Find out more at 🚀 Training machines to read our minds: A good idea?
. Listening to these podcasts triggered brain activity, which was then decoded by the machine, thereby generating text.
As aforementioned, the decoder produces text that matches the intended meanings of the original words, not the actual text itself. For example, if a participant listening to a speaker said, "I don’t have my driver’s license yet", the machine decoded it as "She has not even started to learn to drive yet."
The subjects were also asked to watch four short, silent videos while in the scanner. Using their brain activity, the semantic decoder described certain events from the videos.Currently, it relies on an fMRI machine so that it can be used only within the limits of a lab. According to the researchers, this work could be transferred to others, such as more portable brain-imaging systems, such as functional near-infrared spectroscopy .
"fNIRS measures where there’s more or less blood flow in the brain at different points in time, which, it turns out, is exactly the same kind of signal that fMRI is measuring," Huth said. "So, our exact kind of approach should translate to fNIRS," although, he noted, the resolution with fNIRS would be lower."We take very seriously the concerns that it could be used for bad purposes and have worked to avoid that," Tang said.
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