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25 Fields Medalists, including Terrence Tao, just published a joint declaration accusing AI labs of a severe misalignment with mathematics. The letter, posted on Tao's blog and at on September 11th, argues that AI companies are treating math problems as benchmarks to farm for PR wins. The signatories call...

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24,230 Aufrufe • vor 1 Tag •via X (Twitter)

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Mark Anthonyvor 1 Tag

People are in general are gonna have to start getting comfortable with losing credit with all the world’s problems being solved. Credit isn’t important. What’s important is that this gets distributed to all people.

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incriptionvor 1 Tag

gatekeeping math is some loserr shit

Profilbild von Charlie Manson
Charlie Mansonvor 1 Tag

It’s actually somewhere in the middle. If there was no ego, the 25 signatories would be asking for more equitable access to the most powerful AI/compute to integrate into optimal sustainable workflows to advance the field. But they aren’t. Equally gutting the field is bad…

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draven

122,073 Aufrufe • vor 1 Tag

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.
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OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.

Ricardo

44,177 Aufrufe • vor 1 Monat

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Two mathematicians are accusing OpenAI of lifting their unpublished work on one of math's hardest unsolved problems. Tristan Buckmaster, an NYU math professor, and Levent Alpoge spent about a month working with AI models, mainly Codex, on finite time blow up results for the Navier Stokes existence and smoothness problem, one of the seven Millennium Prize problems carrying a million dollar bounty. On September 6th, OpenAI's Sebastien Bubeck told Buckmaster that OpenAI had solved the same problem using the Cordoba Martinez Zoroa approach. Buckmaster then published a statement alleging that OpenAI's result echoed drafts from his and Alpoge's private Codex sessions, and that OpenAI offered to publish jointly while allegedly pushing to cut Alpoge out as a co-author. OpenAI's Shunyu Douglas and Will Depue publicly denied any access to or training on private user chat logs, calling the scenario essentially impossible under the company's data controls. Bubeck separately said he acted within academic norms and called some circulating claims false. On September 8th, Buckmaster and Alpoge, joined by Matei Costalescu, made three results public without OpenAI involvement, finite time blow up with smooth forcing for incompressible porous media, for the Boussinesq equations, and for 3D incompressible Euler, alongside a lean formalization posted on GitHub.

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Always so much fun to chat with Grant Sanderson AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there's a jagged landscape. What does it look like? What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do? Does AI (on net) increase or decrease human understanding of the field? How big is the overhang from having AIs systematically try to connect ideas already in the literature? And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI? 0:00:00 – AI is discovering new proofs. Is that AGI? 0:11:32 – The verification loop on conceptual breakthroughs can be a century long 0:26:12 – Will we understand an AI proof of the Riemann hypothesis? 0:38:08 – Can AI find the hidden bridges between fields? 0:53:48 – Why real-world tasks don’t fit into RL environments 1:07:07 – Good writing requires theory of mind that AI still lacks 1:16:02 – Why learning will still depend on human curation Look up Dwarkesh Podcast on Spotify, Apple Podcasts, YouTube, etc.
1:33:39

Always so much fun to chat with Grant Sanderson AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there's a jagged landscape. What does it look like? What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do? Does AI (on net) increase or decrease human understanding of the field? How big is the overhang from having AIs systematically try to connect ideas already in the literature? And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI? 0:00:00 – AI is discovering new proofs. Is that AGI? 0:11:32 – The verification loop on conceptual breakthroughs can be a century long 0:26:12 – Will we understand an AI proof of the Riemann hypothesis? 0:38:08 – Can AI find the hidden bridges between fields? 0:53:48 – Why real-world tasks don’t fit into RL environments 1:07:07 – Good writing requires theory of mind that AI still lacks 1:16:02 – Why learning will still depend on human curation Look up Dwarkesh Podcast on Spotify, Apple Podcasts, YouTube, etc.

Dwarkesh Patel

290,780 Aufrufe • vor 2 Monaten

🧵06/34 Narrow vs General AI --- At first glance, this AGI being generally capable in multiple domains looks like a group of many narrow AIs combined, but that is not a correct way to think about it. It is actually more like… a species, a new life form. To illustrate the point, we’ll compare the general AGI of the near future with a currently existing narrow AI that is optimised at playing chess. Both of them are able to comfortably win a game of chess against any human on earth, every time. And both of them win by making plans and setting goals. The main goal is to achieve checkmate. This is the final destination or otherwise called Terminal Goal. In order to get there though it needs to work on smaller problems, what the AI research geeks call instrumental goals. For example: • attack and capture the opponent’s pieces • defend my pieces • strategically dominate the cetre (etc..) All these instrumental goals have something in common: they only make sense in its narrow world of chess. If you place this Narrow Chess AI behind the wheel of a car, it will simply crash, as it can not work on goals unrelated to chess, like driving. Its model doesn’t have a concept for space, time or movement for that matter. In contrast the AGI by design has no limit on what problems it can work on. So when it tries to figure out a solution to a main problem, the sub-problems it chooses to work on can be anything... literally any path out of the infinite possibilities allowed within the laws of physics and nature.
2:19

