So far, humans have had the edge in the ability to identify objects by touch. but not for long. Using Google’s Project Soli, a miniature radar that detects the subtlest of gesture inputs, the [St. Andrews Computer Human Interaction group (SACHI)] at the University of St. Andrews have developed a new platform, named RadarCat, that uses the chip to identify materials, as if by touch.
Realizing that different materials return unique radar signals to the chip, the [SACHI] team combined it with their recognition software and machine learning processes that enables RadarCat to identify a range of materials with accuracy in real time! It can also display additional information about the object, such as nutritional information in the case of food, or product information for consumer electronics. The video displays how RadarCat has already learned an impressive range of materials, and even specific body parts. Can Skynet be far behind?