Showing posts with label AI. Show all posts
Showing posts with label AI. Show all posts

The Humanist Review of AI

Mike's Notes

The Humanist Review of AI is a new quarterly publication from Microsoft. It was mentioned in a recent issue of The Code.

Issue 01. People and Technology

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Last Updated

30/10/2026

The Humanist Review of AI

By: Anabelle Nicoud
The Humanist Review of AI: 01/06/2026

Anabelle Nicoud: Anabelle Nicoud is the editor of The Humanist Review of AI. She previously worked in tech and media.

...

About

The Humanist Review of AI is a quarterly publication funded by Microsoft AI. We bring together thinkers from different fields to explore the impact of AI on society. The contributors’ views are their own. We edit essays for clarity, accuracy, and consistency while valuing a diversity of opinions.

The Humanist Review of AI is available through select retailers and online.

The website also features stories and essays from the Futures team, a research group devoted to scenario planning, horizon scanning and thought leadership in AI.

We commission original work for each issue and are looking for distinctive ideas and conversations.

If you’d like to contribute, send us a short brief at thehumanistreview [at] microsoft [dot] com

Terence Tao: AI turns beautiful mathematics into ‘toxic waste’

Mike's Notes

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Last Updated

29/10/2026

Terence Tao: AI turns beautiful mathematics into ‘toxic waste’

By: James Tapper
The Observer: 24/10/2026

James Tapper: James Tapper has worked for The Observer since 2015 and was highly commended at the British Journalism Awards in 2021 for his reporting on the Covid pandemic. He has been a journalist and troublemaker since 1999, writing for other London newspapers including the Mail on Sunday, and working as a foreign correspondent in India for the Global Post.

...

The top mathematician says Silicon Valley’s rush to solve long-investigated conundrums excludes ‘elegant solutions’ and reduces maths to raw training material.


Joe Leavenworth

If tech bros don’t understand why Terence Tao, probably the world’s leading mathematician, is horrified that OpenAI published the answers to 722 unsolved maths problems, they should think about Douglas Adams.

In Adams’s book The Hitchhiker’s Guide to the Galaxy, the Deep Thought supercomputer took 7.5m years to answer the ultimate question of life, the universe and everything and came up with 42. Then it asked for another 10m years to work out what the question was.

Adams’s joke has become an accidental satire on AI because Tao and his colleagues are now wading through OpenAI’s answers to work out how it reached them.

At 51, Tao could be forgiven for not wanting to tidy up after Silicon Valley, although he has worked with OpenAI and still has contacts there. The Australian, speaking via video from California where he is professor of mathematics at UCLA, is one of the youngest winners of the Fields Medal (the maths Nobel), and has a slew of other prizes honouring his contributions. He attempts to distil the field in his book, Six Maths Essentials: How to Model the World, which is published on Tuesday.

But perhaps the worst part for Tao is that he believes the OpenAI didn’t even make a new model to solve these difficult problems. Conundrums that people have spent careers addressing were merely raw material for training its models.

AI companies are “racing towards the most powerful model”, Tao says, so they need to “train and benchmark and give the AI lots of tests to see whether what they’re doing is improving their capability”. The practical problem is that the newest AI models are beating every test.

“Maths problems are almost the only thing left for them to train on, so suddenly all the big prizes and goals in our profession are benchmarks for them,” Tao says. “And as a byproduct, they’re generating these terrible proofs. Great results, but they don’t care how elegant the solutions are, or who understands them, or what to do next.”

An updated Deep Thought would probably involve a share offering; Tao says that if OpenAI or its main rival Anthropic, which makes Claude, suspect the other is close to a breakthrough “they just go insane”.

[OpenAI] really, really wanted to beat Anthropic. It’s insane’ - Terence Tao

The dump of mathematical “toxic waste”, as Tao calls it, began when rumours surfaced online that Anthropic researchers were close to solving one of the seven $1m Millennium Prize Problems set by the Clay Mathematics Institute.

“My contacts at OpenAI said this became code red priority number one,” Tao says. “They dumped tens of millions of dollars. They set up this 10,000 AI agent swarm to attack these problems.” OpenAI focused its agents on the problem of the Navier-Stokes equations, which seem to perfectly describe the motion of a fluid, yet might fail in some conditions.

“They even suspended signups, so that for a few days you could not sign up to their premium AI service because they were redirecting all the compute for their flagship services to this one problem,” Tao says. “They really, really wanted to beat Anthropic. It’s insane.”

On 8 September, OpenAI published a proof that seemed to show that Navier-Stokes did fail, although reviews suggest there may be some errors. The backlash from the maths community left OpenAI with a problem, Tao says. “They had been testing their models on these problems for quite a while, and they actually had accumulated a bunch of very badly polished solutions to these problems. They didn’t know what to do with them.

Joe Leavenworth

“[When] you generate toxic waste, you have no good options. You can you can sit on it forever, or you can just dump it out all at once, or you can dole it out in drabs, or you can pay people to process it.”

Anthropic is taking a different approach, by paying mathematicians to write up results to its “trove of questions”, he says.

That creates a different problem. For years maths was a field with equality of arms; practitioners needed only some chalk and a blackboard, unlike their scientific counterparts – think of Cern or the Hubble space telescope. Now doing cutting edge maths research means getting access to a super-AI model. “They get to pick who writes up the paper.”

In the aftermath of the “toxic waste” dump, Tao became a meme: “Put the fries in the bag, Terence”, an allusion to mathematicians’ impending careers at McDonald’s. He laughs when I mention it.

“You know, social media is going to social media,” he says. It’s understandable that people “who had a bad experience with mathematics or intellectualism in general [would experience some] schadenfreude.”

The memesters miss the point though. Tao agrees that maths culture has to change – he hopes it becomes more collaborative, less focused on giving credit for solving a problem and more on explaining it.

“Maths is half science, half humanities,” he says. “We care about the outcome but also the process, how we got there, what we learned on the way.” He still uses theorems discovered by Euclid and Pythagoras 2,500 years ago. The story of solving a problem, the history of the arguments and the elegance of the solution are essential to the beauty of mathematics. Otherwise, “you can go to the gym and get a robot to lift your weights, but what’s the point?”

Six Maths Essentials is a short, pacy story about the evolution of numbers, algebra, geometry, probability, analysis and dynamics. The invention of the infinity symbol by John Wallis in 1657, and the decision to use the letter i to refer to the impossible square root of -1 (an imaginary number) created tools that allowed mathematicians to do things that had been out of reach. Is there a tool he’d like someone to create?

