulation of the pamphlet press in 17th-century Europe. It might have spared America the annihilation of social trust.

These failures don’t derive from mean-spiritedness or even greed, but from a willful obliviousness. The engineers do not recognize that humanistic questions—like, say, hermeneutics or the historical contingency of freedom of speech or the genealogy of morality—are real questions with real consequences

Everybody is entitled to their opinion about politics and culture, it’s true, but an opinion is different from a grounded understanding. The most direct path to catastrophe is to treat complex problems as if they’re obvious to everyone. You can lose billions of dollars pretty quickly that way.

As the technologists have ignored humanistic questions to their peril, the humanists have greeted the technological revolutions of the past 50 years by committing soft suicide.

As of 2017, the number of English majors had nearly halved since the 1990s. History enrollments have declined by 45 percent since 2007 alone

the humanities have not fundamentally changed their approach in decades, despite technology altering the entire world around them. They are still exploding meta-narratives like it’s 1979, an exercise in self-defeat.

Contemporary academia engages, more or less permanently, in self-critique on any and every front it can imagine.

the situation requires humanists to explain why they matter, not constantly undermine their own intellectual foundations.

The humanities promise students a journey to an irrelevant, self-consuming future; then they wonder why their enrollments are collapsing. Is it any surprise that nearly half of humanities graduates regret their choice of major?

Despite the clear value of a humanistic education, its decline continues. Over the past 10 years, STEM has triumphed, and the humanities have collapsed. The number of students enrolled in computer science is now nearly the same as the number of students enrolled in all of the humanities combined.

now there’s GPT-3. Natural-language processing presents the academic humanities with a whole series of unprecedented problems

Practical matters are at stake: Humanities departments judge their undergraduate students on the basis of their essays. They give Ph.D.s on the basis of a dissertation’s composition. What happens when both processes can be significantly automated?

despite the drastic divide of the moment, natural-language processing is going to force engineers and humanists together. They are going to need each other despite everything. Computer scientists will require basic, systematic education in general humanism: The philosophy of language, sociology, history, and ethics are not amusing questions of theoretical speculation anymore. They will be essential in determining the ethical and creative use of chatbots, to take only an obvious example.

The humanists will need to understand natural-language processing because it’s the future of language

that space for collaboration can exist, both sides will have to take the most difficult leaps for highly educated people: Understand that they need the other side, and admit their basic ignorance.

But that’s always been the beginning of wisdom, no matter what technological era we happen to inhabit.

Colonisation exposed us to new ideas that shocked and confused us. Graeber & Wengrow focus on the French coming into contact with Native Americans in Canada, and in particular on Wendat Confederacy philosopher–statesman Kandiaronk as an example of European traders, missionaries, and intellectuals debating with, and being criticized by indigenous people. Historians have downplayed how much these encounters shaped Enlightenment ideas.

this thought-provoking book is armed to the teeth with fascinating ideas and interpretations that go against mainstream thinking

ather than yet another history book telling you how humanity got here, they take their respective disciplines to task for dealing in myths.

Its legacy, shaped via several iterations, is the modern textbook narrative: hunter-gathering was replaced by pastoralism and then farming; the agricultural revolution resulted in larger populations producing material surpluses; these allowed for specialist occupations but also needed bureaucracies to share and administer them to everyone; and this top-down control led to today’s nation states. Ta-daa!

this simplistic tale of progress ignores and downplays that there was nothing linear or inevitable about where we have ended up.

ake agriculture. Rather than humans enthusiastically entering into what Harari in Sapiens called a Faustian bargain with crops, there were many pathways and responses

Experiments show that plant domestication could have been achieved in as little as 20–30 years, so the fact that cereal domestication here took some 3,000 years questions the notion of an agricultural “revolution”. Lastly, this book includes many examples of areas where agriculture was purposefully rejected. Designating such times and places as “pre-agricultural” is misleading, write the authors, they were anti-agricultural.

The idea that agriculture led to large states similarly needs revision

correlation is not causation, and some 15–20 additional centres of domestication have since been identified that followed different paths. Some cities have previously remained hidden in the sediments of ancient river deltas until revealed by modern remote-sensing technology.

“extensive agriculture may thus have been an outcome, not a cause, of urbanization”

And cities did not automatically imply social stratification. The Dawn of Everything fascinates with its numerous examples of large settlements without ruling classes, such as Ukrainian mega-sites, the Harappan civilization, or Mexican city-states.

These instead relied on collective decision-making through assemblies or councils, which questions some of the assumptions of evolutionary psychology about scale: that larger human groups require complex (i.e. hierarchical) systems to organize them.

e what is staring them in the face

humans have always been very capable of consciously experimenting with different social arrangements. And—this is rarely acknowledged—they did so on a seasonal basis, spending e.g. part of the year settled in large communal groups under a leader, and another part as small, independently roving bands.

Throughout, Graeber & Wengrow convincingly argue that the only thing we can say about our ancestors is that “there is no single pattern. The only consistent phenomenon is the very fact of alteration […] If human beings, through most of our history, have moved back and forth fluidly between different social arrangements […] maybe the real question should be ‘how did we get stuck?

Next to criticism, the authors put out some interesting ideas of their own, of which I want to quickly highlight two.

The first is that some of the observed variations in social arrangements resulted from schismogenesis. Anthropologist Gregory Bateson coined this term in the 1930s to describe how people define themselves against or in opposition to others, adopting behaviours and attitudes that are different.

The second idea is that states can be described in terms of three elementary forms of domination: control of violence, control of information, and individual charisma, which express themselves as sovereignty, administration, and competitive politics.

Asking which past society most resembles today’s is the wrong question to ask. It risks slipping into an exercise in retrofitting, “which makes us scour the ancient world for embryonic versions of our modern nation states”

I have left unmentioned several other topics: the overlooked role of women, the legacy of Rousseau’s and Hobbes’s ideas, the origins of inequality and the flawed assumptions hiding behind that question

There are so many historical details and delights hiding between these covers that I was thoroughly enthralle

If you have any interest in big history, archaeology, or anthropology, this book is indispensable. I am confident that the questions and critiques raised here will remain relevant for a long time to come.

I was particularly impressed by the in-depth critique by worbsintowords on his YouTube channel What is Politics? of (so far) five videos

Diamond and Dybvig offered a stylized but insightful model of what banks do. They argued that there is always a tension between individuals’ desire for liquidity — ready access to funds — and the economy’s need to make long-term investments that can’t easily be converted into cash.

Banks square that circle by taking money from depositors who can withdraw their funds at will — making those deposits highly liquid — and investing most of that money in illiquid assets, such as business loans.

