In this episode of The Cognitive Revolution, Nathan explores groundbreaking perspectives on AI alignment with MIT PhD student Tan Zhi Xuan. We dive deep into Xuan's critique of preference-based AI alignment and their innovative proposal for role-based AI systems guided by social consensus. The conversation extends into their fascinating work on how AI agents can learn social norms through Bayesian rule induction. Join us for an intellectually stimulating discussion that bridges philosophical theory with practical implementation in AI development.

Check out:

"Beyond Preferences in AI Alignment" paper: https://arxiv.org/pdf/2408.16984

"Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games" paper: https://arxiv.org/pdf/2402.13399

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CHAPTERS:

(00:00:00) Teaser

(00:01:09) About the Episode

(00:04:25) Guest Intro

(00:06:25) Xuan's Background

(00:12:03) AI Near-Term Outlook

(00:17:32) Sponsors: Notion | Weights & Biases RAG++

(00:20:18) Alignment Approaches

(00:26:11) Critiques of RLHF

(00:34:40) Sponsors: Oracle Cloud Infrastructure (OCI)

(00:35:50) Beyond Preferences

(00:40:27) Roles and AI Systems

(00:45:19) What AI Owes Us

(00:51:52) Drexler's AI Services

(01:01:08) Constitutional AI

(01:09:43) Technical Approach

(01:22:01) Norms and Deviations

(01:32:31) Norm Decay

(01:38:06) Self-Other Overlap

(01:44:05) Closing Thoughts

(01:54:23) Outro

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