Showing posts with label terry_tao. Show all posts
Showing posts with label terry_tao. Show all posts

Saturday, October 17, 2026

Math, AI, and Terry Tao on a “war footing”

Elisabeth Bumiller, Why One of the World’s Greatest Mathematicians Is on a ‘War Footing’, NYTimes, Oct. 15, 2026.

Opening paragraphs:

Terence Tao is typically described as one of the world’s greatest living mathematicians, but right now he is doing very little math.

“I’m on a war footing,” he said while grabbing coffee during a two-day math workshop at the University of California, Los Angeles, that was rife with talk about the existential crisis that artificial intelligence has created in his field.

A bit later:

Until recently his job was as a bridge between the slow-moving world of higher math and the scorching speed of A.I. Dr. Tao, who uses A.I. as an assistant to run calculations, write code, search vast troves of literature and brainstorm ideas, has been experimenting with OpenAI’s models since 2023. He was an enthusiast for a “best of both worlds” future.

OpenAI’s releases have now left him feeling used. “As recently as a year ago, I had a pretty good relationship with many employees at OpenAI,” he said in a recent phone conversation.

“A lot of people I interacted with shared a very common vision of how A.I. should be used — you know, to empower scientists.” But the rush to publish new findings, he said, shows their true purpose. They “used math for what they needed, which was to evaluate their models,” he said. “At this point, I don’t see a way to constructively engage.”

His anger was clear in a post on the widely read math blog Proofs and Prompts after OpenAI’s Oct. 6 release. “I am horrified by the many person-years” of research, particularly by young mathematicians, “being casually disrupted or destroyed,” he wrote.

“Much as one cannot unhear a movie spoiler or a crossword clue, one cannot explore a problem as profitably and richly once one is aware of an existing solution.” He mourned, he added, “the path not taken, and the opportunities lost in the frantic race to develop this technology.”

Final paragraphs:

Despite OpenAI’s releases, he said, most problems at the frontiers of math are unsolved, math remains infinite and the difficulty of problems is therefore boundless. Since A.I. has given mathematicians the ability to collect data on such a vast scale, they will increasingly work in large groups collaborating on once-unimagined projects.

Dr. Tao, who sees each of his three students once a week, said every meeting is “increasingly a therapy session more than technical guidance.” His answer to them is that “if you focus on only the things you could do before, it looks bleak.”

So he has offered hope with advice that amounts to acceptance born of necessity rather than fighting a war. Problem solving is not a destination but a journey, and lessons learned along the way are often more important than the answer itself. Rather than solving a single problem on their own, they can use A.I. to solve 10 problems and find new patterns and more questions to ask.

“They will have to use their imagination,” he said. “This is a time for creativity, and I do hope students rise to the challenge. They didn’t ask for it. But I think many of them will surprise us.”

There's much more at the link.

Friday, October 16, 2026

Terence tao says we're being dragged into a new era of mathetics

Saturday, June 6, 2026

Terence Tao Explains The Math Behind AI

On the YouTube page:

Terence Tao has read more mathematics than almost anyone alive, and he uses AI tools every day. So when one of the most cited mathematicians on Earth says these systems still can't ask a genuinely new question, it's worth understanding exactly where he draws the line — because it isn't where the headlines put it.

Watch the full conversation: Terence Tao: Nobody Understands Why AI Actually Works

If AI has absorbed every textbook ever written, why can't it discover anything new? Tao, a Fields Medal winner and professor at UCLA, separates what these systems do brilliantly from what they can't do at all, and the boundary turns out to be sharper and stranger than most people assume.

We cover why reproducing a famous proof is less impressive than it sounds, what a neural network found hidden inside a million knots that humans had missed, why we still can't predict which tasks AI will actually be good at, the "Keating Test" — the benchmark that would actually demonstrate machine thought — and where exhaustive recall ends and real conceptual origination begins.

AI can pass every exam. It just can't ask a question nobody has asked before — yet.

Chapters:
00:00 The question AI can't ask
00:48 Read every textbook, discover nothing
01:42 Why a reproduced proof proves less
02:39 A million knots, one hidden pattern
03:54 The competence we still can't predict
05:11 The Keating Test for machine thought
06:18 Where recall ends and discovery begins

📬 Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt

Sunday, March 22, 2026

Terrence Tao talks with Dwarkesh Patel about Kepler discovering his 3 laws of planetary motion (and other things): A real case of creativity

Dwarkesh Patel, Terence Tao – Kepler, Newton, and the true nature of mathematical discovery, March 20, 2026.

We begin the episode with the absolutely ingenious and surprising way in which Kepler discovered the laws of planetary motion.

People sometimes say that AI will make especially fast progress at scientific discovery because of tight verification loops.

But the story of how we discovered the shape of our solar system shows how the verification loop for correct ideas can be decades (or even millennia) long.

During this time, what we know today as the better theory can often actually make worse predictions (Copernicus’s model of circular orbits around the sun was actually less accurate than Ptolemy’s geocentric model).

And the reasons it survives this epistemic hell is some mixture of judgment and heuristics that we don’t even understand well enough to actually articulate, much less codify into an RL loop.

* * * * *

Terence Tao: I’ve always had an amateur interest in astronomy. I’ve loved stories of how the early astronomers worked out the nature of the universe. Kepler was building on the work of Copernicus, who was himself building on the work of Aristarchus. Copernicus very famously proposed the heliocentric model, that instead of the planets and the Sun going around the Earth, the Sun was at the center of the solar system and the other planets were going around the Sun.