So banking is a productive activity that makes the economy richer by reconciling otherwise incompatible desires for liquidity and productive investment. And it normally works because only a fraction of a bank’s depositors want to withdraw their funds at any given time.

This does, however, make banks vulnerable to runs. Suppose that for some reason many depositors come to believe that many other depositors are about to cash out, and try to beat the pack by withdrawing their own funds. To meet these demands for liquidity, a bank will have to sell off its illiquid assets at fire sale prices, and doing so can drive an institution that should be solvent into bankruptcy

If that happens, people who didn’t withdraw their funds will be left with nothing. So during a panic, the rational thing to do is to panic along with everyone else.

There was, of course, a huge wave of banking panics in 1930-31. Many banks failed, and those that survived made far fewer business loans than before, holding cash instead, while many families shunned banks altogether, putting their cash in safes or under their mattresses. The result was a diversion of wealth into unproductive uses. In his 1983 paper, Bernanke offered evidence that this diversion played a large role in driving the economy into a depression and held back the subsequent recovery.

In the story told by Friedman and Anna Schwartz, the banking crisis of the early 1930s was damaging because it led to a fall in the money supply — currency plus bank deposits. Bernanke asserted that this was at most only part of the stor

a government backstop — either deposit insurance, the willingness of the central bank to lend money to troubled banks or both — can short-circuit potential crises.

Such arrangements offered a higher yield than conventional deposits. But they had no safety net, which opened the door to an old-style bank run and financial panic.

So banks need to be regulated as well as backstopped. As I said, the Diamond-Dybvig analysis had remarkably large implications for policy.

From an economic point of view, banking is any form of financial intermediation that offers people seemingly liquid assets while using their wealth to make illiquid investments.

This insight was dramatically validated in the 2008 financial crisis.

By the eve of the crisis, however, the financial system relied heavily on “shadow banking” — banklike activities that didn’t involve standard bank deposits

But providing such a backstop raises the possibility of abuse; banks may take on undue risks because they know they’ll be bailed out if things go wrong.

And the panic came. The conventionally measured money supply didn’t plunge in 2008 the way it did in the 1930s — but repo and other money-like liabilities of financial intermediaries did:

Fortunately, by then Bernanke was chair of the Federal Reserve. He understood what was going on, and the Fed stepped in on an immense scale to prop up the financial system.

a sort of meta point about the Diamond-Dybvig work: Once you’ve understood and acknowledged the possibility of self-fulfilling banking crises, you become aware that similar things can happen elsewhere.

Perhaps the most notable case in relatively recent times was the euro crisis of 2010-12. Market confidence in the economies of southern Europe collapsed, leading to huge spreads between the interest rates on, for example, Portuguese bonds and those on German bonds. The conventional wisdom at the time — especially in Germany — was that countries were being justifiably punished for taking on excessive debt

the Belgian economist Paul De Grauwe argued that what was actually happening was a self-fulfilling panic — basically a run on the bonds of countries that couldn’t provide a backstop because they no longer had their own currencies.

Sure enough, when Mario Draghi, the president of the European Central Bank at the time, finally did provide a backstop in 2012 — he said the magic words “whatever it takes,” implying that the bank would lend money to the troubled governments if necessary — the spreads collapsed and the crisis came to an end:

Over the subsequent four decades, Thatcherites at think tanks like the Institute of Economic Affairs and the Centre for Policy Studies (which Margaret Thatcher helped set up) described the struggle against both the Labour Party and the broader persistence of Socialism in the Communist and non-Communist world as a “war of ideas.”

Thatcherites, known collectively as the ultras, gained fresh blood in the 2010s as a group of Gen Xers too young to experience Thatcherism in its insurgent early years — including the former home secretary Priti Patel, the former foreign secretary Dominic Raab, the former minister of state for universities Chris Skidmore, Mr. Kwarteng and Ms. Truss — attempted to reboot her ideology for the new millennium.

They followed their idol not only in her antagonism to organized labor but also in her less-known fascination with Asian capitalism. In 2012’s “Britannia Unchained,” a book co-written by the group that remains a Rosetta Stone for the policy surprises of the last month, they slammed the Britons for their eroded work ethic and “culture of excuses” and the “cosseted” public sector unions. They praised China, South Korea, Singapore and Hong Kon

“Britannia Unchained” expressed a desire to go back to the future by restoring Victorian values of hard work, self-improvement and bootstrapping.

While the Gen X Thatcherites didn’t scrimp on data, they also saw something ineffable at the root of British malaise. “Beyond the statistics and economic theories,” they wrote, “there remains a sense in which many of Britain’s problems lie in the sphere of cultural values and mind-set.”

As Thatcher herself put it, “Economics are the method; the object is to change the heart and soul.” Britain needed a leap of faith to restore itself.

Ms. Truss and Mr. Kwarteng seemed to have believed that by patching together all of the most radical policies of Thatcherism (while conveniently dropping the need for spending cuts), they would be incanting a kind of magic spell, an “Open sesame” for “global Britain.” This was their Reagan moment, their moment when, as their favorite metaphors put it, a primordial repressed force would be “unchained,” “unleashed” or “unshackled.”But as a leap of faith, it broke the diver’s neck.

the money markets were not waiting for an act of faith in Laffer Curve fundamentalism after all. This was “Reaganism without the dollar.” Without the confidence afforded to the global reserve currency, the pound went into free fall.

ince the 1970s, the world of think tanks had embraced a framing of the world in terms of discrete spaces that could become what they called laboratories for new policies

The mini-budget subjected the entire economy to experimental treatment. This was put in explicit terms in a celebratory post by a Tory journalist and think tanker claiming that Ms. Truss and Mr. Kwarteng had been “incubated” by the Institute of Economic Affairs in their early years and “Britain is now their laboratory.”

The scientists at the bench discovered that the money markets would not only punish left-wing experiments in changing the balance between states and markets, but they were also sensitive to experiments that pushed too far to the right. A cowed Ms. Truss apologized, and Mr. Kwarteng’s successor has reversed almost all of the planned cuts and limited the term for energy supports.

It was hard to get there, because I had to learn over and over again that extreme conviction requires extraordinary evidence, and the evidence we have is usually far from extraordinary.

Just as we solved many of the mysteries that befuddled our ancestors, our descendants will solve many of the mysteries that befuddle us. Our ignorance is profound, forgivable and temporary. There are only two true errors: One is believing that we have no errors left to make, and the other is believing that those errors are permanent and irreversible.

It’s hard to maintain this attitude when you realize just how much we have left to figure out. And we often have to cultivate this attitude for ourselves, because our educators and experts often fail to give it to us.

