>"However, the current focus on doing AI research via the gathering of data, the deployment of “deep learning” infrastructure, and the demonstration of systems that mimic certain narrowly-defined human skills — with little in the way of emerging explanatory principles — tends to deflect attention from major open problems in classical AI. These problems include the need to bring meaning and reasoning into systems" I'd…
"When you get attacked in the forest by a guy in a clown costume with an axe you don't need to add that as a training input first before you make a run for it." Sure, because it's already a training input. We'd run because we recognize the axe, the signs of aggression, the horror movie trope of an evil clown, and so forth. We have to teach "stranger danger" to children. "There's no agency, liveliness, autonomy or lea…
not in any meaningful sense, no. I can tell you, "if something's fishy about the situation, just leave". You can do this not because of some particular training inputs or examples I give you, but because you have common sense and a sort of personality and intuition for how to behave in the absence of data. If you told that sentence to a state of the art ML model you'd probably get "what fish?" as an answer.
>Well, that's at least in part because we design them that way
It's mostly because we have no idea how to design them anyway else. I think if anyone knew how to build complex agents with rich internal states that have the intent and communication abilities of humans we'd do that. It's not even really conceivable right now how you could have an ML type system that also can just directly adopt high level concepts dynamically just by communicating them.