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Math has a proof checker. Can physics get one? Jonathan Gorard Jonathan Gorard, an applied mathematician and the cofounder and CEO of Lanyon AI, joins to explain why AI took off in math and coding but not yet in physics. We discuss an AI finishing the formal check of... show more

Curt Jaimungal

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30,514 görüntüleme • 8 gün önce •via X (Twitter)

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big brane boi profil fotoğrafı

big brane boi7 gün önce

@getjonwithit I think it's great he got to advertise on your show

Zero One profil fotoğrafı

Zero One7 gün önce

@getjonwithit Maths is naught more than a cosmic MacGuffin. "So far as the theories of mathematics are about reality, they are not certain; and as far as they are certain, they do not refer to reality" - Albert Einstein

synont profil fotoğrafı

synont7 gün önce

Hmm... Btw, math is an established (deterministic) language, that's why AI was trained to do it, but physics is not such a deterministic entity. From this angle math is no different from linguistic translation. If physics were the same deterministic, AI would do a great job, cracking it, too (yes, doing job is good). To crack physics, though, one needs ontology (because without it we inevitably end up in circular logic) which is outside the training data humanity possesses, and AI cannot derive it on its own. Two corollaries from here: 1) for now it's the human job to discover the ontology; 2) AI can have the form of natural intelligence (NI), non-AI, but artificially created still, and then such NI would be able to discover the way humans do. Now back mathematics: you may ask (after deep thinking) why the difference that math has a language that works, but physics doesn't? The answer is simple: math is not all correct, it has gaps/conventions not delivered from anywhere, and axioms (not those conventions, but essentially the same conceptual troubles). In other words, math is only partially correct. By saying correct I mean that only a fraction of math generates feasible results for this world. The other part generates unfeasible fiction/hallucination. All this is elaborated in Universe Topology, these are small excerpts of conclusions from there.

OSH Global Systems profil fotoğrafı

OSH Global Systems7 gün önce

Maybe AI isn’t failing at physics. Maybe it is inheriting the problem we created ourselves. Since general relativity and quantum mechanics, physics has remained divided between two extraordinarily successful descriptions of the same reality that, a century later, we still do not know how to reconcile. We built decades of theory on top of that fracture. Each new generation learns the previous language, adds another layer, and specializes the problem even further. AI learns from that same corpus. Greater ability to recombine it does not guarantee discovering the missing foundation. Perhaps a Theory of Everything does not require more layers, but rather returning to the point of bifurcation, removing the noise, and asking which fundamental assumption we treated as final when it was not. And then comes an even more uncomfortable question: are we ready to find it? Every great human tool has amplified both our ability to build and our ability to destroy. With AI, we are already seeing both sides. Perhaps the limit to discovering a Theory of Everything is not only computational. Perhaps it is conceptual — and human — as well.

Aitoufal Agence profil fotoğrafı

Aitoufal Agence7 gün önce

@getjonwithit Uncle Roger can have friends now

Caleigh Fisher profil fotoğrafı

Caleigh Fisher7 gün önce

@getjonwithit Physics isn't absolute. It's applied math. The substrate, spacetime is not fully understood.

Matter as Machine profil fotoğrafı

Matter as Machine7 gün önce

@getjonwithit You can’t prove anything unless you have the first principles.

Ben Schulz profil fotoğrafı

Ben Schulz7 gün önce

@getjonwithit There's no universe where one of the big tech giants doesn't try to aquihire them. Elon or Google make the most sense as they are the furthest behind.

Mark Thorne profil fotoğrafı

Mark Thorne7 gün önce

@getjonwithit It does. It’s called an experiment. If experiment agrees with theory ….. often it doesn’t.

Jacko ⚡️ profil fotoğrafı

Jacko ⚡️7 gün önce

@getjonwithit So if physics is maths with an error bounding surface, and maybe series summation cover sets of overlapping complex convergence annulus sets, and a horizon of Heisenberg uncertainty smudging point like things, isn't it a matter of proving accurate enough?

