Personally, those "old" methods in the 80s make a lot more sense to me than recent statistical methods.
Old techniques have several things going for them, with one of the more important ones for us being explainability. A random person off the street could hypothetically, with an hour or two of training, diagnose problems just by looking at the structure of the model. That's very helpful for adapting to market needs.
Generality is another big plus. Since the model encodes intuitive ideas there's a lot of room for using it in innovative ways.
Older techniques also tend to produce better results with less data, because big data wasn't as much of a thing back then.
Unless you have a crazy amount of resources, I think it's far better to be bleeding edge in as few things as possible. Solving a new business problem? Perhaps don't spend too much time on also solving all the childhood diseases of a new technology.
I think that going forward, we'll see a mix of "normal" programming, LLMs, and simpler machine learning techniques all combined together, because of economic reasons.
We switched from more modern techniques primarily because they needed too much data to work well, but the other things I mentioned are the benefits we noted along the way. I don't know if that answers your question.
As for GOFAI in the age of DL/LLM, yes, you should know it, for a couple of reasons. A lot of these techniques aren't really considered "AI" anymore, they're just regular CS algorithms everybody should know: graph search, backtracking, optimization, parsing, etc. The other is that a lot of newer DL/LLM is actually going back to these old problems, but bringing all the new techniques to deal with limitations of the classical algorithms.
Same for me.
> I've been keeping my eye on the so called "GOFAI" for a long time but with recent advances in ANN methods (DL, LLM), does it even make sense to further pursue the former?
I think it still matters. Plenty of examples in the tech industry where "old" tech/paradigms became the new "hype". They say it's all a cycle.
Being able to trace how an answer is derived is also worth something.
Sorry if you don't get the metaphor, but it's like so.
Personally I avoid books like this one (similar to how I avoid very esoteric languages) because I want to spend my time on things are interesting and useful instead of only interesting.