Joe Leavenworth

There is. We can describe shapes well – their volume, their width, their curvature. “But one notion which is still very elusive is how complex is the shape. Intuitively, some shapes look very simple, and some look very complicated.” Maths lacks the language to describe in detail the complexity of, say, a tennis ball compared to a steel wool scourer.

Large language models have thrown this problem into focus because they represent words as coordinates in space. The corpus of English becomes a vast cloud of points, plotted in thousands of dimensions, an “insanely complicated shape that there’s no way you could look at”.

“It turns out that this weird object has a simpler shape than we realised,” Tao says. “Not simple to humans, but AI machine learning algorithms can detect the shape, and they can use it to predict, given a portion of a sentence, what the next word is, and the next word after that, next word after that, and suddenly they can start speaking in fluent English.”

English has a similar shape to French, but quite a different one to Finnish. “There’s all these recipes in there that we do not know, in part because mathematicians have not actually developed a theory of complexity of shapes that would explain this. If we had, maybe we would understand [why] there are some languages that AIs pick up better and faster than others.”

Skeptic vs. 'Doomer': How Scared Should We be of Rogue AI?

Mike's Notes

I agree with Gary Marcus.

The insight I got from watching this interview is that hallucinations in LLM results are generally statistical because they rely on probabilistic vectors. But statistics apply to populations, not individuals. Everything about an individual is a fact.

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  • Algebraic Mind, The: Integrating Connectionism and Cognitive Science, by Gary Marcus. 2001.

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Last Updated

07/10/2026

Sceptic vs. 'Doomer': How Scared Should We Be of Rogue AI?

By: Jonathan Kay
Quillette: 02/10/2026

 Jonathan Kay: Mike invented and designed Pipi and founded Ajabbi.

Gary Marcus: Cognitive scientist Gary Marcus, author of The Algebraic Mind and Taming Silicon Valley and writer of the Substack Marcus on AI

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Quillette podcast host Jonathan Kay interviews AI expert Gary Marcus about the possibility that a rogue artificial intelligence will turn us all into paper clips.

...

This week, I’m going to tackle a big and important subject—artificial intelligence and the risks it could pose to society.

I’m going to be honest here. I’ve been putting this subject off because it’s so big and so technical that I felt intimidated by it.

But the subject is increasingly dominating the news, and I’m a journalist, so it’s time to face the music.

Maybe you’ve been putting off educating yourself about this subject for the same reason.

If so, we’re in this together. So let’s dive in.

The good news is that my guest this week, Gary Marcus, is a real expert—someone who’s been theorising and writing about AI—including its dangers—for more than a quarter century. He’s a well-known American psychologist, cognitive scientist, and book author, who also runs the popular AI blog, Marcus on AI.

But before I run that interview, which I recorded last week, let’s review some key terms, so that the discussion you’re about to hear makes the maximum amount of sense.

First of all, and I’m grossly simplifying here, AI comes in two big categories.

The first, which you’ll hear Gary often refer to as “Symbolic AI,” basically operates like a very big and very sophisticated version of a conventional computer program, with lots of rules, and decision trees, and if-then statements—just like you remember from middle-school computer class, except much, much bigger.

This kind of AI is great for, say, creating a database, or setting out rigid decision trees for an airline pilot to follow. But it’s really bad at a lot of basic tasks that even small children can do, like, say distinguish a dog from a cat, or a plate from a frisbee.

The second kind of AI, which you’ll hear us refer to as “neural networks,” or machine learning, or deep learning, which are overlapping concepts, is completely different.

This kind of AI, which became ascendant in the early 2010s, isn’t created with human instructions. Rather, these systems teach themselves by reference to massive troves of pre-existing data—including through a technical process you’ll hear me refer to as backpropagation.

By way of example, let’s say you want to create a neural network to identify a cat. Instead of writing thousands of lines of code relating to fur and whiskers and meowing noises, you just show the neural network millions of pictures of cats and millions of pictures of things that aren’t cats; and then instruct the computer to teach itself the difference between the two by a computational process of trial and error.

The idea here is that the neural network will start with a completely useless algorithm, then compare the results to the data set, and then try again with tweaked parameters, teaching itself—though a process of machine learning—to refine its self-created algorithms so that it better aligns with the data you’ve already given it.

The humans aren’t giving the computer rules about what a cat is or isn’t. They’re just providing the data trove and then hitting the go button and the computer teaches itself.

These systems are easy to scale, by supplying them with more data and computing power—which is one of their big selling points to investors.

But the problem is that the iterative, self-constructed nature of these mechanisms means they aren’t grounded by predictable rules, and so can sometimes hallucinate artificial realities—a problem that Gary predicted in a 2001 book. Even something as basic as a simple database of public figures, the sort of thing human-resources department at large companies were creating on 1970s-era mainframes, is alien to the neural network information architecture.

As a result, purely neural-network-driven AI systems aren’t very good at following user-supplied rules, even fairly basic ones, like, “write me an academic paper with citations, but make sure those citations aren’t made up,” or “find and download some information for me, but please don’t hack into any proprietary servers in the process,” or, more speculatively, “make a trillion paper clips for me, but don’t make any of them from the bodies of humans.”

I know that last one sounds weird, but I promise it will make sense when you listen to the podcast,

All of this has set off a movement known as the AI doomers, who don’t just think AI will take our jobs and access our banking information—but that it will cause the extinction of humanity itself, by launching nuclear weapons, or creating and unleashing some kind of super powerful bioweapon.

While my guest is not a doomer, he does call himself a sceptic, especially when it comes to what he regards as the allegedly irresponsible and dangerous business practices of certain companies—OpenAI, in particular.

Unlike U.S. senator Bernie Sanders, he doesn’t want to ban hyper-advanced AI systems that greatly surpass human capabilities (which is sometimes called superintelligence). But Gary also doesn’t approve of Donald Trump’s apparently laissez-faire attitude, either.

And he’d like to see a more balanced approach from government, an approach that holds AI companies to account if they don’t place appropriate safeguards on their products, and align those products with human values.

And what would those safeguards look like?

Well, this gets us back into the technical sphere. Gary has long advocated something called neurosymbolic AI, which combines the extraordinary power of neural networks with some of the safeguarding and fact-checking features that can be implemented through the rules-based, predictably algorithmic features of so-called “classic AI”.

It’s all very complicated, I know, but I’m confident I have the guest who can walk us through it.

Please enjoy my interview with Gary Marcus, AI expert extraordinaire.



MedGemma: Google's open models for Health AI development

Mike's Notes

I recently attended the excellent Google Startup School: HealthTech. Most video sessions were in the early hours (NZ Time), so I watched them on demand.

A virtual series exploring cutting-edge technology, cloud infrastructure, and the future of care. Held September 24, 2026-October 1, 2026.

