多伦多数学家揭秘AI在数学中的真实能力与局限，AI能计算却无法构建理论。
多伦多大学数学家Daniel Litt与a16z的Lisha Li讨论AI在数学领域的实际作用。AI模型擅长处理长计算和整合多篇论文的技术思想，但无法构建理论或保持足够长久的哲学思考。数学家曾利用AI证明解决多个问题，但AI生成的证明可能正确却无价值。AI数学家可能趋同，而丑陋的证明仍有价值。
University of Toronto mathematician Daniel Litt and a16z's Lisha Li on AI's impact on mathematics: ...
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: youtube.com/watch?v=tQI35C… @littmath @lishali88 Your browser does not support the video tag. 🔗 View on Twitter 💬 2 🔄 2 ❤️ 26 👀 7641 📊 6 ⚡