On the Stanford University campus, you could practically throw a rock and hit 100 graduate students who are building apps that enable people to communicate more effectively. But Terry Winograd is particularly enthusiastic about the app one of his graduate students, Catalin Voss, is working on. Voss, a native of Germany who completed his bachelor's and master's degrees last June at the age of 21, is working on an app that deploys Google Glass, linked to a smartphone, to help autistic children recognize human emotions through facial expressions. Venture capitalists weren't interested, even though Voss had created and sold a startup that used eye-tracking technology to monitor attentiveness to a Toyota subsidiary while still a freshman. But Terry Winograd was interested. "It runs, it has AI [artificial intelligence]," says Winograd, who 20-odd years ago advised another graduate student on the then nascent field of searching the World Wide Web. "It's at a stage where we've actually put 30 devices into homes. Our goal is to have 100 in the trial." Voss says his objective is to build a medical product that insurers will be willing to pay for. "We want to prove the investors wrong, who didn't believe in it, and build an aid for people with autism, and other mental disorders as well," he says. "We believe we've built a fairly holistic system for mental health." Winograd was Voss's first choice for an advisor even though the 70-year-old professor retired from teaching three years ago.

We are on the verge of a very fast rate of technological change. If you think the Internet and mobile communications have changed the world, just wait. Coming years will be even more disruptive and amazing. When most people ponder technology they default to thoughts about compute power and its limitations. In 1965 Gordon Moore, the cofounder of Intel theorized that processing power should be capable of doubling every 18-24 months.

Last summer at the Black Hat cybersecurity conference, the DARPA Cyber Grand Challenge pitted automated systems against one another, trying to find weaknesses in the others' code and exploit them. "This is a great example of how easily machines can find and exploit new vulnerabilities, something we'll likely see increase and become more sophisticated over time," said David Gibson, vice president of strategy and market development at Varonis Systems. His company hasn't seen any examples of hackers leveraging artificial intelligence technology or machine learning, but nobody adopts new technologies faster than the sin and hacking industries, he said. "So it's safe to assume that hackers are already using AI for their evil purposes," he said. "It has never been easier for white hats and black hats to obtain and learn the tools of the machine learning trade," said Don Maclean, chief cybersecurity technologist at DLT Solutions.

In a world where the term "big data" is being thrown around like the next coming, many business leaders still struggle to understand how more information is going to help them make better decisions that drive their businesses forward. But the real challenge goes well beyond merely accessing more data. The key is accessing data in the right way, at the right time, and in the right format to generate beneficial insights. This process is no small feat. It requires both technology and human analysis in order to identify these critical insights for business leaders.

The ability to predict the stock market is, as any Wall Street quantitative trader (or quant) will tell you, a license to print money. So it should be of no small interest to anyone who likes money that a new system that works in a radically different way than previous automated trading schemes appears to be able to beat Wall Street's best quantitative mutual funds at their own game. It's called the Arizona Financial Text system, or AZFinText, and it works by ingesting large quantities of financial news stories (in initial tests, from Yahoo Finance) along with minute-by-minute stock price data, and then using the former to figure out how to predict the latter. Then it buys, or shorts, every stock it believes will move more than 1% of its current price in the next 20 minutes - and it never holds a stock for longer. The system was developed by Robert P. Schumaker of Iona College in New Rochelle and and Hsinchun Chen of the University of Arizona, and was first described in a paper published early this year.

Machine learning gives you unimaginably powerful insights into data. Today, implementations of machine learning have been adopted throughout Industry and its concepts are numerous. This video is a unique blend of projects that teach you what Machine Learning is all about and how you can implement machine learning concepts in practice. Six different independent projects will help you master machine learning in Python. The video will cover concepts such as classification, regression, clustering, and more, all the while working with different kinds of databases.

Science fiction writers describe them as intelligent robots. Industry 4.0 advocates call them autonomous cyber-physical systems. The former often depict them in the worst possible light, as part of their nightmarish visions and dystopian horror stories. From Arthur C. Clarke's HAL to James Cameron's T-800, these AI-powered mechanical monsters have a tendency to use their supernatural strength and intelligence to outwit, outrun and outgun humans. On the other hand, the tech sector tends to view them as the catalyst to exponential productivity growth–the key to automation utopia.

The principles are laid out to ensure that the rapidly developing artificial intelligence systems of the world remain in service of humanity. MANILA, Philippines – Recently, an artificial intelligence (AI) computer program called Libratus defeated 4 poker professionals – a "landmark step" for AI, said its creator, because poker had been particularly challenging for AI up until that point. AI's victory in the poker contest – following wins in chess and the even more complex boardgame "go" – is a reminder that AI is getting smarter all the time. How then does humanity make sure that it remains an ally and doesn't go rogue like Skynet did in the Terminator series? That was the question discussed by global experts at the Beneficial AI conference held in early January in California.

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.

Prof. Tom Griffiths is the director of the Computational Cognitive Science Lab and the Institute of Cognitive and Brain Sciences at UC Berkeley. He studies human cognition and is involved with the Center for Human Compatible Artificial Intelligence. I asked him for insight into the intersection of cognitive science and AI. He offers his thoughts on the historical interaction of the fields and what aspects of human cognition might be relevant to developing AI in the future.