• BombOmOm@lemmy.world
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    3 months ago

    Yep. It leads to a positive feedback loop. They just continue to self-reinforce whatever came out before.

    And with increasing amounts of the internet being polluted with AI text output…

  • TimeSquirrel@kbin.melroy.org
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    3 months ago

    This has been obvious for a while to those of us using GitHub Copilot for programming. Start a function, and then just keep hitting tab to let it autotype based on what it already wrote. It quickly devolves into strange and random bullshit. You gotta babysit it.

    • 0laura@lemmy.world
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      3 months ago

      very unlikely to stem from model collapse. why would they use a worse model? it’s probably because they neutered it or gave it less resources.

      • TimeSquirrel@kbin.melroy.org
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        3 months ago

        It learns from your own code as you type so it can offer more relevant suggestions unlike the web-based LLMs. So you can make it feed back on itself.

    • NekuSoul@lemmy.nekusoul.de
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      3 months ago

      Same thing with Stable Diffusion if you’ve ever used a generated image as an input and repeated the same prompt. You basically get a deep-fried copy.

  • sp3tr4l@lemmy.zip
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    3 months ago

    Holy shit are you telling me…

    Garbage In…

    = Garbage Out?

    No, that can’t be it, throw billions and billions of dollars at this instead of, I don’t know, housing the homeless.

    • FaceDeer@fedia.io
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      3 months ago

      You realize that those “billions of dollars” have actually resulted in a solution to this? “Model collapse” has been known about for a long time and further research figured out how to avoid it. Modern LLMs actually turn out better when they’re trained on well-crafted and well-curated synthetic data.

      Honestly, everyone seems to assume that machine learning researchers are simpletons who’ve never used a photocopier before.

  • fubarx@lemmy.ml
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    3 months ago

    No shit. People have known about the perils of feeding simulator output back in as input for eons. The variance drops off so you end up with zero new insights and a gradual worsening due to entropy.

  • jet@hackertalks.com
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    3 months ago

    Garbage in garbage out

    It’s an old expression, but it still checks out

  • SlopppyEngineer@lemmy.world
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    3 months ago

    Eventually an AI will be developed that can learn with much less data. In the end we don’t need to read the entire internet to get through our education. But, that’s not going to be LLM. No matter how much you tweak LLM models, it won’t get there. It’s like trying to tune a coal fired steam powered car until you can compete in a formula 1 race.

    • conciselyverbose@sh.itjust.works
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      3 months ago

      Yeah, it’s entirely plausible that LLMs are a small part of the answer as basically the language center of the brain, but the brain is a hell of a lot more complex than that. The language center isn’t your whole brain, and is only loosely connected to actual decision making. It confabulates a lot.

      • SlopppyEngineer@lemmy.world
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        3 months ago

        OpenAI stumbled on something that worked and ran with it, and people started proclaiming it to be the answer to everything. The same happened with Deep Learning and every AI invention so far. It’s all just another stepping stone on the way.

    • Even_Adder@lemmy.dbzer0.com
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      3 months ago

      It’s already happening. A quote from Andrej Karpathy :

      Turns out that LLMs learn a lot better and faster from educational content as well. This is partly because the average Common Crawl article (internet pages) is not of very high value and distracts the training, packing in too much irrelevant information. The average webpage on the internet is so random and terrible it’s not even clear how prior LLMs learn anything at all.