{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Epoch After Hours","title":"AI math capabilities could be jagged for a long time – Daniel Litt","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/b2de31d1\"></iframe>","width":"100%","height":180,"duration":7860,"description":"\nDaniel 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. \n\nTopics 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.\n\nWe 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.","thumbnail_url":"https://img.transistorcdn.com/avJlCD61fZ_Bqy1m_pO1QOpnxBT10G4IDIYTLu54WBU/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS82YjNi/MjIzYWYzZGQ3ODgx/ZWQ3ZWZiZmQ4YzFi/ZjZhMS5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}