This is really well put. The term "machine learning" fits the field, but the venn diagram of "what those two words could mean in english" versus "what the term

This is really well put. The term "machine learning" fits the field, but the venn diagram of "what those two words could mean in english" versus "what the term means in the field" is a huge circle enclosing a tiny subset. It's way too broad, and a term that naturally lent itself to a far more narrow interpretation by people first finding it wouldn't have this problem. --- It's fascinating to me, as someone that works with (rudimentary, non-ML) game AI, that - until recently, nobody really even tried doing game AIs that even "trained their heuristics". Like, I get how AIs couldn't form a general plan or any of that, but I was shocked, as an adult, to learn that i.e. FPS AIs were too dumb to even take "guesstimate" values like how much they needed to lead a shot (i.e. honing ballistics calculations), and at least train the aiming value for that based on inputs and success/failure criterion. As a kid, the obviousness of the idea, and triviality of how much effort it ought to take (surely a couple of hours, tops?) had me convinced that of course everybody was doing that. Once I became an adult, I learned the bitter truth that even banally simple ideas are shockingly difficult to put into practice. The devil's in the details.