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Rapidly advancing AI models have sparked breakthroughs in long-standing math problems. From solving problems at the International Math Olympiad to disproving decades-old conjectures - some in the math world are now questioning whether they have a future in the field. But not Professor Shayan Oveis Gharan, who won the...

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57,637 次观看 • 11 天前 •via X (Twitter)

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We asked Elliot Glazer how people should evaluate future AI math breakthroughs. “The buzz phrase you’re gonna be seeing a lot is the difference between problem-solving and theory building.” “You solved the problem using a bunch of techniques, advanced techniques, but very much previously techniques in the human literature. That’s problem-solving.” “We have not yet seen models engage in theory building.” “Coming up with powerful new definitions and techniques that create really great breakthroughs that have ripples across the field.”
1:00

We asked Elliot Glazer how people should evaluate future AI math breakthroughs. “The buzz phrase you’re gonna be seeing a lot is the difference between problem-solving and theory building.” “You solved the problem using a bunch of techniques, advanced techniques, but very much previously techniques in the human literature. That’s problem-solving.” “We have not yet seen models engage in theory building.” “Coming up with powerful new definitions and techniques that create really great breakthroughs that have ripples across the field.”

MTS

20,952 次观看 • 3 个月前

How does math research change when the cost of trying your first dumb idea goes to zero? Daniel Litt joins Greg Burnham and Anson Ho to discuss what today’s models can and can’t do in math, and how far they are from doing high-quality research. 0:00:00 What's the hardest math problem AI can solve today? 00:16:08 How helpful are today’s AI models for math research? 00:23:36 Junk papers, LLM-generated proofs, and the refereeing crisis 00:27:21 AI enables searching through problems at scale 00:33:49 When will AI be good enough to publish in top math journals? 00:42:15 What are the returns to intelligence? 00:59:50 Will AI solve Millennium problems? 01:11:54 Is math full of low-hanging fruit? 01:18:47 How Daniel has adapted his professional life to AI progress 01:25:28 What do AI math benchmarks actually measure? 01:33:05 Designing the Open Problems benchmark 01:56:35 Do mathematicians believe heuristic arguments about conjectures? 02:01:24 What if FrontierMath: Open Problems gets solved? 02:06:53 Is AI on the cusp of accelerating math progress?
2:10:59

How does math research change when the cost of trying your first dumb idea goes to zero? Daniel Litt joins Greg Burnham and Anson Ho to discuss what today’s models can and can’t do in math, and how far they are from doing high-quality research. 0:00:00 What's the hardest math problem AI can solve today? 00:16:08 How helpful are today’s AI models for math research? 00:23:36 Junk papers, LLM-generated proofs, and the refereeing crisis 00:27:21 AI enables searching through problems at scale 00:33:49 When will AI be good enough to publish in top math journals? 00:42:15 What are the returns to intelligence? 00:59:50 Will AI solve Millennium problems? 01:11:54 Is math full of low-hanging fruit? 01:18:47 How Daniel has adapted his professional life to AI progress 01:25:28 What do AI math benchmarks actually measure? 01:33:05 Designing the Open Problems benchmark 01:56:35 Do mathematicians believe heuristic arguments about conjectures? 02:01:24 What if FrontierMath: Open Problems gets solved? 02:06:53 Is AI on the cusp of accelerating math progress?

Epoch AI

178,703 次观看 • 7 个月前

📁 Demis Hassabis says that one of the main reasons we are not yet at AGI is the lack of consistency. He explains that current systems can solve International Math Olympiad level problems but still fail at basic logic tasks or even chess. They are uneven intelligences, exceptional in some areas and surprisingly weak in others, which falls short of true AGI.
1:01

📁 Demis Hassabis says that one of the main reasons we are not yet at AGI is the lack of consistency. He explains that current systems can solve International Math Olympiad level problems but still fail at basic logic tasks or even chess. They are uneven intelligences, exceptional in some areas and surprisingly weak in others, which falls short of true AGI.

