Computers that learn for themselves are with us now. As they become more common in'high-stakes' applications like robotic surgery, terrorism detection and driverless cars, researchers ask what can be done to make sure we can trust them. There would always be a first death in a driverless car and it happened in May 2016. Joshua Brown had engaged the autopilot system in his Tesla when a tractor-trailor drove across the road in front of him. It seems that neither he nor the sensors in the autopilot noticed the white-sided truck against a brightly lit sky, with tragic results.

While ARM introduced two minuscule processor architectures with security features borrowed from larger chips, Intel unveiled its Atom E3900 chips with improved computer vision and industrial-grade timing. They have four vector image processing units to perform video noise reduction, improve low-light image quality, and preserve more color and detail. For industrial uses, the E3900 series gets Intel's TCC (Time Coordinated Computing) technology. This feature lets the chip tightly control the timing of a device's actions.

One of the most significant features of Windows 10's Anniversary Update was the addition of pen computing, known as Windows Ink, which we criticized as falling short of the average consumer's needs. We don't know whether any new inking features will be announced at Microsoft's Windows event on Wednesday (or the event that follows on November 2). Recently, however, we took a deeper dive into the capabilities, when we tried the new Math and Replay features within Windows 10's OneNote UWP app. Math translates and solves inked equations, while Replay records your series of ink strokes and can play them back. But the devil's in the details, and the challenges of both features show how Windows Ink is struggling with the realities of handwriting recognition and data wrangling.

An MIT project distorted photos of the capitol building and other famous sites using an artificial intelligence algorithm to make horror images. An MIT project distorted photos of the capitol building and other famous sites using an artificial intelligence algorithm to make horror images. Welcome to the "Nightmare Machine," a horror-imagery project created by three researchers at the Massachusetts Institute of Technology. Pinar Yanardag, Manuel Cebrian and Iyad Rahwan used artificial intelligence algorithms "to learn how haunted houses, or toxic cities look. Then, we apply the learnt style to famous landmarks and present [to] you: AI-powered horror all over the world!"

The following is a list of, mostly free, machine learning online courses for beginners. First, and arguably the most popular course on this list, Machine Learning provides a broad introduction to machine learning, data mining, and statistical pattern recognition. The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. The course is 11 weeks long and averages a 4.9/5 user rating, currently. It is free to take, but you can pay $79 for a certificate upon course completion.

If there were a World Series of big data baseball analytics, Claudio Silva would be in the starting lineup. By tracking every movement of every player and the ball throughout the game, it's changed how coaches evaluate and train players and how fans watch the game. But Silva is swinging for the fences. He's now using GPU-accelerated deep learning to reveal minute details of player behavior and game patterns, which has the potential to revolutionize how coaches manage players and plan strategy. It could even give them the ability to make predictions about some aspects of the game.

It sounds like something out of a science-fiction movie, but the truth is it's your inevitable reality. The rise of artificial intelligence, or AI, is poised to change the way we think of productivity. That's according to a new report from Accenture, which predicts that AI could boost productivity by up to 40 percent by 2035. Companies are already investing heavily in AI, and not just in the U.S. Put simply, AI is defined as the ability of a machine to mimic intelligent human behavior. It's essentially any sort of technology that is able to make sense of its environment and surroundings, and then act and react accordingly. One of the benefits AI will bring is taking care of mundane responsibilities so human employees can concentrate on others.

Israel's PointGrab has raised $7 million in funding for its sensors for smart buildings. The investors include lighting company Philips Lighting; Mitsubishi UFJ Capital Co. (MUCAP), a venture capital arm of Japan's largest financial group; and existing investors ABB Technology Ventures (ATV) and others. Israel-based PointGrab makes CogniPoint, a smart-sensing solution that detects people in buildings and shuts off lighting if there's no one around who needs it. The goal is to enable effective building management and save money through energy efficiency. By embedding deep learning neural networks technology into cost-effective, miniature, and connected optical sensing devices, PointGrab's CogniPoint sensing solution provides analytics in the detection of occupants' locations, count, and movements.

Masayoshi Son, one of the world's most successful investors, is putting his money behind the artificial intelligence revolution. Son, the world's 82nd richest man by Forbes' reckoning, leads Softbank, a Japanese telecommunications powerhouse that also holds investments in dozens of companies. The newest in the stable is ARM Holdings, developer of the chip designs that power nearly all phones, which Softbank acquired for $32 billion this year. Those ARM chips will help spread artificial intelligence and propel the world toward the so-called singularity, Son said, speaking Tuesday at ARM's developer conference here in Santa Clara, California. The singularity is the moment futurists envision when computing progress has accelerated to the point where we no longer can predict what's next for technology or humanity.

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