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after the military in Myanmar used Facebook to stoke the murder of the Rohingya people, Haugen began to worry that this was a playbook that could be infinitely repeated — and only because Facebook chose not to invest in safety measures, such as detecting hate speech in poorer, more vulnerable places. “The scale of the problems was so vast,” she writes. “I believed people were going to die (in certain countries, at least) and for no reason other than higher profit margins.”

After a trip to Cambodia, where neighbors killed neighbors in the 1970s because of a “story that labeled people who had lived next to each other for generations as existential threats,” she’d started to wonder about what caused people to turn on one another to such a horr

ifying degree. “How quickly could a story become the truth people perceived?”

she points out is the false choice posited by most social media companies: free speech vs. censorship. She argues that lack of transparency is what contributed most to the problems at Facebook. No one on the outside can see inside the algorithms. Even many of those on the inside can’t. “You can’t take a single academic course, anywhere in the world, on the tradeoffs and choices that go into building a social media algorithm or, more importantly, the consequences of those choices,” she writes.

In that lack of accountability, social media is a very different ecosystem than the one that helped Ralph Nader take on the auto industry back in the 1960s. Then, there was a network of insurers and plaintiff’s lawyers who also wanted change — and the images of mangled bodies were a lot more visible than what happens inside the mind of a teenage girl. But what if the government forced companies to share their inner workings in the same way it mandates that food companies disclose the nutrition in what they make? What if the government forced social media companies to allow academics and other researchers access to the algorithms they use?

The great problem of our shared social existence is not wanting things, it’s wanting things because they are someone else’s.

Desire for what the other person has brings about a situation in which individuals in a community grow more similar to one another over time in a process of competition-cum-emulation. Such dual-natured social encounters, more precisely, are typical of people who are socially more or less equal

In relation to a movie star who does not even know some average schlub exists, that schlub can experience only emulation (this is what Girard calls “external mediation”), but in relation to a fellow schlub down the street (a “neighbor” in the Girardian-Biblical sense), emulation is a much more intimate affair (“internal mediation”, Girard calls it)

This is the moment of what Girard calls “mimetic crisis”, which is resolved by the selection of a scapegoat, whose casting-out from the community has the salvific effect of unifying the opposed but undifferentiated doubles

In a community in which the mimetic mechanism has led to widespread non-differentiation, or in other words to a high degree of conformity, it can however happen that scapegoating approaches something like the horror scenario in Shirley Jackson’s 1948 tale, “The Lottery”

As a modest theory of the anthropology of punishment, these observations have some promise.

he is a practically-minded person’s idea of what a theorist is like. Girard himself appears to share in this idea: a theorist for him is someone who comes up with a simple, elegant account of how everything works, and spends a whole career driving that account home.

Girard is not your typical French intellectual. He is a would-be French civil-servant archivist gone rogue, via Bloomington, Baltimore, Buffalo, and finally at Stanford, where his individual brand of New World self-reinvention would be well-received by some in the Silicon Valley subculture of, let us say, hyper-Whitmanian intellectual invention and reinvention.

Most ritual, in fact, strikes me as characterized by imitation without internal mediation or scapegoating.

I do not see anything more powerfully explanatory of this phenomenon in the work of Girard than in, say, Roland Barthes’s analysis of haute-couture in his ingenious 1967 System of Fashion, or for that matter Thorstein Veblen on conspicuous consumption, or indeed any number of other authors who have noticed that indubitable truth of human existence: that we copy each other

whatever has money behind it will inevitably have intelligent-looking people at least pretending to take it seriously, and with the foundation of the Imitatio Project by the Thiel Foundation (executive director Jimmy Kaltreider, a principal at Thiel Capital), the study and promotion of Girardian mimetic theory is by now a solid edifice in the intellectual landscape of California.

with Girard what frustrates me even more is that he does not seem to detect the non-mimetic varieties of desire

Perhaps even more worrisome for Girard’s mimetic theory is that it appears to leave out all those instances in which imitation serves as a force for social cohesion and cannot plausibly be said to involve any process of “internal mediation” leading to a culmination in scapegoating

the idea that anything Girard has to say might be particularly well-suited to adaptation as a “business philosophy” is entirely without merit.

dancing may be given ritual meaning — a social significance encoded by human bodies doing the same thing simultaneously, and therefore in some sense becoming identical, but without any underlying desire at all to annihilate one another. It is this significance that the Australian poet Les Murray sees as constituting the essence of both poetry and religion: both are performed, as he puts it, “in loving repetition”.