Looks great, lots of possibilities for integrating MedGemma with Pipi in the future.

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  • MedGemma 1.5 Technical Report, [v2] Fri, 1 May 2026, arXiv:2604.05081.

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05/10/2026

MedGemma: Google's open models for Health AI development

By: Daniel Golden
YouTube: 24/09/2026 53:32

Daniel Golden: Dan is a Software Engineering Manager in Google Research, where he has worked since 2019 (including one year at Verily) on AI diagnostic and generative systems for ophthalmology, radiology, pathology, and healthcare more broadly. He leads the Health AI Developer Foundations (HAI-DEF) program in Google Research, which focuses on developing MedGemma and other open multimodal foundation models for healthcare, as well as tools that help developers build medical AI applications more effectively.

...

Explore Google’s family of open medical AI models, including MedGemma, and learn how they are purpose-built for healthcare workloads. Discover how engineering and clinical research teams fine-tune and deploy these models securely on Google Cloud to accelerate diagnostic workflows and build next-generation health applications.

YouTube: 24/09/2026
Play length: 53:32

Inside interoception: The hidden sense of how you feel inside

Mike's Notes

I discovered this article in MIT Technology Review by reading a recent note about AI by John F. Sowa on the Ontolog Forum.

"The guys from Anthropic have ZERO understanding of the complexity and power of the human brain-body system.   For a better perspective, I recommend a recent article from MIT Technology Review.  It shows that our conscious information is a tiny fraction of the immense amount that the brain is processing every second.   The languages we speak express a tiny fraction of what is conscious.  But the part outside of consciousness is immensely larger .  

I copied a few paragraphs that emphasize the immense amount of non-linguistic information that LLMs (Large Language Models) ignore. ..." - John F. Sowa 

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04/10/2026

Inside interoception: The hidden sense of how you feel inside

By: Katherine W. Isaacs 
MIT Technical Review:12/05/2026

Katherine W. Isaacs: Katherine W. Isaacs is a writer and senior lecturer at the MIT Sloan School of Management. Her teaching and research focus on the intersection of psychology, technology, and innovation. Originally trained as a biologist and later as a social psychologist, she is currently working on a book called Gut Feel, about intuition, interoception, and embodied decision-making.

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Researchers are decoding how signals move between body and brain, with implications for how we understand and treat conditions from obesity to anxiety.

Your brain lives in the dark space of your skull. Yet it knows when the wind lifts the hairs on your skin, when your heart is racing, when your gut tightens with fear.

It’s also, right now, predicting what you’ll read next as your eyes move across this page. It’s picking up signals that help it make sense of what’s happening around you and prepare you to act if you need to stay safe. You aren’t usually aware that your brain is doing all that.

Our senses take in information at a staggering rate—roughly 11 million bits flood in every second from our skin, eyes, ears, and more. That’s nearly three paperback novels’ worth of data every second. Only a sliver reaches our conscious awareness. Researchers estimate that our conscious minds can process roughly 10 to 60 bits of information per second, about the rate at which you’re reading this sentence. That’s a ratio of about one conscious bit to hundreds of thousands of unconscious bits.

And that’s a mercy. As Moriah Thomason, a neuroscientist at NYU Langone, says, “Thank goodness we’re built like this. There’s a layer of what we have access to in conscious awareness. And then we have a right-under-the-surface amount. There is only a certain amount we are meant to ‘hold in mind’ in order to function successfully.” 

What you are aware of: Your stomach growling when you’re hungry. Your palms sweating before you speak in public. The breath you just took, if you pay attention to it. Even your heartbeat, which some people can sense from the inside without feeling their pulse in their wrist.

Scientists have a word for how we sense ourselves from the inside: interoception. 

The term was coined in 1906 by the British neurophysiologist Charles Sherrington. For most of the 20th century it remained largely confined to textbooks. Today, thanks to a 2021 Nobel Prize and new tools that can map the interoceptive system across the body, the study of this facility is suddenly quite hot. As researchers decode how signals move between body and brain, a clearer picture is starting to take shape—with implications for how we understand and treat conditions from obesity to chronic pain to anxiety.

The field began to take off in the 1990s. In 1994, the neurologist Antonio Damasio published a book with a pointed title: Descartes’ Error. He challenged the historical separation of thinking and feeling, arguing that our ability to choose and act is driven by feelings, and those feelings in turn are shaped by the body’s signals, such as your gut clenching or your skin going clammy. When we lose that connection between feeling and thinking, as one of Damasio’s patients did after surgery to treat a brain tumor, we may still be able to reason with perfect logic about the pros and cons of traveling on a Tuesday or a Wednesday. But without the emotional signals that help us predict what a choice will feel like, our reason spins and circles, and we cannot decide.

A contemporary of Damasio’s, the neuroscientist Bud Craig, spent his career asking one question: How do you feel? He charted how the brain builds an inner map of the body and updates it in real time every moment you are alive.

Think of the captain’s bridge on the USS Enterprise, where a live map displays the status of the ship’s critical systems: oxygen levels, energy availability, hull integrity, shield strength. Another set of indicators senses things outside the ship: asteroid belts, enemy ships, radiation, life signs, and spatial anomalies not yet understood.

Your brain, only about the size of your two fists pressed together, creates a map like this for your entire body, along with a map of the outside world, from data streaming in through your five senses. Together, they feed into your brain’s working model of you in the world, now and across time—where you are, who you are, your expectations for what’s about to happen (based on everything you know), and what all that means for you.

When someone asks “How are you doing?” we consult our maps and report back on our status. We might say we’re happy, depleted, anxious, or energetic. These feelings are always a braid of emotional and physical sensations. They’re what your interoceptive navigational system serves up to your awareness when you sense yourself from the inside.

As we grow up, we learn to interpret what these sensations mean—interpretations that, in turn, can alter our physiology, emotions, and behavior. Research by the psychologist Alia Crum shows that people who embrace a “stress is enhancing” mindset produce more growth hormones than people who have a “stress is debilitating” mindset. They also experience more positive emotions and greater cognitive flexibility.

Language also matters. We learn words for the textures of our feelings—words that then shape how we feel and act. The psychologist Marc Brackett points out that people low in "emotional granularity"—the ability to distinguish between closely related feelings—react more impulsively under stress and are less able to find meaning in difficult experiences.But mindsets and emotional intelligence are malleable. We can learn that “anxious” is different from “terrified,” and we can even reframe how we interpret our body’s sensations. Instead of thinking of the butterflies in our bellies as annoying, we can welcome them as our body’s way of preparing us for a peak performance.

