AI models get convenient amnesia about source material as they grow, MIT boffins find

The process of training an AI model becomes a paradox at scale – the more it remembers, the less it remembers about the source of its memories. MIT computer scientists went looking for a way to attribute AI model output to specific training data, in the hope that understanding could inform AI regulation. What they found, described in a paper titled, "Outputs of Generative Diffusion Models are Often Unattributable," looks like it will actually make regulation more difficult. Scientific jour