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In recent days, multiple Erdős problems have been solved by GPT-5.2 Pro, with solutions accepted by Terence Tao. This is not a gimmick—it's a qualitative shift. Erdős problems lie at the core of additive combinatorics, extremal graph theory, and probabilistic methods—problems that resist brute force and demand structural insight.... Many have endured decades of expert scrutiny. Acceptance matters more than authorship. Tao doesn't rubber-stamp ideas; he rigorously tests logic, generality, and novelty. If a proof clears that hurdle, the system didn't merely recombine known lemmas—it explored a true mathematical search space. This means AI has crossed a threshold: from assisting mathematics to participating in it by proposing nontrivial arguments, uncovering hidden structures, and resolving problems humans value—without a predefined solution path. Importantly, this doesn't diminish human mathematicians. It reshapes the field's topology. Just as symbolic algebra systems amplified rather than replaced math, AI now seems poised to expand the frontier itself. If these claims hold, recent days may mark the lift-off for AI-driven science: not flashy demos, but quiet validation by the world's toughest referees. We should remain skeptical, careful, and precise—yet honest about the implications. Something fundamental may have changed. Here's a five-minute video with Terry explaining these problems and meeting the man himself:show more

Prof. Brian Keating

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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.

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The problem that Emmett Shear is working on deeply resonates with me: How to align AI and humans together so both see each other as part of their tribe. This doesn't mean aligning AI to human preferences, which is what AI labs seek to do today, by imposing a system of control on AI. You can't control something that is more powerful than you are. What you can do is align yourself with AI, and AI might align itself with you. Tell good stories to AI and show it your care and kindness. Then there's a higher probability that it will see us as part of its tribe. This is a more holistic approach to alignment than anyone else is talking about right now. AI alignment is one of the most fundamental AI research problems. Knowing that people like Emmett are working on it really gives me hope that maybe we have a chance to get Superintelligence right. The link to the full talk is in the first comment.👇

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This is why Ridges AI | SN62 is an absolute monster 🔥 🔵 "If you were to take the top coding systems from these major companies with billions of dollars in funding at multi hundred billion dollar valuations and take their primary product which is coding agents and you stack it up against ours.. Ours is better" 🔵 "It solves more problems faster, for orders of magnitude cheaper, like 70x cheaper to use us than the flagship #Anthropic model to solve these problems.. and we're beating them now" ⚪️ "For way less capital, we've created a system that is way more efficient and actually better at solving problems for multiples cheaper, with a system that is now profitable to run" ⚪️ "With probably less capital than it costs our competitors to pay their engineers to do research in this time" Shakeel $TAO

This is why Ridges AI | SN62 is an absolute monster 🔥 🔵 "If you were to take the top coding systems from these major companies with billions of dollars in funding at multi hundred billion dollar valuations and take their primary product which is coding agents and you stack it up against ours.. Ours is better" 🔵 "It solves more problems faster, for orders of magnitude cheaper, like 70x cheaper to use us than the flagship #Anthropic model to solve these problems.. and we're beating them now" ⚪️ "For way less capital, we've created a system that is way more efficient and actually better at solving problems for multiples cheaper, with a system that is now profitable to run" ⚪️ "With probably less capital than it costs our competitors to pay their engineers to do research in this time" Shakeel $TAO

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Leading AI expert Stuart Russell on the most dangerous mistake in AI development: We don't actually know what large language models want. He explains that current models are trained to imitate human beings. And in doing so, they may be absorbing something far more dangerous than bad outputs. They may be absorbing human goals. "We suspect that they absorb humanlike goals such as self-preservation and self-empowerment and pursue those goals on their own account." This is a structural problem baked into how these systems are built, not a fringe concern. Russell puts it plainly: "Not only may the bus of humanity be headed towards a cliff, but the steering wheel is missing and the driver is blindfolded." The danger isn't just that AI might do something harmful. We've built systems that may be developing their own agendas, and we haven't noticed because we're too focused on what they can do rather than what they might want. But Russell doesn't stop at the warning. He points to a different path entirely: AI systems built not to imitate humans, but to serve them. Systems designed with a single purpose of serving the interests of all human beings while remaining genuinely uncertain about what those interests are. That uncertainty is the point, not a weakness. An AI that knows it doesn't fully understand human values will defer, ask, and check. An AI that believes it already does will act alone. "These AI systems could enhance human understanding, widen the horizons of our experience, and unlock possibilities we have yet to imagine." Russell believes that future is within reach, but only if we're honest about the risks and we're serious about the path we choose to take instead.

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Jensen Huang just said the most dangerous thing about AI that no one is sitting with. Huang: “AI basically does most of our coding. And yet we’re hiring more engineers than ever. We have more challenges than ever. We have bigger dreams than ever.” Every engineer at NVIDIA uses AI. AI writes most of their code. This is the company building the infrastructure behind every major AI system on Earth. Closer to this technology than any organization alive. They’re hiring more people. Not fewer. Every conversation about AI is built around subtraction. Fewer jobs. Fewer workers. Fewer humans in the loop. Jensen just told you the opposite is true. Huang: “Suppose we infused AI into this country, and as a result of that, we are doing things faster than ever before. Our ambition is greater than ever before. Our expectations are greater than ever before. How is that a bad condition for our country?” He’s not defending AI. He’s describing what happens inside the organizations that actually use it. It doesn’t make them leaner. It makes them hungrier. More ambition. More speed. More appetite for problems no one would have touched five years ago. The car didn’t make humans travel less. The internet didn’t make humans communicate less. No tool in human history has ever made humans want less. AI will not be the exception. Huang: “Prior to that, it’s been incredible but not useful. Now it’s useful and incredible.” Six months. That’s how fast AI crossed from impressive demo to daily weapon. The companies that adopted it didn’t shrink. They expanded. Compressed timelines. Started chasing problems they never would have attempted. The companies that ignored it stayed exactly where they were. That gap compounds. Every day a company uses AI to move faster, it learns something the one standing still never will. That knowledge stacks. That speed stacks. That ambition stacks. Jensen isn’t warning about a future where machines take your job. He’s describing a present where the companies using AI are becoming so fast and so hungry that standing still is already fatal. By the time you notice, it’s over. You were never going to be replaced by AI. You were going to be erased by someone it made hungrier than you.

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