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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... show more

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“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 views • 8 months ago

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 views • 4 months ago

Robinhood CEO Vlad Tenev believes mathematical superintelligence could eliminate software bugs altogether. "Math skills and coding actually go hand in hand... In the same way that MSI can be used to make sure that your math is correct, when you have a computer program or a piece of software, it could be used to make sure that's correct and that there are no mistakes." "Rather than it being a cat-and-mouse game of escalating model capabilities, finding breaches and preventing breaches from happening, verification and proof that software is immune to these types of bugs, I think is the future."

Robinhood CEO Vlad Tenev believes mathematical superintelligence could eliminate software bugs altogether. "Math skills and coding actually go hand in hand... In the same way that MSI can be used to make sure that your math is correct, when you have a computer program or a piece of software, it could be used to make sure that's correct and that there are no mistakes." "Rather than it being a cat-and-mouse game of escalating model capabilities, finding breaches and preventing breaches from happening, verification and proof that software is immune to these types of bugs, I think is the future."

TBPN

65,315 views • 2 months ago

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 views • 2 months ago

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 views • 8 months ago

Gordian Biotechnology Martin Borch Jensen argues AI drug discovery will crack cancer before Alzheimer's for the same reason it cracks math and code but not financial markets: verifiability "Biotech is the business of building confidence around whether X is the right way to do something. What works really well is when you get a super clear signal about whether the thing worked." "For weight loss, you can order your peptides from China, and two weeks later, if they work, you lost a bunch of weight. If you can get a scan and see your tumor is gone, that will work really well." "For preventing you from having a heart attack or slowing down Alzheimer's, that's gonna be much, much harder." "It's really just verifiability. We do really well at math because you can do formal verification of whether this theorem is true. We do really well at code because you can run the code and see whether it works." "We have yet to make a lot of money on financial markets or invent new forms of government or all this fuzzy, annoying stuff. Same principle applied to bio." Gordian Biotechnology

Gordian Biotechnology Martin Borch Jensen argues AI drug discovery will crack cancer before Alzheimer's for the same reason it cracks math and code but not financial markets: verifiability "Biotech is the business of building confidence around whether X is the right way to do something. What works really well is when you get a super clear signal about whether the thing worked." "For weight loss, you can order your peptides from China, and two weeks later, if they work, you lost a bunch of weight. If you can get a scan and see your tumor is gone, that will work really well." "For preventing you from having a heart attack or slowing down Alzheimer's, that's gonna be much, much harder." "It's really just verifiability. We do really well at math because you can do formal verification of whether this theorem is true. We do really well at code because you can run the code and see whether it works." "We have yet to make a lot of money on financial markets or invent new forms of government or all this fuzzy, annoying stuff. Same principle applied to bio." Gordian Biotechnology

MTS

12,591 views • 1 month ago

Axiom Math's Carina Hong on why verification isn't about catching mistakes, it's how you drive the cost of a proof to zero: "Formal verification is going to make your life slightly better if you're facing a proof with one million lines. Remember the Erdős unit distance problem, the chain of thought being generated? There are actual mathematicians trying to follow it step by step and scrutinize it. That seems very difficult if you're not in that very niche domain of discrete geometry intersecting with algebraic number theory." "But if you have a Lean proof accompanying it, you can just run it. And running the Lean proof gives you that provable guarantee that this proof is sound." "I have a hot take. People think Lean is this library built on the existing Mathlib. I think it's going to grow significantly. A lot of the hurdles where Lean is difficult is that the basic definitions of some mathematical fields are just not in the library." "My hot take is the scaling law, if you go down the formal mathematics path, is going to be a lot steeper than informal mathematics. So it's not just for verification, for trust, it's also for performance, it's also for optimal generation." "Verification is not like insurance. It's not something where, oh, we want to make sure there's no flaw. That's great, but it also helps you generate mathematics, both proofs and conjectures and theories, a lot better." "So imagine the cost of proof goes to zero. Then you can massage the problem statements, and even if it's an open problem, a lot more easily, flexibly, and adaptively." Carina Hong Axiom

