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📁 Terence Tao, Fields Medal mathematician, says mathematics could become AI’s safest and most powerful domain. Language models can be brilliant or completely wrong, but in math every claim is forced through logic and can even be verified by proof assistants. Where other fields struggle with unreliability, mathematics can... show more

Jon Hernandez

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80,288 просмотров • 7 месяцев назад •via X (Twitter)

Образование Наука и технологии

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Why is AI suddenly cracking all kinds of previously unsolved problems in math? My guess is that it’s because: A) Math is verifiable so it's easier for models to improve B) Theoretical math highly benefits from chain of thought reasoning C) The answers and breakthroughs can be tested and verified by expert humans. But this leads to another question: what if progress in other fields is moving just as fast and we can't see it?

Why is AI suddenly cracking all kinds of previously unsolved problems in math? My guess is that it’s because: A) Math is verifiable so it's easier for models to improve B) Theoretical math highly benefits from chain of thought reasoning C) The answers and breakthroughs can be tested and verified by expert humans. But this leads to another question: what if progress in other fields is moving just as fast and we can't see it?

nxthompson

17,404 просмотров • 1 месяц назад

🚨 FULL CONVERSATION Fields medalist Terry Tao sits down with Math Inc's Jesse Michael Han and Jared Duker Lichtman for a conversation on the future of mathematics. "I got convinced that this was the future of mathematics [...] It's a different style of writing proofs that actually is in some ways easier to read—harder to check by humans, but you see more clearly the inputs and outputs of a proof, which traditional writing often conceals [...] I think the definition of a mathematician will broaden."

🚨 FULL CONVERSATION Fields medalist Terry Tao sits down with Math Inc's Jesse Michael Han and Jared Duker Lichtman for a conversation on the future of mathematics. "I got convinced that this was the future of mathematics [...] It's a different style of writing proofs that actually is in some ways easier to read—harder to check by humans, but you see more clearly the inputs and outputs of a proof, which traditional writing often conceals [...] I think the definition of a mathematician will broaden."

Math, Inc.

337,383 просмотров • 8 месяцев назад

Always so much fun to chat with Grant Sanderson AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there's a jagged landscape. What does it look like? What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do? Does AI (on net) increase or decrease human understanding of the field? How big is the overhang from having AIs systematically try to connect ideas already in the literature? And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI? 0:00:00 – AI is discovering new proofs. Is that AGI? 0:11:32 – The verification loop on conceptual breakthroughs can be a century long 0:26:12 – Will we understand an AI proof of the Riemann hypothesis? 0:38:08 – Can AI find the hidden bridges between fields? 0:53:48 – Why real-world tasks don’t fit into RL environments 1:07:07 – Good writing requires theory of mind that AI still lacks 1:16:02 – Why learning will still depend on human curation Look up Dwarkesh Podcast on Spotify, Apple Podcasts, YouTube, etc.

Always so much fun to chat with Grant Sanderson AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there's a jagged landscape. What does it look like? What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do? Does AI (on net) increase or decrease human understanding of the field? How big is the overhang from having AIs systematically try to connect ideas already in the literature? And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI? 0:00:00 – AI is discovering new proofs. Is that AGI? 0:11:32 – The verification loop on conceptual breakthroughs can be a century long 0:26:12 – Will we understand an AI proof of the Riemann hypothesis? 0:38:08 – Can AI find the hidden bridges between fields? 0:53:48 – Why real-world tasks don’t fit into RL environments 1:07:07 – Good writing requires theory of mind that AI still lacks 1:16:02 – Why learning will still depend on human curation Look up Dwarkesh Podcast on Spotify, Apple Podcasts, YouTube, etc.

Dwarkesh Patel

290,780 просмотров • 2 месяцев назад

What is mathematics, really? Mathematician David Bessis joins me to argue that math isn't about numbers in a Platonic realm or a meaningless game of symbols—it's a cognitive technology for rewiring your brain. We explore why the official definitions of mathematics have been unresolved for 2,300 years, why understanding something means finding it obvious, and how the gap between a beginner and Terence Tao looks less like genetic destiny and more like compound interest on intuition. When asked what mathematics fundamentally is, his answer cuts through millennia of philosophy: it's what happens in your head when you pretend something is true until it feels real.

