GE unveiled a machine data system for power plants it claims can increase a facility's efficiency of operation by 1.5%, reduce carbon dioxide emissions by 3%, and reduce coal consumption by 67,000 tons for each megawatt of electricity produced. GE's Digital Power Plant for Steam suite was introduced at the Minds Machines conference in Paris June 14, where GE executives also revealed the results of a hardware and software upgrade using the technology at the Chivasso power plant in Northern Italy. The plant, run by A2A Group, was restarted in November 2015 after a three-year shutdown. Digital Power Plant for Steam is one of the first application suites to sit atop GE's Predix machine data analytics platform and yield practical, industrial results. The reference to "steam" in the product's name reflects the fact that gas and coal-fired power plants produce steam to drive the turbines that generate electricity used in households and industry in most societies.

As technology advances, we will begin to see huge changes in how our world operates. While all the technologies on this list are already being applied all over the world, many of them are still in their infancy. We stand on the precipice of a new technological age in human history, and while it may not yet be The Jetsons, many of these technologies are even more fantastic than anything we could have predicted. By chemically treating ordinary balsa wood and strengthening it with epoxy, scientists have created a clear, biodegradable material that is 4–6 times stronger than its counterpart that is not chemically treated. Scientists even think that it could be used to make renewable solar cells because the material partially traps light, allowing only about 85–90 percent of light to pass through it.

Recent developments in energy management systems and the IoT (Internet of Things), have enabled easy, and low cost visibility of real time energy consumption data of not only main power lines but also individual devices. For anyone skilled in the art of energy management, it is obvious that such data contains incredible value that can help facility managers significantly increase the operational and energy efficiency of their sites. However, due to the shortage and cost of analytical resources, it is always a great challenge to practically and easily deliver such valuable insights out of so much data. As more and more devices are being monitored, the task becomes nearly impossible to manage manually. An article which I recently published as part of the latest research work we're doing in Panoramic Power, introduces an innovative data-science approach that helps automatically generate actionable energy and operational efficiency insights out of real time device level energy consumption data, using machine learning techniques.

The amount of data being handled in chip design is growing significantly at each new node, prompting chipmakers to begin using some of the same concepts, technologies and algorithms used in data centers at companies such as Google, Facebook and GE. While the total data sizes in chip design are still relatively small compared with cloud operations--terabytes per year versus petabytes and exabytes--it's too much to sort through using existing equipment and approaches. "You can take many big data approaches to handle this, but there may be a business problem if you do," said Leon Stok, vice president of EDA at IBM. He said EDA doesn't have the kind of concentrated volume necessary to drive these kinds of techniques, and typically that problem is made worse because the data is often different between design and manufacturing. But for those working on designs, the amount has grown significantly at a time when extracting key data in various parts of the design flow is crucial.

A robotic employee will be deployed instead of human council workers to answer customer queries. IPsoft said Amelia, its technology platform, will be deployed to work for Enfield Council in North London. Capable of analyzing natural language, she understands context, applies logic, learns, resolves problems and even senses emotions. IPsoft said Amelia will be deployed to work for Enfield Council in North London. Capable of analyzing natural language, she understands context and even senses emotions.

IBM's weather forecast and data arm has launched Deep Thunder, a machine learning-powered service designed to provide customised local forecasts. The Weather Company, which IBM recently acquired after a long partnership, uses global weather forecast models and sophisticated analytics to provide forecast and climate data to companies and organisations across the globe. Adding The Weather Company's datasets and forecasting capabilities has given IBM the scope to mix in its own analytics and machine learning technology to create Deep Thunder. The software can provide weather analysis for targeted areas at a 0.2 to 1.2 mile resolution. The application of machine learning-based weather impact models developed by IBM Research allows Deep Learning to assess the effect of weather on a certain area, taking into account historical forecasts and environmental data such as vegetation and soil conditions.

Aerial drones get all the attention, but a new terrestrial drone named the Pegasus:Multiscope is an autonomous treaded vehicle that its makers call "the first unmanned ground vehicle (UGV) for off-road use." Use cases for the Pegasus:Multiscope include surveying challenging terrain for civil engineering projects or agriculture, or in hazardous areas such as near nuclear power stations or in conflict zones. The UGV's treads reduce ground pressure at any one point, allowing the vehicle, which weighs just under 2000 pounds, to traverse any type of terrain, including mud, sand or snow. Contractor Oshkosh Defense designs solutions to turn existing military vehicles into UGV.

We present a new trace estimator of the matrix whose explicit form is not given but its matrix multiplication to a vector is available. The form of the estimator is similar to the Hutchison stochastic trace estimator, but instead of the random noise vectors in Hutchison estimator, we use small number of probing vectors determined by machine learning. Evaluation of the quality of estimates and bias correction are discussed. An unbiased estimator is proposed for the calculation of the expectation value of a function of traces. In the numerical experiments with random matrices, it is shown that the precision of trace estimates with $\mathcal{O}(10)$ probing vectors determined by the machine learning is similar to that with $\mathcal{O}(10000)$ random noise vectors.

On top of providing forecasting, IBM will help businesses by relating other data to the weather. With forecast accuracy down to 0.2 to 1.2 miles of resolution, it can tell companies in very fine detail how the weather affects things like consumer buying behavior, so they can stock and market products appropriately. Utility companies can also use the data to figure out if telephone poles will be damaged in a storm so they can plan accordingly, for instance. The business forecasting helps companies quantify our behavior better than ever, in case you thought we weren't being tracked enough already. But improved weather forecasts will be particularly useful with the recent severe weather weirdness due to climate change.

This paper considers global optimization with a black-box unknown objective function that can be non-convex and non-differentiable. Such a difficult optimization problem arises in many real-world applications, such as parameter tuning in machine learning, engineering design problem, and planning with a complex physics simulator. This paper proposes a new global optimization algorithm, called Locally Oriented Global Optimization (LOGO), to aim for both fast convergence in practice and finite-time error bound in theory. The advantage and usage of the new algorithm are illustrated via theoretical analysis and an experiment conducted with 11 benchmark test functions. Further, we modify the LOGO algorithm to specifically solve a planning problem via policy search with continuous state/action space and long time horizon while maintaining its finite-time error bound. We apply the proposed planning method to accident management of a nuclear power plant. The result of the application study demonstrates the practical utility of our method.