lol yes, the social implications are horrible for this kind of work but I'd say more so for dissidents, journalists, and so on rather than porn. Projects that

lol yes, the social implications are horrible for this kind of work but I'd say more so for dissidents, journalists, and so on rather than porn. Projects that are at the intersection of life sciences and machine learning are rarely interesting along both axes, it's always novel on one side and the other side is just there to make it seem cool. For example you'll find a lot of ML scientists investigating medical issues but lacking the basic medical knowledge to actually make an impact, similarly you'll see doctors using neural networks where a linear regression would do. In both cases the paper seems novel to the people in the field it's being published in. The classifier I was talking about is implicit, if you look at their proposed diagram they want to use fMRI embeddings, they serve as a continuous equivalent. You put a sufficiently large model on top of any embeddings and it will learn to reconstruct their modes. The embeddings are trained across subjects so it's not specific to a single patient but they hope to fine-tune it per patient using latent space alignment. If you look at the papers in their recommended reading list, it's apparent that only low-frequency signals are learned, for example for face reconstruction the model can learn hair color, sex and beard/no-beard, but the actual face doesn't look anything like that of the person being looked at. Similarly for other benchmarks, the model can tell it's looking at a plane, but not much beyond that. This is cool from a technological standpoint but I recall papers doing the same thing at least 7 years ago. The novelty of their approach will be the architecture that comes after the embeddings, which can be interesting but won't add any more information to the fMRI scans.