• jsomae@lemmy.ml
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    17 hours ago

    Right, so this is really only useful in cases where either it’s vastly easier to verify an answer than posit one, or if a conventional program can verify the result of the AI’s output.

    • MangoCats@feddit.it
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      5 hours ago

      It’s usually vastly easier to verify an answer than posit one, if you have the patience to do so.

      I’m envisioning a world where multiple AI engines create and check each others’ work… the first thing they need to make work to support that scenario is probably fusion power.

      • zbyte64@awful.systems
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        5 hours ago

        It’s usually vastly easier to verify an answer than posit one, if you have the patience to do so.

        I usually write 3x the code to test the code itself. Verification is often harder than implementation.

        • jsomae@lemmy.ml
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          59 minutes ago

          It really depends on the context. Sometimes there are domains which require solving problems in NP, but where it turns out that most of these problems are actually not hard to solve by hand with a bit of tinkering. SAT solvers might completely fail, but humans can do it. Often it turns out that this means there’s a better algorithm that can exploit commanalities in the data. But a brute force approach might just be to give it to an LLM and then verify its answer. Verifying NP problems is easy.

          (This is speculation.)

        • MangoCats@feddit.it
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          5 hours ago

          Yes, but the test code “writes itself” - the path is clear, you just have to fill in the blanks.

          Writing the proper product code in the first place, that’s the valuable challenge.