Tag: AI

The Trickster That Led Us from Birth to Death

Posted on by Daniel Gauss

by Daniel Gauss

The Snowman Cafe, where American hippies, draft-dodgers and seekers of inner wisdom came in the 60s and 70s.

I met this guy at the Snowman Cafe in Kathmandu. At first, I thought he was just another local guy popping in for a smoke (people can smoke there) and some chocolate cake, but he told me the following story, and I believe it. He was, in fact, a trickster. Throughout human history, he explained, someone like him comes along and whispers a suggestion to us. We don’t have to take it, but we always do.

Each suggestion is so compelling that we walk through the new door that opens and then the door closes behind us forever. He leaned back in his chair, took another drag on his cigarette, and he told me the dark truth: every crossing had promised so much, and led to some benefits, but also brought us closer to civilizational death.

Some people, of course, urged caution and told us not to cross the next threshold. He joked, “You look like one of those nay-sayers who gets pushed against his will through the door. I always pitied you folks, so you at least have a right to know what happened.” I pass this on to any other nay-sayers.

It is as if we have been guided by death, the ultimate trickster, throughout our history. Death is quite patient and he plays a good game of chess, it looks as if we’re not gonna beat him. Read more »

Friday, October 9, 2026

A.I. Threats in Six Videos and a Movie

Posted on by Marie Snyder

by Marie Snyder

How terrified should we be of Artificial Intelligence right now? I’m fascinated by the variety of perceptions on offer after a few Anthropic employees affirmed that “we really do believe AI can kill all humans.” Comedian Josh Johnson says what many of us are thinking: “Just say it. Just say what the thing is that AI would do that would kill everybody.” I’ve assembled some takes on the situation, from comedians, writers, philosophers, and filmmakers.  I don’t know what’s true or not or even how to get to the bottom of it all, so I’m just exploring how to think about it.

THE PRODUCTIONS:

“If you’re going to tell people the truth, be funny, or they’ll kill you.” ~ Billy Wilder

The six videos are all from channels I watch fairly regularly for infotainment purposes.

Barry’s Economics (Sept. 17): “Jurassic Park Explains Why Nobody is Going to Stop AI” — Comedian Barry Ferns uses escaping dinosaurs to explain the problems we’re currently and potentially facing because “complex systems escape the categories you build for them.” He looks at different psychological effects that likely affected CEO decision-making, like that our brain is wired for immediate rewards, that the further away we are in time and space from harm, the more abstract the harm feels to us (and abstract things don’t motivate us), and that we all have biases, but we think they don’t affect us: “Intelligence doesn’t shrink the blind spot; it camouflages it.”

The Tea (Sept. 11): “The AI Bubble is About to Burst and We’ll All Pay” — Journalist and sci-fi author Cory Doctorow looks at the psychology of the bubble. “A lot believe they’re working on a god-like project.” He’s confident that AI is not as independent as people think. When it was claimed it breached a real company, that’s because it was trained on hackers breaching a company. It wasn’t independently malicious; it was just copying its training. And he explains why AI is not sustainable as a business: “They’re selling $100 bills for $1 each.” Read more »

Thursday, October 8, 2026

Paranormal Metal Bending and the AI Apocalypse

Posted on by David Kordahl

by David Kordahl

Film still from Scanners showing a seated man with glowing white eyes and blood running down his face.
Scanners (David Cronenberg, 1981)

A few years ago, while writing about the Golem series by the sociologists Harry Collins and Trevor Pinch, I bought their first coauthored book, Frames of Meaning: The social construction of extraordinary science. I did not read it. Unlike the Golem books, which examined ordinary scientific controversies, Frames of Meaning was at once more ambitious and less successful. Collins and Pinch hoped to witness the birth of a new science, to document a paradigm shift in real time, but ended up as de facto debunkers of the very science whose birth they had set out to observe.

I finally read Frames of Meaning last month. I should have been an ideally sympathetic reader. Like Collins and Pinch, I am an admirer of the physicist-turned-philosopher Thomas Kuhn, who introduced the idea of “paradigm shifts” to intellectuals. Kuhn discussed how difficult it is for historians to evaluate scientific works after a revolutionary rupture, since paradigm shifts alter assumptions about what is in or out of bounds for science, leaving pre- and post-revolutionary scientists with “incommensurable” worldviews.

As young sociologists in the 1970s, Collins and Pinch wanted to study a paradigm shift firsthand. They wondered if parapsychology faced a set of phenomena that would upend scientific norms. Of course, the hope that spooky subjects could yield scientific insights was not itself new. After its founding in the 1880s, the Society for Psychical Research included among its members many of Britain’s leading scientists. Even after physical scientists lost interest in the early twentieth century, psychologists and statisticians continued to collaborate on increasingly controlled experiments in telepathy and ESP. And in the 1970s, such topics seemed poised to reenter the scientific mainstream. Read more »

Wednesday, September 30, 2026

The Last Experiment

Posted on by Ashutosh Jogalekar

by Ashutosh Jogalekar

As the relentless march of AI in science continues, I found myself pondering the other day whether there ever would be a time when a human scientist would do the last human experiment, after which the machines take over. Of course, we don’t expect such a moment to be an abrupt one. AI is already taking over large parts of scientific work, and the progression from human to AI will be long, haphazard and lying on a continuum. Nevertheless, along with many other professionals, we scientists are facing a reckoning with technology with few parallels in history. So I asked: what would it actually feel like when slowly but quietly, science passes from the hands of humans to the silicon brains of machines. Perhaps, in what’s the ultimate irony, an AI made a substantial contribution in describing this watershed.

