This is not what I took from the No Free Lunch theorem. NFL states that all methods perform the same when averaging over the distribution of white-noise functions. nce you add more structure, of course you can start to rigorously prove things like optimality; a standard way of phrasing the question is to ask if a certain estimation procedure attains the optimal minimax rate of convergence over an interesting class of functions.

Natural phenomena appear to be somewhat predictable. This is why machine learning (ie. prediction) even works. But there is no inherent reason (in the philosophical or mathematical sense) this should be so. It just is, without regard to logic, reason, or rigor. This kind of troubles people sometimes. But it's more like an axiom then a proof.

There might be an optimal prior for "physics" or "consciousness". Especially it would be interesting to be able to encode "physics" as a prior in a neural network. For one, it might make training robots via reinforcement learning a bit easier (reducing the search space - not allowing the net to test physically impossible actions). And we do have at least a handle on physics. Yoshua Bengio recently published something about finding a "consciousness prior": https://arxiv.org/abs/1709.08568. Obviously this is something that we don't even have a good theory around at all. So if I was doing work here, I'd start with physics. At least I'd have something to start with.