Intelligence is collective, not artificial — Prof. Michael I. Jordan (UC Berkeley / Inria)
From Machine Learning Street Talk (MLST) by Machine Learning Street Talk (MLST)
May 21, 2026 · 1h 17m
About this episode
Michael I. Jordan discusses the distinction between AI and collective intelligence, emphasizing the importance of actionable explanations in machine learning.
Michael I. Jordan, described by Science magazine as the most influential computer scientist alive, has never thought of himself as an AI researcher. In this conversation he explains why that distinction matters. SPONSOR: --- Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open. Apply now: https://cyber.fund --- Jordan trained as a statistician and cognitive scientist, and his career has been spent building machine learning systems that work in the real world: supply chains, commerce, healthcare, and large economic systems. When the field rebranded itself as AI and then AGI, he did not follow. Instead he argues that the framing is wrong. AI is better understood as a collective economic system than as a race to build a disembodied superintelligence. We talk about why AGI is mostly a PR term, what machine learning achieved before the LLM hype cycle, and why the assistant-on-your-shoulder vision may be less compelling than it sounds. Jordan explains why explanations need to be actionable, not merely mechanistic; why AlphaFold's missing error bars matter; how prediction-powered inference changes the…
People in this episode
Guest: Michael I. Jordan
Topics covered
- collective intelligence
- machine learning
- AI
- drug discovery
- economic systems
Keywords
- AI
- machine learning
- collective intelligence
- drug discovery
- AlphaFold
Sponsors
Cyber Fund
Mentioned in this episode
Organizations: Science, UC Berkeley, Inria
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