Intel has planted some solid stakes in the ground for the future of deep learning over the last month with its acquisition of deep learning chip startup, Nervana Systems, and most recently, mobile and embedded machine learning company, Movidius. These new pieces will snap into Intel's still-forming puzzle for capturing the supposed billion-plus dollar market ahead for deep learning, which is complemented by its own Knights Mill effort and software optimization work on machine learning codes and tooling. At the same time, just down the coast, Nvidia is firming up the market for its own GPU training and inference chips as well as its own hardware outfitted with the latest Pascal GPUs and requisite deep learning libraries. While Intel's efforts have garnered significant headlines recently with that surprising pair of acquisitions, a move which is pushing Nvidia harder to demonstrate GPU acceleration (thus far the dominant compute engine for model training) for deep learning, they still have some work to do to capture mindshare for this emerging market. Further complicating this is the fact that the last two years have brought a number of newcomers to the field--deep learning chip upstarts touting the idea that general purpose architectures (including GPUs) cannot compare to a low precision, fixed point, specialized approach.

BuildingIQ, a provider of energy management software headquartered in California, provides tools to commercial building owners and managers to improve the energy efficiency of their building operations. BuildingIQ offers solutions that use technology originally developed by the Energy Division of the Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia's national lab. The average commercial building wastes 30% of the energy it uses. Even simple changes, such as adjusting temperature settings and ensuring that lights are not on when no one is around, can produce substantial savings. BuildingIQ has responded by developing an energy management system driven by artificial intelligence (AI) that optimizes HVAC systems for maximum efficiency.

Traditionally, creating a movie trailer is a labor-intensive, completely manual process. Teams have to sort through hours of footage and manually select each and every potential candidate moment. This process is expensive and time consuming – taking anywhere between 10 and 30 days to complete. As with any AI system, the first step was training it to understand a subject area. Using machine learning techniques and experimental Watson APIs, IBM Research team trained a system on the trailers of 100 horror movies by segmenting out each scene from the trailers.

A pair of artificial intelligence experts from Cornell University have joined a nationwide effort to ensure the nightmare science fiction scenarios -- the ones involving corrupted human-killing computers -- don't become a reality. "We are in a period in history when we start using these machines to make judgments," researcher Bart Selman, a professor of computer science at Cornell, explained in a news release. Joseph Halpern, a professor of computer science at Cornell and also a "decision theory" expert, says providing an artificial intelligent agent with as much information as possible will make these difficult decisions more manageable. Scientists at Georgia Tech have been working to instill human values by teaching robots fairy tales.

A pair of artificial intelligence experts from Cornell University have joined a nationwide effort to ensure the nightmare science fiction scenarios -- the ones involving corrupted human-killing computers -- don't become a reality. The effort is organized by the Center for Human-Compatible Artificial Intelligence, based at the University of California, Berkeley. To keep computers behaving properly as they take on more and more responsibility and autonomy, researchers are working to instill their software with human values. Researchers want the algorithms that govern the AI decision-making process to include an understanding of human ethics. "We are in a period in history when we start using these machines to make judgments," researcher Bart Selman, a professor of computer science at Cornell, explained in a news release.

Matt Bencke is CEO of Spare5. Previously, he worked at Getty Images, Microsoft and Boeing. Artificial intelligence is changing at a breathtaking pace the way we interact with our devices, friends and data. It promises to revolutionize how we work, shop, socialize, date, bank, heal, navigate… how we live. Yet here I am in the engine room of the revolution, getting increasingly frustrated with the prevalent AI conversations and memes.

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Recently I posted an article by Jeff Selingo to my LinkedIn feed entitled "What happens when millions of jobs are lost to automation." Predictably, those who actually read the article decided it was about education -- specifically, how a lot of people are being educated to do things that no one will be doing after a few years. Meanwhile, people who read only the title commented that even more jobs will be created in our hyperautomated future. The issue is more complicated. When we defund education and fail to address long-standing social issues, we simply don't prepare future generations (or even much of the current one) for this future.

An agreement to send Canadian authorities passenger name record (PNR) data for flights from the European Union cannot be entered into in its current form, a top European Union judge has said. That's because parts of the draft agreement are incompatible with EU citizens' fundamental privacy rights, according to Paolo Mengozzi, Advocate General of the Court of Justice of the EU, in a legal opinion issued Thursday. His opinion, on a case brought by the European Parliament, is only advisory, and it still remains for the CJEU to make a final ruling on the matter. But if the court follows his advice, it could disrupt the European Commission's plans for a new directive on the sharing of PNR data among EU member states and with other countries. The agreement, which the EU and Canada began negotiating in 2010, concerns the transfer of PNR data to Canadian authorities for the purpose of combatting terrorism and other serious transnational crime. The passenger name records concerned contain 19 categories of information, covering the passenger's identity, nationality, address, contact details of the person making the reservation, payment information such as the number of the credit card used to reserve the flight, luggage details, and additional services requested concerning health problems, mobility, or dietary requirements.