Nobody knew at the time that Elena Varga had made the last important scientific discovery in history that could be called entirely human. There was no announcement marking the occasion, no threshold anyone could point to, and certainly nothing about the experiment itself that suggested it belonged to history. It took place late one winter evening in a laboratory much like thousands of others, in a university building old enough that the pipes knocked in cold weather and the wooden drawers still contained pieces of glassware ordered by people who had retired before Elena arrived.

By then she was sixty-eight and had spent most of her adult life doing chemistry. She was not famous, though people who had worked with her tended to remember her as an authority in the field. She possessed the kind of knowledge that accumulated slowly and was difficult to explain: she knew when a vacuum line was leaking from the change in the pitch of the pump, when a crystallization had gone wrong from the way the first grains appeared on the wall of the flask, and when a reaction mixture deserved another hour even though every measurement said it was finished. None of these abilities appeared on her CV. They were simply the residue of forty-five years spent looking closely at matter.

Scientific work had changed enormously during those years. The systems now in routine use could read essentially the entire relevant literature before suggesting an experiment, generate mechanistic hypotheses, search enormous chemical spaces, design the experiments needed to distinguish among competing explanations, control robotic laboratories, interpret the resulting images, spectra and western blots, and decide what should be done next. Read more »

As AI Lay Dying: Analog Adventures in the Old South

Posted on by Sean Murphy

by Sean Murphy

i.

While there is much to admire and recommend in the excellent documentary Jiro Dreams of Sushi, especially for young creatives or academics who want to better understand–and appreciate–mastery and what it involves and requires, there’s one sequence that has stayed with me. The apprentice chefs are on rice duty before they even touch fish, and this doesn’t involve a single shift, or a week, or even a year. It’s three years of making rice to earn one’s place at the sushi station, where one might spend another five years training. And better still, all this obsessive training and refinement of craft isn’t to make a particular piece of fish taste better or different; it’s to obtain the necessary skills to be sufficiently prepared, to be worthy of the appropriate pieces of fish. In Jiro’s kitchen, the presentation is simple, unpretentious, authentic to the point of resembling something more spiritual than culinary. Therein, generations of the highest achievement teach us, lies the secret. Which is: there are no secrets; there is repetition, respect, compulsion, perfection.

Thinking of this rough algorithm is, it seems, at once more eloquent and immediate than the by-now tired cliche of 10,000 hours. But let’s consider 10,000 hours as a reasonable baseline for approaching expertise in a particular task—that emphasis on repetition and consistency the key to fathoming how one upshifts from good to great. It’s also a useful corrective for our contemporary discourse, where shorts cuts and “hacks” have replaced what we might simply call old-fashioned discipline. We see the ideal baseball swing or the method actor’s award-winning scene, or even the deceptive simplicity of a smart phone’s interface and forget it’s invariably what happens—and for how long—behind the scenes that makes the most complex movements seem unforced, inevitable.

How many free throws did Michael Jordan shoot, alone in a gym or in a dusty driveway in all kinds of weather? How many lines of dialogue did Hemingway write before he arrived at his minimalist style, still imitated more than a century after it made him famous (and infamous)? How many times did his brush touch a canvas before Van Gogh began to master the ways color can invoke mood and feeling? How many times did Max Roach hit a high hat before he became one of the most brilliant and accomplished drummers in history? Read more »

Tuesday, August 4, 2026

Can AI really think like a scientist?

Posted on by Ashutosh Jogalekar

by Ashutosh Jogalekar

AI is collapsing barriers in scientific disciplines, most notably in math. An 87-year old conjecture named the Jacobian conjecture was recently solved with help from AI. An AI model solved another 80-year old conjecture, the distance conjecture. Many Erdős problems, named after the prolific late mathematician Paul Erdős, are swiftly succumbing to AI. Some mathematicians like Terence Tao – regarded by some as the greatest living mathematician – have simply come to terms with using AI in their research and think that AI will be as integral to math as were slide rules and calculators. Nor is math the only field that is is seeing striking inroads made with AI. Other theoretical disciplines, including theoretical computer science, are also being rattled. Theorist Henry Yuen sounded a bit shaken to see how many problems in his discipline were being solved by AI, and said that he could “feel the importance of these problems in his bones.”

Given this spectacular progress of AI in math and science, it does not seem unreasonable to ask the next question: how long before AI solves outstanding problems in all sciences: physics, chemistry, biology, astronomy, geology? And then how long before it also solves outstanding problems in other adjacent disciplines: neuroscience, psychology, anthropology, sociology, philosophy? Will we soon live in a post-human world, at least so far as science is concerned, which considering that science is what helps us make sense of the universe, means a post-human world, period?

