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1. https://www.cell.com/current-biology/fulltext/S0960-9822(11)...
2. https://www.youtube.com/watch?v=nsjDnYxJ0bo
Edit: Just realized the paper above is also from Shinji Nishimoto
I immediately found the results suspect, and think I have found what is actually going on. The dataset it was trained on was 2770 images, minus 982 of those used for validation. I posit that the system did not actually read any pictures from the brains, but simply overfitted all the training images into the network itself. For example, if one looks at a picture of a teddy bear, you'd get an overfitted picture of anot…
Even if true, the result still seems very impressive to me as a layman.
Please continue. /s Governments, three letter agencies and the like would be absolutely excited to see this. The future that no-one has asked for.
As people and groups increasingly move this direction do we think about vectors for abuse in 10, 20 or 50+ years? The human mind is considered the only place where we have true privacy. All these efforts are taking that away. At this rate all notions of privacy will soon be dead.
There was a guy at MIT about ten years ago (edit: 2018! Woah) who made a headset that would read electrical impulses from your face. Apparently when people think in words, the same nerves fire as when they speak, just at a lower activation level. Using those signals it is possible to reconstruct the words being thought. I'm surprised it didn't seem to go anywhere. Edit: found it https://youtu.be/RuUSc53Xpeg
Would be useful if I lost the ability to write or speak, for whatever reason.
Earlier quoted context omitted.
I'm definitely not an expert in this subject, but even if the model is overfitted, doesn't the fact that it can pull out the similar images at all give credit to the idea that a larger, non-overfitted model could actually work as the paper describes? It means that there does exist some correlation between the shown subject, the captured fMRI data, and the resulting location in latent space.
The output part is basically nonsense. It would be more honest if the output was a text. E.g. "Teddybear" instead of a bad image of a random teddybear.
Earlier quoted context omitted.
The output part is basically nonsense. It would be more honest if the output was a text. E.g. "Teddybear" instead of a bad image of a random teddybear.
In this specific case I agree, since the model may be overfitted, it seems like it's currently just a glorified object classifier based on what was in the training data, but the fact that it works at all may indicate that the underlying idea has merit. They would probably have to train a much larger network to see if it's able to separate features distinctly enough using the input fMRI data to be useful.
In this case, the real bear has a blue ribbon and the "reconstructed" bear ha a red ribbon. Is the ribbon in the fMRI data and the computer choose the wrong color, or most of the images in the training set had ribbons and the computer just added one.
Imagine this something like this is used in the future to get something like https://en.wikipedia.org/wiki/Facial_composite . People may give too much importance to the details and arrest someone only because the computer imagined some detail, like the logo in the baseball cap.
Earlier quoted context omitted.
There was a guy at MIT about ten years ago (edit: 2018! Woah) who made a headset that would read electrical impulses from your face. Apparently when people think in words, the same nerves fire as when they speak, just at a lower activation level. Using those signals it is possible to reconstruct the words being thought. I'm surprised it didn't seem to go anywhere. Edit: found it https://youtu.be/RuUSc53Xpeg
People only think in words right before they say something, so I'm not sure how big a deal this is. I guess they'd be able to predict what I'm writing half a second before I write it? Would be useful if I lost the ability to write or speak, for whatever reason.
Earlier quoted context omitted.
The output part is basically nonsense. It would be more honest if the output was a text. E.g. "Teddybear" instead of a bad image of a random teddybear.
In this specific case I agree, since the model may be overfitted, it seems like it's currently just a glorified object classifier based on what was in the training data, but the fact that it works at all may indicate that the underlying idea has merit. They would probably have to train a much larger network to see if it's able to separate features distinctly enough using the input fMRI data to be useful.
From what I understand, regular Stable Diffusion starts by generating a noise and then hallucinating modifications of that noise to make less noise. The more you let it run, the better the results.
So instead of just starting with a meaningless random noise, they're using the fMRI data to start. But if you didn't have the text prompt, you wouldn't get the right image. If you were looking at a cat but told it you were looking at a house, you'd probably end up with a small house, similar to one in its training set, positioned roughly where the cat was located in the original image.
Earlier quoted context omitted.
There was a guy at MIT about ten years ago (edit: 2018! Woah) who made a headset that would read electrical impulses from your face. Apparently when people think in words, the same nerves fire as when they speak, just at a lower activation level. Using those signals it is possible to reconstruct the words being thought. I'm surprised it didn't seem to go anywhere. Edit: found it https://youtu.be/RuUSc53Xpeg
People only think in words right before they say something, so I'm not sure how big a deal this is. I guess they'd be able to predict what I'm writing half a second before I write it? Would be useful if I lost the ability to write or speak, for whatever reason.