Depends on your application. AFAIK they are still used in games because they are easier to control and people want to play against simple AI agents.

> Personally, those "old" methods in the 80s make a lot more sense to me than recent statistical methods.

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.

The company I work for makes a killing on applying a 1980s-era ML technique to a really tough modern business problem -- the resulting product is probably the best in the world at what it's doing.

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.

Cost is also important. LLMs are expensive to run.

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.

Hm. I'm not sure about cost. I haven't done any sort of analysis but I would guess with 75 % certainty that the techniques we use need more watts per byte of data than modern techniques. Modern techniques are very efficient in terms of their operation -- it's just that they achieve performance by needing to do so much operation!

I was assuming they were referring to the data collection, storage, and processing and model training.

What 1980s-era ML technique are you using and why is it better suited for your application than something modern? Is it „just“ the ability to explain solutions, or is there something else like guarantees for XYZ?

The core part of it is doing dimensionality reduction with shallow neural networks, but essentially all user-facing functionality we get by controlling the training methods and hooking into the neural network and querying it all sorts of ways.

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.

You can run it in an air-gapped embedded system with (by today's standards) low RAM, and a slow CPU.

Being able to trace how an answer is derived is also worth something.

If it's for work or for building something useful, the book is a waste of time. If it's for your personal interest, you can read and learn anything you want.

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.

With modern and trendy music, should we listen to old and classical music. Should we forget the history? I think not. My favourite genres are Jazz and classical.

Sorry if you don't get the metaphor, but it's like so.

The metaphor only works if you assume that software has only aesthetic utility.

Jazz is a contemporary pop music, so this doesn't even make sense

This book is a good intro to Lisp itself, which is worth knowing in any case- and symbolic AI is probably one of the best domains for it!

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.

But what is GOFAI?

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

it all loops around, pretty soon we’ll all be back to good old fashioned philosophy

Think of this as a "learn to program better" book, even if you don't care about GOFAI. It has a lot of very nice code, and commentary without blather. I've said this about as long as I knew of it, so this isn't just revisionism.

It depends on what you were expecting from GOFAI. The future state is likely to be a combination of approaches where each makes sense rather than a single one. We're still in the honeymoon phase with deep learning and disillusionment with its ultimate limitations is likely still to come.