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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,... show more

Dwarkesh Patel

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64,547 次观看 • 5 个月前 •via X (Twitter)

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

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

Rohan Paul

174,341 次观看 • 6 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?

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

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.

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 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

61,705 次观看 • 3 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?

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 • 1 个月前

Mathematician Elliot Glazer calls the S6 problem, open since 1947 and now settled using Claude, "by far the most significant" generative math result yet: "By far the most significant generative math result we have seen thus far. The Hopf problem, which asks whether S6 has a complex structure, is pretty far and away the most significant that AI has resolved." "Everyone I talk to says it's kind of a mundane result. Mundane in terms of the techniques used. Some very classical techniques of the field. Humans could've, would've, should've solved it themselves, but almost incidentally, never really stumbled on just the right construction." "But in terms of significance, this is a huge deal problem to see solved."

Mathematician Elliot Glazer calls the S6 problem, open since 1947 and now settled using Claude, "by far the most significant" generative math result yet: "By far the most significant generative math result we have seen thus far. The Hopf problem, which asks whether S6 has a complex structure, is pretty far and away the most significant that AI has resolved." "Everyone I talk to says it's kind of a mundane result. Mundane in terms of the techniques used. Some very classical techniques of the field. Humans could've, would've, should've solved it themselves, but almost incidentally, never really stumbled on just the right construction." "But in terms of significance, this is a huge deal problem to see solved."

MTS

15,212 次观看 • 19 天前

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.”

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 • 6 个月前

The best MOTW in Precure are the one that just fight by making questions like is a exam Mostly because is funny seeing how this girls lose to something like the power of MATH

The best MOTW in Precure are the one that just fight by making questions like is a exam Mostly because is funny seeing how this girls lose to something like the power of MATH

*Kaijus can be cute too

165,665 次观看 • 3 个月前

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

58,847 次观看 • 2 个月前

University of Toronto mathematician Daniel Litt says AI's math capabilities are bottlenecked on verification: "My sense is the reason [AI models are] not producing long, complicated proofs is that they cannot. The ability to check correctness is not yet there." "If you ask the models to produce a short proof, you can then ask, 'Is that correct?' And they will often say no... The problem with producing a very long thing is they might not know they're wrong." "What I wonder is, presumably internally, OpenAI and Anthropic have probably solved a lot more problems than they've released. And I imagine quite a few of them, they're just not sure if they're true." "Someone recently posted a claimed proof of resolution of singularities in positive characteristic, which was 800 AI-generated pages. I haven't read it, I haven't found an error, but there's no way it's correct. This would be a major result... Definitely no human has read it. Definitely the models are not able to check this kind of thing yet." Daniel Litt Lisha

University of Toronto mathematician Daniel Litt says AI's math capabilities are bottlenecked on verification: "My sense is the reason [AI models are] not producing long, complicated proofs is that they cannot. The ability to check correctness is not yet there." "If you ask the models to produce a short proof, you can then ask, 'Is that correct?' And they will often say no... The problem with producing a very long thing is they might not know they're wrong." "What I wonder is, presumably internally, OpenAI and Anthropic have probably solved a lot more problems than they've released. And I imagine quite a few of them, they're just not sure if they're true." "Someone recently posted a claimed proof of resolution of singularities in positive characteristic, which was 800 AI-generated pages. I haven't read it, I haven't found an error, but there's no way it's correct. This would be a major result... Definitely no human has read it. Definitely the models are not able to check this kind of thing yet." Daniel Litt Lisha

a16z

36,746 次观看 • 11 天前

Jony Ive: “Ideas are always fragile” “Ideas—by definition—are always fragile. If they were resolved, they wouldn’t be ideas. They would be products that were ready to ship.” The legendary Apple designer continues: “I’ve come to learn that you have to make an extraordinary effort not to focus on the problems, which are implicated with any new idea. These problems are known. They’re quantifiable and understood. But you have to focus on the actual idea, which is partial, tentative, and unproven.” All new ideas have problems, and if you don’t suspend your disbelief and trust that you will find a solution, you will lose faith in your idea. “That is why criticism and focusing on the problems can be so damaging—particularly in the absence of a constructive idea. Remember, opinions are not ideas. Opinions are not as important as ideas. Opinions are just opinions.” Source: California College of the Arts

