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Tuesday, April 11, 2017

A Big Problem With AI: Even Its Creators Can't Explain How It Works (technologyreview.com)

Last year an experimental vehicle, developed by researchers at the chip maker Nvidia was unlike anything demonstrated by Google, Tesla, or General Motors. 

The car didn't follow a single instruction provided by an engineer or programmer. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it.

Getting a car to drive this way was an impressive feat. But it's also a bit unsettling, since it isn't completely clear how the car makes its decisions, argues an article on MIT Technology Review.

 From the article: 

 The mysterious mind of this vehicle points to a looming issue with artificial intelligence. 

The car's underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation.

 There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries.

 But this won't happen -- or shouldn't happen -- unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. 

Otherwise it will be hard to predict when failures might occur -- and it's inevitable they will. That's one reason Nvidia's car is still experimental.

  A report from TechCrunch:

 Self-driving still seems to be a ways off from active public use on regular roads, but once it arrives, it could ramp very quickly, according to a new study by the Boston Consulting Group.

 The study found that by 2030, up to a quarter of driving miles in the U.S. could be handled by self-driving electric vehicles operating in shared service fleets in cities, due mostly to considerable cost savings for urban drivers. 

The big change BCG sees is a result of the rise in interest in autonomous technologies, paired with the increased electrification of vehicles. 

 There's also more pressure on cities to come up with alternate transportation solutions that address increasing congestion. 

All of that added together could drive reduction in costs by up to 60 percent for drivers who opt into using shared self-driving services vs. owning and operating their own cars.

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