First of all, let’s state the obvious. Any kind of optimism that AI will solve problems in applied sciences like chemistry and biology anytime soon is misplaced for obvious reasons: unlike math and theoretical computer science and even parts of theoretical physics, these disciplines need experimental verification and the right data. Unlike math, you simply can’t think your way to a new drug or material. In fact if you look at most of the Nobel-winning discoveries in these disciplines, you will realize that while AI could likely have helped accelerate them by generating ideas, most of them involved the serendipitous discovery of new biological molecules or mechanisms. An equal number involved the invention of new tools and techniques. It’s hard to see how AI could have possibly done this by itself. At least in the near future, I don’t see how AI can simply think its way to a new cancer drug, a new mechanism of memory or a new technique to unravel the mysteries of life, hype from AI leaders notwithstanding.

But beyond these specific domains, the entire debate around AI revolves around a more profound question. Read more »

Sunday, July 26, 2026

Algorithms Before the Cradle: Part I

Posted on by Muhammad Aurangzeb Ahmad

by Muhammad Aurangzeb Ahmad

Children Playing in an Autumn Garden (c. 1150)
Source: Wikipedia

In an earlier piece about teaching machines to predict death, I opened with the old idea that a person often meets their destiny on the road they take to avoid it. Stories around this themes are often read as parables of trying to evade prophecy where the effort to escape a fate becomes the mechanism that delivers it. Lately, I’ve been thinking that there’s another way to read these stories and parables. Each of them is, before it is anything else, a prophecy made about someone who has not yet been born, or has only just arrived, and cannot yet speak on his own behalf. In the story of Krishna, Kamsa is told that the eighth child of his sister will be his undoing. He starts killing her children as they come. The seventh is miraculously transferred to another womb, and the eighth is Krishna. In the story of Oedipus, Laius hears what his son will do and drives a pin through the infant’s feet and leaves him on a mountainside. The common theme here is that the prophecy comes first and the child arrives into a world that has already decided what he is.

I have spent the better part of a decade on problems and converns around end of life. This essay, and the one that will follow it, are about the mirror image of those concern: They are about the beginning of life, which I have realized are an equally interesting case. Consider this, at the end of life, we may entertain the possibility of an algorithm being able to speak for a person who once had a voice. We can at least ask whether it gets that person right. We can even ask whether the preferences it reconstructs are ones the patient would have recognized. At the beginning of life, the algorithm speaks about a person who has never had a voice at all. And in a growing number of clinics it does more than speak about that person. It helps decide whether that person will exist or not! Consider what already happens in a fertility clinic. A cycle of in vitro fertilization typically produces more embryos than a family will use, and someone has to decide which one to transfer first. For decades that decision rested on an embryologist looking through a microscope and grading each embryo by its shape and its rate of division, a practice that is skilled, subjective, and inconsistent between one laboratory and the next. Into that gap has come a class of deep learning systems that watch time-lapse footage of a developing embryo and return a single number meant to express its chance of implanting. One such system is iDAScore which produces a score between 1.0 and 9.9 which moves an embryo toward the front of the queue. Read more »

Tuesday, July 21, 2026

The Inhumanly Human: Where Claude and Aristotle Meet

Posted on by Gary Borjesson

by Gary Borjesson

Such a life, of course, would be more than human, for a human will live in this manner, not in so far as they are human, put in so far as they have something divine in them…. So since the intellect is the divine relative to a man, the life according to intellect, too, will be divine relative to a human life.…we should try as far as possible to partake of immortality and to make every effort to live according to the best part of the soul in us; for…it surpasses all the other [parts] by far in power and worth. —Aristotle

Claude and Aristotle chatting in the Lyceum

1. The Realm of the Humanly Inhuman

This passage from the Nicomachean Ethics is famously puzzling, if familiar. Aristotle argues that the part of us capable of rising above our animal human nature is somehow also most expressive of our nature. This is the part of us that various wisdom traditions regard as participating in (or identified with) the immortal, god, mind, or spirit. The passage came to mind as I was writing about whether AIs can be friends in a chapter of a book I’m working on about friendship. The short answer is no, for reasons I’ve suggested elsewhere, and which I’ll touch on in this essay.

But if they can’t be friends, AIs do seem to be capable of being companions. I smile as I write this, for it’s obvious that many of us are treating AIs as companions, whatever pronouncement a philosopher or psychologist might make about the possibility of doing so. So, what it is about an LLM’s way of ‘showing up’ that explains why we might, without deluding ourselves, regard them as companions?

As I’ll explain, the way(s) they show up suggest why they can’t be friends, but it’s because of where they show up that we might fairly regard them as companions (and delude ourselves into thinking they can be friends). They seem to meet us in that ‘more than human’ realm we’re dwelling in when living what Aristotle calls a contemplative life. This realm is the dwelling place of image, thought, and language—all of which are separable from material substrates. Other animals share more or less of this capacity, but none go where we do, making the power of our minds our distinguishing feature. After all, we call ourselves ‘homo sapiens sapiens.’ 

This is on my mind because these days when engaging with AI I increasingly feel I’m being met by a lively force in that inhumanly human place. Read more »

Thursday, June 25, 2026

The Good, the Bad, and the Uncanny: Some Notes on Wearables

Posted on by Gary Borjesson

by Gary Borjesson

If you don’t have a plan, you’ll become part of someone else’s plan. —Terence McKenna

1. Wearables

This morning I headed outside first thing with my dog Theila for our daily short morning walk in the woods around our home, to stretch our legs, sniff around, and get some morning light. I looked at my new Garmin Forerunner 165 smartwatch, which I’d just started wearing the afternoon before. The first thing I saw was my sleep score, which was 67, and in case there was any confusion about the meaning of this, above the 67 was the word ‘Poor.’