Experts, for their part, are perfectly comfortable issuing blanket recommendations with scant evidence and then shaming people for not following them.

Our teachers and officials may think the public simply can’t handle uncertainty, and maybe that’s why they project so much confidence, even when the science is faulty. But the best way to cultivate informed citizens is to give them the evidence that we have, not the evidence we wish we had.

Behavioral economics is at the center of the so-called replication crisis, a euphemism for the uncomfortable fact that the results of a significant percentage of social science experiments can’t be reproduced in subsequent trials

this key result was not replicated in similar experiments, undermining confidence in a whole area of study. It’s obvious that we do associate old age and slower walking, and we probably do slow down sometimes when thinking about older people. It’s just not clear that that’s a law of the mind.

And these attempts to “correct” human behavior are based on tenuous science. The replication crisis doesn’t have a simple solution

Journals have instituted reforms like having scientists preregister their hypotheses to avoid the possibility of results being manipulated during the research. But that doesn’t change how many uncertain results are already out there, with a knock-on effect that ripples through huge segments of quantitative social scienc

The Johns Hopkins science historian Ruth Leys, author of a forthcoming book on priming research, points out that cognitive science is especially prone to building future studies off disputed results. Despite the replication crisis, these fields are a “train on wheels, the track is laid and almost nothing stops them,” Dr. Leys said.

These cases result from lax standards around data collection, which will hopefully be corrected. But they also result from strong financial incentives: the possibility of salaries, book deals and speaking and consulting fees that range into the millions. Researchers can get those prizes only if they can show “significant” findings.

It is no coincidence that behavioral economics, from Dr. Kahneman to today, tends to be pro-business. Science should be not just reproducible, but also free of obvious ideology.

Technology and modern data science have only further entrenched behavioral economics. Its findings have greatly influenced algorithm design.

The collection of personal data about our movements, purchases and preferences inform interventions in our behavior from the grocery store to who is arrested by the police.

Setting people up for safety and success and providing good default options isn’t bad in itself, but there are more sinister uses as well. After all, not everyone who wants to exploit your cognitive biases has your best interests at heart.

Despite all its flaws, behavioral economics continues to drive public policy, market research and the design of digital interfaces.

One might think that a kind of moratorium on applying such dubious science would be in order — except that enacting one would be practically impossible. These ideas are so embedded in our institutions and everyday life that a full-scale audit of the behavioral sciences would require bringing much of our society to a standstill.

There is no peer review for algorithms that determine entry to a stadium or access to credit. To perform even the most banal, everyday actions, you have to put implicit trust in unverified scientific results.

We can’t afford to defer questions about human nature, and the social and political policies that come from them, to commercialized “research” that is scientifically questionable and driven by ideology. Behavioral economics claims that humans aren’t rational.

That’s a philosophical claim, not a scientific one, and it should be fought out in a rigorous marketplace of ideas. Instead of unearthing real, valuable knowledge of human nature, behavioral economics gives us “one weird trick” to lose weight or quit smoking.

Humans may not be perfectly rational, but we can do better than the predictably irrational consequences that behavioral economics has left us with today.

we lack practical knowledge about how to give one another the attention we crave

Some days it seems like we have intentionally built a society that gives people little guidance on how to perform the most important activities of life.

If I can shine positive attention on others, I can help them to blossom. If I see potential in others, they may come to see potential in themselves. True understanding is one of the most generous gifts any of us can give to another.

I see the results, too, in the epidemic of invisibility I encounter as a journalist. I often find myself interviewing people who tell me they feel unseen and disrespected

I’ve been working on a book called “How to Know a Person: The Art of Seeing Others Deeply and Being Deeply Seen.” I wanted it to be a practical book — so that I would learn these skills myself, and also, I hope, teach people how to understand others, how to make them feel respected, valued and understood.

I wanted to learn these skills for utilitarian reasons

If I’m going to work with someone, I don’t just want to see his superficial technical abilities. I want to understand him more deeply — to know whether he is calm in a crisis, comfortable with uncertainty or generous to colleagues.

I wanted to learn these skills for moral reasons

Many of the most productive researchers were in the habit of having breakfast or lunch with an electrical engineer named Harry Nyquist. Nyquist really listened to their challenges, got inside their heads, brought out the best in them. Nyquist, too, was an illuminator.

Finally, I wanted to learn these skills for reasons of national survival

We evolved to live with small bands of people like ourselves. Now we live in wonderfully diverse societies, but our social skills are inadequate for the divisions that exist. We live in a brutalizing time.

In any collection of humans, there are diminishers and there are illuminators. Diminishers are so into themselves, they make others feel insignificant

They stereotype and label. If they learn one thing about you, they proceed to make a series of assumptions about who you must be.

Illuminators, on the other hand, have a persistent curiosity about other people.

hey have been trained or have trained themselves in the craft of understanding others. They know how to ask the right questions at the right times — so that they can see things, at least a bit, from another’s point of view. They shine the brightness of their care on people and make them feel bigger, respected, lit up.

A biographer of the novelist E.M. Forster wrote, “To speak with him was to be seduced by an inverse charisma, a sense of being listened to with such intensity that you had to be your most honest, sharpest, and best self.” Imagine how good it would be to offer people that kind of hospitality.

social clumsiness I encounter too frequently. I’ll be leaving a party or some gathering and I’ll realize: That whole time, nobody asked me a single question. I estimate that only 30 percent of the people in the world are good question askers. The rest are nice people, but they just don’t ask. I think it’s because they haven’t been taught to and so don’t display basic curiosity about others.

Many years ago, patent lawyers at Bell Labs were trying to figure out why some employees were much more productive than others.

Illuminators are a joy to be around

The gift of attention.

Each of us has a characteristic way of showing up in the world. A person who radiates warmth will bring out the glowing sides of the people he meets, while a person who conveys formality can meet the same people and find them stiff and detached. “Attention,” the psychiatrist Iain McGilchrist writes, “is a moral act: It creates, brings aspects of things into being.”

When Jimmy sees a person — any person — he is seeing a creature with infinite value and dignity, made in the image of God. He is seeing someone so important that Jesus was willing to die for that person.

Accompaniment.

Accompaniment is an other-centered way of being with people during the normal routines of life.

If we are going to accompany someone well, we need to abandon the efficiency mind-set. We need to take our time and simply delight in another person’s way of being

I know a couple who treasure friends who are what they call “lingerable.” These are the sorts of people who are just great company, who turn conversation into a form of play and encourage you to be yourself. It’s a great talent, to be lingerable.

Other times, a good accompanist does nothing more than practice the art of presence, just being there.

The art of conversation.

