Technology

Collapse faster, please. Sick of ai bullshit clogging up my searches.

My team has been calling models that use ai generated data "Habsberg models"

Maybe we need to label AI-generated content to, you know, avoid confusion.

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.

Looks like we need Low-Background-Content

Back when i was though concept art as a subject at college my teacher had a name for this.

“Incest” cause every generation of art that references other art becomes more and more strange looking and detached from reality.

If you thought Skyrim weapons look ridiculous you should have seen my classmates Skyrim inspired weapons.

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.

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.

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.

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.

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.

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.

This article is from June 12, 2023. That's practically stone-aged as far as AI technology has been progressing.

The paper it's based on used a very simplistic approach, training AIs purely on the outputs of its previous "generation." Turns out that's not a realistic real-world scenario, though. In reality AIs can be trained on a mixture of human-generated and AI-generated content and it can actually turn out better than training on human-generated content alone. AI-generated content can be curated and custom-made to be better suited to training, and the human-generated stuff adds back in the edge cases that might disappear when doing repeated training generations.

It would be hilarious if we entered the deep fried Marquaud era of ai where responses degenerate into rehashed responses that just get progressively more jumbled and unintelligible as the models cannibalise each other's generated content

Like a billion hours of YouTube videos out there I am not seeing the issue plus the entire library of Congress