Historically we had expert systems, formal logic, hand-authored decision trees (eg old school machine translation), domain-specific algorithms (eg A* pathfinding), and more.
Nowadays it seems like everything is some form of ML trained on real world data.
(I’m broadly lumping everything generated automatically from data into the “ML” bucket, including HMMs, Bayesian networks, stochastic modeling, etc.)
Maybe this is a good thing? The results definitely speak for themselves. I might buy that the scale of most modern problems simply overwhelms hand-authored approaches. Are we losing anything by putting all our eggs in the big-data-and-opaque-emergent-models basket, though? Are there any other forms of AI that we’re neglecting? Or am I just being old fashioned and naive?