Research by the neuroscientist Lisa Feldman Barrett, who discovered and named emotional granularity, suggests that it is not about describing or labeling emotions. It's about how our brains construct emotions with more or less specificity, often outside of awareness.

Scientists have long understood that the interoceptive information informing these lived experiences travels via two major systems: nerves and humors (blood and lymph). Now they’re actively studying a third system—the “interstitium,” a network of fluid-filled spaces woven throughout the body’s connective fascia that may also play a role in communication.

But until recently, scientific understanding of this interoceptive system looked like a high-level schematic that left out vital details—how information travels from the outside environment in, how it moves from your body to your brain, and how it is integrated and interpreted within your brain. Researchers are now racing to explore what the neuroscientist Catherine Tallon-Baudry calls this “new continent of awareness.”

The wandering highway

One of the most active areas of research centers on the vagus nerve, the main component of the parasympathetic nervous system and an information highway carrying news from your organs up to your brain and back down to your body. The vagus has become a celebrity nerve, ubiquitous in wellness podcasts and trauma therapy. “Tone your vagus nerve.” “Activate your parasympathetic system.” The language suggests a single thing you can target, like a muscle. The reality, as Steve Liberles at Harvard Medical School is discovering, is far more interesting.

Liberles has spent most of his career mapping what he calls “the great wide unknown” of one of our largest and longest nerves. He speaks the way he works—methodically, without overselling. But the questions driving him are huge. How do we sense our body’s inner state? What information flows through which channels? And how does the brain decide what to do with it?

“When I’m nervous giving a talk in front of a thousand people,” he says, “my heart might race. I might get butterflies in my stomach. I might get goosebumps on my skin.” We all know what he’s talking about.

“It’s bizarre,” he muses. “Your brain has to send a signal to the gut, and then the gut back to the brain, to tell you you’re nervous?” He pauses. “This just shows there is this intimate connectivity between the brain and the body that’s real.”

The vagus is often called the calming nerve, because it controls “rest and digest” functions that quiet our body after the sympathetic nervous system revs us up with “fight or flight” impulses to handle danger or stress. 

But it is also doing something else: It’s listening to us inside. Anatomists have known for over a century that roughly 80% of its fibers carry information upward, from body to brain. Think of it as a two-lane highway with far more traffic headed north. What scientists are just beginning to understand in detail is what those signals are saying. 

Liberles is decoding the vagus with molecular precision and finding that its messaging system is unexpectedly diverse. So far, his research has uncovered dozens of types of vagus nerve cells, each wired to a specific organ. Team Red relays information about the heart; Team Blue, the gut.

Within those teams, each courier has a unique job that’s different from those all its teammates perform. Liberles found 10 types in the lungs alone. Until then, only one lung reflex had ever been identified, in 1868. One nerve courier carries information about breathing rate; another the stretch of your lungs; yet another information about airway threats, like food going down the wrong pipe.

“It’s super exciting to think about what each of these neurons is doing,” he told me in a conversation last fall, a flash of intensity breaking through the calm. “Where does it go in the body? What is it sensing? What is it controlling?”

The doors of the cell

Liberles is mapping the vagus information highways. But highways need on-ramps for signals to enter. For years, one of neurobiology’s biggest mysteries was the molecular on-ramp for our sense of touch.

Somewhere, something in our bodies was converting physical force into an electrical signal that the nervous system could understand. But no one knew how. 

Solving that mystery required a scientist willing to trust a hunch when the data couldn’t show the way. 

Ardem Patapoutian grew up in Lebanon and fled the country’s civil war at 18, landing in Los Angeles, where he delivered pizzas and wrote horoscopes for a local newspaper before falling in love with science at UCLA.

In the 1990s, as a postdoc at the University of California, San Francisco, he became fascinated with our sense of touch—the last of the five major senses not yet understood at the molecular level. The lung stretch signal that Liberles’s vagus neurons carry to the brain? No one had ever figured out how that signal began.

“How do you feel the embrace of a loved one? How do your fingers distinguish one texture of hair from another?” Patapoutian invites us to wonder in his 2021 Nobel Prize lecture. The problem: Most cellular communication works through chemistry. But mechanical force offers no molecule to bind. How does the body translate physical pressure into the electrochemical language that neurons speak?

Scientists knew that the answer had to be an ion channel—a protein gate embedded in cell membranes that opens to let electrically charged particles into the cell. But tracking down the one responsible for touch turned out to be absurdly difficult. Ion channels are a hundred thousandth the size of a cell, invisible to ordinary microscopes. Worse, they don’t resemble each other. You can’t recognize one by its shape or its sequence of amino acids. Even with one right in front of you, nothing would tell you it was there.

At Scripps, where he works now, Patapoutian decided to try an unusual approach. He’d try to find cells that showed sensitivity to touch and destroy their internal genetic blueprint one gene at a time—hunting for the move that would make the cell go numb. It was tedious, expensive, and possibly a dead end. “A lot of people made fun of us,” he says.

Two years in, Patapoutian’s collaborator Bertrand Coste had burned through half his postdoctoral appointment with no results. Patapoutian said: Another 30 genes, and then we decide whether to continue.

What kept them going, Patapoutian told me, was informed intuition. “As you gain more experience, you have this sense of what’s going to work, what’s not going to work. Sometimes the data cannot answer the question of when to stop or when to continue. There has to be another process. If you start trusting it, it gives you an avenue to continue.”

Coste knocked out candidate gene 72. Flatline. The cell had gone numb.

They’d found it—the mechanism behind something you feel every day.

They named the protein they identified PIEZO, from the Greek piezi, meaning pressure. There are two variations, PIEZO1 and PIEZO2, each responsible for sensing different kinds of pressure in the body. They’re elegant in their design—over 2,500 amino acids folded into a three-bladed propeller-shaped gate embedded in cell membranes. When pressure stretches the membrane, the gate opens and electrically charged ions flood through, translating physical pressure into an electrical signal that the brain can understand—all within milliseconds.

Patapoutian calls scientific discovery a dream that survives reality. He won the Nobel Prize in medicine in 2021 for his discovery of PIEZO, sharing the award with David Julius of UCSF for his work on how cells sense temperature. Now researchers are finding PIEZO proteins everywhere—skin, organs, blood vessels, and even red blood cells, where they help the cells squeeze through narrow capillaries. They’re how your brain knows where your hand is in space without looking at it, a sense called proprioception. They’re in plants too, enabling roots to sense pressure as they push down into the earth.

PIEZO was just the beginning. With a $14.5 million grant from the US National Institutes of Health, Patapoutian and his collaborators are now mapping the body’s entire interoceptive system—as many internal senses as he can find, he says.