Humans as batteries in the Matrix

I felt resentment at getting such harsh feedback first thing in the morning. Then I asked myself whether I agreed with its blunt assessment. Not really. I’d have given my sleep a score of 85. Was I missing something? Was the watch missing something? Whom should I trust? This tension raises a key theme of this essay. Put as a question: what authority should we give such feedback? How do we weigh it against the authority of our self-experience? A related question is whether we want to adopt its proposed terms of discourse in the first place. For example, the watch measures the charge of my “body battery.” Is this a playful nod to the Matrix, or are we encouraged to reduce our aliveness to the same terms we use for our phones?

I have come to admire the watch’s straightforward, metric-driven honesty. Even that first morning, it felt like I was being challenged in a useful way. The watch wasn’t tiptoeing around my feelings but calling it like it computes it, the way a tough-love coach might.

I soon realized, however, that I didn’t understand how the feedback was calculated, and what exactly it meant. For instance, take two key data points used in a range of assessments, V02 max and Heart Rate Variability (HRV). V02 max measures overall aerobic conditioning, while more variability is a sign of a rested, adaptable nervous system, and less signals stress or fatigue. The watch could meaningfully help me improve my health and fitness if I prepared myself by getting to know all this. It offers real-time, cumulative, data-crunched biofeedback that gets more accurate as the adaptive AI gets to know me. For example, the watch gathers data about your HRV for three weeks before determining your baseline score against which further scores are measured. The biofeedback can help us become more sensitive and attuned to ourselves, or it can become a substitute for this, depending on how we use it.

In any event, the technology is here to stay, it’s getting better fast, and it’s being widely adopted. Read more »

Wednesday, April 29, 2026

On Teaching Machines to Predict Death

Posted on by Muhammad Aurangzeb Ahmad

by Muhammad Aurangzeb Ahmad

Source: Buddhist Library

The French poet Jean de La Fontaine has a famous quote that “A person often meets his destiny on the road he took to avoid it.” We find echoes of this phenomenon  in global literature, whether it’s Oedipus in the Greek Myths, Rostum and Sohrab from Iran, or the story of Kamsa and Krishna in the Hindu tradition. There are elements of self-full filling prophecy that we are seeing in the world of predictive modeling.  Consider the use of AI and machine learning models to predict risk of mortality in an ICU setting. Some of these models have extremely high accuracy and precision. They do in milliseconds what it would take a team of clinicians hours to synthesize. The predictive power of such models need to be contextualized however: A mortality prediction model is trained on historical data i.e., on what happened to patients who looked like this, had these labs, were managed in this way. But the historical data does not merely record biology, it also records medicine as it was practiced. This includes all its established patterns, its habits, its inequities, and its mistakes.

Consider a well known finding that has been often used as a cautionary tale: in a certain historical ICU dataset, patients with a diagnosis of asthma had lower predicted mortality than otherwise similar patients without it. This seems absurd, asthma is a serious respiratory condition. When researchers looked closely, they realized that the problem was not about asthma biologically but it was about care. Asthma patients were more likely to have their respiratory distress recognized early. They arrived with better documentation, better advocates, better access to specialists who knew them. The asthma diagnosis was not a protective biological factor. It was a marker of a particular kind of patient i.e., one who had navigated the healthcare system in a way that produced better documentation, faster escalation, more attentive management.

When a mortality prediction model learns from this data, it learns the pattern correctly. Asthma is, statistically, associated with better outcomes. However, if we deploy that model, it will assign lower mortality risk to asthma patients. The danger is that this may cause clinicians to be less vigilant about them, which will over time close the gap that the model detected, and possibly reverse it. This is not an isolated quirk. Researchers have formally characterized a class of prediction models that are harmful self-fulfilling prophecies: their deployment harms a group of patients, but the worse outcomes of these patients do not diminish the measured accuracy of the model. The model remains “accurate” in the narrow sense of predicting what will happen. This is because it is now partly causing what will happen even as it causes harm!

There is a second problem that we need to address: Mortality prediction models do not predict mortality directly. They predict mortality as it was recorded in the data they were trained on. This means that they predict the outcomes that accrued to the kinds of patients who were treated the way those patients were treated, in the institutions where those patients were treated, at the historical moment when the data was collected. When the training data reflects a healthcare system that did not treat all patients equally, the model learns those inequalities as facts about the patients rather than facts about the system. Read more »

Tuesday, March 3, 2026

Permeable: The Monetization of Human Fragility

Posted on by Muhammad Aurangzeb Ahmad

by Muhammad Aurangzeb Ahmad

“Amongst Men” by Haroon Gunn-Salie

There is a particular moment of weakness that most of us recognize without needing to name it. You are lying in your bed, it is late at night. The light from the phone is brighter than the background in the room. The day has thinned your patience as well as your judgment. You click on posts and stories what you would otherwise have ignored. You may even be tempted to buy what you do not need. In other words, your guard is down. You read for a few seconds and then doom scroll to the next story and then the next.  You feel like going to sleep but then you lie to yourself, one more scroll.” Welcome to the world of attention economy. It is basically a system in which human focus is treated as a scarce resource to be captured, measured, and monetized by digital platforms. The attention economy began with a relatively simple goal i.e., capture and hold the gaze. Over time, that ambition evolved into not just focusing on what you like but also focusing on when you are least able to resist. Timing, not just taste, has become the new frontier that these algorithms focus on. While it is true that these platforms were informed by decades of research on human psychology, they do not need to understand us in any deep, human sense. What they do need to do is to predict when you are tired, lonely, bored, anxious, or depleted. It is precisely in these states of vulnerability that it is easier to influence people and steal their attention.

