OurobAIros — When AI Goes MAD?
I’m sure you’ve heard of “model autophagy” by now…
Picture a chef who’s spent years mastering their craft. Suddenly, instead of fresh ingredients, they’re given only leftovers from previous dishes. Not only would each dish start tasting like a faint echo of the last, but those new creations would lose the depth, flavour, and novelty they once had. That’s kind of what’s happening with AI models now — welcome to the phenomenon of model autophagy.
“Model autophagy” looks like a big word, but it’s actually a pretty simple in concept: when AI models train on data that’s already AI-generated, we end up in a self-reinforcing loop, and a pretty bad one at that. Basically, over time, this recursive echo chamber could lead AIs to lose their “chef flavour” — their diversity, accuracy, and ultimately, their usefulness.
And since this is definitely a growing concern these days, I thought we could take a quick look at what model autophagy means for AI, the risks it brings, and what we could do to keep AI models fresher, and more innovative.
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