> You don't really need deep-learning or an AI to do a lot of these graph operations. That is right, but we're probably not hunting for absolutely optimal solu

> You don't really need deep-learning or an AI to do a lot of these graph operations. That is right, but we're probably not hunting for absolutely optimal solutions every time. It usually boils down to heuristics - everytime there's a graph reorganization problem where a sub-optimal, but better solution is viable, the solutions always involve short-cuts derived from the domain knowledge in there. This is usually very similar to the AI training operations where there's a guess from an engineer, experiments to validate and modify the heuristic guess to improve the results - finally to give up when the results look acceptable. AIs would do well at parameter tuning, at the very least. My register allocation work would have near infinite loops reordering priorities, until I started forcing invariants into the system which didn't exist in the original design. And the usual arguments about "code is compiled once, executed millions of times" doesn't quite apply to a JIT.