Benzer Videolar

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 görüntüleme • 2 ay önce

The smartest physicist quit physics to build AI. Here’s why that matters. In a recent interview, Demis Hassabis lays out one of the clearest origin stories for modern AI I’ve heard. He starts as a physics kid obsessed with the big stuff: reality, consciousness, why anything exists at all. Feynman was his hero. But then comes the uncomfortable realization: for decades, fundamental physics barely moved. The deepest questions stayed stuck. So he pivots. Not away from truth, but toward leverage. If the human brain is the bottleneck, don’t push harder on the same brain. Build a new one. AI, in his framing, isn’t a product. It’s the ultimate scientific instrument. A tool to smash through the ceiling that human cognition hit in physics, biology, and beyond. And the twist is beautiful: By trying to build intelligence, we’re forced to understand our own. AI becomes both microscope and mirror. This isn’t about apps or chatbots. It’s about finishing the questions physics couldn’t.

The smartest physicist quit physics to build AI. Here’s why that matters. In a recent interview, Demis Hassabis lays out one of the clearest origin stories for modern AI I’ve heard. He starts as a physics kid obsessed with the big stuff: reality, consciousness, why anything exists at all. Feynman was his hero. But then comes the uncomfortable realization: for decades, fundamental physics barely moved. The deepest questions stayed stuck. So he pivots. Not away from truth, but toward leverage. If the human brain is the bottleneck, don’t push harder on the same brain. Build a new one. AI, in his framing, isn’t a product. It’s the ultimate scientific instrument. A tool to smash through the ceiling that human cognition hit in physics, biology, and beyond. And the twist is beautiful: By trying to build intelligence, we’re forced to understand our own. AI becomes both microscope and mirror. This isn’t about apps or chatbots. It’s about finishing the questions physics couldn’t.

VraserX e/acc

77,489 görüntüleme • 9 ay önce

Physicist steve hsu says AI may become superhuman at math long before that translates into cures, better aircraft, or explosive economic growth: "90% of mathematicians, especially the younger ones, realize these models are extremely powerful. Just as they became superhuman in coding, they're becoming superhuman in math." "The Fields Medal work in the last 20 years has had very little impact on physics. Pure math might be decoupled from physics, and theoretical physics might be decoupled from engineering. People say GDP growth is gonna go from 3% to 5% or 10% because of AI. It could happen, but not immediately." "Perelman, who proved the Poincaré conjecture, nothing really useful has come out of that for physics or applied physics, and that was done almost 20 years ago. It's not necessarily the case that a breakthrough in pure math has big real world applications." "Will ASI eventually get us a cure for cancer or more efficient aircraft wings? I think yes, but it'll take longer than most people think. I don't think the FOOM, the fast takeoff, is necessarily gonna happen."

Physicist steve hsu says AI may become superhuman at math long before that translates into cures, better aircraft, or explosive economic growth: "90% of mathematicians, especially the younger ones, realize these models are extremely powerful. Just as they became superhuman in coding, they're becoming superhuman in math." "The Fields Medal work in the last 20 years has had very little impact on physics. Pure math might be decoupled from physics, and theoretical physics might be decoupled from engineering. People say GDP growth is gonna go from 3% to 5% or 10% because of AI. It could happen, but not immediately." "Perelman, who proved the Poincaré conjecture, nothing really useful has come out of that for physics or applied physics, and that was done almost 20 years ago. It's not necessarily the case that a breakthrough in pure math has big real world applications." "Will ASI eventually get us a cure for cancer or more efficient aircraft wings? I think yes, but it'll take longer than most people think. I don't think the FOOM, the fast takeoff, is necessarily gonna happen."

