• AggressivelyPassive@feddit.de
    link
    fedilink
    English
    arrow-up
    4
    arrow-down
    1
    ·
    7 months ago

    That’s maybe because we’ve reached the limits of what the current architecture of models can achieve on the current architecture of GPUs.

    To create significantly better models without having a fundamentally new approach, you have to increase the model size. And if all accelerators accessible to you only offer, say, 24gb, you can’t grow infinitely. At least not within a reasonable timeframe.

    • Kbin_space_program@kbin.social
      link
      fedilink
      arrow-up
      2
      arrow-down
      5
      ·
      edit-2
      7 months ago

      Will increasing the model actually help? Right now we’re dealing with LLMs that literally have the entire internet as a model. It is difficult to increase that.

      Making a better way to process said model would be a much more substantive achievement. So that when particular details are needed it’s not just random chance that it gets it right.

      • AggressivelyPassive@feddit.de
        link
        fedilink
        English
        arrow-up
        9
        ·
        7 months ago

        That is literally a complete misinterpretation of how models work.

        You don’t “have the Internet as a model”, you train a model using large amounts of data. That does not mean, that this model contains any of the actual data. State of the at models are somewhere in the billions of parameters. If you have, say, 50b parameters, each being a 64bit/8 byte double (which is way, way too much accuracy) you get something like 400gb of data. That’s a lot, but the Internet slightly larger than that.

        • Kbin_space_program@kbin.social
          link
          fedilink
          arrow-up
          1
          arrow-down
          6
          ·
          edit-2
          7 months ago

          It’s an exaggeration, but its not far off given that Google literally has all of the web parsed at least once a day.

          Reddit just sold off AI harvesting rights on all of its content to Google.

          The problem is no longer model size. The problem is interpretation.

          You can ask almost everyone on earth a simple deterministic math problem and you’ll get the right answer almost all of the time because they understand the principles behind it.

          Until you can show deterministic understanding in AI, you have a glorified chat bot.

          • AggressivelyPassive@feddit.de
            link
            fedilink
            English
            arrow-up
            8
            ·
            7 months ago

            It is far off. It’s like saying you have the entire knowledge of all physics because you skimmed a textbook once.

            Interpretation is also a problem that can be solved, current models do understand quite a lot of nuance, subtext and implicit context.

            But you’re moving the goal post here. We started at “don’t get better, at a plateau” and now you’re aiming for perfection.

            • Kbin_space_program@kbin.social
              link
              fedilink
              arrow-up
              1
              arrow-down
              4
              ·
              7 months ago

              You’re building beautiful straw men. They’re lies, but great job.

              I said originally that we need to improve the interpretation of the model by AI, not just have even bigger models that will invariably have the same flaw as they do now.

              Deterministic reliability is the end goal of that.

              • AggressivelyPassive@feddit.de
                link
                fedilink
                English
                arrow-up
                3
                ·
                7 months ago

                Will increasing the model actually help? Right now we’re dealing with LLMs that literally have the entire internet as a model. It is difficult to increase that.

                Making a better way to process said model would be a much more substantive achievement. So that when particular details are needed it’s not just random chance that it gets it right.

                Where exactly did you write anything about interpretation? Getting “details right” by processing faster? I would hardly call that “interpretation” that’s just being wrong faster.