Big brains divided over training AI with more AI: Is model collapse inevitable?

AI model collapse – the degradation of quality expected from machine learning models that recursively train on their own output – is not inevitable, at least according to 14 academics.

The risk that ongoing generative AI output, known as synthetic data, will dilute human-created organic data and impair the performance of models trained on this increasingly fabricated corpus was highlighted by a separate group last year, in a paper titled: "The Curse of Recursion: Training on Generated Data Makes Models Forget."

Ilia Shumailov, lead author of that paper, spoke to The Register earlier this year about this phenomenon, which has been documented in other studies.

Now another set of boffins – Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey, Rafael Rafailov, Henry Sleight, John Hughes, Tomasz Korbak, Rajashree Agrawal, Dhruv Pai, Andrey Gromov, Daniel Roberts, Diyi Yang, David Donoho, and Sanmi Koyejo – contend that the problem of training AI on AI-made data isn't significant ...