Deep learning theory should make testable predictions and connect theoretical results to inference about real-world systems.

“It ultimately doesn't matter, even if you can actually make a hard cut. And I think the the important thing is that we need to have something which makes actual predictions, and for that the loop between the the theory people which do like the the really nice things like mean field uh neural tangen kernels um”

“Also, stuff like diffusion like diffusion is also it's a non parametric uh exper uh explanation of what these models do. You just say, well, they fit some density. Reinforcement learning, what I do, it's also they fit some density, be happy. But uh the the actual loop should be to actually then take this and trying to make inference for the real world. Like if I if I prove something converges and I test it and it doesn't”