'As the use of generative models continues to grow rapidly, this situation will only accelerate.'
, scientists at Rice and Stanford University found that feeding AI-generated content to AI models seems to cause their output quality to erode. Train generative AI models — large language models and image generators both included — enough AI-spun stuff, it seems, and this ouroboros-like self-consumption will break the model's digital brain.
"Our primary conclusion across all scenarios is that without enough fresh real data in each generation of an autophagous loop, future generative models are doomed to have their quality or diversity progressively decrease," they added. "We term this condition Model Autophagy Disorder ." In other words, without "fresh real data" — translation: original human work, as opposed to stuff spit out by AI — to feed the beast, we can expect its outputs to suffer drastically. When trained repeatedly on synthetic content, say the researchers, outlying, less-represented information at the outskirts of a model's training data will start to disappear.
Cool paper from my friends at Rice. They look at what happens when you train generative models on their own outputs…over and over again. Image models survive 5 iterations before weird stuff happens.
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