• EarthShipTechIntern@lemm.ee
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    6 months ago

    Set to be revolutionized by AI because AI can’t do math.

    Says my brother, a Math Professor that works with people trying to develop AI

  • Audalin@lemmy.world
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    6 months ago

    The article isn’t about automatic proofs, but it’d be interesting to see a LLM that can write formal proofs in Coq/Lean/whatever and call external computer algebra systems like SageMath or Mathematica.

  • anton@lemmy.blahaj.zone
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    6 months ago

    The absence of coincidence

    Look up the strong law of small numbers.

    Also, one of their examples of AI was an exhaustive search.

  • amzd@kbin.social
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    6 months ago

    AI is insanely bad at distinguishing fact from hallucination, which seems like a terrible match for math

    • magic_lobster_party@kbin.run
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      6 months ago

      The article is about using computers to discover new conjectures (mathematical statements that are not yet known to be true or false). The conjecture can be then later be formally proven (or disproven) by humans.

      Sounds like a good match for me. Formulating conjectures is about finding an interesting pattern and argue that this pattern holds true. Computers are getting increasingly better at pattern matching, so why not use them?

      Title is a bit clickbaity by calling it AI.

      • technocrit@lemmy.dbzer0.com
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        6 months ago

        Title is a bit clickbaity by calling it AI.

        That’s literally every article about “AI”.

        … the London Institute for Mathematical Sciences, of which I am director

        There’s the reason. Self-promotion.

    • blargerer@kbin.social
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      6 months ago

      I haven’t read this article, but the one place machine learning is really really good, is narrowing down a really big solution space where false negatives and false positives are cheap. Frankly, I’m not sure how you’d go about training an AI to solve math problems, but if you could figure that out, it sounds roughly like it would fit the bill. You just need human verification as the final step, with the understanding that humans will rule out like 90% of the tries, but if you only need one success that’s fine. As a real world example machine learning is routinely used in astronomy to narrow down candidate stars or galaxies from potentially millions of options to like 200 that can then undergo human review.