'..The major advance he will discuss is the edge that deep-learning techniques provide in autonomous technology..'<blockquote>'Hotz’s approach isn’t simply a low-cost knockoff of existing autonomous vehicle technology. He says he’s come up with discoveries—most of which he refuses to disclose in detail—that improve how the AI software interprets data coming in from the cameras. “We’ve figured out how to phrase the driving problem in ways compatible with deep learning,” Hotz says. Instead of the hundreds of thousands of lines of code found in other self-driving vehicles, Hotz’s software is based on about 2,000 lines.
The major advance he will discuss is the edge that deep-learning techniques provide in autonomous technology. He says the usual practice has been to manually code rules that handle specific situations. There’s code that helps cars follow other vehicles on the highway, and more code to deal with a deer that leaps into the road. Hotz’s car has no such built-in rules. It learns what drivers typically do in various situations and then tries to mimic and perfect that behavior. If his Acura cruises by a bicyclist, for example, it gives the biker some extra room, because it’s seen Hotz do that in the past. His system has a more general-purpose kind of intelligence than a long series of if/then rules. As Hotz puts it in developer parlance, “ ‘If’ statements kill.” They’re unreliable and imprecise in a real world full of vagaries and nuance. It’s better to teach the computer to be like a human, who constantly processes all kinds of visual clues and uses experience, to deal with the unexpected rather than teach it a hard-and-fast policy.
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Hotz started the autonomous car work because he sees it as Step 1 in the revolution..'
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Ashlee Vance, Self-Driving Car. In His Garage, December 16, 2015</blockquote>
Context<blockquote>
Story (possibility) - A strange electric ‘car’(In The Electric Universe) Open Source Infrastructure, beginning of the Enterprise Nervous System (ENS)</blockquote>