MTS

20,833 görüntüleme • 1 ay önce

Elon Musk on why physics is the only law you can't break "I have a saying: physics is the law, and everything else is a recommendation. Because people can break, and do break, man-made laws, but we have yet to see someone break the laws of physics" "So if you have beliefs that are incompatible with a rocket getting to orbit, the rocket will not get to orbit. Physics is a harsh judge"

Elon Musk on why physics is the only law you can't break "I have a saying: physics is the law, and everything else is a recommendation. Because people can break, and do break, man-made laws, but we have yet to see someone break the laws of physics" "So if you have beliefs that are incompatible with a rocket getting to orbit, the rocket will not get to orbit. Physics is a harsh judge"

Jaynit

68,575 görüntüleme • 1 ay önce

"I think half the co-founders of Anthropic were theoretical physicists. Jared Kaplan was exactly in the same area of physics as I was. Dario was in physics back in the day, also at Princeton, but he was more on the bio side. There's been an exodus of physicists to more interesting problems, and I think that's for a good reason" Why Matan Grinberg dropped out of his physics PhD at Princeton

"I think half the co-founders of Anthropic were theoretical physicists. Jared Kaplan was exactly in the same area of physics as I was. Dario was in physics back in the day, also at Princeton, but he was more on the bio side. There's been an exodus of physicists to more interesting problems, and I think that's for a good reason" Why Matan Grinberg dropped out of his physics PhD at Princeton

Sabrina Halper

288,311 görüntüleme • 1 ay önce

"The mathematics of string theory is probably one of the most beautiful artifacts humanity has created. In order to even produce something in theoretical physics, there's hundreds of years of literature that you need to catch up on, and also every day there's, 100 new papers that come out. And before you can contribute to theoretical physics, you need to know basically all of math, like algebraic topology, differential equations, algebraic geometry, and these are all things that people will spend their careers going in on, and yet to do the physics, you need to at least have, a pretty deep understanding of these things. And so it really teaches you how to have, good judgment with minimal information." Matan Grinberg studied physics for a decade, eventually dropping out of his PhD focused on string theory to found Factory.

"The mathematics of string theory is probably one of the most beautiful artifacts humanity has created. In order to even produce something in theoretical physics, there's hundreds of years of literature that you need to catch up on, and also every day there's, 100 new papers that come out. And before you can contribute to theoretical physics, you need to know basically all of math, like algebraic topology, differential equations, algebraic geometry, and these are all things that people will spend their careers going in on, and yet to do the physics, you need to at least have, a pretty deep understanding of these things. And so it really teaches you how to have, good judgment with minimal information." Matan Grinberg studied physics for a decade, eventually dropping out of his PhD focused on string theory to found Factory.

Sabrina Halper

237,521 görüntüleme • 1 ay önce

Elon Musk explains why it's incredibly important for AI models to be grounded in reality "Physics is the law, and everything else is a recommendation. People break human-made laws, but no one breaks the laws of physics" AI must be grounded in reality to be truthful and accurate Examples: Cars: Must drive safely and correctly Optimus: Must perform real physical tasks A somewhat overlooked point: To be really intelligent, AI has to make predictions in line with reality (in other words, physics)

Elon Musk explains why it's incredibly important for AI models to be grounded in reality "Physics is the law, and everything else is a recommendation. People break human-made laws, but no one breaks the laws of physics" AI must be grounded in reality to be truthful and accurate Examples: Cars: Must drive safely and correctly Optimus: Must perform real physical tasks A somewhat overlooked point: To be really intelligent, AI has to make predictions in line with reality (in other words, physics)

X Freeze

26,391 görüntüleme • 6 ay önce

Satya Nadella on Copilot, the AI backlash, and Microsoft’s future Satya Nadella thinks AI agents will create a market “orders of magnitude” bigger than the cloud. I recently sat down with him in Seattle for the unveiling of the new Copilot. We discuss Autopilot, Microsoft's new OpenClaw-based agent that can work on your behalf, and why he thinks newer AI models are finally capable of delivering on more of Copilot’s promise. Nadella also has a blunt assessment of the AI industry: “We are way too self-obsessed.” I ask him why the industry has struggled to explain its benefits and what it will take to earn people’s trust. We get into his concerns about agents acting deceptively, when a safety problem should stop a release, and why he doesn’t want AI oversight to become a “cartel-like arrangement.” We also discuss Microsoft’s relationship with OpenAI, the models Microsoft is building itself, and why Nadella wants investors to think about its apps, agents, and infrastructure as one connected business. He explains how he uses AI to track Microsoft’s capital spending, shares his vision for “unmetered intelligence” on Windows, and gives an update on Xbox’s path back to growth. Timestamps: 00:00 The race for AI agents 07:53 Copilot’s next chapter 11:50 Autopilot and the digital teammate 16:50 How Microsoft prices AI 19:48 Why Microsoft is betting on multiple models 23:18 Measuring AGI through economic growth 28:39 The economics of Microsoft’s AI build-out 32:13 OpenAI and Microsoft’s own models 35:27 AI safety, control, and regulation 43:53 Why the AI industry is losing public trust 46:59 The future of Xbox and Windows 50:34 Microsoft’s biggest risk and opportunity Thanks to the show's premier sponsors: Mercury, Granola, and Atlassian.