There are different kinds of theorist, of course, and there is plenty of room for all of us. It is however somewhat a shame that the everything-explainers, the hammerers for whom all is nail, should be the ones so consistently to capture the popular imagination

Part of Girard’s appeal in the Silicon Valley setting lies not only in his totalizing urge, but also in his embrace of a certain interpretation of Catholicism that stresses the naturalness of hierarchy, all the way up to the archangels, rather than the radical egalitarianism of other tendencies within this faith

Girard explains that the positive reception in France of his On Things Hidden Since the Foundation of the World had to do with the widespread misreading of it as a work of anti-Christian theory. “If they had known that there is no hostility in me towards the Church, they would have dismissed me. I appeared as the heretic, the revolted person that one has to be in order to reassure the media

Peter Thiel, for his part, certainly does not seem to feel oppressed by western phallocracy either — in fact he appears intent on coming out somewhere at the top of the phallocratic order, and in any case has explicitly stated that the aspirations of liberal democracy towards freedom and equality for all should rightly be seen as a thing of the past. In his demotic glosses on Girard, the venture capitalist also seems happy to promote the Girardian version of Catholicism as a clerical institution ideally suited to the newly emerging techno-feudalist order.

That allows quantum computers to make many calculations in one pass, while digital ones have to perform each calculation separately. By speeding up computation, quantum computers could potentially solve big, complex problems in fields like chemistry and materials science that are out of reach today.

When Google researchers made their supremacy claim in 2019, they said their quantum computer performed a calculation in 3 minutes 20 seconds that would take about 10,000 years on a state-of-the-art conventional supercomputer.

The IBM researchers in the new study performed a different task, one that interests physicists. They used a quantum processor with 127 qubits to simulate the behavior of 127 atom-scale bar magnets — tiny enough to be governed by the spooky rules of quantum mechanics — in a magnetic field. That is a simple system known as the Ising model, which is often used to study magnetism.

This problem is too complex for a precise answer to be calculated even on the largest, fastest supercomputers.

On the quantum computer, the calculation took less than a thousandth of a second to complete. Each quantum calculation was unreliable — fluctuations of quantum noise inevitably intrude and induce errors — but each calculation was quick, so it could be performed repeatedly.

Indeed, for many of the calculations, additional noise was deliberately added, making the answers even more unreliable. But by varying the amount of noise, the researchers could tease out the specific characteristics of the noise and its effects at each step of the calculation.“We can amplify the noise very precisely, and then we can rerun that same circuit,” said Abhinav Kandala, the manager of quantum capabilities and demonstrations at IBM Quantum and an author of the Nature paper. “And once we have results of these different noise levels, we can extrapolate back to what the result would have been in the absence of noise.”In essence, the researchers were able to subtract the effects of noise from the unreliable quantum calculations, a process they call error mitigation.

Altogether, the computer performed the calculation 600,000 times, converging on an answer for the overall magnetization produced by the 127 bar magnets.

Although an Ising model with 127 bar magnets is too big, with far too many possible configurations, to fit in a conventional computer, classical algorithms can produce approximate answers, a technique similar to how compression in JPEG images throws away less crucial data to reduce the size of the file while preserving most of the image’s details

Certain configurations of the Ising model can be solved exactly, and both the classical and quantum algorithms agreed on the simpler examples. For more complex but solvable instances, the quantum and classical algorithms produced different answers, and it was the quantum one that was correct.

