Researchers have found that training successive generations of generative artificial intelligence models on synthetic data gives rise to self-consuming feedback loops.
Generative artificial intelligence models like OpenAI's GPT-4o or Stability AI's Stable Diffusion are surprisingly capable at creating new text, code, images and videos. Training them, however, requires such vast amounts of data that developers are already running up against supply limitations and may soon exhaust training resources altogether.
Mad cow disease is a fatal neurodegenerative illness that affects cows and has a human equivalent caused by consuming infected meat. A major outbreak in the 1980-90s brought attention to the fact that mad cow disease proliferated as a result of the practice of feeding cows the processed leftovers of their slaughtered peers -- hence the term"autophagy," from the Greek auto-, which means"self,"' and phagy --"to eat.
The internet is usually the source of generative AI models' training datasets, so as synthetic data proliferates online, self-consuming loops are likely to emerge with each new generation of a model.
Progressive iterations of the loops revealed that, over time and in the absence of sufficient fresh real data, the models would generate increasingly warped outputs lacking either quality, diversity or both. In other words, the more fresh data, the healthier the AI. The incentive for cherry picking is that data quality is preserved over a greater number of model iterations, but this comes at the expense of an even steeper decline in diversity.
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