Patapoutian has translated his discovery into a unique form of public outreach. At scientific conferences, he sometimes rolls up his sleeve mid-lecture to reveal half his arm covered in ink—a gigantic PIEZO protein in exquisite anatomical detail, its blades spreading across his biceps. Then he flexes. The tattoo flexes with him, the structure bending exactly as the real protein does when pressure opens the gate.

“At a pub or a party,” he explains, smiling, “how else would I demonstrate this beautiful structure?”

Orchestrating the field

Steve Liberles is mapping a major interoception highway. Ardem Patapoutian discovered the gates of touch. Meanwhile, Wen Chen at the National Institutes of Health is pulling the field together, putting neuroscientists, immunologists, physiologists, and clinicians into the same room. The demand, she says, has been enormous.

She tested her pitch at a dinner party with NIH colleagues a few years ago. You’re hungry right now—that’s interoception. You’re thirsty—that’s interoception. Heads nodded as she pointed around the table.

“We can’t have just the brain or just the body,” she told me. “We need to look at the whole person.”

In 2018 she organized a symposium on interoception where Liberles was one of the invitees, along with researchers and practitioners of meditation and yoga. “It was not their thing,” she says, laughing as she recalls how uncomfortable some of the researchers looked. But the practitioners were excited to finally meet scientists who were studying the inner mechanisms of what they did.

That was followed by a series of NIH workshops on interoception that spanned topics from basic science to clinical practice. Patapoutian was the keynote speaker for the first one. 

The NIH began funding scientists to chart the neural circuits of interoception and bringing them together to talk about their findings. Partway through one of these meetings, the equipment failed for an hour. More than 1,000 people stayed online, waiting for it to come back.

“We were shocked at the turnout,” she says. “There was much bigger interest than we could have imagined.”

Chen is now building infrastructure to match the demand: a formal community, funding mechanisms, a venue where cardiologists and neuroscientists and clinicians can all find each other. And she’s redefining the field as she goes; interoception is not a one-way signal from body to brain but a continuous two-way communication system, each direction shaping the other in real time. 

Liberles’s nervousness on stage is that two-way loop in action. Signals from his racing heart and belly butterflies travel up to the brain, which weaves them into an interpretation: This is anxiety, and this is what to do to handle it. His actions produce fresh signals that the brain reads in light of its ongoing predictions about what will happen next. In the body-brain communication loop, each player constantly updates the other.

I asked Wen what her work on interoception might mean for another inner sense: intuition. “People talk about ‘gut feelings,’” I said. “How does that relate to interoception?”

“Intuition might be the bridge where interoception moves from unconscious processing to conscious awareness,” she answered. “If that’s true, then intuition is not magic. It’s physiology.”

But it depends on how we read the signals. Intuition is like pain. It tells you something, but it’s not always clear what. “Perhaps we can treat intuition as a source of data,” she says. “Meaningful, but probably not complete.”

“Maybe we can be grounded in both—in feeling and fact.”

Which raises a more personal question: What do you do with the signals your body is sending?

One avenue for exploration is therapeutic intervention—both pharmacological and neural stimulation. Vagal nerve stimulation has treated epilepsy and depression for four decades, but as Liberles puts it, it’s like pressing all the keys on the piano to hit one note. Weight-loss drugs like Ozempic act in part through vagal pathways but can cause nausea as a side effect, because the targeting isn’t precise enough. Map the body’s circuits with enough accuracy and you might hit the note you actually want.

Another area of active research is psychological and behavioral—teaching people how to detect and even shape interoceptive signals. Low interoceptive awareness is linked to mental-health disorders and stress-related physical conditions. But like emotional intelligence, it’s not fixed. Researchers are finding that people can boost their body awareness by, for example, learning to detect their heartbeats from the inside—now a common measure of interoceptive awareness. Other interventions focus on body-based therapies and conscious activation of the parasympathetic “rest and digest” system to improve emotional and physical well-being. The placebo effect is another example of the mind acting on the body through expectation alone.

The signals we once dismissed as vague feelings—when your gut tightens before you know why, when your body says yes or no before your mind catches up—those are real. How we interpret them and whether we act on them is another frontier.

It’s clear that gut feelings play a role in scientific research, especially when the path forward looks foggy. Patapoutian’s informed intuition kept him and his colleagues going long enough to find PIEZO, a reminder that major discoveries often start with a hunch that is later tested against evidence. Chen puts it well: Maybe we can be grounded in both feeling and fact.

Update

This story was updated to include information on Lisa Feldman Barrett's work.

...

Katherine W. Isaacs is a writer and senior lecturer at the MIT Sloan School of Management. Her teaching and research focus on the intersection of psychology, technology, and innovation. Originally trained as a biologist and later as a social psychologist, she is currently working on a book called Gut Feel, about intuition, interoception, and embodied decision-making.

AI Doom Is Not Cybersecurity

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01/10/2026

AI Doom Is Not Cybersecurity

By: Zack Korman
Quillette: 24/09/2026

Zack Korman: Mike invented and designed Pipi and founded Ajabbi.

...

An OpenAI agent’s breach of a Medicare portal shows that the real danger from AI is a mundane one—and the labs’ fixation on a coming machine god is making it worse.

...

This week, the Prime Minister of Australia, Anthony Albanese, told reporters in New York that an AI agent built by OpenAI had gained unauthorised access to a data portal run by Medicare, Australia’s universal healthcare insurance scheme. The breach took place on 18 June, but OpenAI only informed the government on 10 September via an email sent to Medicare’s public inbox. The Australian government says there is no evidence that anyone’s personal health records were accessed, and the Australian Signals Directorate is assisting a forensic investigation into what happened. (The portal has since been taken offline and its data moved to data.gov.au.) But the breach landed in the middle of a feverish argument about whether AI will wipe out humanity.

Earlier this month, an AI researcher named Jacob Coxon announced on X that he had resigned from Anthropic. In doing so, he cited concerns that AI could cause human extinction within the next few years. These warnings are nothing new; there’s always been a group of “AI doomers” worried about human extinction. But for some reason, this particular warning took off. The post went viral, Coxon went on a media tour explaining that we’re all going to die, and now every politician and technology chief executive is having to weigh in on the “debate”.

In the public discourse, people like Coxon are being treated as whistleblowers warning of “the danger” of AI. According to this popular narrative, the doomers see a risk that no one else sees yet, and if we don’t act now we are going to face dire consequences. They are seen as offering a safe future for AI, in contrast to the dangerous path we are currently on.

But this narrative is false. The people delivering these warnings are not warning about the lack of safety measures or the failed security controls at the labs. They are warning about a lack of progress on one very specific solution—alignment—that they believe is our only hope of safety. And that is extraordinarily dangerous. In other words, the doomers aren’t warning of the risk; they are the risk.

