Future of AI: Reinforcement Learning corrects for Deep Learning’s blind spots

by Kishore Jethanandani

Deep Learning, the staple of Artificial Intelligence, was supposed to create a brave new world where robots replace humans with flawless performance. The Uber autonomous car that killed a female biker in Arizona put a brake on those dreams for now. Reinforcement Learning which minimizes errors by learning in real-time is the new frontier of artificial intelligence expected to correct for the shortcomings of Deep Learning.

Uber autonomous car collides with a pedestrian

The video of the accident in Arizona is puzzling since the car struck a woman who was walking across the road front, and center, in the driver’s field of view. Deep Learning algorithms are adept at object identification, and they did recognize the bicycle albeit only 1.3 seconds before the crash and detected an object 6 seconds before it — classifying it as an unknown object and then a vehicle.

The circumstances of the crash, according to a preliminary report published by NTSB, were unusual: the woman, who was under the influence of drugs, was not using a cross-walk, her pathway was not well lighted, she wore dark clothing, and her bike did not have side reflectors. While the computer system determined the need for emergency braking 1.3 seconds before…