Jon Hernandez

10,541 次观看 • 8 个月前

AI has solved 50 Erdős problems in the last year. But on a wider sweep of problems, the models’ success rate is only about 1-2%: labs have just been publishing the wins. This isn’t because AI isn’t useful for mathematicians. Terence Tao thinks the models are currently at the level of a trustworthy coworker. But while they’ve got a strong ability to apply standard math techniques to problems, often more reliably than humans, Terence thinks they currently aren’t great at iterating on partial successes - their understanding of the mathematical object does not advance from session to session. I swear I wasn’t trying to get him to talk about continual learning.
1:53

AI has solved 50 Erdős problems in the last year. But on a wider sweep of problems, the models’ success rate is only about 1-2%: labs have just been publishing the wins. This isn’t because AI isn’t useful for mathematicians. Terence Tao thinks the models are currently at the level of a trustworthy coworker. But while they’ve got a strong ability to apply standard math techniques to problems, often more reliably than humans, Terence thinks they currently aren’t great at iterating on partial successes - their understanding of the mathematical object does not advance from session to session. I swear I wasn’t trying to get him to talk about continual learning.

Dwarkesh Patel

146,244 次观看 • 5 个月前

Terence Tao thinks AI is already very good at using existing, well-understood math techniques to solve problems. An important question is how many open problems in math could be solved this way, without developing any new ideas. An extreme case of a proof like this is the four-color theorem, which was proved by checking a thousand examples one-by-one. If there are lots of problems that are vulnerable to just mechanically applying existing techniques, AI could start to produce new math results incredibly rapidly. But these proofs might not help advance our civilization’s knowledge the way math has in the past.
1:28

Terence Tao thinks AI is already very good at using existing, well-understood math techniques to solve problems. An important question is how many open problems in math could be solved this way, without developing any new ideas. An extreme case of a proof like this is the four-color theorem, which was proved by checking a thousand examples one-by-one. If there are lots of problems that are vulnerable to just mechanically applying existing techniques, AI could start to produce new math results incredibly rapidly. But these proofs might not help advance our civilization’s knowledge the way math has in the past.

Dwarkesh Patel

64,547 次观看 • 5 个月前

A month ago, we invited 30 of the world’s top mathematicians to Berkeley for a weekend to finish a very hard math exam. The 2025 FrontierMath Symposium wrapped up the hardest tier of FrontierMath, our benchmark for AI’s math abilities. The mathematicians tested AI models on their most challenging math problems, and discussed AI’s future in math. Watch our closing ceremony footage for a glimpse behind the scenes. -Timestamps- (00:00) - Intro by Elliot Glazer - Epoch AI (03:38) - Topology | Sergei Gukov - Caltech (06:53) - Algebraic Geometry | Ravi Vakil - Stanford (09:10) - Number Theory | Ken Ono - University of Virginia (13:19) - Combinatorics | Igor Pak - UCLA (16:54) - Analysis | Paata Ivanisvili - UC Irvine (18:18) - Closing Remarks
19:39

A month ago, we invited 30 of the world’s top mathematicians to Berkeley for a weekend to finish a very hard math exam. The 2025 FrontierMath Symposium wrapped up the hardest tier of FrontierMath, our benchmark for AI’s math abilities. The mathematicians tested AI models on their most challenging math problems, and discussed AI’s future in math. Watch our closing ceremony footage for a glimpse behind the scenes. -Timestamps- (00:00) - Intro by Elliot Glazer - Epoch AI (03:38) - Topology | Sergei Gukov - Caltech (06:53) - Algebraic Geometry | Ravi Vakil - Stanford (09:10) - Number Theory | Ken Ono - University of Virginia (13:19) - Combinatorics | Igor Pak - UCLA (16:54) - Analysis | Paata Ivanisvili - UC Irvine (18:18) - Closing Remarks