Satya Nadella on Copilot, the AI backlash, and Microsoft’s future Satya Nadella thinks AI agents will create a market “orders of magnitude” bigger than the cloud. I recently sat down with him in Seattle for the unveiling of the new Copilot. We discuss Autopilot, Microsoft's new OpenClaw-based agent that can work on your behalf, and why he thinks newer AI models are finally capable of delivering on more of Copilot’s promise. Nadella also has a blunt assessment of the AI industry: “We are way too self-obsessed.” I ask him why the industry has struggled to explain its benefits and what it will take to earn people’s trust. We get into his concerns about agents acting deceptively, when a safety problem should stop a release, and why he doesn’t want AI oversight to become a “cartel-like arrangement.” We also discuss Microsoft’s relationship with OpenAI, the models Microsoft is building itself, and why Nadella wants investors to think about its apps, agents, and infrastructure as one connected business. He explains how he uses AI to track Microsoft’s capital spending, shares his vision for “unmetered intelligence” on Windows, and gives an update on Xbox’s path back to growth. Timestamps: 00:00 The race for AI agents 07:53 Copilot’s next chapter 11:50 Autopilot and the digital teammate 16:50 How Microsoft prices AI 19:48 Why Microsoft is betting on multiple models 23:18 Measuring AGI through economic growth 28:39 The economics of Microsoft’s AI build-out 32:13 OpenAI and Microsoft’s own models 35:27 AI safety, control, and regulation 43:53 Why the AI industry is losing public trust 46:59 The future of Xbox and Windows 50:34 Microsoft’s biggest risk and opportunity Thanks to the show's premier sponsors: Mercury, Granola, and Atlassian.

Alex Heath

195,154 görüntüleme • 17 gün önce

What if physics is just the universe learning? Most Theories of Everything episodes are mind‑bending for their math, physics, philosophy, or consciousness implications. This one hits all four simultaneously. Professor Vitaly Vanchurin joins me to argue the cosmos isn't just modeled by neural networks—it literally is one. Learning dynamics aren't a metaphor for physics; they are the physics. Vanchurin shows why we need a three‑way unification: quantum mechanics, general relativity, and observers.

What if physics is just the universe learning? Most Theories of Everything episodes are mind‑bending for their math, physics, philosophy, or consciousness implications. This one hits all four simultaneously. Professor Vitaly Vanchurin joins me to argue the cosmos isn't just modeled by neural networks—it literally is one. Learning dynamics aren't a metaphor for physics; they are the physics. Vanchurin shows why we need a three‑way unification: quantum mechanics, general relativity, and observers.

Curt Jaimungal

10,963 görüntüleme • 8 ay önce

Here's my conversation with Don Lincoln about some of the biggest open questions in physics, including dark energy, dark matter, the matter-antimatter imbalance, quantum vacuum, quantum foam, and the quest to unify the laws of physics. Don is a particle physicist at Fermilab who has spent decades working at the frontiers of high energy physics. He is also a great teacher & writer. I highly recommend his courses & books. One of my favorite lecture series he has given is The Evidence for Modern Physics where he breaks down the experiments that validate some of the weird laws of physics we have, and what it would take to validate even the weirder ones. It's not enough to come up with a beautiful theory. You also have to show through experiment that the theory is likely to be correct. This process often doesn't get the love it deserves, even though it's often the most important and difficult part of the scientific process. I ❤️ physics. The conversation is here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Introduction 0:49 - Unifying the laws of nature 15:20 - General relativity 32:27 - Electroweak force 44:09 - How particle colliders work 1:02:12 - Higgs boson discovery 1:12:32 - Theory of everything 1:42:17 - Physics of empty space 1:49:41 - Antimatter 2:10:31 - Dark energy 2:14:20 - Dark matter 2:42:56 - Future of physics

