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theneverfox ,
@theneverfox@pawb.social avatar

I really don’t get how people so easily accept this. This is an engineering problem, not a law of the universe… How would someone possibly prove something is impossible, particularly while the entire branch of technology is rapidly changing?

seaQueue ,
@seaQueue@lemmy.world avatar

I for one support the AI centipede and hope it shits into it’s own input until it dies

ColeSloth ,

Well duh. I think a lot of us here learned that lesson from watching the movie Multiplicity.

SendMePhotos ,

Would you recommend it?

ColeSloth ,

Oh, shit. Ummm…it was a funny movie back when it came out, but I haven’t seen it in like 25 years so who knows how bad it seems now. Could still be good?

EgoNo4 ,

More like… Degenerative AI *ba dum tsss

merde , (edited )

deGenerative AI ☞ !degenerate

edit: don’t, if you’re on a bus! i thought lemmynsfw was a warning enough

EgoNo4 ,

No idea this existed.

Also… JFC WHAT THE SHIT?

murmelade ,

deleted_by_author

    •
    merde ,

    comment edited 👍

    NutWrench ,
    @NutWrench@lemmy.world avatar

    Anyone who has made copies of videotapes knows what happens to the quality of each successive copy. You’re not making a “treasure trove.” You’re making trash.

    Alexstarfire ,

    I couldn’t care less.

    Katana314 ,

    If we can work out which data conduits are patrolled more often by AI than by humans, we could intentionally flood those channels with AI content, and push Model Collapse along further. Get AI authors to not only vet for “true human content”, but also pay licensing fees for the use of that content. And then, hopefully, give the fuck up on their whole endeavor.

    BrightCandle ,

    Having now flooded the internet with bad AI content not surprisingly its now eating itself. Numerous projects that aren’t AI are suffering too as the quality of text reduces.

    dog_ ,

    Lol

    SlopppyEngineer ,

    Usually we get an AI winter, until somebody develops a model that can overcome that limitation of needing more and more data. In this case by having some basic understanding instead of just having a regurgitation engine for example. Of course that model runs into the limit of only having basic understanding, not advanced understanding and again there is an AI winter.

    Petter1 ,

    Have you seen the newest model from OpenAI? They managed to get some logic into the system, so that it is now better at math and programming 😄 it is called “o1” and cones in 3 sizes where the largest is not released yet.

    The downside is, that generation of answers takes more time again.

    gravitas_deficiency , (edited )

    Uh, good.

    As an engineer who cares a LOT about engineering ethics, it is absolutely fucking infuriating watching the absolute firehose of shit that comes out of LLMs and public-consumption audio, image, and video ML systems, juxtaposed with the outright refusal of companies and engineers who work there to accept ANY accountability or culpability for the systems THEY FUCKING MADE.

    I understand the nuances of NNs. I understand that they’re much more stochastic than deterministic. So, you know, maybe it wasn’t a great idea to just tell the general public (which runs a WIDE gamut of intelligence and comprehension ability - not to mention, morality) “have at it”. The fact that ML usage and deployment in terms of information generating/kinda-sorta-but-not-really-aggregating “AI oracles” isn’t regulated on the same level as what you’d see in biotech or aerospace is insane to me. It’s a refusal to admit that these systems fundamentally change the entire premise of how “free speech” is generated, and that bad actors (either unrepentantly profit driven, or outright malicious) can and are taking disproportionate advantage of these systems.

    I get it - I am a staunch opponent of censorship, and as a software engineer. But the flippant deployment of literally society-altering technology alongside the outright refusal to accept any responsibility, accountability, or culpability for what that technology does to our society is unconscionable and infuriating to me. I am aware of the potential that ML has - it’s absolutely enormous, and could absolutely change a HUGE number of fields for the better in incredible ways. But that’s not what it’s being used for, and it’s because the field is essentially unregulated right now.

    CarbonatedPastaSauce ,

    Model collapse is just a euphemism for “we ran out of stuff to steal”

    Snowclone ,

    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 ,

    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.

    jimmy90 ,

    or “we’ve hit a limit on what our new toy can do and here’s our excuse why it won’t get any better and AGI will never happen”

    Adderbox76 ,

    Every single one of us, as kids, learned the concept of “garbage in, garbage out”; most likely in terms of diet and food intake.

    And yet every AI cultist makes the shocked pikachu face when they figure out that trying to improve your LLM by feeding it on data generated by literally the inferior LLM you’re trying to improve, is an exercise in diminishing returns and generational degradation in quality.

