>> This is exactly correct, LLMs did scale with huge data, symbolic AI did not. So why?
Like the rock salad you're mixing up two disparate contexts here. Symbolic AI like SAT solvers and planners is not trying to learn from data and there's no context in which it has to "scale with huge data".
Instead, what modern SAT solvers and planners do is even harder than "scaling with data" - which, after all, today means having imba hardware and using it well. SAT solving and planning can't do that: SAT is NP-complete and planning is PSPACE-complete so it really doesn't matter how much you "scale" your hardware, those are not problems you can solve by scaling, ever.
And yet, today both SAT and planning are solved problems. NP complete? Nowadays, that's a piece of cake. There are dedicated solvers for all the classical sub-categories of SAT and modern planners can solve planning problems that require sequences of thousands of actions. Hell, modern planners can even play Atari games from pixels alone, and do very well indeed [1].
So how did symbolic AI manage those feats? Not with bigger computers but precisely with the approach that the article above seems to think has failed to produce any results: heuristic search. In SAT solving, the dominant approach is an algorithm called "Conflict Driven Clause Learning", that is designed to exploit the special structure of SAT problems. In Planning and Scheduling, heuristic search was always used, but work really took off in the '90s when people realised that they could automatically estimate a heuristic cost function from the structure of a planning problem.
There are parallel and similar approaches everywhere you look at, in classical AI problems, like verification, theorem proving, etc, and that work has even produced a few Turing awards [2]. But do you hear about that work at all, when you hear about AI research? No, because it works, and so it's not AI.
But it works, it runs on normal hardware, it doesn't need "scale" and it doesn't need data. You're measuring the wrong thing with the wrong stick.
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[1] Planning with Pixels in (Almost) Real Time: https://arxiv.org/pdf/1801.03354 Competitive results with humans and RL. Bet you didn't know that.
[2] E.g. Pnueli for temporal logic in verification, or Clarke, Emerson and Sifakis, for model checking.
I think the problem with trying to hand-create symbolic rules for AI is that things like natural language, and the real world, are messy. Even with fuzzy rules you are never going to be able to accurately capture all the context dependencies and nuances, which may anyways be dynamic. Learning from real world data is the only realistic approach, although I don't think language models are the answer either - you need a system that is continually learning and correcting it's own errors.
CYC was an interesting experiment though. Even though it might have been expected to be brittle due to the inevitable knowledge gaps/etc, it seems there was something more fundamentally wrong with the approach for it not to have been more capable. An LLM could also be regarded as an expert system of sorts (learning its own rules from the training data), but some critical differences are perhaps that the LLM's rules are as much about recognizing context for when to apply a rule as what the rule itself is doing, and the rules are generative rather than declarative - directly driving behavior rather than just deductive closure.
Yeas, hand-coding rules doesn't work in the long run. But burning through the world's resources to approximate a huge dataset isn't a viable long-term solution for anything either.
SAT is NP-complete and planning is PSPACE-complete so it really doesn't matter how much you "scale" your hardware, those are not problems you can solve by scaling, ever.
It seems like you are not framing NP-completeness properly. An NP complete problem is simply worst case hard. Such a problem can have many solvable instances. With some distributions of randomly selected SAT problem, most instances can be quickly solvable. SAT solving contests often involve hand-constructed SATs translated from other domains and the entrants similarly add methods for these "special cases". So NP-completeness isn't a barrier to SAT-solvers scaling by itself.
I generally agree with your points, my point was mainly that the concept of "scaling" as meant in machine learning doesn't have an analogy in SAT solving and other classical AI tasks. Nobody's building large data centers to solve SAT problems and data is not "the new oil" in SAT solving, and in the other classical AI disciplines I mention above. In short, those are not data-driven fields.
> Symbolic AI like SAT solvers and planners is not trying to learn from data and there's no context in which it has to "scale with huge data".
Actually, they do. Conflict-Driven Clause Learning (CDCL) learns from conflicts encountered during working on the data. The space of inputs they are dealing with oftentimes is in the order of the number of atoms in Universe and that is huge.
"Learning" in CDCL is a misnomer: the learning process is Resolution and it's deductive (reasoning) not inductive (learning).
You invented a new kind of learning that somewhat contradicts usual definition [1] [2].
[1] https://www.britannica.com/dictionary/learning
[2] https://en.wikipedia.org/wiki/Learning
I'm pretty sure most "industrial scale" SAT solvers involve both deduction and heuristics to decide which deductions to make and which to keep. At a certain scale, the heuristics have to be adaptive and then you have "induction".
I don't agree. The derivation of new clauses by Resolution is well understood as deductive and the choice of what clauses to keep doesn't change that.
Resolution can be used inductively, and also for abduction, but that's going into the weeds a bit- it's the subject of my PhD thesis. Let me know if you're in the mood for a proper diatribe :)
Take a look at Satisfaction-Driven Clause Learning [1].
[1] https://www.cs.cmu.edu/~mheule/publications/prencode.pdf
I'd love a diatribe if you're still following this post.
As would I.
You know, this seems like yet another reason to allow HN users to direct message each other, or at least receive reply notifications. Dang, why can't we have nice things?
Oh, hi guys. Sorry just saw this.
Oh gosh I gotta do some work today, so no time to write what I wanted. Maybe watch this space? I'll try to make some time later today.
> No, because it works, and so it's not AI
This is an important point. Hard "AI" problems are no longer "AI" once we have good algorithms and/or heuristics to solve them.