Only Bayes Can Judge Me

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Joined 2 years ago
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Cake day: July 4th, 2023

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  • OK I sped read that thing earlier today, and am now reading it proper.

    The best answer ā€” AI has ā€œjagged intelligenceā€ ā€” lies in between hype and skepticism.

    Hereā€™s how they describe this term, about 2000 words in:

    Researchers have come up with a buzzy term to describe this pattern of reasoning: ā€œjagged intelligence." [ā€¦] Picture it like this. If human intelligence looks like a cloud with softly rounded edges, artificial intelligence is like a spiky cloud with giant peaks and valleys right next to each other. In humans, a lot of problem-solving capabilities are highly correlated with each other, but AI can be great at one thing and ridiculously bad at another thing that (to us) doesnā€™t seem far apart.

    So basically, this term is just pure hype, designed to play up the ā€œintelligenceā€ part of it, to suggest that ā€œAI can be greatā€. The article just boils down to ā€œuse AI for the things that we think itā€™s good at, and donā€™t use it for the things we think itā€™s bad at!ā€ As they say on the internet, completely unserious.

    The big story is: AI companies now claim that their models are capable of genuine reasoning ā€” the type of thinking you and I do when we want to solve a problem. And the big question is: Is that true?

    Demonstrably no.

    These models are yielding some very impressive results. They can solve tricky logic puzzles, ace math tests, and write flawless code on the first try.

    Fuck right off.

    Yet they also fail spectacularly on really easy problems. AI experts are torn over how to interpret this. Skeptics take it as evidence that ā€œreasoningā€ models arenā€™t really reasoning at all.

    Ah, yes, as we all know, the burden of proof lies on skeptics.

    Believers insist that the models genuinely are doing some reasoning, and though it may not currently be as flexible as a humanā€™s reasoning, itā€™s well on its way to getting there. So, whoā€™s right?

    Again, fuck off.

    Moving onā€¦

    The skepticā€™s case

    vs

    The believerā€™s case

    A LW-level analysis shows that the article spends 650 words on the skepticā€™s case and 889 on the believerā€™s case. BIAS!!! /s.

    Anyway, here are the skeptics quoted:

    • Shannon Vallor, ā€œa philosopher of technology at the University of Edinburghā€
    • Melanie Mitchell, ā€œa professor at the Santa Fe Instituteā€

    Great, now the believers:

    • Ryan Greenblatt, ā€œchief scientist at Redwood Researchā€
    • Ajeya Cotra, ā€œa senior analyst at Open Philanthropyā€

    You will never guess which two of these four are regular wrongers.

    Note that the article only really has examples of the dumbass-nature of LLMs. All the smart things it reportedly does is anecdotal, i.e. the author just says shit like ā€œAI can do solve some really complex problems!ā€ Yet, it still has the gall to both-sides this and suggest weā€™ve boiled the oceans for something more than a simulated idiot.







  • Why? Per the poll: ā€œa lack of reliability.ā€ The things being sold as ā€œagentsā€ donā€™t ā€¦ work.

    Vendors insist that the users are just holding the agents wrong. Per Bret Taylor of Sierra (and OpenAI):

    Accept that it is imperfect. Rather than say, ā€œWill AI do something wrongā€, say, ā€œWhen it does something wrong, what are the operational mitigations that weā€™ve put in place to deal with it?ā€

    I think this illustrates the situation of the LLM market pretty well, not just at a shallow level of the base incentives of the parties at play, but also at a deeper level, showing the general lack of humanity and toleration of dogshit exhibited by the AI companies that they are trying to brainwash everyone with.