Weizmann's Brain-IT reconstructs what people see from fMRI scans
A team led by professor Michal Irani at Israel's Weizmann Institute of Science has built an AI model called Brain-IT that reconstructs the images a person is viewing from functional MRI (fMRI) brain scans, and can also run in reverse to predict brain activity for a given image. Trained on the public Natural Scenes Dataset — about 73,000 image-scan pairs from eight volunteers — and expanded with a reverse-translation model, it is said to outperform earlier methods on both image content and detail, and needs only about one hour of fMRI data from a new subject versus the tens of hours other approaches require. The researchers say they next want to decode auditory information and eventually video and dreams, while stressing this is not true mind reading: it does not capture thoughts, memory or language and remains confined to lab-based fMRI.
Israeli scientists have devised an AI model that can reconstruct and even predict what people are seeing with startling accuracy, likening the results to mind reading.
Their work still exists largely in lab settings, though the researchers are hopeful that advances down the line could be extended even as far as reading people’s dreams. More immediately, the technology could have potential for medical purposes, like helping people communicate who otherwise can’t due to injury or disability.
The AI model, called Brain-IT, was developed by professor Michal Irani and fellow researchers at the Weizmann Institute of Science in Israel. They’ve documented their work onGitHub and in a paper presented at a scientific conference earlier this year.
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Brain-IT takes advantage of the thing that AI is best at, which is pattern recognition. It takes in data from functional MRI brain scans, which track changes in blood flow and oxygen in the brain to measure brain activity, and then uses that data to recreate the images the person was thinking about when the scans took place.
#Brain-IT#Weizmann Institute of Science#Michal Irani#Natural Scenes Dataset#Kenneth Norman#Princeton University