    Why has the world gotten both “more intelligent” and yet fundamentally more stupid at the same time? Serious question.

    LANIK2000 ,

    Because the people with power funding this shit have pretty much zero overlap with the people making this tech. The investors saw a talking robot that aced school exams, could make images and videos and just assumed it meant we have artificial humans in the near future and like always, ruined another field by flooding it with money and corruption. These people only know the word “opportunity”, but don’t have the resources or willpower to research that “opportunity”.

    kerrigan778 , (edited )

    Remember Trump every time he’s weighed in on something, like suggesting injecting people with bleach, or putting powerful UV lights inside people, or fighting Covid with a “solid flu vaccine” or preventing wildfires by sweeping the forests, or suggesting using nuclear weapons to disrupt hurricane formation, or asking about sharks and electric boat batteries? Remember these? These are the types of people who are in charge of businesses, they only care about money, they are not particularly smart, they have massive gaps in knowledge and experience but believe that they are profoundly brilliant and insightful because they’ve gotten lucky and either are good at a few things or just had an insane amount of help from generational wealth. They have never had anyone, or very few people genuinely able to tell them no and if people don’t take what they say seriously they get fired and replaced with people who will.

    Croquette ,

    Because the dumdums have access to the whole world at the tip of the fingertip without having to put any efforts in.

    In a time without that, they would be ridiculed for their stupid ideas and told to pipe down.

    Now they can find like minded people and amplify their stupidity, and be loud about it.

    So every dumdum becomes an AI prompt engineer (whatever the fuck that means) and know how to game the LLM, but do not understand how it works. So they are basically just snake oil salesmen that want to get on the gravy train.

    GamingChairModel ,

    Why has the world gotten both “more intelligent” and yet fundamentally more stupid at the same time? Serious question.

    Because it’s not actually always true that garbage in = garbage out. DeepMind’s Alpha Zero trained itself from a very bad chess player to significantly better than any human has ever been, by simply playing chess games against itself and updating its parameters for evaluating which chess positions were better than which. All the system needed was a rule set for chess, a way to define winners and losers and draws, and then a training procedure that optimized for winning rather than drawing, and drawing rather than losing if a win was no longer available.

    Face swaps and deep fakes in general relied on adversarial training as well, where they learned how to trick themselves, then how to detect those tricks, then improve on both ends.

    Some tech guys thought they could bring that adversarial dynamic for improving models to generative AI, where they could train on inputs and improve over those inputs. But the problem is that there isn’t a good definition of “good” or “bad” inputs, and so the feedback loop in this case poisons itself when it starts optimizing on criteria different from what humans would consider good or bad.

    So it’s less like other AI type technologies that came before, and more like how Netflix poisoned its own recommendation engine by producing its own content informed by that recommendation engine. When you can passively observe trends and connections you might be able to model those trends. But once you start actually feeding back into the data by producing shows and movies that you predict will do well, the feedback loop gets unpredictable and doesn’t actually work that well when you’re over-fitting the training data with new stuff your model thinks might be “good.”

    lightsblinken ,

    good commentary, covered a lot of ground - appreciate the effort to write it up :)

    bignate31 ,

    Another great example (from DeepMind) is AlphaFold. Because there’s relatively little amounts of data on protein structures (only 175k in the PDB), you can’t really build a model that requires millions or billions of structures. Coupled with the fact that getting the structure of a new protein in the lab is really hard, and that most proteins are highly synonymous (you share about 60% of your genes with a banana).

    So the researchers generated a bunch of “plausible yet never seen in nature” protein structures (that their model thought were high quality) and used them for training.

    Granted, even though AlphaFold has made incredible progress, it still hasn’t been able to show any biological breakthroughs (e.g. 80% accuracy is much better than the 60% accuracy we were at 10 years ago, but still not nearly where we really need to be).

    Image models, on the other hand, are quite sophisticated, and many of them can “beat” humans or look “more natural” than an actual photograph. Trying to eek the final 0.01% out of a 99.9% accurate model is when the model collapse happens–the model starts to learn from the “nearly accurate to the human eye but containing unseen flaws” images.

    erenkoylu ,

    No it doesn’t.

    All this doomer stuff is contradicted by how fast the models are improving.

    aggelalex ,

    So AI:

    1. Scraped the entire internet without consent
    2. Trained on it
    3. Polluted it with AI generated rubbish
    4. Trained on that rubbish without consent
    5. Are now in need of lobotomy
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