Norvig addresses this at the end of f http://norvig.com/Lisp-retro.html . Basically these old AI techniques are now considered regular programming and modern AI is focused on ML. Both are useful but for different tasks.

Symbolic AI never managed to scale to significant and general real world problems. Neither did Machine Learning, not until the advent of fast processors and massive datasets for training which broke the scale-barrier.

Rule-based expert systems (as opposed to SAT, etc) based on Symbolic AI also have the issue that for non-trivial problems, coding the rules themselves usually requires programming expertise in addition to the domain knowledge required to capture the business logic. Things have improved a lot since the 80s, but applications remain fairly niche.

I think ideas from symbolic AI are relevant here and there but they certainly aren't fashionable.

Almost any financial institution has a copy of IBM iLOG in there somewhere implementing policy in terms of production rules.

Some of the most interesting systems today combine ideas from machine learning with ideas from AI search. For instance there are many game playing programs like AlphaGo that use

https://en.wikipedia.org/wiki/Monte_Carlo_tree_search

which runs a large number of games to the end rather than searching the next few moves exhaustively. Using a machine learning model to play the game for the playouts but sampling a large number of moves with A.I. search turns out to be a winning strategy.

The most relevant field this would apply to is AI for games.

Mostly not, no.