If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Paint-on-the-floor pedestrian crossings don't cut it anymore. They are outdated, and the cause of 20 incidents a day in the UK. Architectural firm Umbrellium reckons it's got a solution: a sensor-packed digital crossing that responds to your movements. "We've been designing a pedestrian crossing for the 21st century," says Usman Haque, Umbrellium's founding partner. "Crossings that you know were designed in the 1950s, when there was a different type of city and interaction."
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.
Wired reports that the cameras Google uses to create imagery on its Street View service have gotten their first upgrade in eight years. Those units record images of stores, road signs, and other objects at the side of the road in incredible detail--and information gleaned from the data will feed Google's ever-hungry machine-learning algorithms. New 360-degree cameras allow users to upload their own panoramas to Street View, and the company hopes cities and other organizations may do the same to keep things fresh. All of that data will be indexed by Google's algorithms--so who knows, maybe one day, a handwritten "sorry we're closed today" sign might stop a wasted journey for a sandwich.
Rules are then written for the computer system to learn about all the data points and make calculations based on the rules of the road. Computer systems are programmed with machine learning algorithms and continuously learn to look at more data more quickly than any human would be able to. It might even notice lots of interactions when "Fly the Friendly Skies" ads are placed next to images of a person being brutally pulled off the plane and place more ads there! Artificial intelligence, machine learning and "self-aware systems" are real.
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.
With NVIDIA PilotNet, we created a neural-network-based system that learns to steer a car by observing what people do. What makes BB8 an AI car, and showcases the power of deep learning, is the deep neural network that translates images from a forward-facing camera into steering commands. This visualization shows us that PilotNet focuses on the same things a human driver would, including lane markers, road edges and other cars. Besides PilotNet, which controls steering, cars will have networks trained and focused on specific tasks like pedestrian detection, lane detection, sign reading, collision avoidance and many more.
Nathan is a Reader in the Department of Computer Science at the University of Warwick, whose research into the application of machine learning for autonomous vehicles (or "driverless cars") has been supported by a Royal Society University Research Fellowship. My research uses machine learning to give insights into how objects or people interact and how patterns emerge and evolve. Machine learning algorithms will examine previous behaviours and learn from these behaviours, to then predict what will happen in the future. An accurate algorithm could then be used to inform the decisions vehicles make and predict vehicle journeys and routes.
A rather high profile area generating headlines this year has been connected vehicles. The technological challenges that must be addressed before autonomous cars can be unleashed onto the streets are quite significant. Vision is one critical factor; your car needs to be able to identify all road hazards as well as navigating from A to B. So, how can a car achieve that in an often over-crowded highway space? Computer vision can be described as graphics in reverse. Rather than us viewing the computer's world, the computer turns around to look at ours.
Recently we've seen a series of startups arise hoping to make robocars with just computer vision, along with radar. That includes recently unstealthed AutoX, the off-again, on again efforts of comma.ai Their optimism is based on the huge progress being made in the use of machine learning, most notably convolutional neural networks, at solving the problems of computer vision. Milestones are dropping quickly in AI and particularly pattern matching and computer vision. There are reasons pushing some teams this way.
How is predictive data changing the automotive industry and what changes can we expect to see in the future? Connected and autonomous cars are going to benefit most from the inclusion of predictive data because their design centers on data collection and processing. As more and more connected cars hit the roads, data management is going to become an essential tool. Predictive data has already shown potential for preventative maintenance, but this same application could be used to predict software problems and security flaws as well.