Collapse faster, please. Sick of ai bullshit clogging up my searches.
It’s not going to. It’s just going to get more widespread and harder to detect. The incentives favor developing better and better AI. Luckily one of the solutions to this issue is - wait for it - AI. With a good enough AI, especially a generally intelligent one you don’t need search engines anymore. You just ask and it gives you the answer. If you think AI couldn’t do this reliably then that is not the AI I’m talking about.
My team has been calling models that use ai generated data “Habsberg models”
I feel there is a good joke here, but I miss the knowledge to understand it. Care to enlighten me?
Lmao that’s a perfect name for it
I thought it was called centipeding
Maybe we need to label AI-generated content to, you know, avoid confusion.
Sorry, best we can do is a race to the bottom fueled by greed and incompetence.
That will be a refeshing change.
That’s what has been happening, and is likely what will continue to happen. Not much change there really…
Yes that’s the joke.
Wouldn’t it have to be funny to be a joke?
I thought I was being funny. Sorry if it didn’t tickle you just right.
Please respect my personal space and refrain from tickling me.
Comedy is hard.
Sounds great, how do we enforce it?
If the AIs want to avoid digital incest they’ll enforce it for themselves.
The AIs dont want anything themselves and those who make the decisions about them want the most profit, what costs more, verifying training data or AI incest?
Sounds like something an advanced language learning model would say…
It’s important to understand that a language modelling AI can only produce responses based on its inputs.
Ah, you’re suggesting using RFC 3514. Good thinking.
Far too late for that now.
Most people here don’t understand what this is saying.
We’ve had “pure” human generated data, verifiably so since LLMs and ImageGen didn’t exist. Any bot generated data was easily filterable due to lack of sophistication.
ChatGPT and SD3 enter the chat, generate nearly indistinguishable data from humans, but with a few errors here and there. These errors while few, are spectacular and make no sense to the training data.
2 years later, the internet is saturated with generated content. The old datasets are like gold now, since none of the new data is verifiably human.
This matters when you’ve played with local machine learning and understand how these machines “think”. If you feed an AI generated set to an AI as training data, it learns the mistakes as well as the data. Every generation it’s like mutations form until eventually it just produces garbage.
Training models on generated sets slowly by surely fail without a human touch. Scale this concept to the net fractionally. When 50% of your dataset is machine generated, 50% of your new model trained on it will begin to deteriorate. Do this long enough and that 50% becomes 60 to 70 and beyond.
Human creativity and thought have yet to be replicated. These models have no human ability to be discerning or sleep to recover errors. They simply learn imperfectly and generate new less perfect data in a digestible form.
For a rough approach, imagine a parrot taught by another parrot, which was in turn taught by another parrot which was taught by a human.
Sure, some things might survive as somewhat understandable vaguelly human sounding sentences, but overall it’s still going to be pretty bad a few parrots down the chain.
Anecdotally speaking, I’ve been suspecting this was happening already with code related AI as I’ve been noticing a pretty steep decline in code quality of the code suggestions various AI tools have been providing.
Some of these tools, like GitHub’s AI product, are trained on their own code repositories. As more and more developers use AI to help generate code and especially as more novice level developers rely on AI to help learn new technologies, more of that AI generated code is getting added to the repos (in theory) that are used to train the AI. Not that all AI code is garbage, but there’s enough that is garbage in my experience, that I suspect it’s going to be a garbage in, garbage out affair sans human correction/oversight. Currently, as far as I can tell, these tools aren’t really using much in the way of good metrics to rate whether the code they are training on is quality or not, nor whether it actually even works or not.
More and more often I’m getting ungrounded output (the new term for hallucinations) when it comes to code, rather than the actual helpful and relevant stuff that had me so excited when I first started using these products. And I worry that it’s going to get worse. I hope not, of course, but it is a little concerning when the AI tools are more consistently providing useless / broken suggestions.
There will soon be a filter on the “best” developers, if there isn’t one already.
That’s it! I’m starting my own internet, with blackjack. And hookers.
The “solutions” to model collapse - essentially retraining on the original data set - suggests LLMs plateau or deteriorate. Especially without a way to separate out good and bad quality data (or ad they euohemistically try and say human vs AI data).
Were increasingly seeing the limitations and flaws with LLMs. “Hallucinations” or better described as serious errors, model collapse and complete collapse suggest the current approach to LLMs is probably not going to lead to some gone of general AI. We have models we don’t really understand that have fundamental flaws and limitations.
Unsurprising that they probably can’t live up to the hype.
Even if it will plateau, same was said with moorrs law, which held up way longer than expected. There are so many ways to improve this. Open source community is getting to the point where you can actually run decent models on normal private hardware (talking about 70-120b model)
I mean it makes sense. Machine learning is fantastic at noticing patterns, and the stuff they generate most definitely do have patterns. We might not notice them, but the models will pick up on them and eventually, if you keep training them on that data, they’ll skew more and more in that direction.
They’ve been marketing things like there isn’t a limit to how good these things can get, but there is. Nothing is infinite.
I’ve tried to make this point several times to folks in the industry. I work in AI, and yet every time I approach some people with “you know it ultimately just repeats patterns”, I’m met with scoffs and those people telling me I’m just not “seeing the big picture”.
But I am, and the truth is that there are limits. This tech is not the digital singularity the marketers and business goons want everyone to think it is.
Legend says that humans developed pattern finding as a skill ages ago…
Its funny how something like this get posted every few days and people keep falling for it like its somehow going to end AI. The people that make these models are acutely aware of how to avoid model collapse.
It’s totally fine for AI models to train on AI generated content that is of high enough quality. Part of the research to train models is building data sets with a text description matching the content, and filtering out content that is not organic enough (or even specifically including it as a ‘bad’ example for the AI to avoid). AI can produce material indistinguishable from human work, and it produces material that wasn’t originally in the training data. There’s no reason that can’t be good training data itself.
Especially since they can just pay someone to sit down and sift through it, or re-use the old training data that they already have from before it all blew up.
Like a billion hours of YouTube videos out there I am not seeing the issue plus the entire library of Congress
If the AI generated content is labeled, or has context, or has comments or descriptions created by people, then wouldn’t it just be the same as synthetic training data? Which is shown to still be very useful for training.
Exactly what percentage of AI data in the wild is labeled?
Close to zero I’d say.
Yes it’s still useful and it’s basically how we made our last couple of jumps. An AI training on AI generated data being graded by another AI. We’ve hit diminishing returns though.
Sorta. This “model collapse” thing is basically an urban legend at this point.
The kernel of truth is this: A model learns stuff. When you use that model to generate training data, it will not output all it has learned. The second generation model will not know as much as the first. If you repeat this process a couple times, you are left with nothing. It’s hard to see how this could become a problem in the real world.
Incest is a good analogy, if you know what the problem with inbreeding is: You lose genetic diversity. Still, breeders use this to get to desired traits and so does nature (genetic bottleneck, founder effect).
The AIrmageddon…