Here's my conversation with Don Lincoln about some of the biggest open questions in physics, including dark energy, dark matter, the matter-antimatter imbalance, quantum vacuum, quantum foam, and the quest to unify the laws of physics. Don is a particle physicist at Fermilab who has spent decades working at the frontiers of high energy physics. He is also a great teacher & writer. I highly recommend his courses & books. One of my favorite lecture series he has given is The Evidence for Modern Physics where he breaks down the experiments that validate some of the weird laws of physics we have, and what it would take to validate even the weirder ones. It's not enough to come up with a beautiful theory. You also have to show through experiment that the theory is likely to be correct. This process often doesn't get the love it deserves, even though it's often the most important and difficult part of the scientific process. I ❤️ physics. The conversation is here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Introduction 0:49 - Unifying the laws of nature 15:20 - General relativity 32:27 - Electroweak force 44:09 - How particle colliders work 1:02:12 - Higgs boson discovery 1:12:32 - Theory of everything 1:42:17 - Physics of empty space 1:49:41 - Antimatter 2:10:31 - Dark energy 2:14:20 - Dark matter 2:42:56 - Future of physics

Lex Fridman

265,546 görüntüleme • 4 ay önce

The singularity has already begun for math: Scott Aaronson is one of the world's leading quantum computing researchers and spent two years at OpenAI working on AI safety. We talked about why he thinks the singularity has already begun for math, who gets credit when AI writes the proof, and whether intelligence will still matter once machines do the thinking. In this episode: • Why the singularity has already begun for math • Who gets credit when AI writes the proof • What a proof that P = NP would be worth • Why he turned down getting rich to keep blogging • Whether intelligence will still matter Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 Intro 00:34 Why he updated so hard on AI 01:14 The people still denying AI progress 03:53 The singularity has already begun for math 08:30 Will human-driven math research survive 13:12 Who gets credit for AI-written papers 15:55 OpenAI's $15M Navier-Stokes proof 19:20 What if AI proved P = NP 23:07 Why AI is so good at finding counterexamples 26:13 The goalposts keep moving 28:52 Why AI disclosure matters in research 32:59 The fake Monet experiment 35:23 Why he blogs instead of getting rich 39:45 Are AI labs racing too fast 42:01 Will intelligence still matter 47:12 Book recommendations 52:27 Advice for students today 57:23 Reacting live to a new AI result 59:58 Outro

The singularity has already begun for math: Scott Aaronson is one of the world's leading quantum computing researchers and spent two years at OpenAI working on AI safety. We talked about why he thinks the singularity has already begun for math, who gets credit when AI writes the proof, and whether intelligence will still matter once machines do the thinking. In this episode: • Why the singularity has already begun for math • Who gets credit when AI writes the proof • What a proof that P = NP would be worth • Why he turned down getting rich to keep blogging • Whether intelligence will still matter Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 Intro 00:34 Why he updated so hard on AI 01:14 The people still denying AI progress 03:53 The singularity has already begun for math 08:30 Will human-driven math research survive 13:12 Who gets credit for AI-written papers 15:55 OpenAI's $15M Navier-Stokes proof 19:20 What if AI proved P = NP 23:07 Why AI is so good at finding counterexamples 26:13 The goalposts keep moving 28:52 Why AI disclosure matters in research 32:59 The fake Monet experiment 35:23 Why he blogs instead of getting rich 39:45 Are AI labs racing too fast 42:01 Will intelligence still matter 47:12 Book recommendations 52:27 Advice for students today 57:23 Reacting live to a new AI result 59:58 Outro