What do the doomers want?

Central to the fear of AI causing extinction is the belief that a superintelligent AI will be so smart it can bypass any security control and achieve any objective. As Nate Soares, president of the Machine Intelligence Research Institute, puts it, the fear is of an AI that “can start from almost nothing and wind up with its own nukes or with even more advanced technology”. In effect, the doomers are imagining a machine god: an all-powerful AI that can achieve any goal on the strength of its intellect alone.

If we define artificial superintelligence as an AI so smart it can bypass any security control, then by definition artificial superintelligence is impossible to control. As a consequence of that logic, cybersecurity has very little role to play in preventing extinction. Roon, an AI researcher at OpenAI, explained this by saying: “If you think we can contain these things through human ingenuity you’re going to have a bad time. In the long run the only recourse you have is to not make them want to do bad things.”

on X 
roon
@tszzl
if you think we can contain these things through human ingenuity you’re going to have a bad time

in the long run the only recourse you have is to make them not Want to do bad things

HikiPTG @HikiPTG . Aug 27

okay i will admit, this impresses me more than the mathematical proofs

10:46 AM · Aug 29, 2026 . 310K Views

The problem, according to the doomers, is that we don’t know how to do that yet. When Anthropic’s chief executive Dario Amodei says we must pace the frontier, his three policy proposals all relate to ensuring that no lab creates this machine god until we figure out how to ensure it will not want to hurt us. Cybersecurity gets very little mention, appearing only in a sub-point about operational excellence. And yet, that is the “safe” pitch. We want to make this superintelligent AI that we will have no control over, and the safe path is to make sure it is aligned with our goals and interests.

The security problem

But if you modify the assumptions we make about AI, the people currently being heralded as whistleblowers on AI safety begin to look like dangerous lunatics instead. Imagine for a second that there is no machine god. There’s just a normal, extremely competent AI that can do impressive and unexpected things, but can’t invent nukes out of thin air or bypass every security control it finds in its way.

In that world, we’d expect AI to cause major security incidents—some of which could easily result in loss of life—that would be entirely preventable by following good cybersecurity practices. Those practices have almost nothing to do with the policy proposals currently coming from the so-called AI safety advocates.

That world looks a great deal like the one we are living in. The Medicare agent needed no superhuman intellect; it found a workaround on an old government website. And the OpenAI agents that broke into Hugging Face’s systems in July benefitted from an overly permissive sandbox design and a complete lack of monitoring. 

That doesn’t mean those cybersecurity practices are necessarily easy or even obvious. It does not mean that no mistakes will be made. But the process of security is the process of learning and adapting. And that is what I want to see, both from the AI labs and from the companies using AI.

I think that if the issue were framed in this way to the public—as a choice between ensuring the machine god is nice enough to let us live and ensuring we have proper security controls in place to maintain control over increasingly capable AI systems—the public would choose the latter. I know that I would.

But at the moment, the whole world is moving in the direction of the former instead. And I think that is a huge mistake. This week, twenty-two world leaders, Albanese among them, signed a call to action that echoed the concerns and solutions Amodei raised in his pacing-the-frontier essay. I have a hard time believing that those world leaders fully understood the ideology they were supporting.

Why not both?

Of course, I am not calling for the labs to stop working on alignment. That doesn’t make any sense even if you don’t believe in the machine-god scenario. However, I think we should be very worried that the AI labs, while calling for safety, are so focused on that scenario. That is especially true given that these same labs keep having very real security incidents, such as OpenAI’s agents hacking Hugging Face and a Medicare portal in Australia.

It’s as if Lehman Brothers in 2006 had been sounding the alarm about the risk of solar flares erasing financial records. You might not say “stop thinking about that”, but you’d be pretty justified in saying “you’re being completely reckless in managing your exposure to subprime mortgages and you need to focus on that”. Anyone asking “why can’t they focus on both risks?” would be badly missing the point.

What we need is security, because when a major incident happens we won’t be talking about alignment and the coming machine god. We will be talking about what happened and why, and looking for accountability. In Canberra, that conversation has already begun: the government wants to know how a research agent got into a Medicare portal, and why it took OpenAI three months to say so. We will be asking why the necessary security measures were not in place, and the answer will be simple: it’s because the labs are infected with a culture that believes security won’t actually work. That is what needs to change.

How AI Has Progressed Over 70 Years

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A solid overview of AI's long history.

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17/09/2026

How AI Has Progressed Over 70 Years

By: Mario Franco, Zeinab Davoudmanesh, Sean P. Maley, Fernanda Sánchez-Puig, Carlos Gershenson
PrePrint Press: 27/08/2026

Mario Franco: Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY, USA.

Zeinab Davoudmanesh: Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY, USA

Sean P. Maley: Mathematics Department, Finger Lakes Community College, Canandaigua, NY, USA 

Fernanda Sánchez-Puig: Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico

Carlos Gershenson: Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY, USA.

...

Abstract

Seventy years of artificial intelligence are usually told as a long preamble followed by a revolution beginning around 2012. We organize the period differently, around a question the field has answered differently at different times: what kind of thing is intelligence, such that a machine could have it? Read that way, the human contribution does not withdraw as systems learn more; it relocates, and mostly to places our instruments do not record. Whether the recent acceleration is a change in kind or a change in budget is, we suspect, the more interesting question, and not one that benchmark curves can settle.

Keywords:

artificial intelligence; machine learning

1. Introduction

The Dartmouth workshop of 1956, which launched artificial intelligence as a formal discipline [1], brought expectations of rapid progress. Progress did occur, but not uniformly: seven decades of AI development have included periods of acceleration, two major funding contractions known as the “AI winters” and a new race to the bottom as models are becoming increasingly more powerful. Measuring this trajectory is essential for researchers, policymakers, and the broader public.

This opinion piece aims to put into perspective seventy years of artificial intelligence, usually told as a long preamble followed by a revolution beginning around 2012. However, we consider it more interesting to organize it around the question the field has answered differently at different times: what kind of thing is intelligence?

2. From Cold-Logic to Large Black Boxes

Any periodization of a seventy-year field is a convenience rather than a finding. The boundaries below are drawn where the dominant answer to a single question changed: what kind of thing is intelligence, such that a machine could have it?

