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There's a raging talent war for AI experts and its costing automakers millions

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The self-driving car space is getting increasingly more cutthroat. The sheer number of lawsuits filed recently are a testament to that. Tesla, for example, is suing its former Autopilot director Sterling Anderson. The lawsuit claims Anderson stole data for a competing venture, Aurora Innovations, that hasn't even come out of stealth mode yet. "In their zeal to play catch-up, traditional automakers have created a get-rich-quick environment.


NVIDIA Boosts IQ of Self-Driving Cars with World's First In-Car Artificial Intelligence Supercomputer Latest News & Updates at Daily News & Analysis

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Accelerating the race to autonomous cars, NVIDIA today launched NVIDIA DRIVE PX 2 – the world's most powerful engine for in-vehicle artificial intelligence. NVIDIA DRIVE PX 2 allows the automotive industry to use artificial intelligence to tackle the complexities inherent in autonomous driving. It utilizes deep learning on NVIDIA's most advanced GPUs for 360-degree situational awareness around the car, to determine precisely where the car is and to compute a safe, comfortable trajectory. "Drivers deal with an infinitely complex world," said Jen-Hsun Huang, co-founder and CEO, NVIDIA. "Modern artificial intelligence and GPU breakthroughs enable us to finally tackle the daunting challenges of self-driving cars.


Garage startup uses deep learning to teach cars to drive

USATODAY - Tech Top Stories

Carol Reiley, president and cofounder of autonomous car tech startup Drive.ai, "How do you create a robot that people can trust? That's what we're working on," says co-founder and president Carol Reiley, 34, a Johns Hopkins University PhD candidate who paused her studies to move west and wrangle her Stanford-trained peers into startup mode. This tidy formula of study hall, garage start-up, technological disruption, is well known and beloved in Silicon Valley. Larry Page and Sergey Brin started Google in a garage near their Palo Alto, Calif.


Company Designs Driverless Car Deep Learning Kit

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Drive.ai is a Silicon Valley startup working on a kit to retrofit your ride If Drive.ai is a success, your first self-driving car might already be parked in the driveway. The Silicon Valley start-up, founded recently by a team of former Stanford University Artificial Intelligence Lab products, is working on a software kit that can be used to retrofit existing vehicles. "We started Drive.ai because we believe there's a real opportunity to make our roads, our commutes, and our families safer," the company announced in a statement on its blog, citing a statistic that more than one million people die each year worldwide in automobile accidents caused by human error. At its foundation, Drive.ai is looking to use deep learning -- which its founders consider the most effective form of artificial intelligence ever developed -- to key a breakthrough in a field that giant companies such as Google and General Motors have been trying to master for years. "Unlike other forms of AI, which involve programming many sets of rules, a deep learning algorithm learns more like a human brain.


Driverless Cars Recognize Peds Better With Deep Learning Algorithm - The New Stack

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Autonomous cars use a variety of technologies like radar, lidar, odometry and computer vision to detect objects and people on the road, prompting it to adjust its trajectory accordingly. To tackle this problem, electrical engineers from University of California, San Diego used powerful machine learning techniques in a recent experiment that incorporated so-called deep learning algorithms in a pedestrian-detection system that performs in near real-time, using visual data only. The findings, which were presented at the International Conference on Computer Vision in Santiago, Chile, are an improvement over current methods of pedestrian detection, which uses something called cascade detection. This traditional form of classification architecture in computer vision takes a multi-stage approach that first breaks down an image into smaller image windows. These sub-images are then processed by whether they contain the presence of a pedestrian or not, using markers like shape and color.