Jony Ive: “Ideas are always fragile” “Ideas—by definition—are always fragile. If they were resolved, they wouldn’t be ideas. They would be products that were ready to ship.” The legendary Apple designer continues: “I’ve come to learn that you have to make an extraordinary effort not to focus on the problems, which are implicated with any new idea. These problems are known. They’re quantifiable and understood. But you have to focus on the actual idea, which is partial, tentative, and unproven.” All new ideas have problems, and if you don’t suspend your disbelief and trust that you will find a solution, you will lose faith in your idea. “That is why criticism and focusing on the problems can be so damaging—particularly in the absence of a constructive idea. Remember, opinions are not ideas. Opinions are not as important as ideas. Opinions are just opinions.” Source: California College of the Arts

Startup Archive

112,457 次观看 • 2 年前

"What is P vs NP and how does it relate to human knowledge?" Avi Wigderson (Turing Award & Abel Prize winner): "In a very basic way, all the problems we want to solve are really NP problems. A subset of it is the problems we can solve. So the question of P versus NP is whether we can solve all the problems we want to solve, whether we can know everything we want to know. This is basically about the limits of our knowledge. You can think there is no limit to what we may want to know but I claim that essentially in any human endeavor, what people are searching for have the property that when a solution is found, it is easily recognized. NP are all problems that we can honestly say we really want to solve and P are those that we can solve. Whether they are equal is just the very fact that a solution can be easily recognized would imply that it can be efficiently found. That means that we can know everything we ever want to know, it's a fundamental question about human knowledge."

"What is P vs NP and how does it relate to human knowledge?" Avi Wigderson (Turing Award & Abel Prize winner): "In a very basic way, all the problems we want to solve are really NP problems. A subset of it is the problems we can solve. So the question of P versus NP is whether we can solve all the problems we want to solve, whether we can know everything we want to know. This is basically about the limits of our knowledge. You can think there is no limit to what we may want to know but I claim that essentially in any human endeavor, what people are searching for have the property that when a solution is found, it is easily recognized. NP are all problems that we can honestly say we really want to solve and P are those that we can solve. Whether they are equal is just the very fact that a solution can be easily recognized would imply that it can be efficiently found. That means that we can know everything we ever want to know, it's a fundamental question about human knowledge."

Ryan Peterman

20,069 次观看 • 3 个月前

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

77,698 次观看 • 4 个月前

.Adam Marblestone explains the value of all these efforts to have AI automate formal math reasoning: "I think of it like when everybody had to write assembly code. The amount of cool startups that got created was a lot less. Fewer people could do it. Progress was more grinding, slow, and lonely. I think there are actually lots of people who could have interesting ideas. Can you have outsider string theorists because the math is just done for them by the computer? Does that lead to more innovation in string theory? Maybe yes."

.Adam Marblestone explains the value of all these efforts to have AI automate formal math reasoning: "I think of it like when everybody had to write assembly code. The amount of cool startups that got created was a lot less. Fewer people could do it. Progress was more grinding, slow, and lonely. I think there are actually lots of people who could have interesting ideas. Can you have outsider string theorists because the math is just done for them by the computer? Does that lead to more innovation in string theory? Maybe yes."

