• Home
  • Technology
  • Gaming
  • Entertainment
  • World & Business
  • Science
  • Sports
  • AI
HomeTechnologyGamingEntertainmentWorld & BusinessScienceSportsAI
    • Home
    • Technology
    • Gaming
    • Entertainment
    • World & Business
    • Science
    • Sports
    • AI
    AI
    Reaction

    Potential AI gains in hard-to-verify reasoning may be hard to spot

    One post argues that possible conceptual or strategic insights are harder to assess than mathematical claims checked with formal proofs.

    Owain EvansOE
    1 Source, 2h ago, first seen 2h ago

    TLDR

    A post speculates that AI models may be improving at “fuzzy” reasoning, such as conceptual thinking, scientific research judgment and strategy, in ways that are difficult to recognize. The author suggests models might struggle to explain novel insights, while people have less agreement about how to judge them than they do for math proofs. Better ways of eliciting those abilities, such as self-play or distillation, could reveal progress.

    Combined views

    1.7K

    1 Source, first seen 2h ago

    Combined views

    1.7K

    1 Source, first seen 2h ago

    36 likes
    36 likes
    8 comments
    18 saves
    1 reposts
    8 comments
    18 saves
    1 reposts
    Featured Source

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    Today's Rank

    —

    Not ranked yet

    Today's Rank

    —

    Not ranked yet

    1 Source

    Owain Evans@OwainEvans_UKSome fuzzy ideas about fuzzy hard-to-verify reasoning in AI models. Fuzzy reasoning = conceptual/philosophical reasoning, research taste in science, strategy in politics or business, etc. It’s plausible models are not as good at fuzzy reasoning as they at math or cyber/hacking, but also that they are improving fast. Pretraining does not favor verifiable over fuzzy tasks and pretrains are improving. In math, when models have new ideas, it seems they are often bad at explaining them to humans. e.g. they produce the “worst proof write-up ever” and a bunch of work is needed by humans and models to make it readable. My guess is that models are worse at explaining things that humans didn’t already explain before. *So if models had impressive fuzzy insights, they would probably be also be bad at explaining them today.* With math, we can have a Lean proof and so only bother reading proofs for verified claims. We don’t have that for fuzzy tasks. For some fuzzy tasks, humans are worse at judging insights and it’s inherently harder. E.g. Mathematicians agree on proofs being correct (even without verification) but we have much less agreement about philosophy insights (even with much human-time to evaluate them). Even in AI research, there’s less agreement on what counts as an insights in advance of experiments. (Sidenote: various people had prescient insights about AI years in advance but they didn’t gain much traction at the time!) Overall, it’s plausible that fuzzy tasks are under-elicited in models and so there could be more rapid progress with some tricks for better elicitation (e.g. some kind of self-play or distillation tricks). Progress in fuzzy tasks may not be as obvious as verifiable ones, and so we should watch out for that. This is especially true if the fuzzy task progress is jagged. (Progress in AI research taste should be easier to recognize than in philosophy because of quick experimentation in the former case.) P.S. Recall Wittgenstein on heliocentrism. What would it look like to be in a world where AIs have (jagged) insights in areas that are particularly hard to evaluate?2h