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Large Language Models Are Neurosymbolic Reasoners

arxiv.org

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Re: Large Language Models Are Neurosymbolic Reasoners

#171

Post by calf »

mjburgess wrote: post #148
calf wrote: post #140

Earlier quoted context omitted.

I've read this thread exchange with interest, but what about the results that quantum computers are simulatable by classical computers? See David Deutsch 1985. This would reduce the issue of infinite Hilbert spaces to simulation using quantum computers, and in turn, Deutsch's result which says classical Turing machines can actually simulate quantum computers.

You can always make local arguments that, say, some g can be substituted with some c. The issue is broader than that. It concerns the premises of vast areas of physics -- you have to show they are more likely false than true. This isnt an argument saying no c can be found for any given g, it's saying, "g-c gaps have empirical consequences we havent observed" and if we did, physics would be foundationally wrong

When they assert theorems like "classical TMs can simulate quantum TMs" they mean the simulation is gapless. Otherwise they use the term approximation.

Re: Large Language Models Are Neurosymbolic Reasoners

#172

Post by calf »

mjburgess wrote: post #161
calf wrote: post #143

Earlier quoted context omitted.

To focus on one issue, the neural machine that is chosen by optimization is one that "best" fits the photos of the sky. But those multiple optima do not preclude a neural machine whose parameter values are computationally equivalent to, say, a 3D representation of the sky projected onto a 2D perspective -- a kind of partial world theory or world model, that was picked randomly out of many optima. First, it's not impo…

> whether neural nets are purely stochastic parrots. Well we know how they work, it isnt speculative. All gradient-based algs on empirical outcome spaces are just kernel machines (ie., they weight their training data and take averages across it using a similarity metric). Insofar as the ooutput seems as-if to reason it is because the input was produced by reasoning (of people). If you input text documents which have…

Or, we don't surely know what deep nets are doing. If I give you an LLM or AlphaGo, you cannot look at it and tell me what it does. It's a bunch of parameters and edge weights. The counterargument is something like, deep nets are overparameterized and the gradient descent process does not reflect the final result. You would think that the large infinities of correct/incorrect 3D models are impossible to choose from, but in practice some have found emergent structural properties - like board positions, formal grammar fragments, etc. - enough to at least suggest that we don't understand how they work, and that it is a conflation/reductive error to call deep nets the same kernel or statistical machines as before.

The above isn't my own argument, as I'm not an expert. But theoreticians have been looking at this, and the ones posing this counterargument come from outside the ML community/Google/OpenAI so you can't attack this argument for being the wild delusions of ML researchers either. The lectures I watched was by an IAS professor in theoretical computer science, not ML people. Another professor's lecture I started watching has a background in signal theory and probability/statistics, if even he says "we don't know what's going on with deep learning", I tend to give that some credence and update my own uncertainty.

Now, I get your argument in that you are repeating everything Chomsky has said regarding explainability, evolution of human cognition and "truth of the world", statistical machines being fed a corpus of human-understandable information be it Internet text or Go game moves. Chomsky's criticism of ML-based "AI" covers all of this and I don't see your argument as introducing anything different from his (feel free to correct me if I've misread your remarks). I myself actually started on his side, now I'm a little on the fence and can see both sides more clearly.

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