{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"AI & I","title":"Building a School Where AI Models Learn About Humanity","html":"","width":"100%","height":180,"duration":2629,"description":"If scaling laws hold—and Surge AI CEO Edwin Chen believes they do—we’re hurtling toward a future where there’s nothing humans can do that AI can’t do better. When OpenAI’s models disproved an open conjecture posed by mathematician Paul Erdős using novel algebraic geometry techniques, Fields medalist Timothy Gowers felt the shift acutely. He initially thought the model had proved an upper bound, and braced himself: that would mean it was “all over for mathematicians very soon.” When he realized it had only found a counterexample, he was relieved—it bought him another year or two before the thing he’s devoted his life to becomes something AI does better.\nAs founder and CEO of the company behind the data environments and evals the major model companies use to train their models, Chen has a unique perspective on how quickly AI models are absorbing tasks we used to think of as uniquely human.\nDan Shipper talked with Chen for AI & I about what the act of creating or building means when AI can do it better—and whether an answer to that question already exists within science fiction.\nIf you found this episode interesting, please like, subscribe, comment, and share!\nJoin the membership for Where You Live at ⁠https://www.joinbilt.com/dan\nTo hear more from Dan Shipper:\nSubscribe to Every: https://every.to/subscribe\nFollow him on X: https://twitter.com/danshipper\nTimestamps:\n00:00:54 Introduction\n00:01:49 Surge as a \"school for AGI\"\n00:04:46 What AI's capacity for novel mathematics says about human achievement\n00:07:29 Motivation in an era when AI can do everything\n00:14:34 The trap of optimizing AI models for engagement\n00:29:34 Training using datasets versus training using environments\n00:35:09 The value of personal data\n00:39:40 Why models are bad at writing\n00:42:00 Chen's AGI timeline\nLinks to resources mentioned in the episode:\nEdwin Chen on X: https://x.com/echen\nSurge: https://surgehq.ai\nRiemann-bench (research-level math benchmark):...","thumbnail_url":"https://img.transistorcdn.com/tpm1hNSy8JXTtPDypo5McPF0S6eDqruRTGYywu9SVrc/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8yNDM2/YzU4NDNmYTQxNTJh/MTEzYjE4YmJmYTg5/ODY1NS5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}