What is mathematics, really? Mathematician David Bessis joins me to argue that math isn't about numbers in a Platonic realm or a meaningless game of symbols—it's a cognitive technology for rewiring your brain. We explore why the official definitions of mathematics have been unresolved for 2,300 years, why understanding something means finding it obvious, and how the gap between a beginner and Terence Tao looks less like genetic destiny and more like compound interest on intuition. When asked what mathematics fundamentally is, his answer cuts through millennia of philosophy: it's what happens in your head when you pretend something is true until it feels real.

Curt Jaimungal

12,681 просмотров • 6 месяцев назад

🚨 BREAKING: Fields medalist Terry Tao on how mathematics will change: “When these tools are perfected, we will change the way we do mathematics. If there's a drudgery or a big computation, we'll just hit it with all our technology and say: 'By Gauss, you can get from here to there,' and now we just keep going. So we can blast through all these obstacles that we avoid almost subconsciously. If you look at what we miss, it's the missed opportunities, and that percentage of the overall opportunities is huge.” Full conversation with Math, Inc.’s Jesse Michael Han and Jared Duker Lichtman coming soon.

🚨 BREAKING: Fields medalist Terry Tao on how mathematics will change: “When these tools are perfected, we will change the way we do mathematics. If there's a drudgery or a big computation, we'll just hit it with all our technology and say: 'By Gauss, you can get from here to there,' and now we just keep going. So we can blast through all these obstacles that we avoid almost subconsciously. If you look at what we miss, it's the missed opportunities, and that percentage of the overall opportunities is huge.” Full conversation with Math, Inc.’s Jesse Michael Han and Jared Duker Lichtman coming soon.

Math, Inc.

138,937 просмотров • 9 месяцев назад

The way math is presented can be really misleading about the actual nature of math. In theory, someone could have discovered quantum information theory decades before quantum mechanics even existed. It's just about combining linear algebra with probability. But nobody would have known it was worth combining these fields - or how, exactly, to combine them - before QM. Math papers and textbooks go rigourously step-by-step, starting with axioms and definitions, then deducing theorems. It's great for making sure proofs can be verified and ensuring the subject is based on sound logic. But for learners, it hides the really important thing: where did the axioms come from? Why these definitions and not others? The thing that's missing is the sense that there were concrete problems that these ideas helped solve.

The way math is presented can be really misleading about the actual nature of math. In theory, someone could have discovered quantum information theory decades before quantum mechanics even existed. It's just about combining linear algebra with probability. But nobody would have known it was worth combining these fields - or how, exactly, to combine them - before QM. Math papers and textbooks go rigourously step-by-step, starting with axioms and definitions, then deducing theorems. It's great for making sure proofs can be verified and ensuring the subject is based on sound logic. But for learners, it hides the really important thing: where did the axioms come from? Why these definitions and not others? The thing that's missing is the sense that there were concrete problems that these ideas helped solve.

Dwarkesh Patel

58,876 просмотров • 2 месяцев назад

A Fields Medalist walked into a UCLA hall and explained prime numbers to people with no mathematics background. Almost nobody watches it. Terence Tao won the Fields Medal, mathematics' highest honour, then a MacArthur Fellowship less than a month later. He was UCLA's first professor to take the Fields. The talk is structure and randomness in the primes. Numbers that look scattered at every scale, yet obey rules strict enough to build cryptography on. That tension is the same one running through every model trained in 2026. Randomness with structure underneath, and the whole question of where one stops and the other starts. Watch how he pitches it. General audience, no prerequisites, one of the strongest living mathematicians choosing clarity over display. A cryptography engineer I know rewatched the middle section twice and said the primes stopped looking arbitrary. Free on YouTube from UCLA, 17 years online. The hardest minds explain most simply. The pattern never changes.

A Fields Medalist walked into a UCLA hall and explained prime numbers to people with no mathematics background. Almost nobody watches it. Terence Tao won the Fields Medal, mathematics' highest honour, then a MacArthur Fellowship less than a month later. He was UCLA's first professor to take the Fields. The talk is structure and randomness in the primes. Numbers that look scattered at every scale, yet obey rules strict enough to build cryptography on. That tension is the same one running through every model trained in 2026. Randomness with structure underneath, and the whole question of where one stops and the other starts. Watch how he pitches it. General audience, no prerequisites, one of the strongest living mathematicians choosing clarity over display. A cryptography engineer I know rewatched the middle section twice and said the primes stopped looking arbitrary. Free on YouTube from UCLA, 17 years online. The hardest minds explain most simply. The pattern never changes.

