Mind-reading technology reconstructs videos from brain
theage.com.au
theage.com.au
Brains of different people are not in a one to one correspondence, they do not have the same number of cells and even if they had, it is not known if the same information will get encoded in the exact same cell. So some form of calibration on test images/video seems unavoidable. However, in spite of the person to person variation there might be some common ground that allows some level of extrapolation from one person to another.
On a different note, artificial intelligence has always had this PR problem. Whenever it becomes possible to solve a problem that has been labeled AI it appears less impressive, because now we understand how it can be done. This has happened with computer vision, reasoning, chess, now jeopardy. AI is a moving frontier, and consists of things we do not understand well enough, and whenever we do, it is taken out from AI.
Another PR problem has been the difficulty to acknowledge the fact that solving an AI task and replicating how a human does it are different tasks. The former may be approached via the latter but it is not necessary. That said, It would indeed be more impressive and fair if the AI problem solvers (vision, chess, Jeopardy, etc. etc) are solved with systems that consume no more power than what a human brain does.
This is true, of course, but irrelevant to this work. At the level they're working at (fMRI scans, which have a resolution on the order of 0.5-4mm or so depending on the temporal resolution, etc.), you can't resolve individual cells anyways so you don't have to be concerned about those kinds of individual variations.
Visual activity in many parts of the brain follows a retinotopic map, where activity in nearby locations on the retina are processed in nearby regions of the brain. So, while you would have to calibrate some details, a lot of things would be constant between brains.
The comment that you quote wasn't made with reference to their work specifically but to any sufficiently accurate technology that can read through your eyes via the brain.
As you say, they're operating at a much larger scale than individual cells (100k-1m cells in each voxel). Likewise, some early visual processing areas are broadly organized kind of like big, noisy bitmaps on the surface of the brain.
But for sophisticated machine learning-style analyses like these, the gross differences in representation and morphology (especially at higher processing levels in the brain) make it very hard to pool the data across multiple people. That's why they're preferring to use many sessions from a small number of participants rather than a single session from many participants (the standard approach).
[I worked on applying machine learning methods to fMRI for my PhD]
This is a remarkable engineering feat, but not a novel motion model. It's known that V1 contains a topographic representation of the visual fields, and subsequent visual cortices encode for features like motion. As they mention in the paper though, dreams evoke activity in higher-level cortices and not so much in the early ones, making it difficult to tell if this method can be used to, e.g., visualize dreams.
I know it cannot be used to visualize what we dream/hallucinate yet, it only shows what the patient directly sees. But if that's the next step... wow, it would change communication and content creation as we know it if we could just dream up videos in our mind and upload them :-)
And what about "uploading" yourself to a computer. What happens if one day we can create exact copies of a person in silicon?
Yes, this is a significant exaggeration, but the scary thing is it is easy to see how that could become a viable interpretation of the law if this technology advanced to use outside a lab.
I'm not sure how far on the AI scale that would be.
Richard Granger has some good articles on brain structures, and he is a very accessible writer. (For a start, Google for his paper "Engines of the Brain".)
Of course, take the "brain re-uses useful structures" hypothesis with a grain of salt. There is some evidence in for some brain structures, but by no means conclusive.
If our brain would have stored all those detailed information, it would have been slow as the computer that we have now