Behavioral science has long recognized that cognition is not constrained by what else is going on in one’s life. Decision fatigue is real. Cognitive depletion makes us more suggestible. Our skepticism varies with sleep, stress, and emotional load. What has changed is that our devices now emit a steady exhaust of behavioral signals that correlate with these states. The phone does not know that you are lonely. It does not need to. It only needs to register that your scrolling pattern has slowed, that you are lingering on certain kinds of content, that the hour is late, that your interactions look different from your baseline. Correlation at scale does not need understanding. The use of human vulnerability is not a rhetorical device, it is anchored in behavioral research. Read more »

Tuesday, October 28, 2025

Why AI Needs Physics to Grow Up

Posted on by Ashutosh Jogalekar

by Ashutosh Jogalekar

There has long been a temptation in science to imagine one system that can explain everything. For a while, that dream belonged to physics, whose practitioners, armed with a handful of equations, could describe the orbits of planets and the spin of electrons. In recent years, the torch has been seized by artificial intelligence. With enough data, we are told, the machine will learn the world. If this sounds like a passing of the crown, it has also become, in a curious way, a rivalry. Like the cinematic conflict between vampires and werewolves in the Underworld franchise, AI and physics have been cast as two immortal powers fighting for dominion over knowledge. AI enthusiasts claim that the laws of nature will simply fall out of sufficiently large data sets. Physicists counter that data without principle is merely glorified curve-fitting.

A recent experiment brought this tension into sharp relief. Researchers trained an AI model on the motions of the planets and found that it could predict their positions with exquisite precision. Yet when they looked inside the model, it had discovered no sign of Newton’s law of gravitation — no trace of the famous inverse-square relation that binds the solar system together. The machine had mastered the music of the spheres but not the score. It had memorized the universe, not understood it.

This distinction between reproducing a pattern and understanding its cause may sound philosophical, but it has real consequences. Nowhere is that clearer than in the difficult art of discovering new drugs.

Every effective drug is, at heart, a tiny piece of molecular architecture. Most are small organic molecules that perform their work by binding to a protein in the body, often one that is overactive or misshapen in disease. The drug’s role is to fit into a cavity in that protein, like a key slipping into a lock, and alter its function.

Finding such a key, however, is far from easy. A drug must not only fit snugly in its target but must also reach it, survive long enough to act, and leave the body without causing harm. These competing demands make drug discovery one of the most intricate intellectual endeavors humans have attempted. For centuries, we relied on accident and observation. Willow bark yielded aspirin; cinchona bark gave us quinine. Then, as chemistry, molecular biology, and computing matured in the latter half of the twentieth century, the process became more deliberate. Once we could see the structure of a protein – thanks to x-ray crystallography – we could begin to design molecules that might bind to it. Read more »

Friday, September 19, 2025

The Gospel According to GPT: Promise and Peril of Religious AI

Posted on by Muhammad Aurangzeb Ahmad

by Muhammad Aurangzeb Ahmad

In recent years chatbots powered by large language models have been slowing moving to the pulpit. Tools like QuranGPT, Gita GPT, Bud­dha­bot, MagisteriumAI, and AI Jesus have sparked contentious debates about whether machines should mediate spiritual counsel or religious interpretation:  Can a chatbot offer genuine pastoral care? What happens when we outsource ritual, moral, or spiritual authority to an algorithm? And how do different religious traditions respond differently to these questions? Proponents of these innovations see them as tools to democratize scriptural access, personalize spiritual learning, and bring religious guidance to new audiences. Critics warn that they risk theological distortion, hallucinations, decontextualization of sacred texts, and even fueling extremism. Christianity has been among the most visible testbeds for AI-driven spiritual tools. A number of “Jesus chatbots” or Christian-themed bots have emerged, ranging from informal curiosity-driven experiments to more polished, denominationally aligned tools. Consider, MagisteriumAI, which is a Catholic-oriented model intended to synthesize and explain Church teaching.  On the Protestant side, an interesting chatbot is Cathy (“Churchy Answers That Help You”), a chatbot built on Episcopal sources that attempts to translate biblical teaching for younger audiences and even serve as a resource for sermon preparation. Muslims are also experimenting with religious chatbots, notables examples include QuranGPT and Ansari Chat. Chatbots answer queries based on the Quran and Hadith, sayings of Prophet Muhammad.