Axiom Math's Carina Hong on why verification isn't about catching mistakes, it's how you drive the cost of a proof to zero: "Formal verification is going to make your life slightly better if you're facing a proof with one million lines. Remember the Erdős unit distance problem, the chain of thought being generated? There are actual mathematicians trying to follow it step by step and scrutinize it. That seems very difficult if you're not in that very niche domain of discrete geometry intersecting with algebraic number theory." "But if you have a Lean proof accompanying it, you can just run it. And running the Lean proof gives you that provable guarantee that this proof is sound." "I have a hot take. People think Lean is this library built on the existing Mathlib. I think it's going to grow significantly. A lot of the hurdles where Lean is difficult is that the basic definitions of some mathematical fields are just not in the library." "My hot take is the scaling law, if you go down the formal mathematics path, is going to be a lot steeper than informal mathematics. So it's not just for verification, for trust, it's also for performance, it's also for optimal generation." "Verification is not like insurance. It's not something where, oh, we want to make sure there's no flaw. That's great, but it also helps you generate mathematics, both proofs and conjectures and theories, a lot better." "So imagine the cost of proof goes to zero. Then you can massage the problem statements, and even if it's an open problem, a lot more easily, flexibly, and adaptively." Carina Hong Axiom

MTS

13,324 views • 2 months ago

Common Core math was introduced in 2010 to address standards across the U.S. and shift the focus from rote memorization to a deeper, conceptual understanding of mathematics. By 2026, it remains a common standard in many states, aiming to prepare students for college and the modern workforce by fostering critical thinking and number sense rather than just memorizing formulas. There has been much debate on using common core math in schools curriculum. Do you agree with using common core math in schools? Or do you think the old way of doing math was the better way to teach math to children?

Common Core math was introduced in 2010 to address standards across the U.S. and shift the focus from rote memorization to a deeper, conceptual understanding of mathematics. By 2026, it remains a common standard in many states, aiming to prepare students for college and the modern workforce by fostering critical thinking and number sense rather than just memorizing formulas. There has been much debate on using common core math in schools curriculum. Do you agree with using common core math in schools? Or do you think the old way of doing math was the better way to teach math to children?

🌸𝓐𝓾𝓭𝓻𝓮𝔂🌸

60,957 views • 4 months ago

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

2,293,886 views • 9 months ago

Terence Tao: "Previously, you needed a PhD to contribute to math research. Now a high school student can." Dwarkesh asks the world's most famous mathematician: what's your advice for someone considering a career in math, especially in light of AI progress? Tao is honest about uncertainty: "We live in a time of change. A particularly unpredictable era. Things that we've taken for granted for centuries may not hold anymore. The way we do everything... not just mathematics... will change." He admits his preference: "In many ways, I would prefer a much more boring, quiet era where things are much the same as they were 10 or 20 years ago. But one just has to embrace this. There's going to be a lot of change. The things you study... some of them may become obsolete or revolutionized. But some things will be retained." On new opportunities: "Previously, you had to go through years and years of education and get a math PhD before you could contribute to the frontier of math research. But now it's quite possible at the high school level 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." His advice: "There will be a lot of non-traditional opportunities to learn. You need a very adaptable mindset. There'll be worth pursuing things just for curiosity and for playing around. Still go through traditional education and learn math and science the old-fashioned way for a while... credentials will still be important. But you should also be open to very, very different ways of doing science. Some of which don't exist yet." He concludes: "It's a scary time. But also very exciting."