Dwarkesh Patel

30,813 次观看 • 8 个月前

New chapter! Context problems. Who can solve these by hand ✍️? Download PDF: The first five: 1. Mark the context: the window as cells you can count 2. What fills the window: the system prompt, the user, the model's reply 3. Turn by turn: every turn is the same block, and most of it is the reply 4. Will it fit? Number a new message into what's left, and see it run out 5. Turns until full: how many rounds before there is no room Previously, I shared Chapter 1: Token Problems 1 ~ 20 Why am I designing these math problems? AI is making people (including me) think less. It's just too easy to ask AI a few questions about a new concept and start to believe I get it. We used to practice on a coding problem, but now AI writes the code, and solving it technically is not the same as knowing anything. So I recast each concept as a simple math problem you have to work with pen and paper. Using our hands is one way to motivate ourselves to start thinking again. ✍️ If you solve them by hand, leave a comment to let me know. I will give you a shout out in my next post. Download, print, solve, and learn. ✍️ ~ Prof. Tom Yeh

New chapter! Context problems. Who can solve these by hand ✍️? Download PDF: The first five: 1. Mark the context: the window as cells you can count 2. What fills the window: the system prompt, the user, the model's reply 3. Turn by turn: every turn is the same block, and most of it is the reply 4. Will it fit? Number a new message into what's left, and see it run out 5. Turns until full: how many rounds before there is no room Previously, I shared Chapter 1: Token Problems 1 ~ 20 Why am I designing these math problems? AI is making people (including me) think less. It's just too easy to ask AI a few questions about a new concept and start to believe I get it. We used to practice on a coding problem, but now AI writes the code, and solving it technically is not the same as knowing anything. So I recast each concept as a simple math problem you have to work with pen and paper. Using our hands is one way to motivate ourselves to start thinking again. ✍️ If you solve them by hand, leave a comment to let me know. I will give you a shout out in my next post. Download, print, solve, and learn. ✍️ ~ Prof. Tom Yeh

Tom Yeh

11,651 次观看 • 1 个月前

“Harmonic is building Mathematical Superintelligence (MSI)” With $295M+ in total funding at a recent $1.45B post-money valuation, Harmonic's mission is to solve math problems that have remained unsolved for centuries, unlocking progress across physics, engineering.. & maybe even time travel? Co-founded by Vlad Tenev (Vlad Tenev) CEO of Robinhood, & Harmonic CEO Tudor Achim (Tudor Achim), the company has raised from leading investors including Ribbit, Sequoia, Kleiner Perkins, Index, Paradigm, DST Global, & more.. Funding history & lead investors: - Series A (Sept 2024): $75M led by steve beaker - Series B (July 2025): $100M led by Kleiner Perkins - Series C (Nov 2025): $120M at a $1.45B post-money valuation led by Ribbit Capital "Harmonic’s flagship Aristotle model recently achieved gold-medal level performance at the International Mathematical Olympiad, considered the most prestigious mathematical competition in the world, and is now available to the public. Unlike other models, Aristotle makes use of formal verification using Lean4 to ensure accuracy and eliminate hallucinations. In the first few weeks since its API beta launch, Aristotle has already been used by mathematicians and researchers to accelerate progress and create novel discoveries." . . . "Harmonic is building what we call mathematical super intelligence, and it's an artificial intelligence that can solve math problems better than any human mathematician. The company's been around for a couple of years. The North Star was, can we actually solve really, really important math problems like the Riemann Hypothesis or Hodge Conjecture? There's this group of math problems that have been open for hundreds of years that are called the Millennium Prize problems, and they're considered very big, difficult, and actually valuable. So that was kind of the North Star, and the reason we wanted to do that was if we could solve those problems, everything downstream of math, like theoretical physics becomes unlocked. So then you can imagine solving really hard physics problems. And actually, if you can solve that, then there's all kinds of exciting engineering developments, like depending on how that theory looks, you can imagine things like faster than light travel and it gets really crazy."

