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MIT engineers develop AI solution to see ‘invisible’ objects in the dark

MIT engineers develop AI solution to see 'invisible' objects in the dark 1

Some of the engineers at the MIT has now developed an Artificial Intelligence mechanism to see the invisible object in the dark.

According to the engineers of the MIT, the latest technology allows one to see the objects as tiny as just like the small perfections in a wine glass on a contact lens which areas of now different to make out even in a good light. Engineers used the “deep neural network” to train a computer to “associate certain inputs with specific outputs — in this case, dark, grainy images of transparent objects and the objects themselves.”

There are more than 10000 engineers who were trained on ‘transparent’ glass like etchings on the grainy photos which has been taken in the low light conditions.

“The images were taken in very low lighting conditions, with about one photon per pixel — far less light than a camera would register in a dark, sealed room. They then showed the computer a new grainy image, not included in the training data, and found that it learned to reconstruct the transparent object that the darkness had obscured,” said MIT in a blog post.

“In the lab, if you blast biological cells with light, you burn them, and there is nothing left to image,” said George Barbastathis, professor of mechanical engineering at MIT. “When it comes to X-ray imaging, if you expose a patient to X-rays, you increase the danger they may get cancer. What we’re doing here is, you can get the same image quality, but with a lower exposure to the patient. And in biology, you can reduce the damage to biological specimens when you want to sample them.” ALSO READ: Google launches AI project in Thailand to screen for diabetic eye disease

At the core of the new AI mechanism is “deep dark learning”, according to some of the engineers the ‘deep neural network’ is much more dynamic than the traditional ones as it also includes some of the more detailed layers of the computation for output and input.

Written by Sony T

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