Navigating High Dimensions
Randall emphasizes the challenges of understanding network behavior in high-dimensional spaces, where intuitive visual inspections can lead to misleading conclusions. He advocates for a more robust geometric control of networks to avoid shortcut solutions and ensure provable guarantees, especially in the context of RLHF extrapolation. Tim highlights the complexities of prompts, illustrating how increasing prompt complexity can bypass the limitations of RLHF, leading to unexpected outcomes.In this clip
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Machine Learning Street Talk (MLST)
Want to Understand Neural Networks? Think Elastic Origami! - Prof. Randall Balestriero
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