Machine learning is an obvious complement to a cloud service that also handles big data. Often the major reason to collect massive amounts of observables is to predict other values of interest to the business. For example, one of the reasons to collect massive numbers of anonymized credit card transactions is to predict whether a new transaction is valid or fraudulent with some likelihood. It's no surprise then that Microsoft, with a large AI research department, would add machine learning facilities to its Azure cloud. Perhaps because the technology originated with the researchers, the commercial offering has all of the complex models and algorithms that a statistics and data weenie could want.

The number of new malware variations that pop up each day runs somewhere between 390,000 (according to AV-TEST Institute) and one million (according to Symantec Corporation). These are new strains of malware that have not been seen in the wild before. Even if we consider just the low end figure, the situation is still dire. Google Now is about to get a lot better in the future, aiming to serve Android users even when they're offline. With smartphones increasingly gaining ground, digital assistants have become widely popular and heavyweight companies are competing to deliver the best software in this category.

Graphics processors power more than the likes of Call of Duty: Black Ops III; they also provide the number-crunching for modern machine learning systems. But GPUs are largely proprietary hardware devices, led in the market by Nvidia, which is notorious for its poor reputation as an open source player. Leave it to Nvidia's competitor AMD, long beleaguered by slumping sales and shrinking market share, to develop a plan with the partial goal of advancing the state of GPU-accelerated high-performance computing. Thus, while AMD hopes to make GPU programming less of a black box with GPUOpen, the company is trying to rescue its own business as well. After all, AMD's reputation with open source users is also shaky, thanks to unfulfilled promises.

For example, Dean's affinity for cats comes in handy with his line of work. In this context, cats are a mere vehicle for determining how much a computer can see, learn, communicate and understand. It also turns out that machines and humans are complementary in skills. While some computers are capable of beating a human opponent in a game such as Go, it's challenging for the same computers to perform more interpretive functions such as identifying and describing images. On the other hand, humans (and cats) are challenged by performing algorithmic functions on large sets of data, a task at that machines excel at.

Every age has its iconic images. One of the more terrifying ones of the 21st century is the image of a man in desert or army fatigues making a "V for victory" sign with raised arm while standing over the decapitated body of a Western victim. In most of these images, the perpetrator's face and head are covered with a scarf or hood to hide his identity. That has forced military and law enforcement agencies to identify these individuals in other ways, such as with voice identification. This is not always easy or straightforward, so there is significant interest in finding new ways.

We all know that alcohol and tweeting is not always a good combination. Yet a surprising number of us indulge in this peculiar form of indiscretion. And this practice has given Nabil Hossain and pals at the University of Rochester an interesting idea. Today, these guys show how they've trained a machine to spot alcohol-related tweets. And they also show how to use this data to monitor alcohol-related activity and the way it is distributed throughout society.

For outsiders, the idea of artificial intelligence in the court room sounds horrifying. AI, however, has been pushing its way into law for decades. The next time you apply to claim child benefits, you may be met with the unexpected: robots. Most might not even notice it, but the future of law is heavily tied to Artificial Intelligence. In fact, the slow integration of AI into the legal sphere has been happening for decades, and several magazines, news sources and committees have been built around the topic.

South Korean Go master Lee Se-dol is now down 0-2 to Google DeepMind's AlphaGo which is on the verge of a milestone achievement in artificial intelligence. Master Se-dol has expressed surprise and amazement at the sophistication and skill of his virtual opponent. It has taken a long time to get here. Games have long been an attractive development tool for artificial intelligence researchers. In 1994, a computer program excelled at checkers and in 1997 it was chess.

At San Francisco's first fully automated restaurant, meals appear in little glass cubbies, just 90 seconds after customers order and pay on wall-mounted iPads. It's a human-less experience – no waitstaff, no cashier, no one to get your order wrong and no one to tip. The moment before the meal appears, the see-through display screen that fronts the cubbies goes black for the few seconds when you might catch sight of the hand that feeds you. Eatsa has not yet achieved total automation. The company admits it employs a small kitchen staff, and one employee is present in the front of the house, answering questions about how to order and dodging questions about what's going on behind the wall of magic cubbies.

Author, inventor, entrepreneur, and futurist Ray Kurzweil has accurately predicted the rise of major technological innovations, from head-mounted displays such as Google Glass to natural language interfaces such as Siri. Speaking Feb. 20 at the annual MIT Tech Conference, Kurzweil offered a vision of 2030, one with nanorobots bolstering the immune system and also connecting to external, cloud-based neocortal modules, or groups of neurons, to access far more knowledge than can fit in the brain. "We have pretty good ideas of how this works," said Kurzweil, who detailed this process in his 2012 book, How to Create a Mind. The thought of nanorobots inside the body fighting disease and connecting to computers may seem far-fetched. But so did, at one time, the World Wide Web, the mobile phone, the 3-D printer, and the fully mapped human genome--all of which Kurzweil also foresaw. Human intuition about the future has always been linear, said Kurzweil, a 1970 MIT graduate and current director of engineering at Google leading efforts to build artificial intelligence and natural language understanding.