The world needs more energy. Governments and companies are investing billions of dollars in technologies to harvest, convert and store power1. And as silicon solar cells approach the limit of their performance, researchers are looking to alternatives based on perovskites and quantum dots2. The batteries that store the energy must get cheaper, more efficient and longer-lasting3. And devices need to be manufactured from safe and abundant materials such as copper, nickel and carbon rather than from lead, platinum or gold.

Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the machine learning process, yielding regional model features whose construction is driven by model prediction performance rather than prior assumptions. Our results demonstrate that Genetic Programming is particularly well suited to this type of feature construction because it can automatically synthesize appropriate aggregations, as well as better incorporate them into predictive models compared to other regression methods we tested. In our experiments we consider a specific problem instance and real-world dataset relevant to predicting snow properties in high-mountain Asia.

We seek to infer the parameters of an ergodic Markov process from samples taken independently from the steady state. Our focus is on non-equilibrium processes, where the steady state is not described by the Boltzmann measure, but is generally unknown and hard to compute, which prevents the application of established equilibrium inference methods. We propose a quantity we call propagator likelihood, which takes on the role of the likelihood in equilibrium processes. This propagator likelihood is based on fictitious transitions between those configurations of the system which occur in the samples. The propagator likelihood can be derived by minimising the relative entropy between the empirical distribution and a distribution generated by propagating the empirical distribution forward in time. Maximising the propagator likelihood leads to an efficient reconstruction of the parameters of the underlying model in different systems, both with discrete configurations and with continuous configurations. We apply the method to non-equilibrium models from statistical physics and theoretical biology, including the asymmetric simple exclusion process (ASEP), the kinetic Ising model, and replicator dynamics.

The two biggest societal challenges for the twenty-first century are also the biggest opportunities – automation and climate change. The epitaph of fossil fuels with its dark cloud burning a hole in the ozone layer is giving way to a rise of solar and wind farms worldwide. Servicing these plantations are fleets of robots and drones, providing greater possibilities of expanding CleanTech to the most remote regions of the planet. As 2017 comes to end, the solar industry for the first time in ten years has plateaued due to the proposed budget cuts by the Trump administration. Solar has had quite a run with an average annual growth rate of more than 65% for the past decade promoted largely by federal subsidies.

This past April, engineer Alex Reben developed and posted to YouTube, "Deeply Artificial Trees", an art piece powered by machine learning, that leveraged old Joy of Painting videos. It generate gibberish audio in the speaking style and tone of Bob Ross, the show's host. Bob Ross' estate was not amused, subsequently issuing a DMCA takedown request and having the video knocked offline until very recently. Much of Reben's art, supported by non-profit Stochastic Labs, seeks to raise such conundrums. "Doing something that's provocative and doing something that's public, I think, starts the conversation and gets them going in a place where the general public can start thinking about them," he told Engadget.

Japanese car maker Toyota unveils a new humanoid robot that mirrors the movements of its remote operator, as Stuart McDill reports. Toyota CEO Akio Toyoda spoke at the company's earnings press conference in Tokyo on May 10. (Photo: Toyota Motor Corporation) Toyota reached a deal to explore a new battery partnership with Panasonic in a move that threatens to encroach on Tesla's territory, heightening the ongoing rivalry between the two automakers. Toyota and Panasonic said Wednesday that they are launching a "feasibility study" to investigate the technological potential of batteries that use prismatic cells, which are grouped together in pouches to power electric cars. The deal places Panasonic in the unusual position of straddling the technological and strategic divide between Toyota and Tesla. Panasonic already has a major partnership with Tesla to jointly manufacture a competing battery technology that relies on different batteries relying on cylindrical cells.

When living and operating in a market largely dominated by a vendor that isn't you, the strategy you must deploy is one of focus. In the early days of Power, IBM tried to take on Intel head to head and that just wasn't working. You can understand why IBM thought it could do this; it was once the most powerful company in the world. But, like Microsoft, Intel's strength largely came from providing technology to firms like IBM, and IBM's decline in the late 1980s and early 1990s not only weakened it substantially, it collectively strengthened other firms. Much like AMD, which has always been weaker than Intel, IBM needed to pick its battles, and given that the company still pretty much owns the market for enterprise-class AI with Watson, and that this segment is slated to become the most lucrative in the industry for servers over the next decade, it chose wisely to make this one of its critical areas of focus.

On the surface, fashion's global impact shows no sign of waning. But look closely and you'll discover an industry struggling to evolve with the times. In December, The State of Fashion report by The Business of Fashion and McKinsey revealed that 67 percent of fashion executives believe conditions in the industry had worsened in the previous year. But, nearly a year later, are the seams still unravelling? The Future Laboratory has identified four major anxieties casting long shadows over the industry.

Our internet-connected devices could be impeding climate change efforts, according to an update to a 2016 peer-reviewed study on power consumption, as reported by Climate Home News. The billions of devices many of us use every day could produce 3.5 percent of global emissions within 10 years and 14 percent by 2040. This would result in the industry using approximately 20 percent of all of the world's electricity by 2025. This growing problem threatens to disrupt progress toward climate change goals and exacerbate increasingly-stressed power grids. These severe consequences are caused by one, major, underlying trend: the rapidly growing power needs of server farms which store data from billions of smart devices.

Abstract--Manufacturers of safety-critical systems must make the case that their product is sufficiently safe for public deployment. Much of this case often relies upon critical event outcomes from real-world testing, requiring manufacturers to be strategic about how they allocate testing resources in order to maximize their chances of demonstrating system safety. This work frames the partially observable and belief-dependent problem of test scheduling as a Markov decision process, which can be solved efficiently to yield closed-loop manufacturer testing policies. By solving for policies over a wide range of problem formulations, we are able to provide high-level guidance for manufacturers and regulators on issues relating to the testing of safety-critical systems. This guidance spans an array of topics, including circumstances under which manufacturers should continue testing despite observed incidents, when manufacturers should test aggressively, and when regulators should increase or reduce the real-world testing requirements for an autonomous vehicle. I. INTRODUCTION Confidence must be established in safety-critical systems such as autonomous vehicles prior to their widespread release. Establishing confidence is difficult because the space of driving scenarios is vast and accidents are rare. Automotive manufacturers can build confidence by conducting test drives on public roadways and make the safety case based on the frequency of observed hazardous events like disengagements and traffic accidents. Each manufacturer must devise a testing strategy capable of providing sufficient evidence that their system is safe enough for widespread adoption. Real-world testing that is too aggressive may yield hazardous events that diminish confidence in system safety. However, a manufacturer that is reluctant to test their product may forfeit opportunities to identify and address shortcomings, and may ultimately not be able to compete in the market. The fundamental tension between the desire to thoroughly test a product and the urgency to forego further testing in favor of bringing the product to market is not unique to the automotive industry.