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The AI feedback loop: Researchers warn of ‘model collapse’ as AI trains on AI-generated content (venturebeat.com)

submitted 8 months ago by mesamunefire@lemmy.world to c/technology@lemmy.world

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[-] Uranium3006@kbin.social 3 points 8 months ago

now that the low hanging fruit of internet scraping is exhausted, we're gonna have to start purpose-building datasets. this will be expensive and might be the new bottleneck on AI progress.

[-] gapbetweenus@feddit.de 3 points 8 months ago

Wasn't there a paper not long time ago that it was possible to generate data with AI as a training set for AI? I was surprised (and the math is to much for me to check out my self) but that seems to solve that problem.

[-] danielbln@lemmy.world 4 points 8 months ago

Microsoft's Phi model was largely trained on synthetic data derived from GPT-4.

[-] gapbetweenus@feddit.de 1 points 8 months ago* (last edited 8 months ago)

I'm to lazy to search for the paper, not sure it was Microsoft, but with my rather basic knowledge of modeling (studied system biology) - it seemed rather crazy and impossible, so I remembered it.

[-] realharo@lemm.ee 3 points 8 months ago

As far as I know, that is mainly used where a better, bigger model generates training data for a more efficient smaller model to bring it a bit closer to its level.

Were there any cases of an already state of the art model using this method to improve itself?

[-] gapbetweenus@feddit.de 1 points 8 months ago* (last edited 8 months ago)

I will search for the paper.

EDIT: can't find it, dang.

[-] General_Effort@lemmy.world 1 points 8 months ago

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).

[-] gapbetweenus@feddit.de 2 points 8 months ago

Training data for models in general was a big problem when I studied systems biology. Interesting that we finding works around, since it sounded rather fundamental to me. I found your metaphor rather helpful, thanks.

[-] jacksilver@lemmy.world 3 points 8 months ago

I wouldn't say we've really found a workaround. AI companies hire lots of people to parse and clean data. That can work for things like pose estimation, which are largely a once and done thing. But for things that are constantly evolving, language/art/videos, it may not be a viable long term strategy.

[-] PoliticallyIncorrect@lemmy.world 0 points 8 months ago* (last edited 8 months ago)

The AIrmageddon..

[+] theneverfox@pawb.social -10 points 8 months ago

Ok, seriously? Fuck this research. It's bullshit.

Want to know how I can declare that so confidently? Because I wrote a program called duo. It's literally two chatbots instead of one, running locally on 5+ year old hardware. These are low powered llama's fine tuned by the community for general purpose last year

I just played a DND campaign with a chatbot and her hallucinated girlfriend (ai 1 wrote the prompt for AI 2, no edits or modifications). I've never played DND before, but they said they wanted to go to a haunted escape room. I have been to one of the most haunted locations in America, so I decided to be DM, and apparently they come with their own dice. Tomorrow I'm going to send the transcript to a friend who was looking for a DND player

Yes, clickbait is terrible training data, and low grade LLMs can really pump it out.

I had enough fun I fell asleep at my desk, and I did nothing but describe a location I've been to and the sounds I heard (and some urban legends)...I could spend a month and have replaced myself in the experience.

Other times I've let them run with no interaction on my part they've hallucinated (feasible) apps I'm not making to the point I could throw it into a design document, and games good enough to land on my to-do list.

Why don't people see this for the miracle technology this is? If it isn't reliable on one pass, do a second to evaluate the first, another to run chain of thought on problem areas, another one to flesh it out and rinse and repeat if you need to.

This is such a simple engineering problem it's not even funny

[-] NotAtWork@startrek.website 3 points 8 months ago

this comment reads like it was written by a LLM.

[-] theneverfox@pawb.social 1 points 8 months ago

That's how someone with ADHD sounds without a filter (we can understand each other at least). All I did is leave out the transitions that links these (to me, obviously related) concepts together

LLMs are the other way around - way to much transition with little substance.

Everything about my experiences experimenting with LLMs sounds unhinged without proof anyways. So I don't see a need to edit my late night rant, eventually I'll start a blog to lay out my methodology and chat logs to support it

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this post was submitted on 28 Feb 2024

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