Current AI works by building up complex statistical models of the world, but it lacks a deeper understanding of reality
It is also something that self-driving cars do not have. And that is a problem. Autonomous vehicles are getting better, but they still don’t understand the world in the way that a human being does. For a self-driving car, a bicycle that is momentarily hidden by a passing van is a bicycle that has ceased to exist.
Similar techniques are used to train self-driving cars to operate in traffic. Cars thus learn how to obey lane markings, avoid other vehicles, hit the brakes at a red light and so on. But they do not understand many things a human driver takes for granted—that other cars on the road have engines and four wheels, or that they obey traffic regulations and the laws of physics . And they do not understand object permanence.
You could, for instance, ask a car fitted with a reasoning engine why it had hit the brakes, and it would be able to tell you that it thought a bicycle hidden by a van was about to enter the intersection ahead. A machine-learning program cannot do that. Besides helping improve program design, such information will, Dr Bhatt reckons, help regulators and insurance companies. It may thus speed up public acceptance of autonomous vehicles.
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