Keith, Karl, and Stephen delve into reverse engineering generative models of neural activity, discussing explainability, uncertainty, and the complexity of self-modeling in neural networks. They explore the fine balance between approximations and capturing dynamics, questioning the observer's role in perceiving the universe's dimensions.
