121–130 of 170 posts
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.
Of course, but what's to be done about it? Should we outlaw research like this?
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.
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…
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.
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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.
Nope. If you train a model where the input is an integer between 1 and 10, and the output is a specific image from a set of ten, the model will be able to get zero loss on the task. That is what's happening here.
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There was a video many years ago (early 2010s?) demoing a similar technology, which would overlay and blend many images on top of each other to make a fuzzy image approximating what was actually being viewed. Edit: found it! https://youtu.be/nsjDnYxJ0bo
The youtube video quotes a paper by the same author, so it's probably the same group's work. I wonder why didn't they used an approach similar to the one in the video using SD - it looks more viable.
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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.
The problem is that it's impossible to know what is in the fMRI data and what is hallucinated by the reconstruction. 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 futu…
Wow, tech not working to tech might kill someone went super fast here.
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…
Its still picking out the correct "overfitted" images, which is remarkable. Theoretically, the results would scale to more training images... we just need to fMRI all of LAION-5B. Easy peasy.
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I'm "thinking in words" this entire thread as I read it. Do some people read without hearing the words in their head?
Yeah, personally - and this might be a controversial opinion - I think most people who say they have an inner monologue are actually misattributing their experience of subvocalization while either reading text, or planning hypothetical conversations with other people (like a child might speak to their imaginary friend). It seems dubious to label that a monologue, because it depends on external stimuli (either the tex…
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Citation definitely needed. I have a nearly constant internal monologue that is 100% composed of words.
Do you know what it's going to say next? If not, who's generating each new word? And if yes, then isn't this just subvocalization?
Do you know what your next thought will be? If so, how? Did you think it before you thought it?