Copernicus proposed that the orbits of the planets were perfect circles. His theory fit the observations that the Greeks, the Arabs, and the Indians had worked out over centuries. Kepler learned about these theories in his studies, and he made this observation that the ratios of the size of the orbits that Copernicus predicted seemed to have some geometric meaning.

He started proposing that if you take the orbit of the Earth and you enclose it in a cube, the outer sphere that encloses the cube almost perfectly matched the orbit of Mars, and so forth. There were six planets known at the time and five gaps between them, and there were five perfect Platonic solids: the cube, the tetrahedron, icosahedron, octahedron, and dodecahedron.

So he had this theory, which he thought was absolutely beautiful, that you could inscribe these Platonic solids between the spheres of the planets. It seemed to fit, and it seemed to him that God’s design of the planets was matching this mathematical perfection of the Platonic solids.

He needed data to confirm this theory. At the time, there was only one really high-quality dataset in existence. Tycho Brahe, this very wealthy, eccentric Danish astronomer, had managed to convince the Danish government to fund this extremely expensive observatory. In fact, it was an entire island where he had taken decades of observations of all the planets, like Mars and Jupiter, at least every night for which the weather was clear, with the naked eye. He was the last of the naked-eye astronomers.

He had all this data which Kepler could use to confirm his theory. Kepler started working with Tycho, but Tycho was very jealous of the data. He only gave him little bits of it at a time. Kepler eventually just stole the data. He copied it and had to have a fight with Brahe’s descendants.

He did get the data, and then he worked out, to his disappointment, that his beautiful theory didn’t quite work. The data was off from his Platonic solid theory by 10% or something. He tried all kinds of fudges, moving the circles around, and it didn’t quite work. But he worked on this problem for years and years, and eventually, he figured out how to use the data to work out the actual orbits of the planets.

That was an incredibly clever, genius amount of data analysis. And then he worked out that the orbits were actually ellipses, not circles, which was shocking for him. So he worked out the two laws of planetary motion: the ellipses, and also that equal areas sweep out equal times.

Then ten years later, after collecting a lot of data—the furthest planets like Saturn and Jupiter were the hardest for him to work out—he finally worked out this third law, that the time it takes for a planet to complete its orbit was proportional to some power of the distance to the Sun. These are the three famous Kepler’s laws of motion. He had no explanation for them. It was all driven by experiment, and it took Newton a century later to give a theory that explained all three laws at once.

Dwarkesh Patel: The take I want to try on you is that Kepler was a high-temperature LLM. Newton comes up with this explanation of why the three laws of planetary motion must be true. Of course, the way that Kepler discovers the laws of planetary motion, or figures out the relative orbits of the different planets, is as you say a work of genius. But through his career, he’s just trying random relationships.

In fact, in the book in which he writes down the third law of planetary motion, it’s an aside on The Harmonics of the World, which is just a book about how all these different planets have these different harmonies. And the reason there’s so much famine and misery on Earth is because the Earth is mi-fa-mi, that’s the note of Earth. It’s all this random astrology, but in there is the cube-square law, which tells you what relationship the period has to a planet’s distance from the Sun. As you were detailing, if you add that to Newton’s F=ma and the equation for centripetal acceleration, you get the inverse-square law. And so Newton works that out.

But the reason I think this is an interesting story is that I feel LLMs can do the kind of thing of trying random relationships for twenty years, some of which make no sense, as long as there’s a verifiable data bank like Brahe’s dataset. “Ok, I’m going to try out random things about musical notes, Platonic objects, or different geometries, I have this bias that there’s some important thing about the geometry of these orbits.”

Then one thing works. As long as you can verify it, these empirical regularities can then drive actual deep scientific progress.

Terence Tao: Traditionally, when we talk about the history of science, idea generation has always been the prestige part of science. A scientific problem comes with many steps. You have to identify a problem, and then you have to identify a good, fruitful problem to work on. Then you need to collect data, figure out a strategy to analyze the data, and make a hypothesis. At this point, you need to propose a good hypothesis, and then you need to validate. Then you need to write things up and explain. There are a dozen different components.

The ones we celebrate are these eureka genius moments of idea generation. Kepler certainly had to cycle through many ideas, several of which didn’t work. I bet there were many that he didn’t even publish at all because they just didn’t fit. That’s an important part of the process, trying all kinds of random things and seeing if they worked.

But as you say, it has to be matched by an equal amount of verification, otherwise it’s slop. We celebrate Kepler, but we should also celebrate Brahe for his assiduous data collection, which was ten times more precise than any previous observation. That extra decimal point of accuracy was essential for Kepler to get his results. He was using Euclidean geometry and the most advanced mathematics he could use at the time to match his models with the data. All aspects had to be in play: the data, the theory, and the hypothesis generation.

I’m not sure nowadays that hypothesis generation is the bottleneck anymore. Science has changed in the century since. Classically, the two big paradigms for science were theory and experiment. Then in the 20th century, numerical simulation came along, so you can do computer simulations to test theories. Finally, in the late 20th century, we had big data. We had the era of data analysis. 

* * * * * 

That’s just the beginning of the conversation. There’s much more to come.