多伦多数学家Daniel Litt亲测AI1小时生成3篇正确但低质量论文，揭示AI如何改变数学研究奖励机制。
多伦多大学数学家Daniel Litt指出AI改变了学术数学的奖励机制。他通过实验发现，使用Codex模型在1小时内能生成3篇正确但质量很低的数学论文。AI模型擅长处理长计算和整合多篇论文的技术思想，但无法构建理论或将模糊哲学转化为精确概念。Litt强调数学的目标不是产生论文，而是产生某种理解。
University of Toronto mathematician Daniel Litt on how AI has changed academic mathematics' reward i...
University of Toronto mathematician Daniel Litt on how AI has changed academic mathematics' reward incentives, and how easily he proved it: "The goal of mathematics is not to produce mathematics papers. It's to produce some kind of understanding." "Maybe some of that understanding resides in model weights or something. To me, that's pretty unsatisfying." "My motivation for doing mathematics is that I would like to satisfy my own personal curiosity, and I think people should have the capability to do that. That requires a pretty substantial apparatus." "Right now, if you're a post-doc on the market... the best way to get a job is to produce a lot of papers which maybe prove old conjectures. And you can do that by playing the slot machine until the model produces a hopefully correct proof." "You don't even have to pick the theorem in advance." "Here's an experiment you can do. You can take Codex, you can say, 'Go online, find five recent conjectures in algebraic geometry and prove them.' I've run this experiment, and with some back and forth I was able to in an hour get like three quite bad papers. But correct papers." @littmath @lishali88 Your browser does not support the video tag. 🔗 View on Twitter a16z @a16z 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 🔗 View Quoted Tweet 💬 3 🔄 2 ❤️ 17 👀 7753 📊 4 ⚡