Terence Tao: "Previously, you needed a PhD to contribute to math research. Now a high school student can." Dwarkesh asks the world's most famous mathematician: what's your advice for someone considering a career in math, especially in light of AI progress? Tao is honest about uncertainty: "We live in a time of change. A particularly unpredictable era. Things that we've taken for granted for centuries may not hold anymore. The way we do everything... not just mathematics... will change." He admits his preference: "In many ways, I would prefer a much more boring, quiet era where things are much the same as they were 10 or 20 years ago. But one just has to embrace this. There's going to be a lot of change. The things you study... some of them may become obsolete or revolutionized. But some things will be retained." On new opportunities: "Previously, you had to go through years and years of education and get a math PhD before you could contribute to the frontier of math research. But now it's quite possible at the high school level 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." His advice: "There will be a lot of non-traditional opportunities to learn. You need a very adaptable mindset. There'll be worth pursuing things just for curiosity and for playing around. Still go through traditional education and learn math and science the old-fashioned way for a while... credentials will still be important. But you should also be open to very, very different ways of doing science. Some of which don't exist yet." He concludes: "It's a scary time. But also very exciting."

Jaynit

77,803 views • 5 months ago

A Canadian math education association put “2 + 2 = 4” on a white supremacy slide. In 2022, the Ontario Mathematics Coordinators Association ran a professional development webinar for math teachers across Ontario school boards. The slide was titled “White Supremacy in Math Education.” Under “covert white supremacy” it listed: standardized testing, “I don’t see colour in my math class,” and “Of course math is neutral because 2 + 2 = 4.” Not a fringe blog. Not a parody account. A professional association shaping how math teachers are trained. When the slide went viral in 2023, the OMCA released an official statement confirming that 2 + 2 = 4 is mathematically true — but argued the equation can be “weaponized to uphold bigotry and white supremacy ideology” when used to claim math is politically neutral. Their defense of the slide was the slide. Someone once wrote about a party that demanded citizens believe two and two made five. In 2022, a professional math association wrote a slide that said believing math is neutral is white supremacy. One was fiction. The other was professional development.

A Canadian math education association put “2 + 2 = 4” on a white supremacy slide. In 2022, the Ontario Mathematics Coordinators Association ran a professional development webinar for math teachers across Ontario school boards. The slide was titled “White Supremacy in Math Education.” Under “covert white supremacy” it listed: standardized testing, “I don’t see colour in my math class,” and “Of course math is neutral because 2 + 2 = 4.” Not a fringe blog. Not a parody account. A professional association shaping how math teachers are trained. When the slide went viral in 2023, the OMCA released an official statement confirming that 2 + 2 = 4 is mathematically true — but argued the equation can be “weaponized to uphold bigotry and white supremacy ideology” when used to claim math is politically neutral. Their defense of the slide was the slide. Someone once wrote about a party that demanded citizens believe two and two made five. In 2022, a professional math association wrote a slide that said believing math is neutral is white supremacy. One was fiction. The other was professional development.

Jake

112,641 views • 6 months ago

The way young kids are being taught math today is beyond insane This American is helping her daughter with her homework. It’s about counting fish You just have to see this math problem to try and understand it for yourself…. Again this is for little kids “I'm just trying to figure out how they gonna come up with 3, or 7, or what is this? The AI create this? — I don't see a correct answer, or this is new math” The math being caught here is called “doubles.” You’re just supposed to find 2 numbers that equal an answer closest to the amount of fish…. It makes no sense Apparently it’s will help kids stop counting with their fingers but to me this is just way more confusing

The way young kids are being taught math today is beyond insane This American is helping her daughter with her homework. It’s about counting fish You just have to see this math problem to try and understand it for yourself…. Again this is for little kids “I'm just trying to figure out how they gonna come up with 3, or 7, or what is this? The AI create this? — I don't see a correct answer, or this is new math” The math being caught here is called “doubles.” You’re just supposed to find 2 numbers that equal an answer closest to the amount of fish…. It makes no sense Apparently it’s will help kids stop counting with their fingers but to me this is just way more confusing

