Peter Norvig on the future of AI: what experts get wrong, why language models are world models, and what we should learn when AI can write code.
I sat down with the co-author of Artificial Intelligence: A Modern Approach, written with Stuart Russell, to talk about what more than 40 years in AI have taught him. Including why he once thought ordinary businesses couldn’t possibly get online.
*We discuss:*
- AI agents, from a textbook banner in 1995 to today’s multi-agent systems
- Why he disagrees with Yann LeCun about the path to better world models
- What Google learned from billions of clicks and a handful of detailed observations
- What students should learn when AI can complete the assignment
- Trust, concentrated power, and who benefits from AI
- What should we still be learning? Share your thoughts in the comments.
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Thank you to The AI Conference https://aiconference.com/ for organizing this conversation.
*About Peter Norvig*
Peter Norvig is an AI researcher, educator, and co-author with Stuart Russell of Artificial Intelligence: A Modern Approach, adopted by more than 1,500 schools. His website brings together his books, essays, courses, and programming projects.
Peter’s website: https://www.norvig.com/
Book and learning resources: https://aima.cs.berkeley.edu/
*Chapters*
00:00 Intro: Peter Norvig on 40+ Years of AI
00:58 Why Experts Get the Future Wrong
02:21 How AI Has Changed Since the 1980s
03:33 What AI Students Still Need to Learn
04:42 The Return of AI Agents
06:11 Do We Need Neuro-Symbolic AI?
07:12 Are Language Models World Models?
08:28 What Today’s AI Systems Are Still Missing
10:00 How AI Is Changing Computer Science Education
11:53 Rethinking Assignments and Exams in the AI Era
13:08 What Excites Peter Norvig Most About AI Today
13:27 Teaching Students to Build with AI
14:46 How AI Is Shifting the Balance of Power
16:31 Can We Trust the Leaders of AI Companies?
17:04 Will AI Power Stay Concentrated?
17:54 Final Thoughts
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