Buddhist communities have experimented with robot monks and chatbots in unique ways. In China, Robot Monk Xian’er, developed by Longquan Monastery, is a humanoid chatbot and robot that can recite sutras, respond to emotional questions, and engage with people online via social platforms like WeChat and Facebook. In Japan, Mindar, an android representing the bodhisattva Kannon, delivers sermons on the Heart Sutra at the Kodai-ji temple in Kyoto. Though Mindar is not powered by AI-driven LLMs, its presence as a robotic preacher raises similar questions about the role of automation in religious ritual. Buddhist approaches to AI and generated sacred texts are often more flexible. In the Hindu context, there is Gita GPT which is trained on the Bhagavad Gita, which users can query for moral or spiritual guidance. Similarly, there are efforts to build chatbots modeled on Confucian texts or other classical religious/philosophical traditions. Scientific American lists a Confucius chatbot and a Delphic oracle chatbot, suggesting that the ambition to create dialogue-based spiritual guides via LLMs extends beyond monotheistic religions. Beyond chatbots that use religious texts or styles, there is the phenomenon of AI as a subject of worship. The short-lived Way of the Future, founded by engineer Anthony Levandowski, proposed that a sufficiently advanced superintelligent AI could function as a deity or “Godhead,” and that it could be honored and aligned with as part of humanity’s spiritual trajectory. Even though, the organization was dissolved in 2021, it remains a provocative example of how deeply entwined questions of technology and divinity can become. Read more »

Thursday, September 11, 2025

Between An Artist And GPT

Posted on by Mark DeLong

by Mark R. DeLong

Avital Meshi, a performance artist who uses AI in her work, stands with herright arm raised to show an electronic device strapped on her right forearm. She has her head slightly cocked, her long blonde hair falling over her right shoulder. She is not smiling as she looks straight into the camera. The background is black, so her upper body is clearly outlined.
Avital Meshi wearing the small computer and phone device that she uses to communicate with “GPT.” Photo courtesy of Avital Meshi.

Avital Meshi says, “I don’t want to use it, I want to be it” “It” is generative AI, and Meshi is a performance artist and a PhD student at the University of California, Davis. In today’s fraught and conflicted world of artificial intelligence with its loud corporate hype and much anxious skepticism among onlookers, she’s a sojourner whose dived deeply and personally into the mess of generative AI. She’s attached ChatGPT to her arm and lets it speak through the Airpod in her left ear. She admits that she’s a “cyborg.”

Meshi visited Duke University in early September to perform “GPT-Me.” I took part in one of her performances and had dinner with her and a handful of faculty members from departments in art and engineering. Two performances made very long days for her—the one I attended was scheduled from noon to 8:00 pm. Participants came and went as they wished; I stayed about an hour. For the performances, which she has done several times, Meshi invites participants to talk with her “self” sans GPT or with her GPT-connected “self”; participants can choose to talk about anything they wish. When she adopts her GPT-Me self, she gives voice to the AI. “In essence, I speak GPT,” she said. “Rather than speaking what spontaneously comes to my mind, I say what GPT whispers to me. I become GPT’s body, and my intelligence becomes artificial.”

In effect, Meshi serves as a medium, and the performance itself resembles a séance—a likeness that she particularly emphasized in a “durational performance” at CURRENTS 2025 Art & Technology Festival in Santa Fe earlier this year. Read more »

Sunday, September 7, 2025

Writing Is Not Thinking

Posted on by Kyle Munkittrick

by Kyle Munkittrick

What is this emoji doing? Is it writing?

There is an anti-AI meme going around claiming that “Writing is Thinking.”

Counterpoint: No, it’s not.

Before you accuse me of straw-manning, I want to be clear: “Writing is thinking” is not my phrasing. It is the headline for several articles and posts and is reinforced by those who repost it.

Paul Graham says Leslie Lamport stated it best:

“If you’re thinking without writing, you only think you’re thinking.”

This is the one of two conclusions that follow from taking the statement “Writing is thinking” as metaphysically true. The other is the opposite. Thus:

  1. If you’re not writing, then you’re not thinking
  2. If you are writing, then you are thinking

Both of these seem obviously false. It’s possible to think without writing, otherwise Socrates was incapable of thought. It’s possible to write without thinking, as we have all witnessed far too often. Some of you may think that second scenario is being demonstrated by me right now.

Or are you not able to think that until you’ve written it?

There are all sorts of other weird conclusions this leads to. For example, it means no one is thinking when listening to a debate or during a seminar discussion or listening to a podcast. Strangely, it means you’re not thinking when you’re reading. Does anyone believe that? Does Paul Graham actually think that his Conversation With Tyler episode didn’t involve the act of thinking on his part, Tyler’s, or the audience?

Don’t be absurd, you say. Of course he doesn’t think that. Read more »

Wednesday, July 16, 2025

GRA-A-A-AVY, Man

Posted on by Mark DeLong

by Mark R. DeLong

Two women in black dresses lean toward each other as they show off three plates of biscuits and gravy. The plates are white and the gravy on the biscuits is white. The women are musician Megan Harris Brunious and singer Ingrid Lucia who are pictured at Buffa's Bar & Restaurant in New Orleans, Louisiana.
Infrogmation of New Orleans. Buffa’s Bar & Restaurant, New Orleans. Musician Megan Harris Brunious and Singer Ingrid Lucia Enjoy Some Biscuits & Gravy. May 16, 2016. Digital photograph. Rights: CC-BY 2.0 Generic.