the lich

50,394 просмотров • 1 месяц назад

I understand why he says this. When you first enter theoretical physics, the mathematics can feel overwhelming. You look at the people around you and think they were simply born able to see structures that you cannot yet see. But I do not think you need to be a world-class mathematician to become a successful physicist. You need to become fluent enough in mathematics that it no longer stands between you and the physical idea. That takes years of frustration, failed calculations, confusion and returning to the same problem again and again. And even then, mathematics is only part of the work. Some people can calculate beautifully but do not always know which question is worth asking. Others may not be the fastest mathematicians in the room, yet they possess a rare physical instinct. They can look at a complicated problem and feel where the essential idea is hiding. The strongest physicists eventually learn to bring both together. So I would not tell a young student, Become a world-class mathematician or give up. I would tell them: learn the mathematics seriously, be patient with yourself, protect your curiosity and keep developing your physical intuition. You do not have to begin as a genius. You have to remain with the problem long enough to become capable.

I understand why he says this. When you first enter theoretical physics, the mathematics can feel overwhelming. You look at the people around you and think they were simply born able to see structures that you cannot yet see. But I do not think you need to be a world-class mathematician to become a successful physicist. You need to become fluent enough in mathematics that it no longer stands between you and the physical idea. That takes years of frustration, failed calculations, confusion and returning to the same problem again and again. And even then, mathematics is only part of the work. Some people can calculate beautifully but do not always know which question is worth asking. Others may not be the fastest mathematicians in the room, yet they possess a rare physical instinct. They can look at a complicated problem and feel where the essential idea is hiding. The strongest physicists eventually learn to bring both together. So I would not tell a young student, Become a world-class mathematician or give up. I would tell them: learn the mathematics seriously, be patient with yourself, protect your curiosity and keep developing your physical intuition. You do not have to begin as a genius. You have to remain with the problem long enough to become capable.

Philosophy Of Physics

16,619 просмотров • 2 месяцев назад

“Math is code. And code is math.” Carina Hong CEO Axiom on why that idea matters for AI: “For centuries, mathematicians reasoned in natural language. But because of the Curry–Howard correspondence, a proof can also be a program.” “You can translate natural-language math into formal code using systems like Lean. Lean is both a theorem-proving language and a programming language.” “That means you can generate solutions as code and then formally verify them. You can even use the same system to verify programs themselves.” “This creates a flywheel of generation and verification, where AI can both write solutions and prove that they’re correct.”

“Math is code. And code is math.” Carina Hong CEO Axiom on why that idea matters for AI: “For centuries, mathematicians reasoned in natural language. But because of the Curry–Howard correspondence, a proof can also be a program.” “You can translate natural-language math into formal code using systems like Lean. Lean is both a theorem-proving language and a programming language.” “That means you can generate solutions as code and then formally verify them. You can even use the same system to verify programs themselves.” “This creates a flywheel of generation and verification, where AI can both write solutions and prove that they’re correct.”

Forward Future

24,729 просмотров • 7 месяцев назад

Why do we do math in school? When I asked this question as a kid, my teachers always told me, "You'll use it one day." That's almost always false. Most adults are never going to be in a sticky situation with two binomials, thinking, thank goodness I can FOIL my way out of this mess. Most of us get by just fine without ever solving for x again. And especially in an Al age, fewer and fewer people will "use math" in the narrow, practical sense. But the origins of math have never been primarily about utility. Math is formative. It trains the mind to love what is true, to recognize what is orderly, and to be drawn toward what is beautiful. It teaches us that the universe is not chaos, but something intelligible, something structured, something that can be known. And you can see that truth made visible in the world's most breathtaking churches and cathedrals: arches, vaults, domes, proportions, symmetry, harmony, light-geometry turned into glory. The mathematics that shapes a cathedral is not cold or sterile. It's the language of wonder, carved into stone. We don't teach math because everyone will use it. We teach math because it forms the kind of person who can see that reality has meaning.

Why do we do math in school? When I asked this question as a kid, my teachers always told me, "You'll use it one day." That's almost always false. Most adults are never going to be in a sticky situation with two binomials, thinking, thank goodness I can FOIL my way out of this mess. Most of us get by just fine without ever solving for x again. And especially in an Al age, fewer and fewer people will "use math" in the narrow, practical sense. But the origins of math have never been primarily about utility. Math is formative. It trains the mind to love what is true, to recognize what is orderly, and to be drawn toward what is beautiful. It teaches us that the universe is not chaos, but something intelligible, something structured, something that can be known. And you can see that truth made visible in the world's most breathtaking churches and cathedrals: arches, vaults, domes, proportions, symmetry, harmony, light-geometry turned into glory. The mathematics that shapes a cathedral is not cold or sterile. It's the language of wonder, carved into stone. We don't teach math because everyone will use it. We teach math because it forms the kind of person who can see that reality has meaning.

