In my experience, most of the time, when people talk about AI nowadays they mostly mean machine learning. Despite this, ML is usually seen as a mere technique to build high-performance software; rarely I see people discuss the foundational questions of it, such as, from which "philosophy" of AI did ML emerge? Why is ML compelling in AI research, if not by its performance? What are the fundamental differences between statistical/probabilistic AI and logical AI? For reference, this hasn't even been mentioned on my master-level course on machine learning. Even myself I used to have a distaste for ML because I thought it was just mindless data-crunching.
But lately I've been reading through "Probability Theory: The Logic Of Science" and I'm starting to appreciate the theoretical side of ML, for instance, how bayesian probability can be seen as a model of plausible reasoning in humans, and how probability theory extends logic (motivating, maybe, why probabilistic AIs were the next logical [no pun intended] step after logical AI). I would like now to delve deeper in the topic.
What are some books/paper that deal with fundamental and philosophical issues of ML and relate it to the global discourse of AIs?