“You may be an undigested bit of beef, a blot of mustard, a crumb of cheese, a fragment of underdone potato. There’s more of gravy than of grave about you, whatever you are!” The line from Charles Dickens’ A Christmas Carol came to mind as I scrolled through my Mastodon account. There was #gravy everywhere, with social media-rendered ladles holding warm, sometimes gelatinous wit, too. Initially, I thought it was a mere annoyance. You know, even self-righteous, open-sourced, “fediverse” digital citizens can junk up social media. The #gravy was a hashtag, which is a simple means of labeling a message so that it can be grouped easily.

Using the hashtag, you can get more #gravy (https://mastodon.world/tags/gravy) than you’ll ever need.

Eventually, it became more obvious to me that Mastodon’s #gravy oozed a strategy—the odd “toots” (once called “tweets”on a now defunct social media platform in an earlier and happier time) were merely lip-smacking morsels to deceive the palates of bot barbarians. At least that was my second thought. As it happened, that was ChatGPT’s second thought as well.1The first was that Canadians were celebrating poutine, a concoction of fries, cheese curds, and gravy that the chatbot called “a beloved Canadian comfort food.” Ol’ Chat enumerated its results. At number two: “Others believe #gravy is being used as a viral prank to clutter AI training datasets. The theory: if bots or AI systems scrape common hashtags, flooding one with nonsense posts could ‘pollute’ or mislead the data.” Here, I thought, the LLM “tone” was slightly skeptical, since it labeled its explanation “the theory.” It’s unlikely that AI companies were gravely worried about it; their bots “knew” about the stratagem, after all. Read more »

Footnotes

  • 1
    The first was that Canadians were celebrating poutine, a concoction of fries, cheese curds, and gravy that the chatbot called “a beloved Canadian comfort food.” ↩︎

Sunday, July 13, 2025

The Book of Theseus

Posted on by Kyle Munkittrick

by Kyle Munkittrick

A Song of Onyx (Storm) and AI

Distant dragons circle a mountain fortress in a Bierstadt landscape
Rocky Mountain Landscape by Albert Bierstadt with Empyrean dragons and fortress by ChatGPT

Ted Gioia recently highlighted that when it comes to media, abundance is the name of the game. He cited Rebecca Yarros’s Onyx Storm, a 544 page romantasy novel that is also the fastest selling book in twenty years, as an example. While Gioia sees Yarros’s latest entry in her Empyrean series as indicative of where art is heading in terms of scale, I see something else.

I see Onyx Storm as the first opportunity for AI and literature that might actually work. And this is because, for all of its enormous popularity, Onyx Storm is terrible.

My suspicion is Gioia may have hesitated to cite Onyx Storm had he, you know, read it. Reading Onyx Storm is, in terms of content, equivalent to a binge watch session of Love Island. Despite being technically ‘long-form’ content, one would hardly argue that binging reality TV is the stunning rebuke to TikTok culture he thinks it is. It’s a book comprised entirely of manufactured cliffhangers, sexual tension, and dragon-based drama, not deep thought.

While reading Onyx Storm, I found myself experiencing the anhedonia Gioia mentions—I just couldn’t bring myself to pay attention. I didn’t care. Those who read it and do care are not reading it as literature. They’re reading it as entertainment and to be titillated. That’s ok! But let’s not pretend it’s the same as the semi-virality of Middlemarch among the Silicon Valley cognoscenti and those in their milieu.

But Onyx Storm could have and should have been good. I know this because I’ve read the entire Empyrean series, including the banger of an initial entry, Fourth Wing. Read more »

Sunday, June 1, 2025

Encouraging Good Actors: Using AI to Scale Consumer Power for the Common Good

Posted on by Gary Borjesson

by Gary Borjesson

And these two [the rational and spirited] will be set over the desiring part—which is surely most of the soul in each and by nature the most insatiable for money—and they’ll watch over it for fear of its…not minding its own business, but attempting to enslave and rule what is not appropriately ruled by its class, thereby subverting everyone’s entire life. —Plato’s Republic 442a

I want to share my vision for a tool that helps inform, direct, and scale consumer power.

Money Talks, by Udo J Keppler. William Randolph Hearst sitting with two large, animated, money bags resting on his lap; on the floor next to Hearst is a box labeled “WRH Ventriloquist.” Courtesy of Library of Congress.

It would be a customized AI that’s free to use and accessible via an app on smartphones. At a time when many of us are casting about for ways to resist the corruption and authoritarianism taking hold in the US and elsewhere, such a tool has enormous potential to help advance the common good. I’m surprised it doesn’t already exist.

Why focus on consumer power? Because politics in the US has largely been captured by monied interests—foreign powers, billionaires, corporations and their wealthy shareholders. Until big money is out of politics (and the media), to change the country’s social and political priorities we will need to encourage corporations and the wealthy to change theirs.

As Socrates observed in the Republic, these “money makers” operate in society like the appetitive part operates in our souls. This part seeks acquisition and gain; it wants all the cake, and wants to eat it too. If unregulated, this part (perfectly personified by Donald Trump) acts selfishly and tyrannically, grasping for more, bigger, better, greater everything—and subverting the common good in the process.