Jeremy Wayne Tate

95,200 просмотров • 8 месяцев назад

Terence Tao says AI can flood you with new mathematical ideas, but maybe 1 in 100 is actually good, and it can't tell the difference Where math is verifiable, you either solved it, or you didn't — AI is already useful "it might occasionally hit gold, then move right past it and give you 99 things that are rubbish"

Terence Tao says AI can flood you with new mathematical ideas, but maybe 1 in 100 is actually good, and it can't tell the difference Where math is verifiable, you either solved it, or you didn't — AI is already useful "it might occasionally hit gold, then move right past it and give you 99 things that are rubbish"

Haider.

23,551 просмотров • 5 месяцев назад

Terence Tao: AI isn’t hype anymore in Math discovery. Terence Tao is one of the greatest living mathematicians, in his new lecture explains how AI and human professional mathematicians are now complementary. "There has been a really visible increase in capability. It is not pure hype by any means. To me, these advances show there is a complementary way to do mathematics. Humans traditionally work in small groups on hard problems for months, and we will keep doing that. But we can also now set AI to scale: sweep a thousand problems and pick up all the low-hanging fruit. Figure out all the ways to match problems to methods. If there are 20 different techniques, apply them all to 1,000 problems and see which ones can be solved by these methods. This is the capability that is present today." From 'Institute for Pure & Applied Mathematics (IPAM)' YT channel.

Terence Tao: AI isn’t hype anymore in Math discovery. Terence Tao is one of the greatest living mathematicians, in his new lecture explains how AI and human professional mathematicians are now complementary. "There has been a really visible increase in capability. It is not pure hype by any means. To me, these advances show there is a complementary way to do mathematics. Humans traditionally work in small groups on hard problems for months, and we will keep doing that. But we can also now set AI to scale: sweep a thousand problems and pick up all the low-hanging fruit. Figure out all the ways to match problems to methods. If there are 20 different techniques, apply them all to 1,000 problems and see which ones can be solved by these methods. This is the capability that is present today." From 'Institute for Pure & Applied Mathematics (IPAM)' YT channel.

Rohan Paul

174,341 просмотров • 7 месяцев назад

Harvey Friedman — the youngest professor in Stanford's history, founder of reverse mathematics, and the mathematician Kurt Gödel personally chose to sponsor his final paper — has spent 60 years on a single, audacious question: can ordinary, finite math be trusted? His theorems suggest otherwise, showing that even the most concrete and natural mathematical statements — involving nothing more exotic than rational numbers — cannot be proved or refuted within the gold standard of mathematical foundations, ZFC. The foundations of mathematics, Friedman argues, are not settled bedrock but something far more vertiginous: totally up in the air, and made more mysterious, not less, by his own work.

Harvey Friedman — the youngest professor in Stanford's history, founder of reverse mathematics, and the mathematician Kurt Gödel personally chose to sponsor his final paper — has spent 60 years on a single, audacious question: can ordinary, finite math be trusted? His theorems suggest otherwise, showing that even the most concrete and natural mathematical statements — involving nothing more exotic than rational numbers — cannot be proved or refuted within the gold standard of mathematical foundations, ZFC. The foundations of mathematics, Friedman argues, are not settled bedrock but something far more vertiginous: totally up in the air, and made more mysterious, not less, by his own work.

Curt Jaimungal

113,554 просмотров • 4 месяцев назад

Ten days after winning the Fields Medal — math's highest honor — Jacob Tsimerman joins to explain why he's leaving academia temporarily for OpenAI's AI safety team, and why he's grieving even as he does it. The conversation covers why math is one of the first fields being radically reshaped by AI, whether a proof still counts if no one can understand it, what mathematical "understanding" really means, the ethics of elite access to frontier models, his disagreements with the Leiden Declaration, and his advice for young people weighing a career in pure math in an age of rapidly advancing AI. This is an exclusive first podcast with Jacob Tsimerman.

