Machine Learning for Systems and Systems for Machine Learning [pdf]
Great presentation. Far as application, I already thought this might be useful in lightweight, formal methods to spot problems and suggest corrections for failures in Rust's borrow checkers, separation logic on C programs, proof tactics, and static analysis tooling. For Rust example, the person might try to express a solution in the language that fails the borrow checker. If they can't understand why, they submit it to the system that attempts to spot where the problem is. The system might start with humans spotting it and restructuring the code to pass borrow checker. Every instance of those will feed into the learning system that might eventually do that on its own. There's also potential to use automated, equivalence checks/tests between user-submitted code and the AI's suggestions to help human-in-the-loop decide if it's worth review before passing onto the other person.In hardware, both digital and analog designers seem to use lots of heuristics in how they design things. Certainly could help there. Might be especially useful in analog due to small number of experienced engineers available.