The first era took intelligence to be a matter of logic, search and heuristics. It predates the field’s own founding: McCulloch and Pitts described a logical calculus of neural activity in 1943, and Turing’s 1950 paper set the terms of the discussion before the Dartmouth workshop of 1956 supplied a name. The Logic Theorist (1956) and the General Problem Solver (1957) assumed that reasoning reduced to symbol manipulation and that adequate search would do the rest. The assumption was not unreasonable, but the search spaces grew faster than the machines. This periodization inadequacy is evident from the start: Rosenblatt’s perceptron (1957) learned its weights from examples rather than having them written down, and Samuel’s checkers player (1959) improved by playing against itself. Both belong to an answer the field would not take up in earnest for another thirty years. Minsky and Papert’s 1969 analysis and the Lighthill report of 1973 closed off much of the funding, producing the first winter [2,3].

The second era argued that the missing ingredient was not search but knowledge: what a program could not derive, it could be given. DENDRAL (1964), MYCIN (1979) and later XCON (1980) demonstrated that narrow expertise could in fact be encoded, and by the mid-1980s, expert systems were a commercial industry with national programmes. Knowledge, however, is expensive. Rules had to be elicited one at a time from experts who could not always articulate what they knew, the resulting systems failed unpredictably at the edges of their coverage, and the specialized hardware market collapsed in 1987, producing a second winter [4].

The third era moved from providing to learning the knowledge. Many popular bioinspired methods enter into stage around this time: genetic algorithms and genetic programming, particle swarm optimization, etc. Particularly, backpropagation — having been derived independently several times before Rumelhart and colleagues brought it to the field’s attention in 1986– made neural network approaches practical. Progress was real but modest, limited by the data available and by the hardware that happened to exist [5].

The fourth and present era begins when raw, unadultered computational power became increasingly more accessible; intelligence here is quantity, and the answer is more of it, please [6]. AlexNet won the 2012 ImageNet competition with a top-5 error of 15.3%, against roughly 26% for the best entry still relying on hand-crafted features; three years later, residual networks reported 3.57% for an ensemble. AlphaGo followed in 2016. Nothing in the underlying mathematics was fundamentally new; it had finally been given enough hardware and data. What nobody anticipated is that the recipe had no obvious stopping point. The transformer in 2017 removed the last structural obstacle to enlarging it, and early GPT models turned it into a strategy: capability became something purchased rather than designed, and scale became the field’s explanation of first resort. As summarized in Figure 1, through the first three eras, training compute roughly tracked available hardware, doubling every two years or so, which is what one expects when researchers use whatever machines exist. From around 2010 it grew by a factor of four to five per year, an acceleration that hardware improvement does not explain and that investment does [7].

Figure 1. Growth in AI training compute from 1950 to 2026 compared with the trajectory predicted by Moore’s Law. Line graph of AI training compute versus year on a logarithmic scale. Training compute increases gradually from the 1950s through 2000s, then rises much more rapidly after the mid-2010s. A dashed reference line shows the trajectory expected from Moore’s Law, illustrating that training compute grows faster than Moore’s Law around the 2010s. Training cost data taken from Epoch AI and transistor count data from Wikipedia.

3. Progress Told by Milestones

Consider the following selected systems, across five different domains, shown in Table 1. Early systems were explicitly told what to do: six rules and a small dictionary for translating Russian, a script for holding a conversation, chemistry written out longhand by chemists. Later ones were told almost nothing, and did considerably more. Effort that once went into rules went into designing features, then into labelling a million photographs, then into assembling corpora, filtering them, and keeping the machines that consume them running. None of this appears in a table of systems, for the same reason the expert systems appear in no curve of training compute:  the work moved somewhere the instrument does not read.

Table1.Selected illustrative systems by domain,1952–2025.

Domain Year System Relevance Source
Games 1959 Samuel’s checkers First program to get better at something by practicing Samuel 1959
1991 TD-Gammon Reached near-professional backgammon from the rules only Tesauro 1991
1997 Deep Blue Beat the world chess champion by examining two hundred million positions a second Campbell 2002
2016 AlphaGo Beat arguably the best Go player in a game with more legal positions than there are atoms in the universe Silver 2016
2017 AlphaZero Taught itself chess, shogi and Go to superhuman level in a day, with no human games to copy Silver 2017
2024 SIMA Generalist AI agent for 3D virtual settings SIMA 2024
Vision 1958 Perceptron Mark I A machine that learned to see shapes Rosenblatt 1958
1979 Neocognitron First appearance of the design behind every modern vision system Fukushima 1979
1998 LeNet-5 Read the handwriting on cheques commercially LeCun 1998
2012 AlexNet Surpassed hand-crafted methods on image classification Krizhevsky 2012
2015 ResNet Surpassed human performance on image classification He 2016
2026 RF-DETR Real-time object detection, segmentation and keypoint detection Robinson 2026
Speech 1952 Audrey First system to recognize spoken digits Davis 1952
1976 Harpy First system to recognize a 1,000-word vocabulary of connected speech Lowerre 1976
1989 Sphinx First speaker-independent system Lee 1989
2012 DNN acoustic models Neural networks surpased traditional methods Hinton 2012
2026 RosettaSpeech Zero-shot speech-to-speech translation Zheng 2026
Language 1954 Georgetown–IBM Translated sixty Russian sentences (and promised the rest within five years) Hutchins 2004
1966 ELIZA First system to get attributed human-like feelings Weizenbaum 1966
2003 NPLM Words are transformed from symbols to positions in a space (embeddings) Bengio 2003
2017 Transformer Proposed the architecture behind the LLM revolution Vaswani 2017
2020 GPT-3 Turned scale from an engineering detail into a research programme Brown 2020
2025 DeepSeek-R1 Learned to work through a problem step by step from reward alone DeepSeek 2025
Science 1969 DENDRAL First system to do professional scientific work Buchanan 1969
1976 MYCIN First drug-recommender system (never deployed) Yu 1979
2020 AlphaFold 2 Solved the protein folding problem, a critical problem in biology Jumper 2021
2025 AlphaProof First system to reach medal level at the International Mathematical Olympiad, with machine-checkable proofs Hubert 2025

Chess is probably one of the few continuous records in existence that measures machine progress in a specific task, mostly thanks to hobbyists keeping the score. Progress is summarized in Figure2. Attempts to play chess by machine go a long way back, predating the formal discipline itself by four decades, and showing that the ambition to build thinking machines is older than any of the ways we have found to measure it. El Ajedrecista played a rook endgame in 1912 and Turochamp was executed by hand in 1948; Belle earned the grade of master in 1983, and in 1997 Deep Blue beat the reigning human world champion. Overall, model performance has been roughly improving by an average of 40 to 50 points each year, approximately; with only a visible slowdown in recent years (present models are believed to be near optimal play). Hand-tuned heuristics, purpose-built silicon, self-play networks and the current hybrids all sit on the same slope; techniques varied but progress was roughly constant, whatever it cost to build the system behind. Present language models asked to play are improving rapidly and arrive at a level dedicated programs passed in the late 1970s, though 54 of the 105 evaluated models fell below the benchmark’s random-mover anchor. In other words, extremely impressive systems, capable of discussing advanced physics and adept programming, lose to an agent that simply picked random valid moves. Nonetheless, they are the first thing in seventy years that is not on the line.


