It's not that no one has any idea how to avoid choosing building blocks but that it's a tradeoff. You're building in an inductive bias when you choose the building blocks for your deep RL or evolutionary algorithm optimizer. Nothing stops you from choosing building blocks which are Turing-complete - Schmidhuber, for example, experimented with evolving Brainfuck programs back in the '90s or '00s, I forget which. Turing-complete, can solve anything you set it eventually, as minimal and general as it gets. The problem is that there's so little inductive bias there that it takes forever to get anywhere useful, you have to evolve far too many samples. With the evolving CNNs, researchers are already joking about how you would need a nuclear reactor to reproduce some of these papers which train thousands of CNNs, so the inductive bias is really important in keeping samples down to a feasible level. As computing power gets cheaper and the AutoML-like tools learn more domain knowledge, it'll be possible to let them work on a more raw level than architecture design choices like 'convolution or fully-connected layer? ReLu or PRelu?'