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OpenAI Says Astra Solved 10 Open Math Problems With Lean Proofs: The proof files are public, but the new model is still private. (www.implicator.ai)
submitted 3 weeks ago by to c/technology@lemmy.world
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[+] -6 points 3 weeks ago (4 children)

Holy shit. If an AI actually solves a Millennium Prize Problems we're seriously entering superintelligence (but not AGI) territory.

In any case this should disprove the "AI is just a statistically typing monkey" theorem some AI skeptics are fond of.

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  • [–] 9 points 3 weeks ago*

    The Decoder reported on August 1 that Brown said the lab had not spent much on each problem and that there were "no Millennium Prize Problems (yet)".

    Just to be clear.

    But yeah, if that happens, it will certainly be a major milestone.

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  • [–] 3 points 3 weeks ago (1 child)

    Ok, not sure I understand "AI is just a statistically typing monkey"? As far as I know AI simplified is just predicting what is statistically the most likely next word or is that wrong? I mean sure the way it predicts is complicated and word vectors are fascinating etc but highly simplified its a statistically typing monkey?

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  • [–] 5 points 3 weeks ago*

    It's a "category error" like saying hydrodynamics are just quantum mechanics. It does not describe the complex emergent behavior and doesn't really tell you anything. Or it's like saying psychology is how the neurons and synapses connect with each and exchange signals and that is all - this would not explain the mental processes between a marmot and a human. So it's not an accurate simplified description, just a description of the basic building blocks.

    Afaik nobody knows how LLMs actually think, we only know sort of how to train them. They are researching how LLMs think with an equivalent to MRI brain scanning: Vista do LLM-MRI Python module: a brain scanner for LLMs

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  • [–] 0 points 3 weeks ago* (last edited 3 weeks ago) (1 child)

    The current examples of AI could never be described as a general intelligence let alone Superintelligence. These LLMs are definitely just a typing monkey (that was eloquently put).

    They are literally generating text word by word with a next-word-prediction methodology.

    With some tweaking they can be made to produce mostly coherent code and apparently also equations that balance. This does not make these algorithms geniuses. A calculator can also balance an equation, so that alone is not impressive.

    The techniques LLM designers use to leverage statistical models in order to generate coherent sentences are actually rather impressive. It is legitimately quite a feat to make LLMs work as well as they do. But that does not mean that these outputs are anything like "thinking", they're just outputs. Data goes in, the machine churns, does some mystery math, and different data comes out.

    These systems are not worth anthropomorphizing.

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  • [–] 0 points 3 weeks ago (1 child)

    Depends how you define superintelligence, what I meant is a more specialized superintelligence than AGI. We already seen that with Go and Chess. But if AI overtakes humans in mathematics it takes on a different quality. But we will see. I doubt it stops here.

    the machine churns, does some mystery math

    Yeah, the sauce is in the mystery math. See my other reply. You're making a category error.

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  • [–] 2 points 3 weeks ago* (1 child)

    My understanding was that any hitherto success of LLMs in mathematics was in its trying approaches and information from different fields where they weren't traditionally applied (within mathematics), they have surfaced potential links but haven't created anything in any real sense. Still, a potential legitimate use for them that I personally hadn't anticipated.

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  • [–] 1 point 3 weeks ago

    I mean that math is so far beyond me I have no clue if or how the proofs would "create anything" or lead to anything useful. I'm sure they do, besides adding a little bit to our civilization's knowledge.

    I do suspect that LLMs think very "broadly" with a very broad knowledge but can't really reason very deep without training for a special problem. So rapidly trying different approaches does sound like how they do it. So maybe it is a little bit similar to a typing monkey except that it apparently did these proofs with a relatively limited budget.

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