“Harmonic is building Mathematical Superintelligence (MSI)” With $295M+ in total funding at a recent $1.45B post-money valuation, Harmonic's mission is to solve math problems that have remained unsolved for centuries, unlocking progress across physics, engineering.. & maybe even time travel? Co-founded by Vlad Tenev (Vlad Tenev) CEO of Robinhood, & Harmonic CEO Tudor Achim (Tudor Achim), the company has raised from leading investors including Ribbit, Sequoia, Kleiner Perkins, Index, Paradigm, DST Global, & more.. Funding history & lead investors: - Series A (Sept 2024): $75M led by steve beaker - Series B (July 2025): $100M led by Kleiner Perkins - Series C (Nov 2025): $120M at a $1.45B post-money valuation led by Ribbit Capital "Harmonic’s flagship Aristotle model recently achieved gold-medal level performance at the International Mathematical Olympiad, considered the most prestigious mathematical competition in the world, and is now available to the public. Unlike other models, Aristotle makes use of formal verification using Lean4 to ensure accuracy and eliminate hallucinations. In the first few weeks since its API beta launch, Aristotle has already been used by mathematicians and researchers to accelerate progress and create novel discoveries." . . . "Harmonic is building what we call mathematical super intelligence, and it's an artificial intelligence that can solve math problems better than any human mathematician. The company's been around for a couple of years. The North Star was, can we actually solve really, really important math problems like the Riemann Hypothesis or Hodge Conjecture? There's this group of math problems that have been open for hundreds of years that are called the Millennium Prize problems, and they're considered very big, difficult, and actually valuable. So that was kind of the North Star, and the reason we wanted to do that was if we could solve those problems, everything downstream of math, like theoretical physics becomes unlocked. So then you can imagine solving really hard physics problems. And actually, if you can solve that, then there's all kinds of exciting engineering developments, like depending on how that theory looks, you can imagine things like faster than light travel and it gets really crazy."

Molly O’Shea

51,281 次观看 • 8 个月前

Marginal Revolution co-creator Alex Tabarrok says that AI replacing all jobs (which he doesn't think will happen) is a rich man's problem. "People are worried AI is going to do all the jobs. But this means we are going to be fabulously wealthy." "Even without any jobs, just being fabulously wealthy — we'll figure things out. This is the sort of problem you want to have." "This is not like a natural disaster, which destroys wealth. This is a tsunami which creates wealth." "Yes, it could be a tsunami in the sense that it's going to be very dramatic. But it's going to be very dramatic in the sense of like, Santa Claus coming and leaving us stuff under the Christmas tree. That's drama we can handle." "It won't be without problems. But problems where the pie gets bigger are problems we can solve." "It's problems when the pie gets smaller, when we are forced into a zero-sum society of one person versus another, that's when society breaks down." "But not when the pie is getting bigger. We'll figure out ways to make sure everyone gets a decent slice. We can solve the problem of dividing it up with everybody being happy. I'm much less worried about that."

Marginal Revolution co-creator Alex Tabarrok says that AI replacing all jobs (which he doesn't think will happen) is a rich man's problem. "People are worried AI is going to do all the jobs. But this means we are going to be fabulously wealthy." "Even without any jobs, just being fabulously wealthy — we'll figure things out. This is the sort of problem you want to have." "This is not like a natural disaster, which destroys wealth. This is a tsunami which creates wealth." "Yes, it could be a tsunami in the sense that it's going to be very dramatic. But it's going to be very dramatic in the sense of like, Santa Claus coming and leaving us stuff under the Christmas tree. That's drama we can handle." "It won't be without problems. But problems where the pie gets bigger are problems we can solve." "It's problems when the pie gets smaller, when we are forced into a zero-sum society of one person versus another, that's when society breaks down." "But not when the pie is getting bigger. We'll figure out ways to make sure everyone gets a decent slice. We can solve the problem of dividing it up with everybody being happy. I'm much less worried about that."

TBPN

165,208 次观看 • 3 个月前

"No one actually wants robots… people want solutions to their problems. And if you happen to solve that problem using robots because they’re the right way to do it, great." "But no one really wants AI, they want a solution to their problem, which might be writing a great email, or a great video, or a great image. AI is just a means to an end. Same way robots are a means to an end"

"No one actually wants robots… people want solutions to their problems. And if you happen to solve that problem using robots because they’re the right way to do it, great." "But no one really wants AI, they want a solution to their problem, which might be writing a great email, or a great video, or a great image. AI is just a means to an end. Same way robots are a means to an end"

Joe Rogan Podcast News

59,805 次观看 • 25 天前