A lot of the early AI community was using lisp or lisp derivatives. I remember playing with expert systems and Bayesian believe networks in university in the mid nineties. Bayesian believe networks were basically about assigning probabilities to correlations between facts. Early ML became about trying to learn the probabilities.

I also did a Prolog course and I got to teach programming as a teaching assistant in Java to philosophy students that had Prolog as their only programming experience. So, they understood logic but not loops or assignments.