Google says it is on course to have its global datacentre footprint powered by renewable energy sources by 2017, having pledged to purchase enough wind and solar power to support its global operations. A collection of our most popular articles on datacentre management, including: Cloud vs. Colocation: Why both make sense for the enterprise right now; AWS at 10: How the cloud giant shook up enterprise IT and Life on the edge: The benefits of using micro datacenters Corporate E-mail Address: This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. Corporate E-mail Address: This email address is already registered. This email address is already registered.

The spacecraft, whose name means stork in Japanese, will deliver some 5.9 tons of supplies, the heaviest load transported by a Kounotori ship. It is the largest among the cargo ships owned by Japan, the United States and Russia. Friday's launch, which followed Russia's failure in the launch of a Soyuz rocket carrying a Progress supply ship on Dec. 1, has raised the success rate of Japan's H-2 rocket launches to 97.3 percent. Of the 37 launches so far, all but one succeeded. In addition to water and food, Kounotori 6 will also deliver Japanese-made large lithium-ion batteries to replace batteries used at the ISS, experimental equipment for a new cooling system and equipment to measure cosmic radiation in real time.

Machine learning, a field focused on training computers to recognize patterns in data and make new predictions, is helping doctors more accurately diagnose diseases and stock analysts forecast the rise and fall of financial markets. And now materials scientists have pioneered another important application for machine learning--helping to accelerate the discovery and development of new materials. Researchers at the Center for Nanoscale Materials and the Advanced Photon Source, both U.S. Department of Energy (DOE) Office of Science User Facilities at DOE's Argonne National Laboratory, announced the use of machine learning tools to accurately predict the physical, chemical and mechanical properties of nanomaterials. In a study published in The Journal of Physical Chemistry Letters, a team of researchers led by Argonne computational scientist Subramanian Sankaranarayanan described their use of machine learning tools to create the first atomic-level model that accurately predicts the thermal properties of stanene, a two-dimensional (2-D) material made up of a one-atom-thick sheet of tin. The study reveals for the first time an approach to materials modeling that applies machine learning and is more accurate at predicting material properties compared to past models.

Huawei needs new tricks to differentiate its products from the crowd of Chinese phones permeating the US market, and it's turning to artificial intelligence to set it apart. The Mate 9 is a new Android device that offers a "Machine Learning Algorithm" that purports to learn your habits over time and optimize performance so that the device is more responsive. The Mate 9, which is expected to arrive in the US soon (although the exact timing is unknown), also has one of the largest displays on the market. We don't yet know how much it'll cost in the US, but we expect the Mate 9 to sell for about the same as it does in Europe (€699), which would make it slightly more affordable than other leading big-screen flagships too. That, along with the promised performance boost and supposedly safer battery tech, might be reason enough to consider the Mate 9 as your next large-screen smartphone.

Koichiro Otaki started taking aerial pictures of photovoltaic power stations in April 2015. At first, it was an innocent desire to capture their sheer scale and aesthetic value that motivated him, he says. Solar parks, mostly in rural, desolate areas, were also among the few places where he could practice flying a drone without having to worry about hitting people or tall structures, he says. Weather permitting, the 38-year-old freelance photographer would toss a compact drone into his backpack and venture out to the suburbs of Tokyo or up north to the Tohoku region by motorcycle, snapping away at solar panels neatly lined up along river banks, mountain slopes and even abandoned golf courses. "I was simply captivated by their geometric beauty," Otaki said of the panels.

What would the future be without the toys, tools and technology to definitively herald in a new age? An unchanging world would be uninspiring and unexciting if tomorrow was exactly like today. Although the 21st century through the mind's eye of Jules Verne and Issac Isamov, two of history's greatest dreamers and visionaries, has not yet come to pass, there have been a number of ground breaking scientific developments that indicate that we are well on our way to that fantasy world of flying cars and autonomous robots. Although some of these pioneering new endeavours still require successive rounds of improvement and some will initially only be the preserve of the most well-heeled, it is assured that all will profoundly alter our lives as we know it. Standing at the cusp of tomorrow, it is both exciting and encouraging to take stock of the new technologies in development now that will undoubtedly be a part of our future.

Granville V., Rasson J.P. Multivariate discriminate analysis and maximum penalized likelihood.... Journal of the Royal Statistical Society, Series B, 57 (1995), 501-517.

No one can predict how the future will shake out, but we can make some educated guesses. Global design and strategy firm frog has released its 2017 forecasts for the technologies that will define the upcoming year. Last year, the firm correctly predicted that virtual reality would explode in popularity and that sensors in things like appliances and thermometers would continue to shrink in size. Around the world, large companies are leading the way in building solar-powered offices that don't rely on fossil fuels. Frog strategist Agnes Pyrchla expects the trend to continue in 2017. "Taking a nod from natural patterns," she writes, "material scientists and architects have developed bricks with bacteria, made cement that captures carbon dioxide, and created building cooling systems using nothing but the available wind and our vibrant sun." Business bots are going to be huge.

IMAGE: The Argonne research team that has pioneered the use of machine learning tools in 2-D material modeling. Machine learning, a field focused on training computers to recognize patterns in data and make new predictions, is helping doctors more accurately diagnose diseases and stock analysts forecast the rise and fall of financial markets. And now materials scientists have pioneered another important application for machine learning -- helping to accelerate the discovery and development of new materials. Researchers at the Center for Nanoscale Materials and the Advanced Photon Source, both U.S. Department of Energy (DOE) Office of Science User Facilities at DOE's Argonne National Laboratory, announced the use of machine learning tools to accurately predict the physical, chemical and mechanical properties of nanomaterials. In a study published in The Journal of Physical Chemistry Letters, a team of researchers led by Argonne computational scientist Subramanian Sankaranarayanan described their use of machine learning tools to create the first atomic-level model that accurately predicts the thermal properties of stanene, a two-dimensional (2-D) material made up of a one-atom-thick sheet of tin.

Chinese vessel Dong Hai Jiu 101 concluded its underwater search operations in a remote part of the southern Indian Ocean to locate the missing Malaysia Airlines Flight MH370. The Australian Transport Safety Bureau (ATSB) said in its latest search update that the vessel "commenced passage to Fremantle to demobilise the Phoenix Remora III Remotely Operated Vehicle (ROV) before the vessel returns to Shanghai." The agency, which is leading the search for the missing Boeing 777-200, moved from deep tow operations to AUV (Autonomous Underwater Vehicle) and ROV operations in October 2016. Dong Hai Jiu 101 vessel has completed 33 dives with the ROV since October 2016. The vessel departed the search area on Dec. 3 and has completed its missions in the search for MH370.