[marcosdumay]

> no one could really explain why exactly they worked

That is the first time I have heard that claim, and since we have a large body of knowledge describing how evolution works (that sampo description is one the clearest I've seen) and how it can be optimized, I imagine you are talking about some other problem.

Is it about predicting the causes of some learned trait? Is there some interesting research on that?

[Cacti]

They are referring to Genetic Algorithms. There was a theory called the "building block hypothesis" but no one could prove it (turned out it was impossible to prove). The field was sort of run on hand waving for several decades.

[marcosdumay]

The fact that it is impossible to prove is what is new to me.

Even more because it's clearly not a property of genetic algorithms in general, but a very powerful effect that one aims into achieving with genetic algorithms and a good domain modeling. I don't really understand what is the meaning of something like that being impossible to prove.

[eafan]

Clearly lack of a sound theoretical basis or proof for why deep learning works has not stopped its proliferation. For a practitioner, the proof is in the pudding: generalized results, novel solutions that provably work, new designs that fulfill the given objective(s). At the end of the day, those are what really matter for practical applications.