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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... show more

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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

95,314 Aufrufe • vor 9 Monaten

Terence Tao summarized how AI is massively accelerating math career and math research. "In math, you previously had to basically go through years and years of education to be a math PhD before you could contribute to the frontier of math research. But now it's quite possible at the high school level or whatever, that you could get involved in a math project and actually make a real contribution because of all these AI tools and lean and everything else." From Dwarkesh Patel podcast (link to full video in comment)

Terence Tao summarized how AI is massively accelerating math career and math research. "In math, you previously had to basically go through years and years of education to be a math PhD before you could contribute to the frontier of math research. But now it's quite possible at the high school level or whatever, that you could get involved in a math project and actually make a real contribution because of all these AI tools and lean and everything else." From Dwarkesh Patel podcast (link to full video in comment)

Rohan Paul

61,874 Aufrufe • vor 5 Monaten

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.”

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 Aufrufe • vor 8 Monaten

AI is on the cusp of solving math and physics. The past two months have a been a race between OpenAI and Anthropic, first with Navier-Stokes and now with Yang-Mills (two of the seven million-dollar math problems). Everything is changing. Here's my take at the end of this video (about Yang-Mills and the Mass Gap): "AI is getting so smart that it’s solving math’s most difficult problems, one by one. I wouldn’t be surprised in a year or two if all the remaining million dollar math problems are solved by AI. I’m reminded back in the day when AI basically solved chess by beating the best human. Now AI is beating the best mathematicians, and it’s not even close. But I think this time is different because AI is now a generalized intelligence, meaning it covers almost all areas and disciplines. So the potential and possibilities are nearly endless. When you think about math and physics, it’s like the building blocks or foundation for a lot of how we understand the world. So, as AI helps solve the hardest math and physics problems it’s going to lead to countless discoveries in fields that are built on math or physics. This is one of the reasons why Anthropic is so bullish on biology and medicine and why they’re building their own lab efforts. I think we’re going to see countless new discoveries in material sciences too. We’re figure out new ways to make things, build things. Basically AI is helping us figure out how the world and our body works, and that understanding is what will lead to mind-blowing discoveries at a rate never seen in history. This is why this topic is so interesting. We’re at a big turning point in history. Things are speeding up. I’m not saying that there aren’t going to be challenges. There are. But the pace of new discoveries is speeding up, and it’s just the beginning." 0:00 Intro 1:20 The Glue 4:22 The Race 5:23 The Drawer 6:51 What Changes 8:46 The Bigger Picture Also posted on YT:

AI is on the cusp of solving math and physics. The past two months have a been a race between OpenAI and Anthropic, first with Navier-Stokes and now with Yang-Mills (two of the seven million-dollar math problems). Everything is changing. Here's my take at the end of this video (about Yang-Mills and the Mass Gap): "AI is getting so smart that it’s solving math’s most difficult problems, one by one. I wouldn’t be surprised in a year or two if all the remaining million dollar math problems are solved by AI. I’m reminded back in the day when AI basically solved chess by beating the best human. Now AI is beating the best mathematicians, and it’s not even close. But I think this time is different because AI is now a generalized intelligence, meaning it covers almost all areas and disciplines. So the potential and possibilities are nearly endless. When you think about math and physics, it’s like the building blocks or foundation for a lot of how we understand the world. So, as AI helps solve the hardest math and physics problems it’s going to lead to countless discoveries in fields that are built on math or physics. This is one of the reasons why Anthropic is so bullish on biology and medicine and why they’re building their own lab efforts. I think we’re going to see countless new discoveries in material sciences too. We’re figure out new ways to make things, build things. Basically AI is helping us figure out how the world and our body works, and that understanding is what will lead to mind-blowing discoveries at a rate never seen in history. This is why this topic is so interesting. We’re at a big turning point in history. Things are speeding up. I’m not saying that there aren’t going to be challenges. There are. But the pace of new discoveries is speeding up, and it’s just the beginning." 0:00 Intro 1:20 The Glue 4:22 The Race 5:23 The Drawer 6:51 What Changes 8:46 The Bigger Picture Also posted on YT:

Dave Lee

19,783 Aufrufe • vor 3 Tagen

➕Math can be tough, but it doesn’t have to be! Our Math Song shows kids that math is everywhere—in counting, cooking, playing, and more. This catchy tune makes learning math fun and part of everyday life. 🎧 Listen now on YouTube via #linkinbio and help your child see the fun side of math!

➕Math can be tough, but it doesn’t have to be! Our Math Song shows kids that math is everywhere—in counting, cooking, playing, and more. This catchy tune makes learning math fun and part of everyday life. 🎧 Listen now on YouTube via #linkinbio and help your child see the fun side of math!

SuperSemaOfficial

10,800 Aufrufe • vor 2 Jahren

Text is a string. Math isn’t. It grows in every direction into multi-line structures. That’s why people still do math on paper. We’re bringing that experience to computers. Corca is WYSIWYG math editor, and it's fast!