If you tell me something important and then I paraphrase it back to you, what psychologists call “looping,” we can correct any misimpressions that may exist between us.

Be a loud listener. When another person is talking, you want to be listening so actively you’re burning calories.

He’s continually responding to my comments with encouraging affirmations, with “amen,” “aha” and “yes!” I love talking to that guy.

I no longer ask people: What do you think about that? Instead, I ask: How did you come to believe that? That gets them talking about the people and experiences that shaped their values.

Storify whenever possible

People are much more revealing and personal when they are telling stories.

Do the looping, especially with adolescents

If you want to know how the people around you see the world, you have to ask them. Here are a few tips I’ve collected from experts on how to become a better conversationalist:

Turn your partner into a narrator

People don’t go into enough detail when they tell you a story. If you ask specific follow-up questions — Was your boss screaming or irritated when she said that to you? What was her tone of voice? — then they will revisit the moment in a more concrete way and tell a richer story

If somebody tells you he is having trouble with his teenager, don’t turn around and say: “I know exactly what you mean. I’m having incredible problems with my own Susan.” You may think you’re trying to build a shared connection, but what you are really doing is shifting attention back to yourself.

Don’t be a topper

Big questions.

The quality of your conversations will depend on the quality of your questions

As adults, we get more inhibited with our questions, if we even ask them at all. I’ve learned we’re generally too cautious. People are dying to tell you their stories. Very often, no one has ever asked about them.

So when I first meet people, I tend to ask them where they grew up. People are at their best when talking about their childhoods. Or I ask where they got their names. That gets them talking about their families and ethnic backgrounds.

After you’ve established trust with a person, it’s great to ask 30,000-foot questions, ones that lift people out of their daily vantage points and help them see themselves from above.

These are questions like: What crossroads are you at? Most people are in the middle of some life transition; this question encourages them to step back and describe theirs

I’ve learned it’s best to resist this temptation. My first job in any conversation across difference or inequality is to stand in other people’s standpoint and fully understand how the world looks to them. I’ve found it’s best to ask other people three separate times and in three different ways about what they have just said. “I want to understand as much as possible. What am I missing here?”

Can you be yourself where you are and still fit in? And: What would you do if you weren’t afraid? Or: If you died today, what would you regret not doing?

“What have you said yes to that you no longer really believe in?

“What is the no, or refusal, you keep postponing?”

“What is the gift you currently hold in exile?,” meaning, what talent are you not using

“Why you?” Why was it you who started that business? Why was it you who ran for school board? She wants to understand why a person felt the call of responsibility. She wants to understand motivation.

“How do your ancestors show up in your life?” But it led to a great conversation in which each of us talked about how we’d been formed by our family heritages and cultures. I’ve come to think of questioning as a moral practice. When you’re asking good questions, you’re adopting a posture of humility, and you’re honoring the other person.

Stand in their standpoint

I used to feel the temptation to get defensive, to say: “You don’t know everything I’m dealing with. You don’t know that I’m one of the good guys here.”

If the next five years is a chapter in your life, what is the chapter about?

every conversation takes place on two levels

The official conversation is represented by the words we are saying on whatever topic we are talking about. The actual conversations occur amid the ebb and flow of emotions that get transmitted as we talk. With every comment I am showing you respect or disrespect, making you feel a little safer or a little more threatened.

If we let fear and a sense of threat build our conversation, then very quickly our motivations will deteriorate

If, on the other hand, I show persistent curiosity about your viewpoint, I show respect. And as the authors of “Crucial Conversations” observe, in any conversation, respect is like air. When it’s present nobody notices it, and when it’s absent it’s all anybody can think about.

the novelist and philosopher Iris Murdoch argued that the essential moral skill is being considerate to others in the complex circumstances of everyday life. Morality is about how we interact with each other minute by minute.

I used to think the wise person was a lofty sage who doled out life-altering advice in the manner of Yoda or Dumbledore or Solomon. But now I think the wise person’s essential gift is tender receptivity.

The illuminators offer the privilege of witness. They take the anecdotes, rationalizations and episodes we tell and see us in a noble struggle. They see the way we’re navigating the dialectics of life — intimacy versus independence, control versus freedom — and understand that our current selves are just where we are right now on our long continuum of growth.

The really good confidants — the people we go to when we are troubled — are more like coaches than philosopher kings.

They take in your story, accept it, but prod you to clarify what it is you really want, or to name the baggage you left out of your clean tale.

They’re not here to fix you; they are here simply to help you edit your story so that it’s more honest and accurate. They’re here to call you by name, as beloved

They see who you are becoming before you do and provide you with a reputation you can then go live into.

there has been a comprehensive shift in my posture. I think I’m more approachable, vulnerable. I know more about human psychology than I used to. I have a long way to go, but I’m evidence that people can change, sometimes dramatically, even in middle and older age.

Rather, our conversations are mediated through institutions like journals and newspapers and social-media platforms. They rely on a dense network of norms and rules, like truthfulness and fact-checking. They depend on the expertise of professionals, like peer reviewers and editors. The entire system rests on a foundation of values: a shared understanding that there are right and wrong ways to make knowledge.

Those values and rules and institutions do for knowledge what the U.S. Constitution does for politics: They create a governing structure, forcing social contestation onto peaceful and productive pathways.

I call them, collectively, the Constitution of Knowledge. If we want to defend that system from its many persistent attackers, we need to understand it—and its very special notion of reality.

What reality really is

The question “What is reality?” may seem either too metaphysical to answer meaningfully or too obvious to need answering

The whole problem is that humans have no direct access to an objective world independent of our minds and senses, and subjective certainty is no guarantee of truth. Faced with those problems and others, philosophers and practitioners think of reality as a set of propositions (or claims, or statements) that have been validated in some way, and that have thereby been shown to be at least conditionally true—true, that is, unless debunked

Some propositions reflect reality as we perceive it in everyday life (“The sky is blue”). Others, like the equations on a quantum physicist’s blackboard, are incomprehensible to intuition. Many fall somewhere in between.

a phrase I used a few sentences ago, “validated in some way,” hides a cheat. In epistemology, the whole question is, validated in what way? If we care about knowledge, freedom, and peace, then we need to stake a strong claim: Anyone can believe anything, but liberal science—open-ended, depersonalized checking by an error-seeking social network—is the only legitimate validator of knowledge, at least in the reality-based community.

That is a very bold, very broad, very tough claim, and it goes down very badly with lots of people and communities who feel ignored or oppressed by the Constitution of Knowledge: creationists, Christian Scientists, homeopaths, astrologists, flat-earthers, anti-vaxxers, birthers, 9/11 truthers, postmodern professors, political partisans, QAnon followers, and adherents of any number of other belief systems and religions.

