The automobile is being dismantled, reimagined, and rebuilt in Silicon Valley. Intel's proposed $15.3 billion acquisition of Mobileye, an Israeli company that supplies carmakers with a computer-vision technology and advanced driver assistance systems, offers a chance to measure the scale of this rebuild. In particular, it shows how valuable on-the-road data is likely to be in the evolution of automated driving. While the price tag might seem steep, especially with so many players in automated driving today, Mobileye has some key technological strengths and strategic advantages. It's also developing new technologies that could help solidify this position.
Before autonomous trucks and taxis hit the road, manufacturers will need to solve problems far more complex than collision avoidance and navigation (see "10 Breakthrough Technologies 2017: Self-Driving Trucks"). These vehicles will have to anticipate and defend against a full spectrum of malicious attackers wielding both traditional cyberattacks and a new generation of attacks based on so-called adversarial machine learning (see "AI Fight Club Could Help Save Us from a Future of Super-Smart Cyberattacks"). As consensus grows that autonomous vehicles are just a few years away from being deployed in cities as robotic taxis, and on highways to ease the mind-numbing boredom of long-haul trucking, this risk of attack has been largely missing from the breathless coverage. It reminds me of numerous articles promoting e-mail in the early 1990s, before the newfound world of electronic communications was awash in unwanted spam. Back then, the promise of machine learning was seen as a solution to the world's spam problems.
As a designer, you will be facing more demands and opportunities to work with digital systems that embody machine learning. As a designer, you will be facing more demands and opportunities to work with digital systems that embody machine learning. This will help with making actual design decisions and identifying the right design patterns, including situations when no directly applicable solution exists and you must transfer ideas across domains. In rare cases, machine learning might enable a computer to perform tasks that humans simply can't perform because of speed requirements or the scale of data.
I am spending some cycles on my algorithmic rotoscope work -- which is basically a stationary exercise bicycle for my learning about what is and what is not Machine Learning. I am using it to help me understand and tell stories about Machine Learning by creating images using Machine Learning that I can use in my Machine Learning storytelling. Picture a bunch of Machine Learning gears all working together to help make sense of what I'm doing, and WTF I am talking about? As I'm writing a story on how image style transfer Machine Learning could be put to use by libraries, museums, and collection curators, I'm reminded of what a con machine learning will be in the future, and how it will be a vehicle for the extraction of value and outright theft. My image style transfer work is just one tiny slice of this pie.
Among all of the self-driving startups working toward Level 4 autonomy (a self-driving system that doesn't require human intervention in most scenarios), Mountain View, Calif.-based Drive.ai's Drive sees deep learning as the only viable way to make a truly useful autonomous car in the near term, says Sameep Tandon, cofounder and CEO. While a pedestrian in a camera image is a perceptual pattern, there are also patterns in decision making and motion planning--the right behavior at a four way stop, or when turning right on red, to name two examples--to which deep learning can be applied. This is why many companies working on vehicle autonomy are more comfortable with using traditional robotics approaches for decision making, and restrict deep learning to perception. Reiley agrees: "Your decisions have to be software driven and optimized for deep learning, for software and hardware integration.
Intel's proposed $15.3 billion acquisition of Mobileye, an Israeli company that supplies carmakers with a computer-vision technology and advanced driver assistance systems, offers a chance to measure the scale of this rebuild. The company's vision systems are a simple, low-cost solution that offers surprisingly sophisticated sensing. This involves capturing images as cars drive around, and annotating them to identify things like road markings, traffic signs, other vehicles, and pedestrians. Stephen Zoepf, executive director of the Center for Automotive Research at Stanford, agrees that Intel's acquisition of Mobileye shows how critical data and machine learning are to the auto industry's future.