Text is a string. Math isn’t. It grows in every direction into multi-line structures. That’s why people still do math on paper. We’re bringing that experience to computers. Corca is WYSIWYG math editor, and it's fast!

Corca

269,285 Aufrufe • vor 10 Monaten

🚨BREAKING: Sam Altman just gave a mind-blowing podcast. He said in a conversation released with Raj Shamani today that the hardest problem he is working on right now is pacing progress 'as we enter this RSI era', including deciding 'how smart an AI system should be allowed to be relative to a person' and how fast it can improve. Asked why Dario Amodei, Ilya Sutskever, Mira Murati, and Elon Musk all left OpenAI to build rival labs, Altman said 'there is something that happens in many people's brains where they only trust themselves to build this, because the stakes feel so high.' He summed up the mindset as: 'His approach is bad, and so I can't let power get concentrated in his hands. He said: 'One of the most confusing things I have ever observed in my career' is why from 2015 through 2021, other researchers looked at the same scaling data and did not say, 'This is crazy, like we have to invest much more in this.' He said it was so obvious inside OpenAI that 'almost we felt delusional', while outsiders assumed 'there's no way that something so dumb can just fix all of its problems by scaling it up.' Sam Altman says the last time he felt 'heart-pounding' nervousness was right before OpenAI announced its Navier-Stokes math result. He said OpenAI's recent math results convinced him 'that we are closer to this milestone than I expected us to be' on building an AI to discover new science. 'It's an extraordinary time in terms of just all of the model progress.' And on what he ultimately wants AI to become: 'I am extremely excited to see how far we can push AI to discover new science. Math - the math results were quite amazing to me, and it really made me think that we are closer to this milestone than I expected us to be. But a machine that can help us understand the world better, discover, you know, the deepest insights in physics, figure out new medical treatments, new sources of energy, things that I think we can't even imagine, but that better scientific understanding will enable. I am extremely excited for us to go build that machine.'

🚨BREAKING: Sam Altman just gave a mind-blowing podcast. He said in a conversation released with Raj Shamani today that the hardest problem he is working on right now is pacing progress 'as we enter this RSI era', including deciding 'how smart an AI system should be allowed to be relative to a person' and how fast it can improve. Asked why Dario Amodei, Ilya Sutskever, Mira Murati, and Elon Musk all left OpenAI to build rival labs, Altman said 'there is something that happens in many people's brains where they only trust themselves to build this, because the stakes feel so high.' He summed up the mindset as: 'His approach is bad, and so I can't let power get concentrated in his hands. He said: 'One of the most confusing things I have ever observed in my career' is why from 2015 through 2021, other researchers looked at the same scaling data and did not say, 'This is crazy, like we have to invest much more in this.' He said it was so obvious inside OpenAI that 'almost we felt delusional', while outsiders assumed 'there's no way that something so dumb can just fix all of its problems by scaling it up.' Sam Altman says the last time he felt 'heart-pounding' nervousness was right before OpenAI announced its Navier-Stokes math result. He said OpenAI's recent math results convinced him 'that we are closer to this milestone than I expected us to be' on building an AI to discover new science. 'It's an extraordinary time in terms of just all of the model progress.' And on what he ultimately wants AI to become: 'I am extremely excited to see how far we can push AI to discover new science. Math - the math results were quite amazing to me, and it really made me think that we are closer to this milestone than I expected us to be. But a machine that can help us understand the world better, discover, you know, the deepest insights in physics, figure out new medical treatments, new sources of energy, things that I think we can't even imagine, but that better scientific understanding will enable. I am extremely excited for us to go build that machine.'

Choblin

44,562 Aufrufe • vor 8 Tagen

Don't be tricked by this math problem! There are 2 common answers to this but only one is correct. Which is it and why?

Don't be tricked by this math problem! There are 2 common answers to this but only one is correct. Which is it and why?

Howie Hua

10,405 Aufrufe • vor 1 Jahr

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?

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 Aufrufe • vor 9 Monaten

📁 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 eliminate the noise and preserve only what is true.

📁 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 eliminate the noise and preserve only what is true.

Jon Hernandez

80,288 Aufrufe • vor 9 Monaten

Sam Altman and Dario Amodei just spoke at the UN Security Council, and it's hard to unhear. Amodei: "four years ago AI could barely write a line of code, today it writes most of the code at Anthropic and solves world-famous unsolved math problems." it's moving so fast that Dari

Sam Altman and Dario Amodei just spoke at the UN Security Council, and it's hard to unhear. Amodei: "four years ago AI could barely write a line of code, today it writes most of the code at Anthropic and solves world-famous unsolved math problems." it's moving so fast that Dari

shobhanai

277,809 Aufrufe • vor 1 Monat

American mother says her daughter is 1 of only 4 children in her entire math class that speaks English “She doesn't get to learn math because there's 20 plus other kids who don't know English and the teacher has to accommodate them” “Why am I paying property taxes? Why am I paying income tax? Why am I paying taxes for anything for my children to have a the right to a free and public education? — This this is what they get. You don't get to learn math because you know English, so you just don't get to learn math.”

