Basically a deer with a human face. Despite probably being some sort of magical nature spirit, his interests are primarily in technology and politics and science fiction.

Spent many years on Reddit and then some time on kbin.social.

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Joined 7 months ago
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Cake day: March 3rd, 2024

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  • It’s a common pattern. Something actually bad exists, and a word is invented to describe that bad thing. People want to call the things they don’t like by that bad word, even if it’s not quite right, so the definition starts to widen a bit. It’s a very bad thing so it’s good to call things you don’t like by that word, it makes everyone else hate them too! The word stretches and stretches, and eventually everything vaguely bad is called that word. It loses its meaning.

    A new word is invented to describe some specific actually bad thing. Repeat.



  • They’re not both true, though. It’s actually perfectly fine for a new dataset to contain AI generated content. Especially when it’s mixed in with non-AI-generated content. It can even be better in some circumstances, that’s what “synthetic data” is all about.

    The various experiments demonstrating model collapse have to go out of their way to make it happen, by deliberately recycling model outputs over and over without using any of the methods that real-world AI trainers use to ensure that it doesn’t happen. As I said, real-world AI trainers are actually quite knowledgeable about this stuff, model collapse isn’t some surprising new development that they’re helpless in the face of. It’s just another factor to include in the criteria for curating training data sets. It’s already a “solved” problem.

    The reason these articles keep coming around is that there are a lot of people that don’t want it to be a solved problem, and love clicking on headlines that say it isn’t. I guess if it makes them feel better they can go ahead and keep doing that, but supposedly this is a technology community and I would expect there to be some interest in the underlying truth of the matter.