"Most current advanced driver assistance systems based on radar and cameras are not capable of accurately detecting and classifying objects – such as cars, pedestrians or bicycles – at a level required for autonomous driving," said Sachin Lawande, president and CEO of Visteon, a leading global cockpit electronics supplier. "We need to achieve virtually 100 percent accuracy for autonomous driving, which will require innovative solutions based on deep machine learning technology. Our Silicon Valley team, with its focus on machine learning software development, will be a critical part of our autonomous driving technology initiative." Visteon's recently opened facility in the heart of Silicon Valley will house a team of engineers specializing in artificial intelligence and machine learning. The center is located close to the West Coast offices of various automakers and tech companies, as well as Stanford University and the University of California, Berkeley – two of the leading universities for artificial intelligence and deep learning in the U.S. In addition to leading Visteon's artificial intelligence efforts, the Silicon Valley office will play a key role in delivering control systems, localization and vision processing – interpreting live camera data and converting it to information required for autonomous driving.
Computer vision and image recognition are integral parts of artificial intelligence (AI), which has quickly gone from niche to mainstream in the past few years. And nowhere was this more evident than at CES 2017 earlier this month. From a few days of wandering the floor, here are some of the coolest new uses of computer vision. The biggest displays of computer vision are coming from the automotive industry, because computer vision, after all, is one of the central enabling technologies of semi- and fully-autonomous cars. NVIDIA, which already helped supercharge the deep learning revolution with its deep learning GPU tools, is powering many of the autonomous car innovations with the NVIDIA Drive PX 2, a self-driving car reference platform that Tesla, Volvo, Audi, BMW, and Mercedes-Benz are already using for semi- and fully-autonomous functions.
The Consumer Electronics Show, one of the world's tentpole technology shows, is a flashy vehicle for the engineering underneath. In the world of chipsets, the show was dominated by Nvidia, one of many companies seeking to power the artificial intelligence in next-generation cars and image processors. There were also plenty of opportunities for component manufacturers to find places in the flashy new cars and fleets that dominated some of the show floors. Chipmakers and sensor manufacturers are also keeping an eye on what customers want in the automotive space. Just seven states – Nevada, California, Florida, Michigan, Hawaii, Washington, and Tennessee -- and the District of Columbia have passed bills related to autonomous driving.
Elon Musk has hired a new director of AI research at Tesla, and it may signal a plan to rethink the way its automated driving works. This week, Musk poached Andrej Karpathy, an expert on vision, deep learning, and reinforcement learning, from OpenAI, a nonprofit that Musk and others are funding that's dedicated to "discovering and enacting the path to safe artificial general intelligence." Karpathy, who will apparently report directly to Musk, is a rising star in the world of AI, having studied at Stanford with Fei-Fei Li, a leading AI expert who is now the chief scientist of Google Cloud. Li is famous in tech circles for having developed a data set of images that helped inspire a breakthrough in machine vision. Many have pointed to Karpathy's expertise in computer vision as a key asset for Tesla, and that's true.