Epoch AI

21,379 次观看 • 1 年前

Peter Thiel just told Silicon Valley it’s automating away its own cognitive moat. Nobody there is paying attention. Thiel: “It is striking to me how bad Silicon Valley is at talking about these sorts of things.” The industry is either arguing over 20% improvements in the next transformer model or jumping straight to simulation theory. They’re missing the massive real-world shift happening right in the middle. Thiel: “My intuition would be it’s going to be quite the opposite, where it seems much worse for the math people than the word people.” For decades, Silicon Valley worshipped quantitative intelligence. Math and coding were the ultimate safety nets. Thiel: “Within three to five years, the AI models will be able to solve all the US Math Olympiad problems.” Once a machine instantly solves the hardest math problems on earth, the economic value of being a human calculator doesn’t just decline. It disappears. And the historical irony is brutal. The societal bias toward math over verbal ability started during the French Revolution. Not because math was more valuable. Because verbal ability ran in aristocratic families, and math was elevated as the great equalizer to break nepotism. A 200-year-old political accident became the foundation of Silicon Valley’s entire hiring philosophy. AI is about to snap it back. The people who built the models that can now outperform them mathematically spent their careers optimizing for the wrong skill. The future belongs to the word people. The engineers didn’t see it coming because they were too busy calculating.
1:45

Peter Thiel just told Silicon Valley it’s automating away its own cognitive moat. Nobody there is paying attention. Thiel: “It is striking to me how bad Silicon Valley is at talking about these sorts of things.” The industry is either arguing over 20% improvements in the next transformer model or jumping straight to simulation theory. They’re missing the massive real-world shift happening right in the middle. Thiel: “My intuition would be it’s going to be quite the opposite, where it seems much worse for the math people than the word people.” For decades, Silicon Valley worshipped quantitative intelligence. Math and coding were the ultimate safety nets. Thiel: “Within three to five years, the AI models will be able to solve all the US Math Olympiad problems.” Once a machine instantly solves the hardest math problems on earth, the economic value of being a human calculator doesn’t just decline. It disappears. And the historical irony is brutal. The societal bias toward math over verbal ability started during the French Revolution. Not because math was more valuable. Because verbal ability ran in aristocratic families, and math was elevated as the great equalizer to break nepotism. A 200-year-old political accident became the foundation of Silicon Valley’s entire hiring philosophy. AI is about to snap it back. The people who built the models that can now outperform them mathematically spent their careers optimizing for the wrong skill. The future belongs to the word people. The engineers didn’t see it coming because they were too busy calculating.

Dustin

623,823 次观看 • 6 个月前

If we're going to have AIs that fully automate math, they not only need to solve existing problems. We also need to teach them how to recognize what problem to solve next. Human mathematicians have heuristic models that they use to decide what to work on, like, “There’s something important going on with the randomness of primes - let’s codify some patterns we see into specific conjectures.” But these aren’t currently precise enough for RL.
2:33

If we're going to have AIs that fully automate math, they not only need to solve existing problems. We also need to teach them how to recognize what problem to solve next. Human mathematicians have heuristic models that they use to decide what to work on, like, “There’s something important going on with the randomness of primes - let’s codify some patterns we see into specific conjectures.” But these aren’t currently precise enough for RL.

Dwarkesh Patel

54,583 次观看 • 5 个月前

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?
2:59

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 次观看 • 24 天前

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.
0:45

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

174,109 次观看 • 6 个月前

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.
3:16

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

50,330 次观看 • 17 天前

Nigerians are waking up to reality. Lots of Nigerians are now discovering that the Yorubas are indeed the problems of Nigeria. They have been hiding it for a long time but Nigerians have discovered who their problems are currently. I’m glad Nigerians are discovering this now. I have known youbas as the problems of this country for a very long time.
2:04

Nigerians are waking up to reality. Lots of Nigerians are now discovering that the Yorubas are indeed the problems of Nigeria. They have been hiding it for a long time but Nigerians have discovered who their problems are currently. I’m glad Nigerians are discovering this now. I have known youbas as the problems of this country for a very long time.

OSE MMUO

59,601 次观看 • 9 个月前

Yann LeCun says AI already outperforms humans at tasks we’re not great at -- like chess, Go, or solving tough math problems that’s how computers have always helped us: by doing what we find hard in the future, smart devices with AI could make everyone feel like they have expert help, like a CEO or professor with a brilliant team
1:29

Yann LeCun says AI already outperforms humans at tasks we’re not great at -- like chess, Go, or solving tough math problems that’s how computers have always helped us: by doing what we find hard in the future, smart devices with AI could make everyone feel like they have expert help, like a CEO or professor with a brilliant team

Haider.

