[joe_the_user]

It's like asking for a theory of which programming language is better.

There's nothing wrong with just ignoring programming-language theory and just deciding on one, seat of the pants style. But this is because programming as it exists now is a static "art form" with only marginal progress expected.

However, assuming deep learning currently works unexplainably well and one aims to scientifically explain that good working, one would want an explanation which guides one's approach to extending the process.

I've done a bit of applied math, where knowing which kind of function to pull out of one's toolbox for which situation was the really-smart-people's purview, a fairly well guarded folk-knowledge, actually. I'm used to the "little bit of this, little bit of that" kind of explanation for which functions to use when and why. If one weighs them long enough, I assume one can intuitively figure out what to do.

But if we're aiming to advance fundamentally beyond the state-of-the-art, we would aim to quantify these advantages and disadvantages, to automate one more layer. So here we really should know and have a "real" theory here.

[nightski]

Do you think no one is trying? Should researchers just ignore all results until the underlying theory is found? What if we don't find it for another 50 years? I find it incredibly hard to be critical in this situation.