#210 Pedro Domingos: Exploring AI’s Impact on Politics and Society

Eye On A.I.

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In this episode of the Eye on AI podcast, we sit down with Pedro Domingos, professor of computer science and author of The Master Algorithm and 2040, to dive deep into the future of artificial intelligence, machine learning, and AI governance.

Pedro shares his expertise in AI, offering a unique perspective on the real dangers and potential of AI, far from the apocalyptic fears of superintelligence taking over. We explore his satirical novel, 2040, where an AI candidate for president—Prezibot—raises questions about control, democracy, and the flaws in both AI systems and human decision-makers.

Throughout the episode, Pedro sheds light on Silicon Valley’s utopian dreams clashing with its dystopian realities, highlighting the contrast between tech innovation and societal challenges like homelessness. He discusses how AI has already integrated into our daily lives, from recommendation systems to decision-making tools, and what this means for the future.

We also unpack the ongoing debate around AI safety, the limits of current AI models like ChatGPT, and why he believes AI is more of a tool to amplify human intelligence rather than an existential threat. Pedro offers his insights into the future of AI development, focusing on how symbolic AI and neural networks could pave the way for more reliable and intelligent systems.

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(00:00) Preview and Introduction

(01:06) Pedro's Background and Contributions to AI

(03:36) The Satirical Take on AI in '2040'

(05:42) AI Safety Debate: Geoffrey Hinton vs. Yann LeCun

(08:06) Debunking AI's Real Risks

(12:45) Satirical Elements in '2040': HappyNet and Prezibot

(17:57) AI as a Decision-Making Tool: Potential and Risks

(22:55) The Limits of AI as an Arbiter of Truth

(27:35) Crowdsourced AI: PreziBot 2.0 and Real-Time Decision Making

(29:54) AI Governance and the Kill Switch Debate

(37:42) Integrating AI into Society: Challenges and Optimism

(47:11) Pedro's Current Research and Future of AI

(55:17) Scaling AI and the Future of Reinforcement Learning