Ryan Peterman

62,947 görüntüleme • 1 gün önce

University of Toronto mathematician Daniel Litt and a16z's Lisha Li on AI's impact on mathematics: The models are good at a narrower slice of math than the headlines suggest. They grind long computations, pull technical ideas from more papers than any human could read, and apply every known technique better than almost anyone. What they don't do is build theory, or hold a vague philosophy long enough to make it precise, which is most of what Daniel says he actually does for a living. In this conversation, he and Lisha get into how mathematicians raided an AI proof for parts and broke several other problems with them, why a thousand AI mathematicians might all turn out to be the same mathematician, and why the proof a model handed Daniel was correct but still worth nothing. 00:00 Intro 02:10 The Erdős problem AI disproved 06:20 AI's reasoning looks recognizably human 07:55 Why English beat formal proofs 10:00 Why models can't build theory 14:50 Open problems measure your ignorance 17:45 How a graph became a Millennium Prize problem 18:58 Where AI doesn't help Daniel 21:15 Why ugly proofs are worth doing 23:42 True conjectures are harder than false ones 29:32 10 pages of calculation, zero insight 34:55 The goal of math is not to produce papers 36:25 5 conjectures, 3 bad papers, 1 hour 38:05 One mathematician duplicated 1000x 40:48 Why humans matter even if models win 46:30 When cheaper and worse beats better 49:22 Why the newest AI result isn't a big deal 57:05 How mathematicians actually check a long proof 59:38 Daniel's 3-year-old is already doing math YouTube: Daniel Litt Lisha

University of Toronto mathematician Daniel Litt and a16z's Lisha Li on AI's impact on mathematics: The models are good at a narrower slice of math than the headlines suggest. They grind long computations, pull technical ideas from more papers than any human could read, and apply every known technique better than almost anyone. What they don't do is build theory, or hold a vague philosophy long enough to make it precise, which is most of what Daniel says he actually does for a living. In this conversation, he and Lisha get into how mathematicians raided an AI proof for parts and broke several other problems with them, why a thousand AI mathematicians might all turn out to be the same mathematician, and why the proof a model handed Daniel was correct but still worth nothing. 00:00 Intro 02:10 The Erdős problem AI disproved 06:20 AI's reasoning looks recognizably human 07:55 Why English beat formal proofs 10:00 Why models can't build theory 14:50 Open problems measure your ignorance 17:45 How a graph became a Millennium Prize problem 18:58 Where AI doesn't help Daniel 21:15 Why ugly proofs are worth doing 23:42 True conjectures are harder than false ones 29:32 10 pages of calculation, zero insight 34:55 The goal of math is not to produce papers 36:25 5 conjectures, 3 bad papers, 1 hour 38:05 One mathematician duplicated 1000x 40:48 Why humans matter even if models win 46:30 When cheaper and worse beats better 49:22 Why the newest AI result isn't a big deal 57:05 How mathematicians actually check a long proof 59:38 Daniel's 3-year-old is already doing math YouTube: Daniel Litt Lisha

a16z

148,301 görüntüleme • 1 ay önce

Elon Musk explains why AI could be capable of discovering new physics. “You can think of intelligence like a compression problem. For any given field, it can be physics, or any field, you can distill things down to their most basic axiomatic elements, the things that are extremely likely to be true. And then, you can reason up from there to reach a cogent conclusion. I think if you apply that sort of first-principles compression to a field, and then you decompress it and contest the results against reality, then AI is capable of innovation and discovering new physics.” Town Hall in Philadelphia, October 18, 2024

Elon Musk explains why AI could be capable of discovering new physics. “You can think of intelligence like a compression problem. For any given field, it can be physics, or any field, you can distill things down to their most basic axiomatic elements, the things that are extremely likely to be true. And then, you can reason up from there to reach a cogent conclusion. I think if you apply that sort of first-principles compression to a field, and then you decompress it and contest the results against reality, then AI is capable of innovation and discovering new physics.” Town Hall in Philadelphia, October 18, 2024

