With Watch Room, our goal is to contribute to the budding conversation around the promise and perils of Artificial Intelligence research, in a way that respects the complexities involved. As such, we've done our best to create a story that touches on everything from simulation theory, to brain emulation, to Roko's Basilisk... to that most hallowed of science fiction questions: "What makes us human?" Another goal of ours is to illustrate the possibilities within the realm of virtual reality. Of course, Watch Room's scientific roots drink deeply from rich dramatic soil. On one level, we're just plain old excited to make a film that's a joy to watch: smart and twisting in a way that respects the audience and keeps you guessing right up to the end.

Google said it now gets 3.5 times as much computing power out of the same amount of energy as it did five years ago thanks to custom-built servers, more efficient cooling systems that use outside air, and investment in green energy. The company wants to cap its increase in energy use at four per cent a year between 2014 and 2020 even as data use grows at a faster rate. It also plans to be 100 per cent powered by renewable energy. But it hasn't said when it will reach that goal, or how much of its power currently comes from renewable sources. The data centre algorithm can eventually be used to improve efficiency in other areas, according to Google, including getting more energy from the same amount of input at power plants, and reducing energy and water usage in semiconductor manufacturing.

We are long, long past the point where humans can manage this network expansion. Our network management systems, billing systems and customer management systems cannot do this job the way we want it done. SoftBank said this week that it is buying chipmaker ARM for 31.4 billion. But the presence of a major chipmaker and telecom companies in the same AI-friendly portfolio makes it seem likely that deep attention will be paid to the topic.

The definition of artificial intelligence (AI) is a bit fuzzy, so when AT&T claims that it has been using AI for more than 20 years, it should be kept in mind that the AI of 1996 is a lot different from the AI of 2016. The bigger point is that the newer version is doing a lot for the carrier. AT&T is settling on an AI platform that can be used for different things instead of developing "one-off" solutions every time a task requiring the predictive capabilities and massive number-crunching abilities of AI presents itself. AI can be leveraged to anticipate rather than simply react to events, as less sophisticated AI platforms have done in the past. The driver is software-defined networks (SDNs), according to Computerworld.

The Intergovernmental Panel on Climate Change (IPCC) highlights six main lines of evidence for climate change. First, we have tracked (see chart) the unprecedented recent increase in the amount of atmospheric carbon dioxide and other greenhouse gases since the beginning of the industrial revolution. By burning coal, oil, and natural gas, we accelerate the process, releasing vast amounts of carbon (carbon that took millions of years to accumulate) into the atmosphere every year.

Your brain is a strange three-pound lump in your head that also happens to determine your personality, control your movements, and hold all of your hopes and dreams. Neuroscientists have been mapping the brain for centuries to try to tease apart its inner workings. But people are complicated, and so are brains--intricate bits of biology packed with neurons and axons and all the synapses that tie them together. That hasn't stopped neuroscientists from trying. Just like regular maps, brain maps are useful points of reference.

I think there's a rule somewhere that says "You can't call yourself a data scientist until you've used a Naive Bayes classifier". This article is my attempt at laying the groundwork for Naive Bayes in a practical and intuitive fashion. Let's start with a problem to motivate our formulation of Naive Bayes. Suppose we own a professional networking site similar to LinkedIn. Users sign up, type some information about themselves, and then roam the network looking for jobs/connections/etc. Until recently, we only required users to enter their current job title, but now we're asking them what industry they work in.

Recent advances in deep learning have enabled research and industry to master many challenges in computer vision and natural language processing that were out of reach until just a few years ago. Yet computer vision and natural language processing represent only the tip of the iceberg of what is possible. In this article, I will demonstrate how Sebastian Heinz, Roland Vollgraf and I (Calvin Seward) used deep neural networks in steering operations at Zalando's fashion warehouses. As Europe's leading online fashion retailer, there are many exciting opportunities to apply the latest results from data science, statistics, and high-performance computing. Zalando's vertically integrated business model means that I have dealt with projects as diverse as computer vision, fraud detection, recommender systems and, of course, warehouse management.

The age of computers is over. You are now living in the age of intelligent processing by just about everything else. Like vacuum tubes and tape drives, desktops and laptops are on their way to becoming odd relics of a distant age, if people remember them at all. That may sound a bit extreme, but the fact is that applications are not married to any technological substrate, not even the most advanced mobile devices. That is why smart developers have already turned their attention to using JavaScript for building out next-generation technology like drone controllers, big data management tools, and connectors for the Internet of Things (IoT). The World After Web Apps In Fabio Nelli's "Beginning JavaScript Charts: With jqPlot, d3, and Highcharts," he starts off by saying "JavaScript is experiencing a rebirth as a result of the new libraries developed precisely to take advantage of the innovation introduced by HTML5."

Macy's is testing a mobile tool using artificial intelligence that lets shoppers get answers customized to the store they're in -- like where a particular brand is located or what's in stock -- that they would normally ask a sales associate face-to-face. The tool, which the nation's largest department store chain calls a "mobile companion," can be accessed for now through a browser and will accept questions in 10 U.S. locations about products, services and facilities. It uses natural language and offers feedback in seconds. It's developed by IBM Watson -- the Jeopardy-winning "cognitive computing" service and is designed to keep learning more about the store's customers. That's a key element as Macy's seeks to spur sluggish sales, make being at the store more enjoyable and distinguish itself from online portals and specialty retailers.