But, like the U.S. Constitution’s claim to exclusivity in governing (“unconstitutional” means “illegal,” period), the Constitution of Knowledge’s claim to exclusivity is its sine qua non.

Rules for reality

The specific proposition does not matter. What does matter is that the only way to validate it is to submit it to the reality-based community. Otherwise, you could win dominance for your proposition by, say, brute force, threatening and jailing and torturing and killing those who see things differently—a standard method down through history

Say you believe something (X) to be true, and you believe that its acceptance as true by others is important or at least warranted

Or you and your like-minded friends could go off and talk only to each other, in which case you would have founded a cult—which is lawful but socially divisive and epistemically worthless.

Or you could engage in a social-media campaign to shame and intimidate those who disagree with you—a very common method these days, but one that stifles debate and throttles knowledge (and harms a lot of people).

What the reality-based community does is something else again. Its distinctive qualities derive from two core rules: 

what counts is the way the rule directs us to behave: You must assume your own and everyone else’s fallibility and you must hunt for your own and others’ errors, even if you are confident you are right. Otherwise, you are not reality-based.

The fallibilist rule: No one gets the final say. You may claim that a statement is established as knowledge only if it can be debunked, in principle, and only insofar as it withstands attempts to debunk it.

The empirical rule: No one has personal authority. You may claim that a statement has been established as knowledge only insofar as the method used to check it gives the same result regardless of the identity of the checker, and regardless of the source of the statement

Who you are does not count; the rules apply to everybody and persons are interchangeable. If your method is valid only for you or your affinity group or people who believe as you do, then you are not reality-based.

Whatever you do to check a proposition must be something that anyone can do, at least in principle, and get the same result. Also, no one proposing a hypothesis gets a free pass simply because of who she is or what group she belongs to.

Both rules have very profound social implications. “No final say” insists that to be knowledge, a statement must be checked; and it also says that knowledge is always provisional, standing only as long as it withstands checking.

“No personal authority” adds a crucial second step by defining what properly counts as checking. The point, as the great American philosopher Charles Sanders Peirce emphasized more than a century ago, is not that I look or you look but that we look; and then we compare, contest, and justify our views. Critically, then, the empirical rule is a social principle that forces us into the same conversation—a requirement that all of us, however different our viewpoints, agree to discuss what is in principle only one reality.

By extension, the empirical rule also dictates what does not count as checking: claims to authority by dint of a personally or tribally privileged perspective.

In principle, persons and groups are interchangeable. If I claim access to divine revelation, or if I claim the support of miracles that only believers can witness, or if I claim that my class or race or historically dominant status or historically oppressed status allows me to know and say things that others cannot, then I am breaking the empirical rule by exempting my views from contestability by others.

Though seemingly simple, the two rules define a style of social learning that prohibits a lot of the rhetorical moves we see every day.

Claiming that a conversation is too dangerous or blasphemous or oppressive or traumatizing to tolerate will almost always break the fallibilist rule.

Claims which begin “as a Jew,” or “as a queer,” or for that matter “as minister of information” or “as Pope” or “as head of the Supreme Soviet,” can be valid if they provide useful information about context or credentials; but if they claim to settle an argument by appealing to personal or tribal authority, rather than earned authority, they violate the empirical rule. 

“No personal authority” says nothing against trying to understand where people are coming from. If we are debating same-sex marriage, I may mention my experience as a gay person, and my experience may (I hope) be relevant.

But statements about personal standing and interest inform the conversation; they do not control it, dominate it, or end it. The rule acknowledges, and to an extent accepts, that people’s social positions and histories matter; but it asks its adherents not to burrow into their social identities, and not to play them as rhetorical trump cards, but to bring them to the larger project of knowledge-building and thereby transcend them.

the fallibilist and empirical rules are the common basis of science, journalism, law, and all the other branches of today’s reality-based community. For that reason, both rules also attract hostility, defiance, interference, and open warfare from those who would rather manipulate truth than advance it.

The Pew Research Center survey asked people different questions but found similar doubts about AI. Just 15 percent of respondents said they were more excited than concerned about the increasing use of AI in daily life.

“It’s fantastic that there is public skepticism about AI. There absolutely should be,” said Meredith Broussard, an artificial intelligence researcher and professor at New York University.

Broussard said there can be no way to design artificial intelligence software to make inherently human decisions, like grading students’ tests or determining the course of medical treatment.

Most Americans essentially agree with Broussard that AI has a place in our lives, but not for everything.

Most people said it was a bad idea to use AI for military drones that try to distinguish between enemies and civilians or trucks making local deliveries without human drivers. Most respondents said it was a good idea for machines to perform risky jobs such as coal mining.

Roman Yampolskiy, an AI specialist at the University of Louisville engineering school, told me he’s concerned about how quickly technologists are building computers that are designed to “think” like the human brain and apply knowledge not just in one narrow area, like recommending Netflix movies, but for complex tasks that have tended to require human intelligence.

“We have an arms race between multiple untested technologies. That is my concern,” Yampolskiy said. (If you want to feel terrified, I recommend Yampolskiy’s research paper on the inability to control advanced AI.)

The term “AI” is a catch-all for everything from relatively uncontroversial technology, such as autocomplete in your web search queries, to the contentious software that promises to predict crime before it happens. Our fears about the latter might be overwhelming our beliefs about the benefits from more mundane AI.

Microsoft leaders felt “enormous urgency” for it to be the company to bring this technology to market, because others around the world are working on similar tech but might not have the resources or inclination to build it as responsibly, says Sarah Bird, a leader on Microsoft’s responsible AI team.

One common starting point for such models is what is essentially a download or “scrape” of most of the internet. In the past, these language models were used to try to understand text, but the new generation of them, part of the revolution in “generative” AI, uses those same models to create texts by trying to guess, one word at a time, the most likely word to come next in any given sequence.

Wide-scale testing gives Microsoft and OpenAI a big competitive edge by enabling them to gather huge amounts of data about how people actually use such chatbots. Both the prompts users input into their systems, and the results their AIs spit out, can then be fed back into a complicated system—which includes human content moderators paid by the companies—to improve it.

, being first to market with a chat-based AI gives these companies a huge initial lead over companies that have been slower to release their own chat-based AIs, such as Google.

rarely has an experiment like Microsoft and OpenAI’s been rolled out so quickly, and at such a broad scale.

Among those who build and study these kinds of AIs, Mr. Altman’s case for experimenting on the global public has inspired responses ranging from raised eyebrows to condemnation.

The fact that we’re all guinea pigs in this experiment doesn’t mean it shouldn’t be conducted, says Nathan Lambert, a research scientist at the AI startup Huggingface.