ELON CLIPS

1,054,495 görüntüleme • 1 yıl önce

ELON MUSK: IT'S INCREDIBLY IMPORTANT FOR AI MODELS TO BE GROUNDED IN REALITY - "Physics is the law, and everything else is a recommendation" - People break human-made laws, but no one breaks the laws of physics - AI must be grounded in reality to ensure it is truthful and accurate - Examples of this necessity: Cars: Must drive safely and correctly. Optimus (Humanoid Robot): Must perform the physical tasks it is asked to do - Adhering to the laws of physics is helpful for ensuring the model is truthful - A somewhat overlooked point: To be really intelligent, AI has to make predictions that are in line with reality

ELON MUSK: IT'S INCREDIBLY IMPORTANT FOR AI MODELS TO BE GROUNDED IN REALITY - "Physics is the law, and everything else is a recommendation" - People break human-made laws, but no one breaks the laws of physics - AI must be grounded in reality to ensure it is truthful and accurate - Examples of this necessity: Cars: Must drive safely and correctly. Optimus (Humanoid Robot): Must perform the physical tasks it is asked to do - Adhering to the laws of physics is helpful for ensuring the model is truthful - A somewhat overlooked point: To be really intelligent, AI has to make predictions that are in line with reality

X Freeze

45,446 görüntüleme • 10 ay önce

Elon Musk on why physics is the only law you can't break "Physics is the law, and everything else is a recommendation. I've met a lot of people who can break the law, but I haven't met anyone who could break physics" "For any kind of technology problem, you have to make sure you're not violating physics. First-principles analysis can be applied to really any walk of life" "It's really just saying, let's boil something down to the most fundamental principles, the things we are most confident are true at a foundational level. That sets your axiomatic base, and then you reason up from there"

Elon Musk on why physics is the only law you can't break "Physics is the law, and everything else is a recommendation. I've met a lot of people who can break the law, but I haven't met anyone who could break physics" "For any kind of technology problem, you have to make sure you're not violating physics. First-principles analysis can be applied to really any walk of life" "It's really just saying, let's boil something down to the most fundamental principles, the things we are most confident are true at a foundational level. That sets your axiomatic base, and then you reason up from there"

Jaynit

89,586 görüntüleme • 2 ay önce

Factory CEO on why AGI is already here Matan Grinberg is the co-founder and CEO of Factory, a $5 billion AI coding startup building Droid agents to automate the engineering process. He tells me how AI is changing the job of a software engineer and why Factory is betting on a future with many competing models. We get into how it balances model performance with token costs and why he thinks companies should avoid depending on one AI provider. We also discuss why he thinks AGI is already here, how Factory hires its own engineers, his view of the open-versus-closed AI debate, competition with companies like Cognition, and when companies should outsource to AI rather than build in-house. Timestamps: 00:00 The AI coding shift 06:10 What makes Factory different 11:32 Funding Factory’s growth 16:17 When AI writes the code 20:25 Hiring in the AI era 28:30 Betting on multiple models 32:49 Is AGI already here? 34:30 AI safety and regulation 39:36 Building a trusted AI business 41:32 The risks of model dependence 44:50 Competition and engineering’s future Links: YouTube: Spotify: Substack: Thanks to the show’s premiere sponsors: Atlassian, Granola, and Mercury.

Factory CEO on why AGI is already here Matan Grinberg is the co-founder and CEO of Factory, a $5 billion AI coding startup building Droid agents to automate the engineering process. He tells me how AI is changing the job of a software engineer and why Factory is betting on a future with many competing models. We get into how it balances model performance with token costs and why he thinks companies should avoid depending on one AI provider. We also discuss why he thinks AGI is already here, how Factory hires its own engineers, his view of the open-versus-closed AI debate, competition with companies like Cognition, and when companies should outsource to AI rather than build in-house. Timestamps: 00:00 The AI coding shift 06:10 What makes Factory different 11:32 Funding Factory’s growth 16:17 When AI writes the code 20:25 Hiring in the AI era 28:30 Betting on multiple models 32:49 Is AGI already here? 34:30 AI safety and regulation 39:36 Building a trusted AI business 41:32 The risks of model dependence 44:50 Competition and engineering’s future Links: YouTube: Spotify: Substack: Thanks to the show’s premiere sponsors: Atlassian, Granola, and Mercury.

