Google apologizes for ‘missing the mark’ after Gemini generated racially diverse Nazis::Google says it’s aware of historically inaccurate results for its Gemini AI image generator, following criticism that it depicted historically white groups as people of color.

  • fidodo@lemmy.world
    link
    fedilink
    English
    arrow-up
    2
    ·
    10 个月前

    I think a lot of the improvement will come from breaking down the problem using sub assistant for specific actions. So in this case you’re asking for an image generation action involving people, then an LLM specifically designed for that use case can take over tuned for that exact use case. I think it’ll be hard to keep an LLM on task if you have one prompt trying to accomplish every possible outcome, but you can make it more specific to handle sub tasks more accurately. We could even potentially get an LLM to dynamically create sub assistants based on the use case. Right now the tech is too slow to do all this stuff at scale and in real time, but it will get faster. The problem right now isn’t that these fixes aren’t possible, it’s that they’re hard to scale.

    • kromem@lemmy.world
      link
      fedilink
      English
      arrow-up
      1
      ·
      10 个月前

      Yes, this is exactly correct. And it’s not actually too slow - the specialized models can be run quite quickly, and there’s various speedups like Groq.

      The issue is just more cost of multiple passes, so companies are trying to have it be “all-in-one” even though cognitive science in humans isn’t an all-in-one process either.

      For example, AI alignment would be much better if it took inspiration from the prefrontal cortex inhibiting intrusive thoughts rather than trying to prevent the generation of the equivalent of intrusive thoughts in the first place.

      • fidodo@lemmy.world
        link
        fedilink
        English
        arrow-up
        1
        ·
        10 个月前

        The issue is just more cost of multiple passes, so companies are trying to have it be “all-in-one”

        Exactly, that’s where the too slow part comes in. To get more robust behavior it needs multiple layers of meta analysis, but that means it would take way more text generation under the hood than what’s needed for one shot output.

        • kromem@lemmy.world
          link
          fedilink
          English
          arrow-up
          1
          ·
          10 个月前

          Yes, but in terms of speed you don’t need the same parameters and quantization for the secondary layers.

          If you haven’t seen it, see how fast a very capable model can actually be: https://groq.com/

          • fidodo@lemmy.world
            link
            fedilink
            English
            arrow-up
            1
            ·
            10 个月前

            Yeah I’ve seen that. I think things will get much faster very quickly, I’m just commenting on the first Gen tech we’re seeing right now.