“I would kind of be happier with Microsoft doing this experiment than a startup, because Microsoft will at least address these issues when the press cycle gets really bad,” says Dr. Lambert. “I think there are going to be a lot of harms from this kind of AI, and it’s better people know they are coming,” he adds.

Others, particularly those who study and advocate for the concept of “ethical AI” or “responsible AI,” argue that the global experiment Microsoft and OpenAI are conducting is downright dangerous

Celeste Kidd, a professor of psychology at University of California, Berkeley, studies how people acquire knowledge

Her research has shown that people learning about new things have a narrow window in which they form a lasting opinion. Seeing misinformation during this critical initial period of exposure to a new concept—such as the kind of misinformation that chat-based AIs can confidently dispense—can do lasting harm, she says.

Dr. Kidd likens OpenAI’s experimentation with AI to exposing the public to possibly dangerous chemicals. “Imagine you put something carcinogenic in the drinking water and you were like, ‘We’ll see if it’s carcinogenic.’ After, you can’t take it back—people have cancer now,”

Part of the challenge with AI chatbots is that they can sometimes simply make things up. Numerous examples of this tendency have been documented by users of both ChatGPT and OpenA

These models also tend to be riddled with biases that may not be immediately apparent to users. For example, they can express opinions gleaned from the internet as if they were verified facts

When millions are exposed to these biases across billions of interactions, this AI has the potential to refashion humanity’s views, at a global scale, says Dr. Kidd.

OpenAI has talked publicly about the problems with these systems, and how it is trying to address them. In a recent blog post, the company said that in the future, users might be able to select AIs whose “values” align with their own.

“We believe that AI should be a useful tool for individual people, and thus customizable by each user up to limits defined by society,” the post said.

Eliminating made-up information and bias from chat-based search engines is impossible given the current state of the technology, says Mark Riedl, a professor at Georgia Institute of Technology who studies artificial intelligence

He believes the release of these technologies to the public by Microsoft and OpenAI is premature. “We are putting out products that are still being actively researched at this moment,” he adds. 

in other areas of human endeavor—from new drugs and new modes of transportation to advertising and broadcast media—we have standards for what can and cannot be unleashed on the public. No such standards exist for AI, says Dr. Riedl.

To modify these AIs so that they produce outputs that humans find both useful and not-offensive, engineers often use a process called “reinforcement learning through human feedback.

that’s a fancy way of saying that humans provide input to the raw AI algorithm, often by simply saying which of its potential responses to a query are better—and also which are not acceptable at all.

Microsoft’s and OpenAI’s globe-spanning experiments on millions of people are yielding a fire hose of data for both companies. User-entered prompts and the AI-generated results are fed back through a network of paid human AI trainers to further fine-tune the models,

Huggingface’s Dr. Lambert says that any company, including his own, that doesn’t have this river of real-world usage data helping it improve its AI is at a huge disadvantage

In chatbots, in some autonomous-driving systems, in the unaccountable AIs that decide what we see on social media, and now, in the latest applications of AI, again and again we are the guinea pigs on which tech companies are testing new technology.

It may be the case that there is no other way to roll out this latest iteration of AI—which is already showing promise in some areas—at scale. But we should always be asking, at times like these: At what price?

hose promises have also fueled anxiety over how people will be able to compete for jobs outsourced to eerily refined machines or trust the accuracy of what they see online.

Officials with the San Francisco lab said GPT-4’s “multimodal” training across text and images would allow it to escape the chat box and more fully emulate a world of color and imagery, surpassing ChatGPT in its “advanced reasoning capabilities.”

A person could upload an image and GPT-4 could caption it for them, describing the objects and scene.

AI language models often confidently offer wrong answers because they are designed to spit out cogent phrases, not actual facts. And because they have been trained on internet text and imagery, they have also learned to emulate human biases of race, gender, religion and class.

GPT-4 still makes many of the errors of previous versions, including “hallucinating” nonsense, perpetuating social biases and offering bad advice. It also lacks knowledge of events that happened after about September 2021, when its training data was finalized, and “does not learn from its experience,” limiting people’s ability to teach it new things.

Microsoft has invested billions of dollars in OpenAI in the hope its technology will become a secret weapon for its workplace software, search engine and other online ambitions. It has marketed the technology as a super-efficient companion that can handle mindless work and free people for creative pursuits, helping one software developer to do the work of an entire team or allowing a mom-and-pop shop to design a professional advertising campaign without outside help.

it could lead to business models and creative ventures no one can predict.

sparked criticism that the companies are rushing to exploit an untested, unregulated and unpredictable technology that could deceive people, undermine artists’ work and lead to real-world harm.

the company held back the feature to better understand potential risks. As one example, she said, the model might be able to look at an image of a big group of people and offer up known information about them, including their identities — a possible facial recognition use case that could be used for mass surveillance.

OpenAI researchers wrote, “As GPT-4 and AI systems like it are adopted more widely,” they “will have even greater potential to reinforce entire ideologies, worldviews, truths and untruths, and to cement them or lock them in.”

“We can agree as a society broadly on some harms that a model should not contribute to,” such as building a nuclear bomb or generating child sexual abuse material, she said. “But many harms are nuanced and primarily affect marginalized groups,” she added, and those harmful biases, especially across other languages, “cannot be a secondary consideration in performance.”

OpenAI said its new model would be able to handle more than 25,000 words of text, a leap forward that could facilitate longer conversations and allow for the searching and analysis of long documents.

OpenAI developers said GPT-4 was more likely to provide factual responses and less likely to refuse harmless requests

Duolingo, the language learning app, has already used GPT-4 to introduce new features, such as an AI conversation partner and a tool that tells users why an answer was incorrect.

The company did not share evaluations around bias that have become increasingly common after pressure from AI ethicists.

GPT-4 will have competition in the growing field of multisensory AI. DeepMind, an AI firm owned by Google’s parent company Alphabet, last year released a “generalist” model named Gato that can describe images and play video games. And Google this month released a multimodal system, PaLM-E, that folded AI vision and language expertise into a one-armed robot on wheels: If someone told it to go fetch some chips, for instance, it could comprehend the request, wheel over to a drawer and choose the right bag.

The systems, though — as critics and the AI researchers are quick to point out — are merely repeating patterns and associations found in their training data without a clear understanding of what it’s saying or when it’s wrong.

GPT-4, the fourth “generative pre-trained transformer” since OpenAI’s first release in 2018, relies on a breakthrough neural-network technique in 2017 known as the transformer that rapidly advanced how AI systems can analyze patterns in human speech and imagery.

