How Deep Learning and Humans in the Loop Will Make Autonomous Cars Work

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sysby Angela Guess

Naveen Rao of Nervana Systems recently wrote in VentureBeat, “In a not-too-distant future, autonomous cars, driven largely by AI systems, will hit the road in large numbers. But getting autonomous vehicles on the road is only half the battle. That’s because, even after the cars are out there, system operators will need to frequently update their software models and deploy updates to their fleets. While we can all get away with updating our smartphone apps only once every few months for fun, autonomous cars aren’t Angry Birds, and their software will need to be updated regularly in order to keep passengers safe. Across the board, auto manufacturers agree that continuously training and deploying updated software models to their fleets is their biggest challenge. Deep learning AI technology platforms, logistics, and, surprisingly, humans will all play a role in the final solution.”

Rao goes on, “The production release of fully autonomous cars is probably at least five years away still, as these machines are not nearly safe enough for widespread consumer use. Google’s self-driving cars still make mistakes, like getting confused by cyclists on fixed-gear bikes at stop signs. Tesla’s Autopilot has run into trouble when driving on local streets instead of highways. In fact, there are an unlimited number of such corner cases that autonomous vehicles must respond to, and many still need to be discovered and factored in. Only when a sufficient number of scenarios have been addressed will autonomous cars be considered ‘safe enough.’  As Tesla recently blogged: ‘Getting [an autonomous car] to be 99% correct is relatively easy, but getting it to be 99.9999% correct, which is where it ultimately needs to be, is vastly more difficult. Making mistakes at 70 mph would be highly problematic’.”

Read more here.

Photo credit: Flickr/ bovinity

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