Part of the solution, I think, is making implementations of existing techniques available (and of course, rewriting them in Rust). Deep learning is popular beca

Part of the solution, I think, is making implementations of existing techniques available (and of course, rewriting them in Rust). Deep learning is popular because it's so widely applicable that it justifies the effort of learning to use it: deep nets work with image processing and robot control, and it's kinda unusual to have the mostly the same bag of tricks such different tasks. But a reliable way to improve your performance (or at least write a publishable paper) is to examine why a specific technique worked, rewrite it as a differentiable function or finite automaton, and then implement it as a component in a deep net. So it's a cause for concern, but also an opportunity for academic arbitrage. I think it will work out eventually, but I become uncertain when I meet machine learning "experts" who are only familiar with deep learning and unwilling to consider anything else.