When a proven convergence result does not hold up in testing, deep learning theorists should investigate and explain the discrepancy.

“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”

“Then we would then kind of and I think this is where the like there should be a deep a theory of deep learning people look at we should now go and say exactly that we need to explain this the problem is right now it's really really difficult because we have so little theory that the reason it could not work or it did not work could be just an implementation error because we don't have a good enough idea of what we do yet.”