Wall Street Apes

99,966 views • 22 days ago

Several years ago, Peter Thiel shocked many people by telling Tyler Cowen he had it backwards: AI would be worse for math people than for wordcels. Tyler Cowen: “Large language models are already quite powerful. They’re only going to get better. In this world to come, will the wordcels just lose their influence? People who write, people who play around with ideas, pundits — are they just toast?” Peter 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. What people have told me is that they think within three to five years, the Al models will be able to solve all the US Math Olympiad problems. That would shift things quite a bit. There's a longer history I always have on the math versus verbal riff. If you ask, "When did our society bias to testing people more for math ability?" I believe it was during the French Revolution because it was believed that verbal ability ran in families. Math ability was distributed in this idiot savant way throughout the population. If we prioritized math ability, it had this meritocratic but also egalitarian effect on society. Then, I think, by the time you get to the Soviet Union, Soviet Communism in the 20th century, where you give a number theorist or chess grandmaster a medal-which was always a part I was somewhat sympathetic to in the Soviet Union— maybe it's actually just a control mechanism, where the math people are singularly clueless. They don’t understand anything, but if we put them on a pedestal, and we tell everyone else you need to be like the math person, then it’s actually a way to control. Or the chess grandmaster doesn’t understand anything about the world. That’s a way to really control things. If I fast-forwarded to, let’s say, Silicon Valley in the early 21st century, it’s way too biased toward the math people. I don’t know if it’s a French Revolution thing or a Russian-Straussian, secret-cabal, control thing where you have to prioritize it. That’s the thing that seems deeply unstable, and that’s what I would bet on getting reversed, where it’s like the place where math ability — it’s the thing that’s the test for everything. It’s like if you want to go to medical school, okay, we weed people out through physics and calculus, and I’m not sure that’s really correlated with your dexterity as a neurosurgeon. I don’t really want someone operating on my brain to be doing prime number factorizations in their head while they’re operating on my brain, or something like that. In the late ’80s, early ’90s, I had a chess bias because I was a pretty good chess player. And so my chess bias was, you should just test everyone on chess ability, and that should be the gating factor. Why even do math? Why not just chess? That got undermined by the computers in 1997. Isn’t that what’s going to happen to math? And isn’t that a long-overdue rebalancing of our society?” More in clip:

Several years ago, Peter Thiel shocked many people by telling Tyler Cowen he had it backwards: AI would be worse for math people than for wordcels. Tyler Cowen: “Large language models are already quite powerful. They’re only going to get better. In this world to come, will the wordcels just lose their influence? People who write, people who play around with ideas, pundits — are they just toast?” Peter 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. What people have told me is that they think within three to five years, the Al models will be able to solve all the US Math Olympiad problems. That would shift things quite a bit. There's a longer history I always have on the math versus verbal riff. If you ask, "When did our society bias to testing people more for math ability?" I believe it was during the French Revolution because it was believed that verbal ability ran in families. Math ability was distributed in this idiot savant way throughout the population. If we prioritized math ability, it had this meritocratic but also egalitarian effect on society. Then, I think, by the time you get to the Soviet Union, Soviet Communism in the 20th century, where you give a number theorist or chess grandmaster a medal-which was always a part I was somewhat sympathetic to in the Soviet Union— maybe it's actually just a control mechanism, where the math people are singularly clueless. They don’t understand anything, but if we put them on a pedestal, and we tell everyone else you need to be like the math person, then it’s actually a way to control. Or the chess grandmaster doesn’t understand anything about the world. That’s a way to really control things. If I fast-forwarded to, let’s say, Silicon Valley in the early 21st century, it’s way too biased toward the math people. I don’t know if it’s a French Revolution thing or a Russian-Straussian, secret-cabal, control thing where you have to prioritize it. That’s the thing that seems deeply unstable, and that’s what I would bet on getting reversed, where it’s like the place where math ability — it’s the thing that’s the test for everything. It’s like if you want to go to medical school, okay, we weed people out through physics and calculus, and I’m not sure that’s really correlated with your dexterity as a neurosurgeon. I don’t really want someone operating on my brain to be doing prime number factorizations in their head while they’re operating on my brain, or something like that. In the late ’80s, early ’90s, I had a chess bias because I was a pretty good chess player. And so my chess bias was, you should just test everyone on chess ability, and that should be the gating factor. Why even do math? Why not just chess? That got undermined by the computers in 1997. Isn’t that what’s going to happen to math? And isn’t that a long-overdue rebalancing of our society?” More in clip:

