Exploring Attention Schema Theory in AI Consciousness

This chapter examines attention schema theory and its relevance to understanding consciousness in AI systems. It discusses the relationship between self-modeling, predictability, and cooperation, emphasizing the need for simplified models to enhance AI alignment and address moral concerns. The chapter also addresses the challenges of over-parameterization in large models while highlighting the importance of singularities in learning algorithms.

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In this episode of The Cognitive Revolution, Nathan explores unconventional approaches to AI safety with Judd Rosenblatt and Mike Vaiana from AE Studio. Discover how this innovative company pivoted from brain-computer interfaces to groundbreaking AI alignment research, producing two notable results in cooperative and less deceptive AI systems. Join us for a deep dive into biologically-inspired approaches that offer hope for solving critical AI safety challenges.

Self-Modeling: https://arxiv.org/abs/2407.10188

Self-Other Distinction Minimization: https://www.alignmentforum.org/posts/hzt9gHpNwA2oHtwKX/self-other-overlap-a-neglected-approach-to-ai-alignment

Neglected approaches blog post: https://www.lesswrong.com/posts/qAdDzcBuDBLexb4fC/the-neglected-approaches-approach-ae-studio-s-alignment

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CHAPTERS:

(00:00:00) About the Show

(00:00:22) Sponsors: WorkOS

(00:01:22) About the Episode

(00:05:18) Introduction and AE Studio Background

(00:11:37) Keys to Success in Building AE Studio

(00:16:57) Sponsors: Weights & Biases Weave | 80,000 Hours

(00:19:37) Universal Launcher and Productivity Gains

(00:24:44) 100x Productivity Increase Explanation

(00:31:46) Brain-Computer Interface and AI Alignment

(00:38:05) Sponsors: Omneky