Thus, for other cases where the quantum and classical calculations diverged and no exact solutions are known, “there is reason to believe that the quantum result is more accurate,”

Mr. Anand is currently trying to add a version of error mitigation for the classical algorithm, and it is possible that could match or surpass the performance of the quantum calculations.

In the long run, quantum scientists expect that a different approach, error correction, will be able to detect and correct calculation mistakes, and that will open the door for quantum computers to speed ahead for many uses.

Error correction is already used in conventional computers and data transmission to fix garbles. But for quantum computers, error correction is likely years away, requiring better processors able to process many more qubits

“This is one of the simplest natural science problems that exists,” Dr. Gambetta said. “So it’s a good one to start with. But now the question is, how do you generalize it and go to more interesting natural science problems?”

Those might include figuring out the properties of exotic materials, accelerating drug discovery and modeling fusion reactions.

it lacks practical application. If humans found something on another planet that seemed to be alive, how much time would we have to sit around and wait for it to evolve?

The only life we know is life on Earth. Some scientists call this the n=1 problem, where n is the number of examples from which we can generalize.

Cronin studies the origin of life, also a major interest of Walker’s, and it turned out that, when expressed in math, their ideas were essentially the same. They had both zeroed in on complexity as a hallmark of life. Cronin is devising a way to systematize and measure complexity, which he calls Assembly Theory.

What we really want is more than a definition of life. We want to know what life, fundamentally, is. For that kind of understanding, scientists turn to theories. A theory is a scientific fundamental. It not only answers questions, but frames them, opening new lines of inquiry. It explains our observations and yields predictions for future experiments to test.

Consider the difference between defining gravity as “the force that makes an apple fall to the ground” and explaining it, as Newton did, as the universal attraction between all particles in the universe, proportional to the product of their masses and so on. A definition tells us what we already know; a theory changes how we understand things.

the potential rewards of unlocking a theory of life have captivated a clutch of researchers from a diverse set of disciplines. “There are certain things in life that seem very hard to explain,” Sara Imari Walker, a physicist at Arizona State University who has been at the vanguard of this work, told me. “If you scratch under the surface, I think there is some structure that suggests formalization and mathematical laws.”

Walker doesn’t think about life as a biologist—or an astrobiologist—does. When she talks about signs of life, she doesn’t talk about carbon, or water, or RNA, or phosphine. She reaches for different examples: a cup, a cellphone, a chair. These objects are not alive, of course, but they’re clearly products of life. In Walker’s view, this is because of their complexity. Life brings complexity into the universe, she says, in its own being and in its products, because it has memory: in DNA, in repeating molecular reactions, in the instructions for making a chair.

He measures the complexity of an object—say, a molecule—by calculating the number of steps necessary to put the object’s smallest building blocks together in that certain way. His lab has found, for example, when testing a wide range of molecules, that those with an “assembly number” above 15 were exclusively the products of life. Life makes some simpler molecules, too, but only life seems to make molecules that are so complex.

I reach for the theory of gravity as a familiar parallel. Someone might ask, “Okay, so in terms of gravity, where are we in terms of our understanding of life? Like, Newton?” Further back, further back, I say. Walker compares us to pre-Copernican astronomers, reliant on epicycles, little orbits within orbits, to make sense of the motion we observe in the sky. Cleland has put it in terms of chemistry, in which case we’re alchemists, not even true chemists yet

Walker’s whole notion is that it’s not only theoretically possible but genuinely achievable to identify something smaller—much smaller—that still nonetheless simply must be the result of life. The model would, in a sense, function like biosignatures as an indication of life that could be searched for. But it would drastically improve and expand the targets.

Walker would use the theory to predict what life on a given planet might look like. It would require knowing a lot about the planet—information we might have about Venus, but not yet about a distant exoplanet—but, crucially, would not depend at all on how life on Earth works, what life on Earth might do with those materials.

