What 40 Years in AI Taught Brown’s Chief AI Officer

Professor Michael Littman has been an AI researcher since the 1980s, co-created foundational reinforcement learning frameworks like Markov games, and now serves as Brown University's Associate Provost for Artificial Intelligence. In this conversation, we get into: why AI "solved" the problems researchers spent decades on, but not exactly in an expected way the 1980s math crisis that predicted today's fight over whether AI "understands" anything why LLMs are like a house the size of a planet — and why nobody can check what's in every room Brown's brand-new report on generative AI in the classroom, and the one simple rule Littman proposes for what counts as cheating why he thinks both the AI doomers and the AI utopians are missing the actual picture why he's not convinced there will ever be a "Newton's laws" for neural networks 🔔 Subscribe for more conversations with the people actually shaping how AI gets built, used, and regulated TIMESTAMPS 00:00 preview & introduction 00:56 explaining the role of associate provost for ai 03:00 how ai development surprised longtime researchers 06:01 can ai truly create new research and discoveries? 12:33 what is intelligence? (philosophical perspectives on ai) 15:10 ai culture, robot rights, and "clankers" 16:27 responsible ai, bias, and large language models 19:24 ai policy and academic integrity in higher education 27:47 rethinking teaching, homework, and class structures 29:54 pitting ai doomers vs. ai optimists 33:28 final takeaways and the future of ai