Zarathustra

141,991 views • 1 month ago

I had a fantastic time discussing with the learning legend Justin Skycak from Math Academy about learning math in the modern age. we've talked about his quite impressive self-learning journey (3000h of math in high school) all the way to how he hand curated the initial knowledge graph for math academy to make that process more efficient. great lively 3h discussion here are the chapters: 0:00:00 - intro: 0:02:10 - justin background 0:05:45 - 3000h math self study in high school 0:11:45 - what a day looked like for that 3000h stretch 0:16:10 - meta-learning vs pure math learning 0:21:50 - when did you get into cognitive neuro? 0:29:55 - how did the fundamental math helped in your research projects 0:43:10 - what does the math academy learning system looks like 0:47:34 - how did you guys build the 2000 topic knowledge graph 1:01:15 - would LLM be useful as an interface to that knowledge graph for the students? 1:10:46 - how does the FIRe spaced repetition algorithm works? 1:17:34 - does the same knowledge graph structure would work for physics? or other topic?: 1:34:05 - how do you understand the subject vs the curiculum 1:35:50 - is there a connection between studying math and learning a sport? 1:42:00 - do you think in math doing and teaching requires different skills? 1:56:25 - could you get understanding without automaticy? 2:05:35 - do you see any upside of confusion in learning? 2:14:11 - learning math as an adult? 2:19:20 - how to fill the motivation gap after learning the fundamental? 2:24:10 - how should teaching math for kids and adults balance fundamentals and creativity? 2:33:55 - is it ever too late to learn math seriously? 2:46:00 - mastery learning vs ultra learning 2:51:30 - top-down vs bottom-up 2:53:40 - mastery learning for domain without a structured hierarchical structure? 2:56:30 - neurodivergence / adhd for structured math learning? 3:06:20 - amateur mathematician augmented with technology will be able to contribute to research? 3:14:37 - what are you most excited about right now in term of learning enjoy!

I had a fantastic time discussing with the learning legend Justin Skycak from Math Academy about learning math in the modern age. we've talked about his quite impressive self-learning journey (3000h of math in high school) all the way to how he hand curated the initial knowledge graph for math academy to make that process more efficient. great lively 3h discussion here are the chapters: 0:00:00 - intro: 0:02:10 - justin background 0:05:45 - 3000h math self study in high school 0:11:45 - what a day looked like for that 3000h stretch 0:16:10 - meta-learning vs pure math learning 0:21:50 - when did you get into cognitive neuro? 0:29:55 - how did the fundamental math helped in your research projects 0:43:10 - what does the math academy learning system looks like 0:47:34 - how did you guys build the 2000 topic knowledge graph 1:01:15 - would LLM be useful as an interface to that knowledge graph for the students? 1:10:46 - how does the FIRe spaced repetition algorithm works? 1:17:34 - does the same knowledge graph structure would work for physics? or other topic?: 1:34:05 - how do you understand the subject vs the curiculum 1:35:50 - is there a connection between studying math and learning a sport? 1:42:00 - do you think in math doing and teaching requires different skills? 1:56:25 - could you get understanding without automaticy? 2:05:35 - do you see any upside of confusion in learning? 2:14:11 - learning math as an adult? 2:19:20 - how to fill the motivation gap after learning the fundamental? 2:24:10 - how should teaching math for kids and adults balance fundamentals and creativity? 2:33:55 - is it ever too late to learn math seriously? 2:46:00 - mastery learning vs ultra learning 2:51:30 - top-down vs bottom-up 2:53:40 - mastery learning for domain without a structured hierarchical structure? 2:56:30 - neurodivergence / adhd for structured math learning? 3:06:20 - amateur mathematician augmented with technology will be able to contribute to research? 3:14:37 - what are you most excited about right now in term of learning enjoy!