26,568 次观看 • 1 年前

Axiom Math founder Carina Hong on why math is the path to general superintelligence: “Math is the sandbox for reality. You very quickly see verifiable rewards because in math, there’s an absolute right or wrong.” “And especially when you have Lean, you can check the proof or solution step by step. You’ll be able to apply reinforcement learning in a much more efficient way.” “We have currently scaled from winning a Putnam perfect score to solving a batch of research problems that professional mathematicians find really challenging. And we also see this transfer to code verification.”
0:56

Axiom Math founder Carina Hong on why math is the path to general superintelligence: “Math is the sandbox for reality. You very quickly see verifiable rewards because in math, there’s an absolute right or wrong.” “And especially when you have Lean, you can check the proof or solution step by step. You’ll be able to apply reinforcement learning in a much more efficient way.” “We have currently scaled from winning a Putnam perfect score to solving a batch of research problems that professional mathematicians find really challenging. And we also see this transfer to code verification.”

TBPN

39,589 次观看 • 5 个月前

Mathematician Yu Deng of The University of Chicago has won the 2026 Fields Medal. Through his work on partial differential equations, Deng derived the Boltzmann equation from “billiard ball” dynamics for rarefied gases as well as the wave-kinetic equations from nonlinear dispersive systems. Deng is also a researcher with the Simons Collaboration on Wave Turbulence and a recipient of our travel support for mathematicians. Watch his full video and see all of the Fields Medal winners: #math #ICM2026 #FieldsMedal
0:42

Mathematician Yu Deng of The University of Chicago has won the 2026 Fields Medal. Through his work on partial differential equations, Deng derived the Boltzmann equation from “billiard ball” dynamics for rarefied gases as well as the wave-kinetic equations from nonlinear dispersive systems. Deng is also a researcher with the Simons Collaboration on Wave Turbulence and a recipient of our travel support for mathematicians. Watch his full video and see all of the Fields Medal winners: #math #ICM2026 #FieldsMedal

Simons Foundation

28,249 次观看 • 1 个月前

Who is a Historian? Dr. Vikram Sampath, FRHistS knocking it out of the park... Jadhunath Sarkar was an English professor, later a historian. Kosambi, the Marxist historian had a degree in Math not history. I have a PhD in history, fellow of Royal Historical Society, but its still not enough for leftists as I'm not dyed in the wool with a JNU stamp" 🤣🤣
3:00

Who is a Historian? Dr. Vikram Sampath, FRHistS knocking it out of the park... Jadhunath Sarkar was an English professor, later a historian. Kosambi, the Marxist historian had a degree in Math not history. I have a PhD in history, fellow of Royal Historical Society, but its still not enough for leftists as I'm not dyed in the wool with a JNU stamp" 🤣🤣

Anu Lall

33,797 次观看 • 6 个月前

Tudor Achim (Tudor Achim) is the co-founder and CEO of Harmonic, a startup working to solve one of AI’s hardest problems: mathematical reasoning. In our conversation, we explore: • Why Tudor believes math is the fundamental toolkit to understand the world • How Aristotle works and the applications beyond pure mathematics • The reinforcement learning process that lets Harmonic generate synthetic training data and solve problems humans have never attempted • Why Tudor believes AI could surpass human mathematicians on specific tasks within 2–3 years • Why the future of mathematics looks more like GitHub than academic journals Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Guru_HQ: The AI source of truth for work: - Rippling: Stop wasting time on admin tasks, build your startup faster: Timestamps (00:00) Intro (06:28) The mathematical foundations of music (and why Tudor keeps them separate) (12:52) Defining intelligence (22:55) The two breakthroughs that made mathematical AI possible in 2023 (35:25) An overview of Aristotle: the world’s first always-correct mathematical agent (45:34) Math in AI now and what’s next (56:02) History’s alternating rhythm of thinking and measuring (1:00:18) Final meditations
1:04:43

