If a Driverless Car Goes Bad, We May Never Know Why

Will Knight comments on how the recent Tesla autopilot crash highlights that  the increasing complexity of modern AI systems potentially outstrips our ability to comprehend them:

Tesla hasn’t disclosed precisely how Autopilot works. But machine learning techniques are increasingly used to train automotive systems, especially to recognize visual information. MobileEye, an Israeli company that supplies technology to Tesla and other automakers, offers software that uses deep learning to recognize vehicles, lane markings, road signs, and other objects in video footage.

Machine learning can provide an easier way to program computers to do things that are incredibly difficult to code by hand. For example, a deep learning neural network can be trained to recognize dogs in photographs or video footage with remarkable accuracy provided it sees enough examples. The flip side is that it can be more complicated to understand how these systems work.

Fortunately, the industry is already starting to respond:

As these algorithms become more common, regulators will need to consider how they should be evaluated. Carmakers are aware that increasingly complex and automated cars may be difficult for regulators to probe. Toyota is funding a research project at MIT that will explore ways for automated vehicles to explain their actions after the fact. The Japanese automaker is funding a number of such research projects related to challenges with self-driving cars.

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