Yacine Mahdid

57,320 views • 6 months ago

This is a day in the life of a Flat Earther You tell us your math We show you real life examples of how your math doesn’t fit reality You throw in some excuses that don’t explain anything while simultaneously refusing to actually look at the evidence Because science dudes! 😎

This is a day in the life of a Flat Earther You tell us your math We show you real life examples of how your math doesn’t fit reality You throw in some excuses that don’t explain anything while simultaneously refusing to actually look at the evidence Because science dudes! 😎

Ted Logan

89,187 views • 3 years ago

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.

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 views • 6 months ago

OpenAI President Greg Brockman on solving the Navier-Stokes problem: "If we have models that can solve this kind of problem, what happens from here?" "I think that talking about curing diseases and new medicines we're going to be able to develop, all of that starts to become much more real when you have models that are at this level of assistance and capability." "[This result] is really about the broader insights and methods that can help scientists and mathematicians further accelerate their research."

OpenAI President Greg Brockman on solving the Navier-Stokes problem: "If we have models that can solve this kind of problem, what happens from here?" "I think that talking about curing diseases and new medicines we're going to be able to develop, all of that starts to become much more real when you have models that are at this level of assistance and capability." "[This result] is really about the broader insights and methods that can help scientists and mathematicians further accelerate their research."

TBPN

40,394 views • 1 month ago

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

174,863 views • 7 months ago

OUR PATH ONLY APPEARS AFTER WE TAKE THE FIRST STEP We often tell ourselves that we'll begin once we know exactly where we're going-once we have a plan, a guarantee, or proof that everything will work out. But life rarely gives us the entire path in advance. Sometimes clarity is not what allows us to move. Sometimes movement is what creates clarity. The first step may feel terrifying because there is nothing visible beneath it. But courage is not walking forward because you can see the way. Courage is moving forward while trusting that the way will meet you. You may not need to know how the whole journey unfolds. You may only need enough faith for the step directly in front of you. And perhaps the path you've been waiting to see has been waiting for you to begin. As we start to walk on the path, the path appears. Take the step.

OUR PATH ONLY APPEARS AFTER WE TAKE THE FIRST STEP We often tell ourselves that we'll begin once we know exactly where we're going-once we have a plan, a guarantee, or proof that everything will work out. But life rarely gives us the entire path in advance. Sometimes clarity is not what allows us to move. Sometimes movement is what creates clarity. The first step may feel terrifying because there is nothing visible beneath it. But courage is not walking forward because you can see the way. Courage is moving forward while trusting that the way will meet you. You may not need to know how the whole journey unfolds. You may only need enough faith for the step directly in front of you. And perhaps the path you've been waiting to see has been waiting for you to begin. As we start to walk on the path, the path appears. Take the step.

🧬Maxpein🧬

12,010 views • 1 month ago

"Literally, Newton vs. Leibniz on the founding of calculus is *still* one of the biggest arguments that people have. This was back in the 1600s." Cognition founder Scott Wu on the Navier-Stokes controversy: "A lot of people who aren't familiar with the math academia world might not know that this is what always happens in math academia." "I believe that both sides meant well. I think the accomplishment itself is going to be the biggest thing that we all remember from this... The AI accomplishment itself is the biggest thing by far." "The rest kind of feels par for the course, to be 100% honest."

"Literally, Newton vs. Leibniz on the founding of calculus is still one of the biggest arguments that people have. This was back in the 1600s." Cognition founder Scott Wu on the Navier-Stokes controversy: "A lot of people who aren't familiar with the math academia world might not know that this is what always happens in math academia." "I believe that both sides meant well. I think the accomplishment itself is going to be the biggest thing that we all remember from this... The AI accomplishment itself is the biggest thing by far." "The rest kind of feels par for the course, to be 100% honest."

TBPN

39,132 views • 1 month ago