Tudor Achim (Tudor Achim) is the co-founder and CEO of Harmonic, a startup working to solve one of AI’s hardest problems: mathematical reasoning. In our conversation, we explore: • Why Tudor believes math is the fundamental toolkit to understand the world • How Aristotle works and the applications beyond pure mathematics • The reinforcement learning process that lets Harmonic generate synthetic training data and solve problems humans have never attempted • Why Tudor believes AI could surpass human mathematicians on specific tasks within 2–3 years • Why the future of mathematics looks more like GitHub than academic journals Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Guru_HQ: The AI source of truth for work: - Rippling: Stop wasting time on admin tasks, build your startup faster: Timestamps (00:00) Intro (06:28) The mathematical foundations of music (and why Tudor keeps them separate) (12:52) Defining intelligence (22:55) The two breakthroughs that made mathematical AI possible in 2023 (35:25) An overview of Aristotle: the world’s first always-correct mathematical agent (45:34) Math in AI now and what’s next (56:02) History’s alternating rhythm of thinking and measuring (1:00:18) Final meditations

Mario Gabriele 🦊

17,899 次观看 • 4 个月前

Peter Thiel: The US has a lot of problems but we're not declining as fast as the rest of the world. "Demographically people in the US are not having enough children—but we are beating China. China will disappear before the US disappears." "We have too much debt. We have not enough growth in the US economy—but the technological future is being built in the US with computers, internet, and AI." "Maybe it's not enough. Maybe these things are dangerous. But the US is beating not just Russia and Iran—but countries like France. France is not even on AI." "It seems that the US is winning against the whole world." Via 月刊文藝春秋（文藝春秋PLUS）
1:18

Peter Thiel: The US has a lot of problems but we're not declining as fast as the rest of the world. "Demographically people in the US are not having enough children—but we are beating China. China will disappear before the US disappears." "We have too much debt. We have not enough growth in the US economy—but the technological future is being built in the US with computers, internet, and AI." "Maybe it's not enough. Maybe these things are dangerous. But the US is beating not just Russia and Iran—but countries like France. France is not even on AI." "It seems that the US is winning against the whole world." Via 月刊文藝春秋（文藝春秋PLUS）

Jawwwn

1,143,760 次观看 • 4 个月前

At OpenAI, Chief Scientist Jakub Pachocki helps lead the research roadmap to AGI including a research intern-level AI system by September 2026 and a fully automated AI researcher by March 2028. I sat down with Jakub to check on those timelines and ask him all of my top-of-mind AI questions including: ▪️ How OpenAI thinks about extending RL beyond code and math ▪️ The current state of alignment research as more powerful models loom ▪️ The future of continual learning ▪️ How startups should think about building their own models/harnesses And he also shared some great stories around OpenAI’s pioneering work on math. YouTube: Spotify: Apple: 0:00 Intro 1:53 Research Intern Capability Timelines 4:59 Math Breakthroughs 7:59 RL Beyond Verifiable Tasks 12:32 RL vs In-Context 19:01 Allocating Compute Internally 28:18 AI for Science 31:40 Pattern Matching 33:23 Solving the Hardest Math Problems 37:40 Chain of Thought Monitoring 44:33 Generalization and Value Alignment in Models 47:57 Inside OpenAI 51:55 Quickfire
58:45

At OpenAI, Chief Scientist Jakub Pachocki helps lead the research roadmap to AGI including a research intern-level AI system by September 2026 and a fully automated AI researcher by March 2028. I sat down with Jakub to check on those timelines and ask him all of my top-of-mind AI questions including: ▪️ How OpenAI thinks about extending RL beyond code and math ▪️ The current state of alignment research as more powerful models loom ▪️ The future of continual learning ▪️ How startups should think about building their own models/harnesses And he also shared some great stories around OpenAI’s pioneering work on math. YouTube: Spotify: Apple: 0:00 Intro 1:53 Research Intern Capability Timelines 4:59 Math Breakthroughs 7:59 RL Beyond Verifiable Tasks 12:32 RL vs In-Context 19:01 Allocating Compute Internally 28:18 AI for Science 31:40 Pattern Matching 33:23 Solving the Hardest Math Problems 37:40 Chain of Thought Monitoring 44:33 Generalization and Value Alignment in Models 47:57 Inside OpenAI 51:55 Quickfire

Jacob Effron

138,223 次观看 • 4 个月前