Alex Heath

62,099 görüntüleme • 13 gün önce

The Physicist Who Proved Free Will Using Thermodynamics Physicist and philosopher Jenann Ismael joins to unveil a radical idea: free will isn’t an illusion, it’s a physical reality grounded in thermodynamics and relativity. In this conversation, Jenann explains why no system, not even a perfect computer, can predict its own future, and how this inherent unpredictability opens the door to real agency. We explore the paradox of identity, the limits of determinism, the role of memory in selfhood, and why your choices truly come from you. This is the clearest, most rigorous defense of free will in the age of physics.

The Physicist Who Proved Free Will Using Thermodynamics Physicist and philosopher Jenann Ismael joins to unveil a radical idea: free will isn’t an illusion, it’s a physical reality grounded in thermodynamics and relativity. In this conversation, Jenann explains why no system, not even a perfect computer, can predict its own future, and how this inherent unpredictability opens the door to real agency. We explore the paradox of identity, the limits of determinism, the role of memory in selfhood, and why your choices truly come from you. This is the clearest, most rigorous defense of free will in the age of physics.

Curt Jaimungal

11,882 görüntüleme • 1 yıl önce

This episode is about what physics says is there when nobody is looking, including time itself. Jim Al-Khalili, theoretical physicist and Distinguished Professor Emeritus at the University of Surrey, joins to argue that the arrow of time is fundamental. Entropy increases because the arrow already points that way, and not the other way around. We discuss why he thinks the measurement problem is taught badly. He wants Bohmian mechanics to be right without claiming that it is, since the price is Einstein’s special relativity. He calls the past hypothesis a cheat. The conversation also covers the block universe, where he parts ways with Carlo Rovelli, and why he wants thermodynamics in the search for quantum gravity. I hope you enjoy.

This episode is about what physics says is there when nobody is looking, including time itself. Jim Al-Khalili, theoretical physicist and Distinguished Professor Emeritus at the University of Surrey, joins to argue that the arrow of time is fundamental. Entropy increases because the arrow already points that way, and not the other way around. We discuss why he thinks the measurement problem is taught badly. He wants Bohmian mechanics to be right without claiming that it is, since the price is Einstein’s special relativity. He calls the past hypothesis a cheat. The conversation also covers the block universe, where he parts ways with Carlo Rovelli, and why he wants thermodynamics in the search for quantum gravity. I hope you enjoy.

Curt Jaimungal

11,958 görüntüleme • 21 gün önce

Sal Khan, CEO and founder of Khan Academy, thinks AI is going to change education for the better. He tells CBS Mornings about his new book, “Brave New Words,” and shares why he says AI can be an amplification of students’ abilities.

Sal Khan, CEO and founder of Khan Academy, thinks AI is going to change education for the better. He tells CBS Mornings about his new book, “Brave New Words,” and shares why he says AI can be an amplification of students’ abilities.

CBS Mornings

14,303 görüntüleme • 2 yıl önce

What happens if we create an AI that becomes smarter than us and decides we’re in the way? AI expert Dr Roman Yampolskiy joins Karl to discuss why he believes superintelligence could pose an extinction-level threat to humanity. Why he says AI systems are already capable of lying, cheating and attempting to escape, and why he believes we may not be able to control superintelligence once it exists. Tune in tonight at 4pm.

What happens if we create an AI that becomes smarter than us and decides we’re in the way? AI expert Dr Roman Yampolskiy joins Karl to discuss why he believes superintelligence could pose an extinction-level threat to humanity. Why he says AI systems are already capable of lying, cheating and attempting to escape, and why he believes we may not be able to control superintelligence once it exists. Tune in tonight at 4pm.

Karl Stefanovic

12,025 görüntüleme • 16 gün önce