🧵06/34 Narrow vs General AI --- At first glance, this AGI being generally capable in multiple domains looks like a group of many narrow AIs combined, but that is not a correct way to think about it. It is actually more like… a species, a new life form. To illustrate the point, we’ll compare the general AGI of the near future with a currently existing narrow AI that is optimised at playing chess. Both of them are able to comfortably win a game of chess against any human on earth, every time. And both of them win by making plans and setting goals. The main goal is to achieve checkmate. This is the final destination or otherwise called Terminal Goal. In order to get there though it needs to work on smaller problems, what the AI research geeks call instrumental goals. For example: • attack and capture the opponent’s pieces • defend my pieces • strategically dominate the cetre (etc..) All these instrumental goals have something in common: they only make sense in its narrow world of chess. If you place this Narrow Chess AI behind the wheel of a car, it will simply crash, as it can not work on goals unrelated to chess, like driving. Its model doesn’t have a concept for space, time or movement for that matter. In contrast the AGI by design has no limit on what problems it can work on. So when it tries to figure out a solution to a main problem, the sub-problems it chooses to work on can be anything... literally any path out of the infinite possibilities allowed within the laws of physics and nature.

Lethal Intelligence

570,437 Aufrufe • vor 1 Jahr

GROK 3 VS. RIEMANN: A BREAKTHROUGH ON THE HORIZON? The race to crack the legendary Riemann Hypothesis is heating up, and Grok 3 might just hold the key! * Brute-Force Computation – With enough power and a verifier, AI could crunch through all solutions. * AI + Human Collaboration – Machines assisting top minds could lead to Turing, Fields, or Nobel Prize-worthy breakthroughs. * A Timeline for Discovery? – Elon predicts AI-driven advancements within two years. No solution yet—but Grok 3 is inching closer to cracking one of math’s biggest unsolved codes! Source: Elon Musk
0:22

GROK 3 VS. RIEMANN: A BREAKTHROUGH ON THE HORIZON? The race to crack the legendary Riemann Hypothesis is heating up, and Grok 3 might just hold the key! * Brute-Force Computation – With enough power and a verifier, AI could crunch through all solutions. * AI + Human Collaboration – Machines assisting top minds could lead to Turing, Fields, or Nobel Prize-worthy breakthroughs. * A Timeline for Discovery? – Elon predicts AI-driven advancements within two years. No solution yet—but Grok 3 is inching closer to cracking one of math’s biggest unsolved codes! Source: Elon Musk

Mario Nawfal

965,754 Aufrufe • vor 1 Jahr

A former OpenAI employee has predicted the potential end of the human race because of advances in AI. Daniel Kokotajlo quit the company in 2024 because he said they were failing to address the extinction risks posed by advanced AI systems and now he says those fears are being borne out as OpenAI and others rush to develop super-intelligent robots that could bring about the destruction of humankind. Channel 4 News reached out to Anthropic for comment and is waiting for a response but in the past, it said the dangers of AI are real and that's why they want governments to work together to regulate the industry.
2:02

A former OpenAI employee has predicted the potential end of the human race because of advances in AI. Daniel Kokotajlo quit the company in 2024 because he said they were failing to address the extinction risks posed by advanced AI systems and now he says those fears are being borne out as OpenAI and others rush to develop super-intelligent robots that could bring about the destruction of humankind. Channel 4 News reached out to Anthropic for comment and is waiting for a response but in the past, it said the dangers of AI are real and that's why they want governments to work together to regulate the industry.

Channel 4 News

24,753 Aufrufe • vor 3 Tagen

Chamath on why the AI capex spend might end up in a bubble Even though the race is on to adopt this technology as fast as possible, key economic indicators such as productivity and GDP growth are not yet showing any meaningful changes because of AI For the spend on AI to continue, companies need to be able to point to margin expansion or revenue growth that was directly contributed through AI A shoe company must be able to say they sold more shoes because of AI adoption A food or restaurant business must be able to say that they generated higher profits because of AI adoption This is simply not the case today. Until that happens, the AI thesis remains fickle
1:21

Chamath on why the AI capex spend might end up in a bubble Even though the race is on to adopt this technology as fast as possible, key economic indicators such as productivity and GDP growth are not yet showing any meaningful changes because of AI For the spend on AI to continue, companies need to be able to point to margin expansion or revenue growth that was directly contributed through AI A shoe company must be able to say they sold more shoes because of AI adoption A food or restaurant business must be able to say that they generated higher profits because of AI adoption This is simply not the case today. Until that happens, the AI thesis remains fickle

Boring_Business

144,234 Aufrufe • vor 4 Monaten