Raw LLM Responses

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Ok, I went back and read the Nightshade paper (Shan et al), and the authors know what they're doing, it's actually pretty cool - basically, modern AIs work by converting collections of pixels into a simplified 'latent space' where the concepts behind the art are grouped together as detected by a different image-to-text AI model, and Nightshade works by finding a way to trick that model into putting the image into completely the wrong place in the latent space, so conceptually it's doing something similar to what I suggested mislabeling the art but much more efficiently and effectively - against this particular AI. In the long term, though, I still think that if you trained a new image-to-text model from scratch using this data, which to be fair none of these companies are doing because it'd be way too expensive, then these poisoned images would be valuable in the way I assumed they would be before.
youtube Viral AI Reaction 2024-10-20T21:2…
Coding Result
DimensionValue
Responsibilitynone
Reasoningunclear
Policynone
Emotionapproval
Coded at2026-04-27T06:24:53.388235
Raw LLM Response
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