American mother says her daughter is 1 of only 4 children in her entire math class that speaks English “She doesn't get to learn math because there's 20 plus other kids who don't know English and the teacher has to accommodate them” “Why am I paying property taxes? Why am I paying income tax? Why am I paying taxes for anything for my children to have a the right to a free and public education? — This this is what they get. You don't get to learn math because you know English, so you just don't get to learn math.”

Wall Street Apes

2,294,048 Aufrufe • vor 10 Monaten

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 Aufrufe • vor 3 Monaten

Gagan Biyani of Horowitz Andreessen Academy (HAA) on why the best way to future-proof a career is being AI-native in whatever field you love: "The question of, 'What field should I study so that I can be sure that I'm resilient to the future?' is not the right question." "The question is, 'What field am I interested in?' I have enough faith that if you're AI-native in any field, it does not matter." "A lot of people are concerned about coding or math. There's gonna be a whole new type of coding and math that happens with the AI-native mathematicians." "The new students who love math, they're gonna do just fine. In fact, they're gonna go and do things none of us ever thought possible." "That's actually something that we were trying to screen for at HAA. We really don't want someone who's like, 'I wanna make sure I'm resilient to the future.' It's a very defensive way of approaching the world, and we want you to be on offense." Gagan Biyani 🏛

Gagan Biyani of Horowitz Andreessen Academy (HAA) on why the best way to future-proof a career is being AI-native in whatever field you love: "The question of, 'What field should I study so that I can be sure that I'm resilient to the future?' is not the right question." "The question is, 'What field am I interested in?' I have enough faith that if you're AI-native in any field, it does not matter." "A lot of people are concerned about coding or math. There's gonna be a whole new type of coding and math that happens with the AI-native mathematicians." "The new students who love math, they're gonna do just fine. In fact, they're gonna go and do things none of us ever thought possible." "That's actually something that we were trying to screen for at HAA. We really don't want someone who's like, 'I wanna make sure I'm resilient to the future.' It's a very defensive way of approaching the world, and we want you to be on offense." Gagan Biyani 🏛

a16z

26,957 Aufrufe • vor 15 Tagen

Business is problems. The best companies are just highly effective problem-solving machines. Travis Kalanick on why he loves problems: “I don’t think about problems in a negative way.” “I think about problems the way a math professor would think about a problem.” “A math professor without interesting problems to solve is a sad math professor.” “So you wanna create interesting things to solve. You wanna create problems to solve. You have to predict well the nature of the problem and your ability and capacity to solve it.” — travis kalanick

Business is problems. The best companies are just highly effective problem-solving machines. Travis Kalanick on why he loves problems: “I don’t think about problems in a negative way.” “I think about problems the way a math professor would think about a problem.” “A math professor without interesting problems to solve is a sad math professor.” “So you wanna create interesting things to solve. You wanna create problems to solve. You have to predict well the nature of the problem and your ability and capacity to solve it.” — travis kalanick

David Senra

25,888 Aufrufe • vor 6 Tagen

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 Aufrufe • vor 4 Monaten

.Sheel Mohnot explains why AI is transforming Silicon Valley but barely showing up in the wider economy, and why 2027 could change everything: "For people like us in the center, AI is everything. We're using it 100 times a day. And what we are experiencing has not shown up in national numbers yet." "Coding is a great use case, and technologists use it first. Math is verifiable. Law is writing, and AI was fairly decent at writing already in 2023. Word cells were f*cked already." "But then models like o3 come out and all of a sudden it's not making math errors. Remember how many Rs in strawberry? All that shit is gone." "Now we are very close to solving accounting. Accounting is the base of so many things, and we have a shortage of accountants. More and more companies are getting AI pilled. I think 2027 is a big year." Better Tomorrow Ventures

.Sheel Mohnot explains why AI is transforming Silicon Valley but barely showing up in the wider economy, and why 2027 could change everything: "For people like us in the center, AI is everything. We're using it 100 times a day. And what we are experiencing has not shown up in national numbers yet." "Coding is a great use case, and technologists use it first. Math is verifiable. Law is writing, and AI was fairly decent at writing already in 2023. Word cells were f*cked already." "But then models like o3 come out and all of a sudden it's not making math errors. Remember how many Rs in strawberry? All that shit is gone." "Now we are very close to solving accounting. Accounting is the base of so many things, and we have a shortage of accountants. More and more companies are getting AI pilled. I think 2027 is a big year." Better Tomorrow Ventures

MTS

15,313 Aufrufe • vor 12 Tagen

“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 Aufrufe • vor 9 Monaten

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 2026 Abacus Medal for his work on the theory of algorithms. He believes the power of AI is harnessed not as replacement for human mathematicians, but as a partner. CBS’s Kamal Afzali explains.

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 2026 Abacus Medal for his work on the theory of algorithms. He believes the power of AI is harnessed not as replacement for human mathematicians, but as a partner. CBS’s Kamal Afzali explains.

CBS News

57,767 Aufrufe • vor 2 Monaten