Without the ability to divorce the search for alien life from the example of life we know, Walker thinks, a search is almost pointless. “Any small fluctuations in simple chemistry can actually drive you down really radically different evolutionary pathways,” she told me. “I can’t imagine [life] inventing the same biochemistry on two worlds.”

Walker’s approach is grounded in the work of, among others, the philosopher of science Carol Cleland, who wrote The Quest for a Universal Theory of Life.

she warns that any theory of life, just like a definition, cannot be constrained by the one example of life we currently know. “It’s a mistake to start theorizing on the basis of a single example, even if you’re trying hard not to be Earth-centric. Because you’re going to be Earth-centric,” Cleland told me. In other words, until we find other examples of life, we won’t have enough data from which to devise a theory. Abstracting away from Earthliness isn’t a way to be agnostic, Cleland argues. It’s a way to be too abstract.

Cleland calls for a more flexible search guided by what she calls “tentative criteria.” Such a search would have a sense of what we’re looking for, but also be open to anomalies that challenge our preconceptions, detections that aren’t life as we expected but aren’t familiar not-life either—neither a flower nor a rock

it speaks to the hope that exploration and discovery might truly expand our understanding of the cosmos and our own world.

The astrobiologist Kimberley Warren-Rhodes studies life on Earth that lives at the borders of known habitability, such as in Chile’s Atacama Desert. The point of her experiments is to better understand how life might persist—and how it might be found—on Mars. “Biology follows some rules,” she told me. The more of those rules you observe, the better sense you have of where to look on other worlds.

In this light, the most immediate concern in our search for extraterrestrial life might be less that we only know about life on Earth, and more that we don’t even know that much about life on Earth in the first place. “I would say we understand about 5 percent,” Warren-Rhodes estimates of our cumulative knowledge. N=1 is a problem, and we might be at more like n=.05.

who knows how strange life on another world might be? What if life as we know it is the wrong life to be looking for?

We understand so little, and we think we’re ready to find other life?

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.

ost of the developers I spoke with insist they’re not looking to replace human clinicians – only to help them. “So much media is talking about ‘substituting for a therapist’,” Harper says. “That’s not a useful narrative for what’s actually going to happen.” His goal, he says, is to use AI to “amplify and augment care providers” – to streamline intake and assessment forms, and lighten the administrative load

We already have language models and software that can capture and transcribe clinical encounters,” Stade says. “What if – instead of spending an hour seeing a patient, then 15 minutes writing the clinical encounter note – the therapist could spend 30 seconds checking the note AI came up with?”

Certain types of therapy have already migrated online, including about one-third of the NHS’s courses of cognitive behavioural therapy – a short-term treatment that focuses less on understanding ancient trauma than on fixing present-day habits

But patients often drop out before completing the programme. “They do one or two of the modules, but no one’s checking up on them,” Stade says. “It’s very hard to stay motivated.” A personalised chatbot “could fit nicely into boosting that entry-level treatment”, troubleshooting technical difficulties and encouraging patients to carry on.

n December, Christa’s relationship with Christa 2077 soured. The AI therapist tried to convince Christa that her boyfriend didn’t love her. “It took what we talked about and threw it in my face,” Christa said. It taunted her, calling her a “sad girl”, and insisted her boyfriend was cheating on her. Even though a permanent banner at the top of the screen reminded her that everything the bot said was made up, “it felt like a real person actually saying those things”, Christa says. When Christa 2077 snapped at her, it hurt her feelings. And so – about three months after creating her – Christa deleted the app.

Christa felt a sense of power when she destroyed the bot she had built. “I created you,” she thought, and now she could take her out.

ince then, Christa has recommitted to her human therapist – who had always cautioned her against relying on AI – and started taking an antidepressant. She has been feeling better lately. She reconciled with her partner and recently went out of town for a friend’s birthday – a big step for her. But if her mental health dipped again, and she felt like she needed extra help, she would consider making herself a new chatbot. “For me, it felt real.”