A physics-like approach to deep learning theory is more useful because it links predictions to experiments.

“But the important thing is, and I think it's also missing from from many of these theory papers, is that you go and you actually check whether the test works, uh basically you try to falsify your theory. Or try to figure out where does this theory actually break. In this case I would actually think that something like like physicsy is a lot more useful.”

“Uh just because kind of this have this loop of I make a prediction, then I go, I make an experiment. It I think is much more uh much more useful. And I think that the the big thing is that theory will never be able to explain literally anything, because uh it might be able to explain things on like a pure functional point of view. Uh let's say like the mean field people, they can completely explain if I have let's say uh a density composed of let's uh let's say a Gaussian mixture model.”