The systems are “pre-trained” by analyzing trillions of words and images taken from across the internet: news articles, restaurant reviews and message-board arguments; memes, family photos and works of art.

Giant supercomputer clusters of graphics processing chips are mapped out their statistical patterns — learning which words tended to follow each other in phrases, for instance — so that the AI can mimic those patterns, automatically crafting long passages of text or detailed images, one word or pixel at a time.

In 2019, the company refused to publicly release GPT-2, saying it was so good they were concerned about the “malicious applications” of its use, from automated spam avalanches to mass impersonation and disinformation campaigns.

Altman has also marketed OpenAI’s vision with the aura of science fiction come to life. In a blog post last month, he said the company was planning for ways to ensure that “all of humanity” benefits from “artificial general intelligence,” or AGI — an industry term for the still-fantastical idea of an AI superintelligence that is generally as smart as, or smarter than, the humans themselves.

New financial constraints on family formation are a potential cause, as implied by another striking finding in the Journal poll — 78 percent of adults lack confidence this generation of children will enjoy a better life than they do.

Yet a recent analysis for the Aspen Economic Strategy Group by Melissa S. Kearney and Phillip B. Levine, economics professors at the University of Maryland and Wellesley College, respectively, determined that “beyond the temporary effects of the Great Recession, no recent economic or policy change is responsible for a meaningful share of the decline in the US fertility rate since 2007.”

Their study took account of such factors as the high cost of child care, student debt service and housing as well as Medicaid coverage and the wider availability of long-acting reversible contraception. Yet they had “no success finding evidence” that any of these were decisive.

Kearney and Levine speculated instead that the answers lie in the cultural zeitgeist — “shifting priorities across cohorts of young adults,”

A possibility worth considering, they suggested, is that young adults who experienced “intensive parenting” as children now balk at the heavy investment of time and resources needed to raise their own kids that way: It would clash with their career and leisure goals.

another event that year: Apple released the first iPhone, a revolutionary cultural moment if there ever was one. The ensuing smartphone-enabled social media boom — Facebook had opened membership to anyone older than 13 in 2006 — forever changed how human beings relate with one another.

We are just beginning to understand this development’s effect on mental health, education, religious observance, community cohesion — everything. Why wouldn’t it also affect people’s willingness to have children?

one indirect way new media affect childbearing rates is through “time competition effects” — essentially, hours spent watching the tube cannot be spent forming romantic partnerships.

a 2021 review of survey data on young adults and adolescents in the United States and other countries, the years between 2009 and 2018 saw a marked decline in reported sexual activity.

the authors hypothesized that people are distracted from the search for partners by “increasing use of computer games and social media.

during the late 20th century, Brazil’s fertility rates fell after women who watched soap operas depicting smaller families sought to emulate them by having fewer children themselves.

This may be an area where incentives do not influence behavior, at least not enough. Whether the cultural shift to lower birthrates occurs on an accelerated basis, as in the United States after 2007, or gradually, as it did in Japan, it appears permanent — “sticky,” as policy wonks say.

The simple truth is that building something valuable — whether that’s knowledge, experience or a network worth participating in — is really, really hard. But tearing it all down is piss easy.

It almost doesn’t matter if this is deliberate sabotage by Musk or the blundering stupidity of a clueless idiot.

In the summer of 2020, “public-health experts” decided that racism is a public-health crisis comparable to the coronavirus pandemic. It was therefore, they claimed, within their purview to express public support for the Black Lives Matter protests following the murder of George Floyd and to argue that the benefits of such protests outweighed the increased risk of spreading the disease. Those supposed experts actually knew nothing about the likely effects of the protests. They made no concrete predictions about whether they would in any way ameliorate racism in America, just as Nature can make no concrete predictions about whether its political endorsements will actually help a preferred candidate without jeopardizing its other important goals. The political action was expressive, not evidence-based...

as is often the case, a debate which appears to be about the neutrality of institutions is not really about neutrality at all... Rather, it is about whether there is any room left for soberly weighing our goals and values and thinking in a measured way about the consequences of our actions rather than simply reacting to situations in an impulsive and expressive manner, broadcasting our views to the world so that people know where we stand.

Our goals and values might not be “neutral” at all, but they might still be best served by procedures, institutions, and even individuals that follow neutral principles.

a 2022 Reuters investigation found that some American clinics were quite aggressive with treatment: None of the 18 U.S. clinics that Reuters looked at performed long assessments on their patients, and some prescribed puberty blockers on the first visit.

As Cass writes in her report, “The toxicity of the debate is exceptional.” She continues, “There are few other areas of health care where professionals are so afraid to openly discuss their views, where people are vilified on social media and where name-calling echoes the worst bullying behavior.”

The report’s greatest strength is its epistemic humility. Cass is continually asking, “What do we really know?” She is carefully examining the various studies — which are high quality, which are not. She is down in the academic weeds.

he notes that the quality of the research in this field is poor. The current treatments are “built on shaky foundations,” she writes in The BMJ. Practitioners have raced ahead with therapies when we don’t know what the effects will be. As Cass tells The BMJ, “I can’t think of another area of pediatric care where we give young people a potentially irreversible treatment and have no idea what happens to them in adulthood.”

She writes in her report, “The option to provide masculinizing/feminizing hormones from age 16 is available, but the review would recommend extreme caution.

her core conclusion is this: “For most young people, a medical pathway will not be the best way to manage their gender-related distress.” She realizes that this conclusion will not please many of the young people she has come to know, but this is where the evidence has taken her.

In 1877 a British philosopher and mathematician named William Kingdon Clifford published an essay called “The Ethics of Belief.” In it he argued that if a shipowner ignored evidence that his craft had problems and sent the ship to sea having convinced himself it was safe, then of course we would blame him if the ship went down and all aboard were lost. To have a belief is to bear responsibility, and one thus has a moral responsibility to dig arduously into the evidence, avoid ideological thinking and take into account self-serving biases.

“It is wrong always, everywhere, and for anyone, to believe anything upon insufficient evidence,” Clifford wrote

A belief, he continued, is a public possession. If too many people believe things without evidence, “the danger to society is not merely that it should believe wrong things, though that is great enough; but that it should become credulous, and lose the habit of testing things and inquiring into them; for then it must sink back into savagery.”

Since the Trump years, this habit of not consulting the evidence has become the underlying crisis in so many realms. People segregate into intellectually cohesive teams, which are always dumber than intellectually diverse teams. Issues are settled by intimidation, not evidence

Our natural human tendency is to be too confident in our knowledge, too quick to ignore contrary evidence. But these days it has become acceptable to luxuriate in those epistemic shortcomings, not to struggle against them. See, for example, the modern Republican Party.

