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.
I rarely 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 in 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 into the topic.
What are some books/papers that deal with fundamental and philosophical issues of ML and relate it to the global discourse of AIs?