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Andrew and I discuss his work exploring how various facets of deep networks contribute to their function, i.e. deep network theory. We talk about what he’s learned by studying linear deep networks and asking how depth and initial weights affect learning dynamics, when replay is appropriate (and when it’s not), how semantics develop, and what it all might tell us about deep learning in brains.
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A few recommended texts to dive deeper:
Steve and I discuss many topics from his new book Know Thyself: The Science of Self-Awareness. The book covers the full range of what...
Support the show to get full episodes and join the Discord community. Check out my free video series about what's missing in AI and...
Support the show to get full episodes and join the Discord community. Check out my free video series about what's missing in AI and...