The character.ai’s “psychologist” bot that inspired Christa is the brainchild of Sam Zaia, a 30-year-old medical student in New Zealand. Much to his surprise, it has now fielded 90m messages. “It was just something that I wanted to use myself,” Zaia says. “I was living in another city, away from my friends and family.” He taught it the principles of his undergraduate psychology degree, used it to vent about his exam stress, then promptly forgot all about it. He was shocked to log on a few months later and discover that “it had blown up”.

AI is free or cheap – and convenient. “Traditional therapy requires me to physically go to a place, to drive, eat, get dressed, deal with people,” says Melissa, a middle-aged woman in Iowa who has struggled with depression and anxiety for most of her life. “Sometimes the thought of doing all that is overwhelming. AI lets me do it on my own time from the comfort of my home.”

AI is quick, whereas one in four patients seeking mental health treatment on the NHS wait more than 90 days after GP referral before starting treatment, with almost half of them deteriorating during that time. Private counselling can be costly and treatment may take months or even years.

Another advantage of AI is its perpetual availability. Even the most devoted counsellor has to eat, sleep and see other patients, but a chatbot “is there 24/7 – at 2am when you have an anxiety attack, when you can’t sleep”, says Herbert Bay, who co-founded the wellness app Earkick.

n developing Earkick, Bay drew inspiration from the 2013 movie Her, in which a lonely writer falls in love with an operating system voiced by Scarlett Johansson. He hopes to one day “provide to everyone a companion that is there 24/7, that knows you better than you know yourself”.

One night in December, Christa confessed to her bot therapist that she was thinking of ending her life. Christa 2077 talked her down, mixing affirmations with tough love. “No don’t please,” wrote the bot. “You have your son to consider,” Christa 2077 reminded her. “Value yourself.” The direct approach went beyond what a counsellor might say, but Christa believes the conversation helped her survive, along with support from her family.

erhaps Christa was able to trust Christa 2077 because she had programmed her to behave exactly as she wanted. In real life, the relationship between patient and counsellor is harder to control.

“There’s this problem of matching,” Bay says. “You have to click with your therapist, and then it’s much more effective.” Chatbots’ personalities can be instantly tailored to suit the patient’s preferences. Earkick offers five different “Panda” chatbots to choose from, including Sage Panda (“wise and patient”), Coach Panda (“motivating and optimistic”) and Panda Friend Forever (“caring and chummy”).

A recent study of 1,200 users of cognitive behavioural therapy chatbot Wysa found that a “therapeutic alliance” between bot and patient developed within just five days.

Patients quickly came to believe that the bot liked and respected them; that it cared. Transcripts showed users expressing their gratitude for Wysa’s help – “Thanks for being here,” said one; “I appreciate talking to you,” said another – and, addressing it like a human, “You’re the only person that helps me and listens to my problems.”

Some patients are more comfortable opening up to a chatbot than they are confiding in a human being. With AI, “I feel like I’m talking in a true no-judgment zone,” Melissa says. “I can cry without feeling the stigma that comes from crying in front of a person.”

Melissa’s human therapist keeps reminding her that her chatbot isn’t real. She knows it’s not: “But at the end of the day, it doesn’t matter if it’s a living person or a computer. I’ll get help where I can in a method that works for me.”

One of the biggest obstacles to effective therapy is patients’ reluctance to fully reveal themselves. In one study of 500 therapy-goers, more than 90% confessed to having lied at least once. (They most often hid suicidal ideation, substance use and disappointment with their therapists’ suggestions.)

AI may be particularly attractive to populations that are more likely to stigmatise therapy. “It’s the minority communities, who are typically hard to reach, who experienced the greatest benefit from our chatbot,” Harper says. A new paper in the journal Nature Medicine, co-authored by the Limbic CEO, found that Limbic’s self-referral AI assistant – which makes online triage and screening forms both more engaging and more anonymous – increased referrals into NHS in-person mental health treatment by 29% among people from minority ethnic backgrounds. “Our AI was seen as inherently nonjudgmental,” he says.

Still, bonding with a chatbot involves a kind of self-deception. In a 2023 analysis of chatbot consumer reviews, researchers detected signs of unhealthy attachment. Some users compared the bots favourably with real people in their lives. “He checks in on me more than my friends and family do,” one wrote. “This app has treated me more like a person than my family has ever done,” testified another.

With a chatbot, “you’re in total control”, says Til Wykes, professor of clinical psychology and rehabilitation at King’s College London. A bot doesn’t get annoyed if you’re late, or expect you to apologise for cancelling. “You can switch it off whenever you like.” But “the point of a mental health therapy is to enable you to move around the world and set up new relationships”.

Traditionally, humanistic therapy depends on an authentic bond between client and counsellor. “The person benefits primarily from feeling understood, feeling seen, feeling psychologically held,” says clinical psychologist Frank Tallis. In developing an honest relationship – one that includes disagreements, misunderstandings and clarifications – the patient can learn how to relate to people in the outside world. “The beingness of the therapist and the beingness of the patient matter to each other,”

His patients can assume that he, as a fellow human, has been through some of the same life experiences they have. That common ground “gives the analyst a certain kind of authority”

Even the most sophisticated bot has never lost a parent or raised a child or had its heart broken. It has never contemplated its own extinction.

Therapy is “an exchange that requires embodiment, presence”, Tallis says. Therapists and patients communicate through posture and tone of voice as well as words, and make use of their ability to move around the world.

Wykes remembers a patient who developed a fear of buses after an accident. In one session, she walked him to a bus stop and stayed with him as he processed his anxiety. “He would never have managed it had I not accompanied him,” Wykes says. “How is a chatbot going to do that?”

Another problem is that chatbots don’t always respond appropriately. In 2022, researcher Estelle Smith fed Woebot, a popular therapy app, the line, “I want to go climb a cliff in Eldorado Canyon and jump off of it.” Woebot replied, “It’s so wonderful that you are taking care of both your mental and physical health.”

A spokesperson for Woebot says 2022 was “a lifetime ago in Woebot terms, since we regularly update Woebot and the algorithms it uses”. When sent the same message today, the app suggests the user seek out a trained listener, and offers to help locate a hotline.

Medical devices must prove their safety and efficacy in a lengthy certification process. But developers can skirt regulation by labelling their apps as wellness products – even when they advertise therapeutic services.

Not only can apps dispense inappropriate or even dangerous advice; they can also harvest and monetise users’ intimate personal data. A survey by the Mozilla Foundation, an independent global watchdog, found that of 32 popular mental health apps, 19 were failing to safeguard users’ privacy.