Comment by ChuckMcM
1 year ago
I had the funny thought that this is exactly what a sentient AI would write "stop looking here, there is nothing to see, move along." :-)
I (like vannevar apparently) didn't feel Cyc was going anywhere useful, there were ideas there, but not coherent enough to form a credible basis for even a hypothesis of how a system could be constructed that would embody them.
I was pretty impressed by McCarthy's blocks world demo, later he and a student formalized some of the rules for creating 'context'[1] for AI to operate within, I continue to think that will be crucial to solving some of the mess that LLMs create.
For example, the early failures of LLMs suggesting that you could make salad crunchy by adding rocks was a classic context failure, data from the context of 'humor' and data from the context of 'recipes' intertwined. Because existing models have no context during training, there is nothing in the model that 'tunes' the output based on context. And you get rocks in your salad.
[1] https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&d...
> there remains no evidence of its general intelligence
This seems like a high bar to reach.
We all know that symbolic AI didn't scale as well as LLMs trained on huge amounts of data. However, as you note, it also tried to address many things that LLMs still don't do well.
This is exactly correct, LLMs did scale with huge data, symbolic AI did not. So why? One of the things I periodically ask people working on LLMs is "what does a 'parameter' represent? The simplistic answer is 'it's a weight in a neural net node' but that doesn't much closer. Consider something like a bloom filter where a '0' bit represents the nth bit of all hashes of strings this filter has not seen. I would be interested in reading a paper that does a good job of explaining what a parameter ends up representing in an LLM model.[1]
I suspect that McCarthy was on to something with the context thing. Organic intelligence certainly fails in creative ways without context it would not be disqualifying to have AI fail in similarly spectacular ways.
[1] I made a bit of progress on this considering it to be the permeability for progress such that the higher the weight the easier it was to 'pass thorough' this particular neuron but the cyclic nature of the graph makes a purely topological explanation pretty obtuse :-).
> LLMs did scale with huge data, symbolic AI did not.
Symbolic AI have not had a privilege to be applied or "trained" with huge data. 30 millions assertions is not a big number.
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>I would be interested in reading a paper that does a good job of explaining what a parameter ends up representing in an LLM model.
https://distill.pub/2020/circuits/ https://transformer-circuits.pub/2025/attribution-graphs/bio...
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LLMs did scale with huge data, symbolic AI did not. So why? [1]
Neural networks, not LLMs in particular, were just about the simplest thing that could scale - they scaled and everything else has been fine-tuning. Symbolic AI basically begins with existing mathematical models of reality and of human reason and indeed didn't scale.
The problem imo is: The standard way mathematical modeling works[2] is you have a triple of <data, model-of-data, math-formalism>. The math formalism characterizes what the data could be, how data diverges from reality etc. The trouble is that the math formalism really doesn't scale even if a given model scales[3]. So even if you were to start plugging numbers into some other math model and get a reality-approximation like an LLM, it would be a black box like an LLM because the meta-information would be just as opaque.
Consider the way Judea Pearl rejected confidence intervals and claimed probabilities were needed as the building blocks for approximate reasoning systems. But a look at human beings, animals or LLMs shows that things that "deal with reality" don't have and couldn't access to "real" probabilities.
I'd just offer that I believe that for a model to scale, the vast majority of it's parameters would have to be mathematically meaningless to us. And that's for the above reasons.
[1]. Really key point, imo [2]. That innclude symbolic and probabilistic model "at the end of the day" [3]. Contrast the simplicity of plugging data into a regression model versus the multitudes of approaches explaining regression and loss/error functions etc.
>> 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.
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Well, we haven't tried symbolic AI with huge amounts of data. It's a hard problem.
(And ironically this problem is much easier now that we have LLMs to help us clean and massage textual data.)
Such as what? What can GOFAI do well that LLMs still cannot?
I think logical reasoning - so reasoning about logical problems, especially those with transitive relations like two way implication. A way round this is to get them to write prolog relations and then reason over them... with prolog. This isn't a fail - it's what things like prolog do, and not what things like nns do. If I was asked to solve these problems I would write prolog too.
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!
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"Tried to address" is not the same as "can do well."
I was responding to PP, but some other (maybe obvious?) examples are logical reasoning and explainability.
As PP suggests, some of the classical symbolic ideas may be applicable or complementary to current approaches.
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SAT solving, verification and model checking, automated theorem proving, planning and scheculing, knowledge representation and reasoning. Those are fields of AI research where LLMs have nothing to offer.
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Theorem proving.