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I haven't yet had a chance to dig into this pre-print but I know from @elmahdi its been in the works for a while now, so looking forward to reading. Although I wouldn't call them foundation models. I'd call them multimodal pretrained models, or anything that doesn't confuse people further about them being close to foundational.
https://arxiv.org/pdf/2209.15259.pdf
@timnitGebru @elmahdi fascinating read, thanks for sharing!
@timnitGebru @elmahdi
done for the necessary title change, and some other updates (I wasn't totally ok with "foundation models" either in the earlier version). https://arxiv.org/abs/2209.15259
arXiv.orgOn the Impossible Safety of Large AI ModelsLarge AI Models (LAIMs), of which large language models are the most prominent recent example, showcase some impressive performance. However they have been empirically found to pose serious security issues. This paper systematizes our knowledge about the fundamental impossibility of building arbitrarily accurate and secure machine learning models. More precisely, we identify key challenging features of many of today's machine learning settings. Namely, high accuracy seems to require memorizing large training datasets, which are often user-generated and highly heterogeneous, with both sensitive information and fake users. We then survey statistical lower bounds that, we argue, constitute a compelling case against the possibility of designing high-accuracy LAIMs with strong security guarantees.