We’re one AI and brainwave experiment away from x-ray vision

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The future is here, and it is every bit as cool and creepy as you might have hoped.

X-ray vision has always been pretty far down on my list of superpowers I’d like to possess, far behind time travel and reading minds. But x-ray vision might be closer to reality than the other options, and I’ll take what I can get. Researchers at the University of Glasgow are working to combine artificial intelligence and human brainwaves to identify objects around the corner — objects that humans can’t normally see because it’s around a corner. It’s called a “ghost imaging” system and will be presented at the Optica Imaging and Applied Optics Congress this month.

“We believe that this work provides ideas that one day might be used to bring together human and artificial intelligence,” Daniele Faccio, a professor of quantum technologies at the school of physics and astronomy at the University of Glasgow told Optica. “The next steps in this work range from extending the capability to provide 3D depth information to looking for ways to combine multiple information from multiple viewers at the same time.”

The research is part of non-line-of-sight imaging, according to New Atlas, which is a branch of technology that allows people to see objects that are covered up. Sometimes it requires a laser light being beamed onto a surface, which sounds a lot like a power Superman might have.

But Faccio’s experiment worked like this: An object was projected onto a cardboard cut-out. A person, wearing an electroencephalography headset to monitor their brainwaves, can only see the diffused light on a wall instead of the actual light patterns that are projected. The EEG helmet reads signals in the person’s visual cortex, which are fed into a computer, which then works to identify the object using AI the person’s brainwaves. And it was successful: Within about one minute, the researchers could successfully reconstruct 16 x 16-pixel images of simple objects that people couldn’t see through the obstacle.

“This is one of the first times that computational imaging has been performed by using the human visual system in a neurofeedback loop that adjusts the imaging process in real-time,” Faccio said. “Although we could have used a standard detector in place of the human brain to detect the diffuse signals from the wall, we wanted to explore methods that might one day be used to augment human capabilities.”





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