Mitsubishi Heavy Industries Ltd. on Tuesday unveiled a robot that can operate in the presence of flammable gases, such as after a gas leak following a disaster. A joint project with Chiba Institute of Technology, the Sakura No. 2 is the country's first mobile inspection unit that can operate in the presence of high concentrations of explosive gases such as methane and hydrogen. There is an increasing need for an inspection robot that is not a fire hazard as Japan steers toward becoming a hydrogen-based society, said Ken Onishi, a senior engineer in charge of the project for Mitsubishi Heavy. "There was a debate over whether to develop robots that can operate near hydrogen gas, as doing so requires an extremely high level of technology," Onishi said. "As we may encounter accidents such as collisions involving hydrogen cars or a truck loaded with hydrogen tanks rolling over inside a road tunnel, we decided to develop a robot that can deal with such situations."

When I attended translation courses, I was assigned to write a commentary on George F. Will's column Reading, Writing and Rationality on the Newsweek issue of March 17, 1986. Even then, with no Internet, and television as the dominant media, students were urged to read. That day, green activists were giving a demonstration of solar energy applications in a public park near the school, and our professor opened his lesson with a witty comment about the experiment he had witnessed during his lunch break. The history of innovation is full of inventors and manufacturers unable to understand the impact and actual use of their own work. Similarly, most innovations do not necessarily use the most recent and sophisticated technology, with their makers showing an outstanding capacity of interpreting and accelerating the transformations that are already underway.

We propose and analyze sequential design methods for the problem of ranking several response surfaces. Namely, given $L \ge 2$ response surfaces over a continuous input space $\cal X$, the aim is to efficiently find the index of the minimal response across the entire $\cal X$. The response surfaces are not known and have to be noisily sampled one-at-a-time. This setting is motivated by stochastic control applications and requires joint experimental design both in space and response-index dimensions. To generate sequential design heuristics we investigate stepwise uncertainty reduction approaches, as well as sampling based on posterior classification complexity. We also make connections between our continuous-input formulation and the discrete framework of pure regret in multi-armed bandits. To model the response surfaces we utilize kriging surrogates. Several numerical examples using both synthetic data and an epidemics control problem are provided to illustrate our approach and the efficacy of respective adaptive designs.

Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine. However, there are often external factors or variables which are not captured by sensors leading to time-series which are inherently unpredictable. For instance, manual controls and/or unmonitored environmental conditions or load may lead to inherently unpredictable time-series. Detecting anomalies in such scenarios becomes challenging using standard approaches based on mathematical models that rely on stationarity, or prediction models that utilize prediction errors to detect anomalies. We propose a Long Short Term Memory Networks based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD) that learns to reconstruct 'normal' time-series behavior, and thereafter uses reconstruction error to detect anomalies. We experiment with three publicly available quasi predictable time-series datasets: power demand, space shuttle, and ECG, and two real-world engine datasets with both predictive and unpredictable behavior. We show that EncDec-AD is robust and can detect anomalies from predictable, unpredictable, periodic, aperiodic, and quasi-periodic time-series. Further, we show that EncDec-AD is able to detect anomalies from short time-series (length as small as 30) as well as long time-series (length as large as 500).

Machine learning artificial intelligence has unlocked big data as a source of military, weather and business intelligence that has opened up multiple options. Social Media giants Twitter and Facebook have been spending millions trying to keep their companies ahead of the flock, highlighted by Twitter Buys Machine Learning Artificial Intelligence Star Magic Pony Technology Pavel Machalek co-founder of Silicon Valley data analytics firm Spaceknow working with commercial satellite data says the convergence of computing power, machine learning and satellite imagery is a perfect storm that s just beginning to peak, ... We could not have done this five years ago. Chinese government economic reports are notoriously inaccurate. Spaceknow's China Satellite Manufacturing Index uses satellite imagery to monitor changes at 6,000 industrial facilities in China as an alternative. The above image courtesy of DigitalGlobe shows how geospatial data companies can track activity by identifying surface material as seen here with individual trees in a forest (above) and aircrafts on the tarmac (below).

The price of oil has fallen over 60% since the summer 2014, and anybody reading the news sees it mentioned on a daily basis. With all of the negativity portrayed in the day-to-day headlines, many forget that the oil and gas business is one of the largest, most globally-important industries in the world. As the Motley Fool has called out, there is a huge amount of opportunity in the oil and gas industry, notably because it has weathered times like this before. One of the things that happens during downturns is that companies innovate. As we have seen historically, the companies that emerge the strongest during these times are the ones who adopt innovative technologies to promote growth.

Acquisition cost is a crucial bottleneck for seismic workflows, and low-rank formulations for data interpolation allow practitioners to `fill in' data volumes from critically subsampled data acquired in the field. Tremendous size of seismic data volumes required for seismic processing remains a major challenge for these techniques. We propose a new approach to solve residual constrained formulations for interpolation. We represent the data volume using matrix factors, and build a block-coordinate algorithm with constrained convex subproblems that are solved with a primal-dual splitting scheme. The new approach is competitive with state of the art level-set algorithms that interchange the role of objectives with constraints. We use the new algorithm to successfully interpolate a large scale 5D seismic data volume, generated from the geologically complex synthetic 3D Compass velocity model, where 80% of the data has been removed.

Ten to twenty years from now if you're going to be an effective lawyer, doctor, or financial analyst, it will be in part your ability to use the technological implements, loosely going under the name of artificial intelligence," Hoffman explained. However, the jobs that do exist will be technology enabled and part of the skill set is having the necessary technology skills," Hoffman said. It is a common and an intelligent worry that our education is insufficiently S.T.E.M., insufficiently technical, and insufficiently aggressive at younger ages. It's not just learning new programs its learning to use them to work effectively," Hoffman noted.

Halfway into 2016, it is clear that we are living in a new era of innovation. Beyond Silicon Valley, corporations and startup hubs worldwide are tackling big problems like water scarcity and cancer. The concept of the "next big thing" is becoming redundant because breakthroughs have become normal. Artificial intelligence that can learn and function independent of human overlords seems like science fiction. Yet, this may become our new reality within the next 3–5 years.

Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These characterizations are pivotal for endeavors such as searching for Earth-like planets and solar twins, understanding the mechanisms that govern stellar evolution, and tracing the dynamics of our Galaxy. The volume of data that is becoming available, however, brings with it the need to process this information accurately and rapidly. While existing methods can constrain fundamental stellar parameters such as ages, masses, and radii from these observations, they require substantial computational efforts to do so. We develop a method based on machine learning for rapidly estimating fundamental parameters of main-sequence solar-like stars from classical and asteroseismic observations. We first demonstrate this method on a hare-and-hound exercise and then apply it to the Sun, 16 Cyg A & B, and 34 planet-hosting candidates that have been observed by the Kepler spacecraft. We find that our estimates and their associated uncertainties are comparable to the results of other methods, but with the additional benefit of being able to explore many more stellar parameters while using much less computation time. We furthermore use this method to present evidence for an empirical diffusion-mass relation. Our method is open source and freely available for the community to use. The source code for all analyses and for all figures appearing in this manuscript can be found electronically at https://github.com/earlbellinger/asteroseismology