AI math capabilities could be jagged for a long time – Daniel Litt

Daniel Litt is a professor of mathematics at the University of Toronto. He has been a careful observer of AI’s progress toward accelerating mathematical discovery, sometimes skeptical and sometimes enthusiastic. Topics we cover: the hardest problems models can solve today, whether there is convincing evidence that AI is speeding up math research, and what’s missing before AI might have a shot at solving Millennium Prize problems. We also discuss how to measure progress in math, including Epoch AI’s new FrontierMath: Open Problems benchmark which evaluates models on meaningful unsolved math research problems. – Episode links – Transcript & references: https://epoch.ai/epoch-after-hours/daniel-litt-ai-math-capabilities-could-be-jagged-for-a-long-time Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-math-capabilities-could-be-jagged-for-a-long/id1790976895?i=1000747213829 Spotify: https://open.spotify.com/episode/1brOmv2FYLzH6TdEZacZ3S?si=ae56bad05c9e4f1c – Timestamps – 0:00:00 What's the hardest math problem AI can solve today? 00:16:08 How helpful are today’s AI models for math research? 00:23:36 Junk papers, LLM-generated proofs, and the refereeing crisis 00:27:21 AI enables searching through problems at scale 00:33:49 When will AI be good enough to publish in top math journals? 00:42:15 What are the returns to intelligence? 00:59:50 Will AI solve Millennium problems? 01:11:54 Is math full of low-hanging fruit? 01:18:47 How Daniel has adapted his professional life to AI progress 01:25:28 What do AI math benchmarks actually measure? 01:33:05 Designing the Open Problems benchmark 01:56:35 Do mathematicians believe heuristic arguments about conjectures? 02:01:24 What if FrontierMath: Open Problems gets solved? 02:06:53 Is AI on the cusp of accelerating math progress?