Deep Teaching Takes Humans Out of AV Training

Image of AV Deep Teaching Could Change the Game

Deep Teaching Could Change the Game

How do you beat Tesla, Google, Uber and the entire multi-trillion dollar automotive industry with massive brands like Toyota, General Motors, and Volkswagen to a full self-driving car? Just maybe, by finding a way to train your AI systems that is 100,000 times cheaper. It’s called Deep Teaching.

From an article in Forbes, by John Koetsier.

Perhaps not surprisingly, it works by taking human effort out of the equation.

And Helm.ai says it’s the key to unlocking autonomous driving. Including cars driving themselves on roads they’ve never seen … using just one camera.

“Our Deep Teaching technology trains without human annotation or simulation,” Helm.ai CEO Vladislav Voroninski told me recently on the TechFirst podcast. “And it’s on a similar level of effectiveness as supervised learning, which allows us to actually achieve a higher levels of accuracy as well as generalization … than the traditional methods.”

Artificial intelligence runs on data the way an army marches on its stomach. Most self-driving car projects use annotated data, Voroninski says.

That means thousands upon thousands of images and videos that a human has viewed and labeled, perhaps identifying things like “lane” or “human” or “truck.” Labeling images costs at least dollars per image, which means the cost of annotation becomes the bottleneck.

“The cost of annotation is about a hundred thousand X more than the cost of simply processing an image through a GPU,” Voroninski says.

And that means that even with budgets of tens of billions of dollars, you’re going to be challenged to drive enough training data through your AI to make it smart enough to approach level five autonomy: full capability to drive anywhere at any time in any conditions.

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