• BlackLaZoR@fedia.io
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    24 hours ago

    So they made garbage AI content, without any filtering for errors, and they fed that garbage to the new model, that turned out to produce more garbage. Incredible discovery!

    • Pennomi@lemmy.world
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      24 hours ago

      Yeah, in practice feeding AI its own outputs is totally fine as long as it’s only the outputs that are approved by users.

      • Bezier@suppo.fi
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        18 hours ago

        I would expect some kind of small artifacting getting reinforced in the process, if the approved output images aren’t perfect.

        • Pennomi@lemmy.world
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          15 hours ago

          Only up to the point where humans notice it. It’ll make AI images easier to detect, but still pretty for humans. Probably a win-win.

          • Bezier@suppo.fi
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            8 hours ago

            Didn’t think of that, good point.

            The inbreeding could also affect larger decisions in sneaky ways, like how it wants to compose the image. It would be bad if the generator started to exaggerate and repeat some weird ai tropes.

      • WalnutLum@lemmy.ml
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        6 hours ago

        I don’t know if thinking that training data isn’t going to be more and more poisoned by unsupervised training data from this point on counts as “in practice”

  • Admiral Patrick@dubvee.org
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    1 day ago

    Let’s go, already!

    How you can help: If you run a website and can filter traffic by user agent, get a list of the known AI scrapers agent strings and selectively redirect their requests to pre-generated AI slop. Regular visitors will see the content and the LLM scraper bots will scrape their own slop and, hopefully, train on it.

    • azl@lemmy.sdf.org
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      23 hours ago

      This would ideally become standardized among web servers with an option to easily block various automated aggregators.

      Regardless, all of us combined are a grain of rice compared to the real meat and potatoes AI trains on - social media, public image storage, copyrighted media, etc. All those sites with extensive privacy policies who are signing contracts to permit their content for training.

      Without laws (and I’m not sure I support anything in this regard yet), I do not see AI progress slowing. Clearly inbreeding AI models has a similar effect as in nature. Fortunately there is enough original digital content out there that this does not need to happen.

      • Admiral Patrick@dubvee.org
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        23 hours ago

        Regardless, all of us combined are a grain of rice compared to the real meat and potatoes AI trains on

        Absolutely. It’s more a matter of principle for me. Kind of like the digital equivalent of leaving fake Amazon packages full of dog poo out front to make porch pirates have a bad day.

      • Snowclone@lemmy.world
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        19 hours ago

        Well it means they need some ability to reject some content, which means they need a level of transparency they would never want otherwise.

    • FaceDeer@fedia.io
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      23 hours ago

      AI already long ago stopped being trained on any old random stuff that came along off the web. Training data is carefully curated and processed these days. Much of it is synthetic, in fact.

      These breathless articles about model collapse dooming AI are like discovering that the sun sets at night and declaring solar power to be doomed. The people working on this stuff know about it already and long ago worked around it.

      • Wrench@lemmy.world
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        20 hours ago

        Both can be true.

        Preserved and curated datasets to train AI on, gathered before AI was mainstream. This has the disadvantage of being stuck in time, so-to-speak.

        New datasets that will inevitably contain AI generated content, even with careful curation. So to take the other commenter’s analogy, it’s a shit sandwich that has some real ingredients, and doodoo smeared throughout.

        • FaceDeer@fedia.io
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          18 hours ago

          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.

    • db2@lemmy.world
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      21 hours ago

      In case anyone doesn’t get what’s happening, imagine feeding an animal nothing but its own shit.

      • BassTurd@lemmy.world
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        21 hours ago

        Not shit, but isn’t that what brought about mad cow disease? Farmers were feeding cattle brain matter that had infected prions. Idk if it was cows eating cow brains or other animals though.

        • _cnt0@sh.itjust.works
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          21 hours ago

          It was the remains of fish which we ground into powder and fed to other fish and sheep, whose remains we ground into powder and fed to other sheep and cows, whose remains we ground to powder and fed to other cows.

      • Stern@lemmy.worldOP
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        19 hours ago

        I use the “Sistermother and me are gonna have a baby!” example personally, but I am a awful human so

    • Snowclone@lemmy.world
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      19 hours ago

      It’s more ''we are so focused on stealing and eating content, we’re accidently eating the content we or other AI made, which is basically like incest for AI, and they’re all inbred to the point they don’t even know people have more than two thumb shaped fingers anymore."

    • rottingleaf@lemmy.world
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      11 hours ago

      All such news make me want to live to the time when our world is interesting again. Real AI research, something new instead of the Web we have, something new instead of the governments we have. It’s just that I’m scared of what’s between now and then. Parasites die hard.

  • rickdg@lemmy.world
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    18 hours ago

    Old news? Seems to be a subject of several papers for some time now. Synthetic data has been used successfully already for very specific domains.

    • SomeGuy69@lemmy.world
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      10 hours ago

      Yup, old news and wrong news. Also so many people who hate AI but don’t understand how it works. Pretty disappointing for a technology community.

  • draughtcyclist@lemmy.world
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    1 day ago

    I’ve been assuming this was going to happen since it’s been haphazardly implemented across the web. Are people just now realizing it?

    • DeathbringerThoctar@lemmy.world
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      1 day ago

      People are just now acknowledging it. Execs tend to have a disdain for the minutiae. They’re like kids that only want to do the exciting bits. As a result things get fucked because they don’t really understand what they’re doing. As Muskrat would say “move fast and break things.” It’s a terrible mindset.

    • FaceDeer@fedia.io
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      22 hours ago

      No, researchers in the field knew about this potential problem ages ago. It’s easy enough to work around and prevent.

      People who are just on the lookout for the latest “aha, AI bad!” Headline, on the other hand, discover this every couple of months.

  • Hugin@lemmy.world
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    17 hours ago

    The solution for this is usually counter training. Granted my experience is on the opposite end training ai vision systems to id real objects.

    So you train up your detector ai on hand tagged images. When it gets good you use it to train a generator ai until the generator is good at fooling the detector.

    Then you train the detector on new tagged real data and the new ai generated data. Once it’s good at detection again you train the generator ai on the new detector.

    Repeate several times and you usually get a solid detector and a good generator as a side effect.

    The thing is you need new real human tagged data for each new generation. None of the companies want to generate new human tagged data sets as it’s expensive.

  • pyre@lemmy.world
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    6 hours ago

    oh no are we gonna have to appreciate the art of human beings? ew. what if they want compensation‽