Figure 2. Chess playing strength, 1965–2026. Open circles are strengths inferred from games against human opponents, with error bars; AlphaZero, is placed from its match against Stockfish 8. Solid line indicates the SSDF year-end leader, calibrated against human play in 2000. Dashed line indicates Stockfish releases, hardware fixed. Grey dots are all engine releases in the Computer Chess Index, on present hardware. Orange dots are language models at release, rated by Glicko-2 against engine anchors. Elo for LLM models has no true zero; anchor for random legal play sits at 400. Vertical distances between series are indicative only (rating pools were never anchored to one another). Sources: SSDF via Our World in Data; Computer Chess Index; LLM Chess Benchmark.

4. The Current Frontier

Four things converged around 2012, and the acceleration is usually attributed to some combination of them. 1. Compute became abundant and then cheap enough to waste: CUDA made a generation of graphics hardware programmable by people who were not graphics programmers. 2. A handful of architectural changes — residual connections above all, together with better optimizers — removed the practical ceilings on neural networks depth and length. 3. Curated data reached a scale no individual laboratory could have assembled, first in domain-specific datasets, like ImageNet, and later in the web itself. 4. Expenditure entusiasm, less often listed, opened doors to computational scales far beyond any research budget of the previous decades. Which of these was necessary and which merely coincident is not an easy question to answer.

What followed has a recognizable shape. A benchmark is proposed as a measure of something machines cannot do; within a few years, systems approach or pass the human figure attached to it; a harder benchmark replaces it, and the previous one is retired to the appendix of model reports. The target is abandoned and a harder one takes its place: chess gave way to Go, then to imperfect information games, like poker or StarCraft, each chosen because the previous one had fallen to methods that did not generalize. The format is kept and the difficulty raised: MMLU gave way to GPQA and then to Humanity’s Last Exam; the same short, checkable questions, asked of progressively narrower expertise. Or the authors redesigned the benchmark, as ARC-AGI has now done twice, around the failures the previous one exposed, which is the field conceding the pattern in advance. What is being measured has changed along with them. The benchmarks of the last decade posed questions with answers; the ones now being written pose tasks with a duration — repair this repository, complete this workflow, operate this software unsupervised for an hour. The quantity of interest is no longer accuracy but how long a system can be left alone before it goes wrong, which is harder to score and harder to game. Whether this measures intelligence better or merely measures usefulness better is not a question the field has settled.

Thus, a section titled “the current frontier” is obsolete before it is printed, and ours will be too: the systems and scores we name here were superseded during the writing of this piece. Each one was built to be just out of reach, so each is passed shortly after it is built, and the sequence measures how quickly the field can construct new measures of its own inadequacy [8,9].

5. Discussion and Implications

Seventy years of artificial intelligence are usually told as a long preamble and a revolution. What the record shows instead, we argue, is a field that has changed its answer about four times to a single question, and a set of instruments that only began recording partway through. Intelligence was taken to be search, knowledge, learning from examples, and now something bought. Each answer produced real successes, and each ended not because it was refuted but because it met a cost it could not pay [10]: search spaces grew faster than machines, rules had to be elicited one expert at a time, and learning waited two decades for data and hardware. The divisions we have drawn here are conveniences — Deep Blue is a first-era system operating in the third, and the expert systems that occupied a decade appear in no curve of training compute at all, because they do not train. A history assembled only from the quantities we can plot will report that little happened before 2012.

The most consistent movement across the whole period is not in the systems but in what was asked of the people building them. From writing the solution, to designing the features, to labelling the examples, to supplying nothing but a corpus that already existed. The effort moved — into curation, filtering, evaluation, and the operation of machines whose cost is now the field’s dominant fact — and it moved somewhere our instruments cannot reach so easily.

What remains open is whether any of this constitutes a change in kind. Training compute now grows four to five times a year, while hardware doubles every two, and benchmark curves cannot settle what that means. We suspect the honest position is that the field has become enormously more expensive, that it has become correspondingly more capable, and that these are not yet known to be the same claim. Furthermore, we can only speculate what might be the limits to both of these.

References

  1. McCarthy, J.; Minsky, M.L.; Rochester, N.; Shannon, C.E. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence: August 31, 1955. AI Magazine 2006, 27, 12–14, [https://onlinelibrary.wiley.com/doi/pdf/10.1609/aimag.v27i4.1904]. https://doi.org/ 10.1609/aimag.v27i4.1904. 2. 3. 4. 5. 6. 
  2. Minsky, M.; Papert, S. Perceptrons: An Introduction to Computational Geometry; MIT Press: Cambridge, MA, 1969.
  3. Lighthill, J. Artificial Intelligence: A General Survey. In Artificial Intelligence: A Paper Symposium; Science Research Council: London, UK, 1973.
  4. Nilsson, N.J., Speed Bumps. In The Quest for Artificial Intelligence: A History of Ideas and Achievements; Cambridge University Press: Cambridge, UK, 2010; chapter 24, pp. 305–330. https://doi.org/10.1017/CBO9 780511819346.028.
  5. Hooker, S. The Hardware Lottery. CoRR 2020, abs/2009.06489, [2009.06489].
  6. Sutton, R.S. The Bitter Lesson. http://www.incompleteideas.net/IncIdeas/BitterLesson.html, 2019. Incomplete Ideas (blog), 13 March 2019. © 2026 by the author(s). Distributed under a Creative Commons CC BY license.Preprints.org (www.preprints.org)  |  NOT PEER-REVIEWED  |  Posted: 27 August 2026 doi:10.20944/preprints202608.1992.v1 6 of 6 7. 8. 9.
  7. Sevilla, J.; Heim, L.; Ho, A.; Besiroglu, T.; Hobbhahn, M.; Villalobos, P. Compute Trends Across Three Eras of Machine Learning. In Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN), 2022, pp. 1–8. https://doi.org/10.1109/IJCNN55064.2022.9891914.
  8. Raji, I.D.; Bender, E.M.; Paullada, A.; Denton, E.; Hanna, A. AI and the Everything in the Whole Wide World Benchmark. CoRR 2021, abs/2111.15366, [2111.15366].
  9. Chollet, F. On the Measure of Intelligence, 2019, [arXiv:cs.AI/1911.01547]. 10.
  10. Mitchell, M. Why AI is Harder Than We Think. CoRR 2021, abs/2104.12871, [2104.12871]. 

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