George Matus was still in high school when he began raising millions for his startup, Teal. The former quad drone racer\u2019s pitch to investors was a wish list of what he thought a drone should be. More than just an aerial camera, his quad would be freaky fast and easy to use \u2014 even fly in the rain.<\/p>\n
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And, most challenging of all, Teal would think and learn. It would be a platform that developers might use for all kinds of complex applications, from counting a farmer\u2019s cows to following a target without using GPS.<\/p>\n
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To do all that, Teal would need a tiny supercomputer\u2026and a digital brain.<\/p>\n
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That would have been impossible just a couple years ago. But a handful of new technologies \u2014 sprung from research labs, small startups, and major tech companies \u2014 have converged to make this kind of innovation possible. It\u2019s paving the way for quadcopters and self-driving cars that can navigate by themselves. They can recognize what they\u2019re seeing and make independent decisions accordingly, freeing them from the old need for an internet connection.<\/p>\n
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Breakthroughs in artificial intelligence (AI) lie at the root of this advancement. AI, the scientific shorthand for a machine\u2019s ability to copy human traits like thinking and learning, has transformed how we use technology. AI now permeates our life through Apple\u2019s Siri, Google search, and Facebook newsfeeds.<\/p>\n
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But that tech taps into the cloud. Ask Siri for help splitting the dinner tab, and your voice is sent off to Apple servers for some speedy calculations. It doesn\u2019t work without the web, or often even with it.<\/p>\n
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\u201cRobots and UAVs can\u2019t depend on that connection back to the data center,\u201d says Jesse Clayton, Nvidia\u2019s senior manager of product for intelligent machines. Imagine the delay if your quadcopter\u2019s live feed had to bounce off the cloud before a computer could calculate the safest route. You\u2019d be better off flying manual.<\/p>\n
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That bottleneck has companies racing to build tiny, AI-capable supercomputers.<\/p>\n
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When Max Versace started working on AI algorithms 25 years ago, computers weren\u2019t advanced enough to achieve his vision of an artificial brain. But by 2006, he and a colleague had cooked up a method for computing AI algorithms much faster. They patented it and formed a company, Neurala, around their equations. Then DARPA, the U.S. government\u2019s secretive military research agency, asked Neurala to build a software system that could emulate a fully functional brain.<\/p>\n
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The physical part of that brain is made from computer processors built by Hewlett Packard and IBM. Neurala wrote the software. \u201cIn a sense, we build minds, which are algorithms,\u201d Versace says.<\/p>\n
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Neurala took its inspiration from a rat brain. With just half a gram of gray matter, a rodent can navigate obstacles, forage food, and evade predators using complex and efficient senses. Yet its brain is far simpler to model than a human brain.<\/p>\n
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Then, once Neurala built DARPA this fake brain, or neural network, NASA asked them to make it work in a Mars rover. It can take half an hour to bounce signals off the Red Planet and hear back, which makes it somewhat tough to steer a robot. NASA wanted the rover to be able to make more decisions on its own. Neurala\u2019s brain never flew to Mars, but that request from NASA pushed the company to start working on autonomous robots with artificial brains. And that brain will soon power Teal\u2019s drones.<\/p>\n
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Three major advances have made the fusion of drones and AI possible. In recent years, researchers have amassed staggering amounts of data \u2014 mainly, vast image sets. This data is the proving ground for training new and complicated AI algorithms, the second major advancement. This progress in AI allows self-driving cars to recognize and track obstacles on the road. But that skill doesn\u2019t matter much if you can\u2019t free it from a supercomputer. So the third major advancement had to come from new computer processors.<\/p>\n
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\u201cWe are really at the invention of the wheel in terms of AI,\u201d Versace says. \u201cThis is just the beginning.\u201d<\/p>\n
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Versace adds that many current AI algorithms are trained on a supercomputer and then immediately stop learning. He compares it to graduating college at 25 years old and never getting any smarter.<\/p>\n
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\u201cYou go to work every day, perform your duties, wake up tomorrow and you don\u2019t know anything new,\u201d he says. \u201cYou just know what you learned the last day of school.\u201d<\/p>\n
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But he believes AI shouldn\u2019t stop learning.<\/p>\n
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\u201cWe have come up with a different solution, which relies on how the brain works, how the cerebral cortex works,\u201d he adds. \u201cIt enables machines to learn a little bit every day, every time they\u2019re used.\u201d<\/p>\n
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Versace and other scientists are now working on what\u2019s called deep learning: You show a computer thousands of pictures of pedestrians, and eventually it will spot a little old lady in a crosswalk that it\u2019s never seen before. Today, that kind of processing usually happens in the cloud.<\/p>\n
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