Lex Fridman PodcastPeter Norvig: Artificial Intelligence: A Modern Approach | Lex Fridman Podcast #42
Episode Details
EPISODE INFO
- Released
- September 30, 2019
- Duration
- 1h 3m
- Channel
- Lex Fridman Podcast
- Watch on YouTube
- ▶ Open ↗
EPISODE DESCRIPTION
Peter Norvig is a research director at Google and the co-author with Stuart Russell of the book Artificial Intelligence: A Modern Approach that educated and inspired a whole generation of researchers including myself to get into the field. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep42-sb See below for timestamps, and to give feedback, submit questions, contact Lex, etc. *CONTACT LEX:* *Feedback* - give feedback to Lex: https://lexfridman.com/survey *AMA* - submit questions, videos or call-in: https://lexfridman.com/ama *Hiring* - join our team: https://lexfridman.com/hiring *Other* - other ways to get in touch: https://lexfridman.com/contact *OUTLINE:* 0:00 - Introduction 0:37 - Artificial Intelligence: A Modern Approach 9:11 - Covering the entire field of AI 15:42 - Expert systems and knowledge representation 18:31 - Explainable AI 23:15 - Trust 25:47 - Education - Intro to AI - MOOC 32:43 - Learning to program in 10 years 37:12 - Changing nature of mastery 40:01 - Code review 41:17 - How have you changed as a programmer 43:05 - LISP 47:41 - Python 48:32 - Early days of Google Search 53:24 - What does it take to build human-level intelligence 55:14 - Her 57:00 - Test of intelligence 58:41 - Future threats from AI 1:00:58 - Exciting open problems in AI *PODCAST LINKS:*
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SPEAKERS
Lex Fridman
hostPeter Norvig
guestNarrator
other
EPISODE SUMMARY
In this episode of Lex Fridman Podcast, featuring Lex Fridman and Peter Norvig, Peter Norvig: Artificial Intelligence: A Modern Approach | Lex Fridman Podcast #42 explores peter Norvig on AI’s Past, Present, Ethics, and Human-Centered Future Peter Norvig discusses how AI has evolved since the 1990s, reflected through successive editions of *Artificial Intelligence: A Modern Approach*, emphasizing shifts from logic and knowledge engineering to probability, machine learning, and now deep learning. He highlights that defining the “utility function” and human values in AI systems is becoming harder than optimization itself, especially around fairness, bias, and societal impact. The conversation also explores explainability versus trust, the adversarial nature of search and the web, and how education and programming have changed with MOOCs, high‑level tools, and ubiquitous data. Norvig closes by stressing useful, human-aligned AI (e.g., conversational assistants, programming tools) over abstract pursuit of “human-level” intelligence, while acknowledging real risks like inequality and misuse of powerful technologies.
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