There is a lot of interesting work going on in this area right now in the research community - using ML for things like program synthesis, meta-learning, and co

There is a lot of interesting work going on in this area right now in the research community - using ML for things like program synthesis, meta-learning, and combining ideas from constraint satisfaction with ML approaches. The rules based approach has (as you mention) years of history, so people are currently exploring the green-ish fields of raw learning approaches with good success on tasks where rules based approaches performed much worse or didn't work at all (cf image recognition, speech recognition), and in some areas it seems like the more you let the model learn / get rid of classical rules based approaches (with enough data), the better it does. Whether that is true for field X, not true yet for field X, or will never be true for field X depends on who you ask. There is definitely a recent tide of models which are focused more on rule learning, function generation, and so on. The general thing I see is that rule based approaches with good approximators/probability models to guide heuristic or exact search can do crazy things - this is the story of AlphaGo at a 10k foot level. People in the ML community are just more focused on the new-ish part (learning good probability/function approximators from data) right now. Just because rules aren't incorporated widely yet, doesn't mean they won't be in the future. I am personally very interested in this direction, and a bunch of work from Sony CSL (Pachet et. al.) has focused heavily on this idea in the past. As an aside, whenever you hear an ML researcher say "prior", it is generally functioning as some kind of soft or occasionally hard rule - so maybe there are more rules floating around than it seems. Soft rules aka priors are generally (much!) easier for gradient descent style optimization and incorporating directly into models, so we tend to have priors rather than hard rules as seen in many other parts of computer science. Even the structure of the model itself can be seen as a prior.