Ten days after winning the Fields Medal — math's highest honor — Jacob Tsimerman joins to explain why he's leaving academia temporarily for OpenAI's AI safety team, and why he's grieving even as he does it. The conversation covers why math is one of the first fields being radically reshaped by AI, whether a proof still counts if no one can understand it, what mathematical "understanding" really means, the ethics of elite access to frontier models, his disagreements with the Leiden Declaration, and his advice for young people weighing a career in pure math in an age of rapidly advancing AI. This is an exclusive first podcast with Jacob Tsimerman.

Curt Jaimungal

50,690 просмотров • 1 месяц назад

Living near the Power Lines? You’re probably being exposed to AC magnetic fields One of the strongest & most stubborn forms of nnEMF (non-native electromagnetic fields) The closer you are to these lines & the higher the current running through them, the stronger the exposure. Unlike RF radiation, which you can block with shielding, AC magnetic fields are much harder to mitigate. They pass through most materials, including walls, floors and even shielding fabrics. Only specialized, heavy materials like mu-metal can redirect fields but they are expensive, impractical and usually not realistic for home use. Distance is your best friend when it comes to reducing exposure to AC magnetic fields.

Living near the Power Lines? You’re probably being exposed to AC magnetic fields One of the strongest & most stubborn forms of nnEMF (non-native electromagnetic fields) The closer you are to these lines & the higher the current running through them, the stronger the exposure. Unlike RF radiation, which you can block with shielding, AC magnetic fields are much harder to mitigate. They pass through most materials, including walls, floors and even shielding fabrics. Only specialized, heavy materials like mu-metal can redirect fields but they are expensive, impractical and usually not realistic for home use. Distance is your best friend when it comes to reducing exposure to AC magnetic fields.

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Introducing Trace. Trace is our flagship, public audit and search platform where every asset permanently registered on DATA Network can be accessed. AI Labs can search every human contributed photo or voice sample and drill into any individual record to see the full audit trail. Trace is where labs and regulators can filter by dataset, app, data type, modality, and time to verify data provenance so AI models can be trained with confidence.

Introducing Trace. Trace is our flagship, public audit and search platform where every asset permanently registered on DATA Network can be accessed. AI Labs can search every human contributed photo or voice sample and drill into any individual record to see the full audit trail. Trace is where labs and regulators can filter by dataset, app, data type, modality, and time to verify data provenance so AI models can be trained with confidence.

The DATA Foundation

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This lady is named JJ. She is a mystic who teaches people how to speak "Light Language." She admits there is no difference between Light Language and charismatic tongues - they are the same thing. And "Light Language" can even be interpreted (so she says). Much like charismatics claim tongues can be interpreted (but they cannot). The biblical gift of tongues could be interpreted (better said, translated) because it was an actual language. Light Language and charismatic tongues ae just gibberish that means nothing.

This lady is named JJ. She is a mystic who teaches people how to speak "Light Language." She admits there is no difference between Light Language and charismatic tongues - they are the same thing. And "Light Language" can even be interpreted (so she says). Much like charismatics claim tongues can be interpreted (but they cannot). The biblical gift of tongues could be interpreted (better said, translated) because it was an actual language. Light Language and charismatic tongues ae just gibberish that means nothing.

Justin Peters

41,478 просмотров • 4 месяцев назад

Google artificial intelligence says it’s wrong to dislike Muslims or any other religion except Christianity. Google says you can hate Christians Google says it’s safer to be alone with a satanist than it is with a Christian Google says you can be proud to be black or any other race except white people Google is extremely openly racist against Christian’s and white people and only Christians and white people They are also actively going and covering their tracks as things go viral and covering up the evidence by changing the search results, but it’s all on video

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Wall Street Apes

319,199 просмотров • 25 дней назад

Demis Hassabis recommendation for college students. He’s still do STEM, math and computer science. Expertise in those fields will help better leverage AI for at least next decade. Those in non-techical majors, really “lean in to” using latest models. AI labs spending so much time creating new models that they’ve only “scratched the surface” of what the models can actually do (huge “capability overhang”). And expertise in any field can be turbocharged by smart use of AI. “Double down on your own agency. The future is still to be written. Don’t listen to anyone that says it’s not.”

Demis Hassabis recommendation for college students. He’s still do STEM, math and computer science. Expertise in those fields will help better leverage AI for at least next decade. Those in non-techical majors, really “lean in to” using latest models. AI labs spending so much time creating new models that they’ve only “scratched the surface” of what the models can actually do (huge “capability overhang”). And expertise in any field can be turbocharged by smart use of AI. “Double down on your own agency. The future is still to be written. Don’t listen to anyone that says it’s not.”

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