Differentiable learning of automata

Learning stack machines, random access machines, nested hierarchical parsing machines, Turing machines and whatever other automata-with-memory that you wish, from data. In other words, teaching computers to program themselves, via a deep learning formalism.

This is a kind of obvious idea and there are some charming toy examples. Indeed this is wort-of what we have traditionally imagined AI might do.

Obviously a hypothetical superhuman Artificial General Intelligence would be good at handling computer-science problems; It’s not the absolute hippest research area right now though, on account of being hard in general, just like we always imagined from earlier attempts. Some progress has been made. My sense is that most of the hyped research that looks like differentiable computer learning is in the slightly-better-contained area of reinforcement learning where more progress can be made, or in the hot area of transformer networks which are harder to explain but solve the same kind of problems whilst looking different inside..

Related: grammatical inference.