I think quite a lot of planning.
I think scheduling - I tried something recently and GPT4 wrote python code which worked for very naive cases but then failed at any scale.
Basically though - trusted reasoning. Where you need a precise and correct answer LLM's aren't any good. They fail in the limit. But where you need a generally decent answer they are amazing. You just can't rely on it.
Whereas GOFAI you can, because if you couldn't the community thew it out and said it was impossible!
ML is good at fuzzy stuff, where you don't have a clear definition of a problem (what is spam? what is porn?), "I know it when I see it", or when you don't have a clear mathematical algorithm to solve the problem (think "distinguishing dogs and cats").
When you have both (think sorting arrays, adding numbers), traditional programming (and that includes Prolog and the symbolic AI stuff) is much better.
LLMs will always be much worse than traditional computer programs at adding large numbers, just as traditional computer programs will always be worse at telling whether the person in the image is wearing proper safety equipment.
For best results, you need to combine both. Use LLMs for the "fuzzy stuff", converting imprecise English or pictures into JSON, Python, Wolfram, Prolog or some other representation that a computer can understand and work with, and then use the computer to do the rest.
Let's say you're trying to calculate how much proteins there are per 100 grams of a product, you have a picture of the label, but the label only gives you proteins per serving and the serving size in imperial units. The naive way most programmers try is to ask an LLM to give them proteins per 100g, which is obviously going to fail in some cases. The correct way is to ask the LLM for whatever unit it likes, and then do the conversion on the backend.
Agentic LLMs can solve complicated reasoning and scheduling problems, by writing special-purpose solutions (which might resemble the things we call GOFAI). It's the nature of AGI--which LLMs assuredly are--that they can solve problems by inventing specialized tools, just as we do.
1: I need to schedule scientific operations for a space probe, given a lot of hard instrument and schedule constraints. Please write a program to do this. Use the best tool for the job, no matter how obscure.
2: This is a high-value NASA space mission and so we only get one shot at it. We need to make absolutely sure that the solution is correct and optimal, ideally with proofs.
3: Please code me up a full example, making up appropriate input data for the purpose of illustration
I got an implementation that at first glance looks correct using the MiniZinc constraint solver. I’m sure people could quibble, but I was not trying to lead the model in any way. The second prompt was because the first generated a simple python program, and I think it was because I didn’t specify that it was a high value project that needed mission assurance at the start. A better initial prompt would’ve gotten the desired result on the first try.