Navigating AI Alignment and State Dynamics

This chapter explores the philosophical and technical challenges of AI alignment, intertwined with the speaker's personal narrative. It examines the dual influence of cultural perspectives and the complexities of state capitalism, while questioning the feasibility of achieving significant automation in the near future.

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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)