A quick, hassle-free way to stay on top of robotics news, our robotics digest is released on the first Monday of every month. Sign up to get it in your inbox. February is only just gone, and already 2017 is shaping up to be a year full of big ideas and ambitions. The Future of Life Institute, for example, just published the Asilomar AI principles: 23 guidelines to ensure AI developments are beneficial to humanity. They are calling for shared responsibility and caution against an AI arms race.

We discuss a Bayesian formulation to coarse-graining (CG) of PDEs where the coefficients (e.g. material parameters) exhibit random, fine scale variability. The direct solution to such problems requires grids that are small enough to resolve this fine scale variability which unavoidably requires the repeated solution of very large systems of algebraic equations. We establish a physically inspired, data-driven coarse-grained model which learns a low- dimensional set of microstructural features that are predictive of the fine-grained model (FG) response. Once learned, those features provide a sharp distribution over the coarse scale effec- tive coefficients of the PDE that are most suitable for prediction of the fine scale model output. This ultimately allows to replace the computationally expensive FG by a generative proba- bilistic model based on evaluating the much cheaper CG several times. Sparsity enforcing pri- ors further increase predictive efficiency and reveal microstructural features that are important in predicting the FG response. Moreover, the model yields probabilistic rather than single-point predictions, which enables the quantification of the unavoidable epistemic uncertainty that is present due to the information loss that occurs during the coarse-graining process.

Researchers from Argonne National Laboratory, using supercomputers at Berkeley Lab's National Energy Research Scientific Computing Center (NERSC), are employing machine learning algorithms to accurately predict the physical, chemical and mechanical properties of nanomaterials, reducing the time it takes to yield such predictions from years to months--in some cases even weeks. This approach could help accelerate the discovery and development of new materials. Using a modeling framework built around a molecular dynamics code (LAMMPS), the research team ran a series of simulations to study the structure and temperature-dependent thermal conductivity of stanene, a 2D material made up of a one-atom-thick sheet of tin. This work, which involved a set of parameters known as the "many-body interatomic potential" or "force field," yielded the first atomic-level computer model that accurately predicts stanene's structural, elastic and thermal properties. The findings were published in The Journal of Physical Chemistry Letters.

The electrical grid has become a network of billions of linked devices with highly complex energy and information flows. Add to this the elevated role of the consumer as a producer and you are looking at a massive volume, velocity and variety of data from smart meters, transformers, and substations that remains largely untapped. One extremely interesting solution to the impending challenges is cognitive computing, which is gaining traction among industry experts as a way to better manage data and improve both operating efficiencies and customer service. According to GTM Research, applying cognitive computing is expected to deliver an estimated $121 billion global return on investment (ROI) on grid analytics by 2020. There are three key areas where energy companies are already engaging with cognitive systems to experience real benefits.

Geologists have long been able to work out the approximate risk of an earthquake. Their approach is to work out when the fault moved in the past and use any periodicity to predict the future. The most famous example involves the Parkfield segment of the San Andreas Fault in California, one of the most carefully studied faults on the planet. Earthquakes occurred here in 1857, 1881, 1901, 1922, 1934, and 1966, suggesting a pattern in which quakes occur every 22 years give or take a few years. Geologists therefore predicted that a quake would occur between 1988 and 1993, but they had to wait until 2004 for their temblor.

The company in charge of the ruined Fukushima No. 1 nuclear plant revealed Thursday it needed new ideas to design robots capable of surviving the high levels of radiation inside the site's reactors, which were damaged in a 2011 earthquake and its resulting tsunami. The Tokyo Electric Power Company (Tepco) has hit a new obstacle since being tasked with cleaning up the worst nuclear incident since the 1986 Chernobyl disaster in the Soviet Union. The exploratory robot, specially designed to navigate the underwater sections of the reactor, died last month after being exposed to "unimaginable" levels of radiation nearly nine times more potent than the previous highest dose recorded. Naohiro Masuda, president of Tepco's Fukushima Daiichi Decommissioning project, told reporters that the company had to rethink its methods in order to examine and extract the hazardous material stuck in the plant's second reactor. "We should think out of the box so we can examine the bottom of the core and how melted fuel debris spread out," Masuda said, according to the Japan Times.

NEXTracker, who makes devices that shift solar panels to soak in as much direct sunlight as possible, acquired a startup called BrightBox Technologies out of Berkeley (shocker) to add some intelligence to its hardware. NEXTracker will use software developed by BrightBox, originally made to monitor and control temperatures in large buildings, to increase energy production of solar farms, thereby enabling faster operations and easier maintenance. Can you say streamlining processes? AI is great for that.

Robot probes sent in to study Japan's Fukushima site keep failing during their recon missions. The radiation levels at the site are so high that previous clean-up bots have struggled to withstand conditions within the reactor for long. Now the head of decommissioning for the damaged Fukushima nuclear plant said on Thursday that the company's robots were not able to get close enough to the core area. Robot probes sent in to study Japan's Fukushima site keep failing during their recon missions. This image shows a photograph taken by a probe sent into Fukushima's No. 2 reactor Tokyo Electric Power Co (TEPCO), the company that owns the Fukushima plant, took radiation readings using a robot probe.

Google's cryostats reach temperatures of 10 millikelvin to run its quantum processors. From aspects of quantum entanglement to chemical reactions with large molecules, many features of the world cannot be described efficiently with conventional computers based on binary logic. The solution, as physicist Richard Feynman realized three decades ago1, is to use quantum processors that adopt a blend of classical states simultaneously, as matter does. Many technical hurdles must be overcome for such quantum machines to be practical, however. These include noise control and improving the fidelity of operations acting on the quantum states that encode the information.

We harness the power of Bayesian emulation techniques, designed to aid the analysis of complex computer models, to examine the structure of complex Bayesian analyses themselves. These techniques facilitate robust Bayesian analyses and/or sensitivity analyses of complex problems, and hence allow global exploration of the impacts of choices made in both the likelihood and prior specification. We show how previously intractable problems in robustness studies can be overcome using emulation techniques, and how these methods allow other scientists to quickly extract approximations to posterior results corresponding to their own particular subjective specification. The utility and flexibility of our method is demonstrated on a reanalysis of a real application where Bayesian methods were employed to capture beliefs about river flow. We discuss the obvious extensions and directions of future research that such an approach opens up.