The Race For Robot Clairvoyance

This week a Harvard Business School student challenged me to name a startup capable of producing an intelligent robot — TODAY! At first I did not understand the question, as artificial intelligence (AI) is an implement like any other in a roboticist’s toolbox. The student persisted, she demanded to know if I thought that the current co-bots working in factories could one day evolve to perceive the world like humans. It’s a good question that I didn’t appreciate at the time as robots are best deployed for specific repeatable tasks, even with deep learning systems. By contrast, mortals comprehend their surroundings (and other organisms) using a sixth sense, intuition.

As an avid tennis player, I also enjoyed meeting Tennibot this week. The autonomous ball-gathering robot sweeps the court like a roomba sucking up dust off a rug. In order to accomplish this task, without knocking over players, it navigates around the cage utilizing six cameras on each side. This is a perfect example of the type of job that an unmanned system excels at performing, freeing up athletes from wasting precious court time with tedious cleanup. Yet, Tennibot, at the end of the day, is a dumb appliance. While it gobbles up balls quicker than any person, it is unable to discern the quality of the game or the health of players.

As stated by Adler’s Medium post, Perceptive’s technology uses “behavioral science techniques to characterize the way human drivers understand the state-of-mind of other humans and then train deep learning models to acquire that human ability. These deep learning models are designed for integration into autonomous driving stacks and next-generation driver assistance systems, sandwiched between the perception and planning layers. These deep learning, predictive models provide real-time information on the intention, awareness, and other state-of-mind attributes of pedestrians, cyclists and other motorists.”

Unlike vision sensors, Neteera is a cost-effective micro-radar on a chip that leverages its own network of proprietary algorithms to provide “the first contact free vital sign detection platform.” Its FDA-level of accuracy is not only being utilized by the automative sector, but healthcare systems across the United States for monitoring such elusive conditions as sleep apnea and sudden infant death syndrome. To date, the challenge of monitoring vital signs through micro-skin motion in the automotive industry has been the displacement caused by a moving vehicles. However, Litman’s team has developed a a patent-pending “motion compensation algorithm” that tracks “quasi-periodic signals in the presence of massive random motions,” providing near perfect accuracy (see tables below).

While the automotive industry races to launch fleets of autonomous vehicles, Litman estimates that the most successful players will be the ones that install empathic engines into the machines’ framework. Unlike the crowded field of AI and computer vision startups that are enabling robocars to safely navigate city streets, Neteera’s “intuition on a chip” is probably one of the only mechatronic ventures that actually report on the psychological state of drivers and passengers. Litman’s innovation has wider societal implications, as social robots begin to augment humans in the workplace and support the infirm and elderly in coping with the fragility of life.

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