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EP 316 Ken Stanley on the AI Representation Problem

The Jim Rutt Show

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Unraveling Neural Network Representations

This chapter explores the intricacies of neural networks, emphasizing weight sweeps to assess representation accuracy across architectures. It uncovers the contrast between coherent and chaotic image compositions, raising concerns about knowledge integrity in AI models. The discussion further delves into adversarial attacks, the challenges of representation in smaller versus larger models, and the concept of 'grokking' as a means for AI systems to transition from rote memorization to deeper understanding.

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