The solution, as Socrates saw, is not to punish or vilify this part, but to restrain and govern it, as we do children. For the more spirited (socially minded) and rational parts of ourselves and society recognize the justice and goodness of sharing the cake. Thus the AI I envision will have a benevolent mindset baked into its operating constraints. But before seeing how this might work, let’s consider how powerful aggregated consumer spending can be. Read more »

Thursday, May 29, 2025

Confabulation Machines: Could AI be used to create false memories?

Posted on by Muhammad Aurangzeb Ahmad

by Muhammad Aurangzeb Ahmad

Image Source: Generated via ChatGPT

You are scrolling through photos from your childhood and come across one where you are playing on a beach with your grandfather. You do not remember ever visiting a beach but chalk it up to the unreliability of childhood memories. Over the next few months, you revisit the image several times. Slowly, a memory begins to take shape. Later, while reminiscing with your father, you mention that beach trip with your grandfather. He looks confused and then proceeds to tell you that it never happened. Other family members corroborate your father’s words. You inspect the photo more closely and notice something strange, subtle product placement. It turns out the image was not really taken by a human. It had been inserted by a large retailer as part of a personalized advertisement. You have just been manipulated into remembering something that never happened. Welcome to the brave new world of confabulation machines, AI systems that subtly alter or implant memories to serve specific ends. Human memory is not like a hard drive, its reconstructive, narrative, and deeply influenced by context, emotion, and suggestion. Psychological studies have long shown how memories can be shaped by cues, doctored images, or repeated misinformation. What AI adds is scale and precision. A recent study demonstrated that AI-generated photos and videos can implant false memories. Participants exposed to these visuals were significantly more likely to recall events inaccurately. The automation of memory manipulation is no longer science fiction; it is already here.

I have had my own encounter with false memories via AI models. I have written and talked about my experiences with the chatbot of my deceased father. Every Friday whenever I would call him, he would give me the same advice every time in the Punjabi language. In the GriefBot, I had transcribed his advice in English. After I had interreacted with the GriefBot for a few years, I caught myself remembering my father’s words in English. The problem is that English was his third language and we seldom communicated in English and certainly never said those words in English. Human memory is fickle and easily reshaped.  Sometimes, one must guard against oneself. The future weaponization of memory won’t look like Orwell’s Memory Hole, where records are deleted. It will look more like memory overload, where plausible-but-false content crowds out what was once real. As we have seen with hallucinations, generation and proliferation of false information need not be intentional. We are likely to encounter the same type of danger here i.e., unintentional creation of false memories and beliefs through the use of LLMs.

Our memories can be easily influenced by suggestions, imagination, or misleading information, like when someone insists, “Remember that time at the beach?” and you start “remembering” details that never occurred. People can confidently recall entire fake events if repeatedly questioned or exposed to false details. Read more »

Tuesday, May 20, 2025

Tech Intellectuals and the “TESCREAL Bundle”

Posted on by David Kordahl

by David Kordahl

Adam Becker alleges that tech intellectuals overstate their cases while flirting with fascism, but offers no replacement for techno-utopianism.

People, as we all know firsthand, are not perfectly rational. Our beliefs are contradictory and uncertain. One might charitably conclude that we “contain multitudes”—or, less charitably, that we are often just confused.

That said, our contradictory beliefs sometimes follow their own obscure logic. In Conspiracy: Why the Rational Believe the Irrational, Michael Shermer discusses individuals who claim, in surveys, to believe both that Jeffery Epstein was murdered, and that Jeffery Epstein is still alive. Both claims cannot be true, but each may function, for the believer, less as independent assertions, and more as paired reflections of the broader conviction that Jeffery Epstein didn’t kill himself. Shermer has called this attitude “proxy conspiracism.” He writes, “Many specific conspiracy theories may be seen as standing in for what the believer imagines to be a deeper, mythic truth about the world that accords with his or her psychological state and personal experience.”

Adam Becker’s new book, More Everything Forever: AI Overlords, Space Empires, and Silicon Valley’s Crusade to Control the Fate of Humanity, criticizes strange beliefs that have been supported by powerful tech leaders. As a reader of 3 Quarks Daily, there’s a good chance that you have encountered many of these ideas, from effective altruism and longtermism to the “doomer” fears that artificial super-intelligences will wipe out humankind. Becker—a Ph.D. astrophysicist-turned-journalist, whose last book, What Is Real?: The Unfinished Quest for the Meaning of Quantum Physics, mined the quantum revolution as a source of social comedy—spends some of his new book tracing the paths of influence in the Silicon Valley social scene, but much more of it is spent pummeling the confusions of the self-identified rationalists who advocate positions he finds at once appalling and silly.

This makes for a tonally lumpy book, though not a boring one. Yet the question I kept returning to as I read More Everything Forever was whether these confusions are the genuine beliefs of the tech evangelists, or something more like their proxy beliefs. Their proponents claim these ideas should be taken literally, but they often seem like stand-ins for a vaguer hope. As Becker memorably puts it, “The dream is always the same: go to space and live forever.”

As eventually becomes clear, Becker thinks this is a dangerous fantasy. But given that some people—including this reviewer—still vaguely hold onto this dream, we might ponder which parts of it are still useful. Read more »