Chemical plants are complex and dynamical systems consisting of many components for manipulation and sensing, whose state transitions depend on various factors such as time, disturbance, and operation procedures. For the purpose of supporting human operators of chemical plants, we are developing an AI system that can semi-automatically synthesize operation procedures for efficient and stable operation. Our system can provide not only appropriate operation procedures but also reasons why the procedures are considered to be valid. This is achieved by integrating automated reasoning and deep reinforcement learning technologies with a chemical plant simulator and external knowledge. Our preliminary experimental results demonstrate that it can synthesize a procedure that achieves a much faster recovery from a malfunction compared to standard PID control.

Apocalyptic asteroids heading for Earth may be harder to destroy than the Hollywood sci-fi films would have us believe. Scientists studying just how easy it would be to blow up a life-threatening space rock found they are stronger and more resilient than previously imagined. They say the discovery could aid in the creation of asteroid deflection weapons and for designing efficient asteroid mining techniques. Researchers found the fallout from the enormous collision would be split into two different stages. 'We used to believe that the larger the object, the more easily it would break, because bigger objects are more likely to have flaws,' says Charles El Mir, a recent PhD graduate from Johns Hopkins University, who led the study.

Despite the recent progress in deep reinforcement learning field (RL), and, arguably because of it, a large body of work remains to be done in reproducing and carefully comparing different RL algorithms. We present catalyst.RL, an open source framework for RL research with a focus on reproducibility and flexibility. Main features of our library include large-scale asynchronous distributed training, easy-to-use configuration files with the complete list of hyperparameters for the particular experiments, efficient implementations of various RL algorithms and auxiliary tricks, such as frame stacking, n-step returns, value distributions, etc. To vindicate the usefulness of our framework, we evaluate it on a range of benchmarks in a continuous control, as well as on the task of developing a controller to enable a physiologically-based human model with a prosthetic leg to walk and run. The latter task was introduced at NeurIPS 2018 AI for Prosthetics Challenge, where our team took the 3rd place, capitalizing on the ability of catalyst.RL to train high-quality and sample-efficient RL agents.

Artificial intelligence is no longer the stuff of science fiction. The technology is already disrupting multiple industries, many of which impact you on a daily basis. Own an iPhone X? Its facial recognition system is powered by AI. Ever been redirected by Google Maps because of an accident or construction ahead? And those are just a couple of small examples.

Their work is published in Environmental Research Letters. At the southern tip of the Himalayas, farmers in Himachal Pradesh graze cattle among rolling hills and forests. While policies to manage the region's forest have been but in place by the Indian government, the impact that these policies have had remains unclear. In this study, scientists led by Dr. Pushpendra Rana at the University of Illinois applied machine learning algorithms to examine natural resources policy and governance, evaluating how policies actually work on the ground. Using satellite images from NASA, Rana's machine learning algorithm was able to simultaneously evaluate policy effectiveness in over 200 forest management regions in Kangra, covering a 14-year period.

One endeavour of modern physical chemistry is to use bottom-up approaches to design materials and drugs with desired properties. Here we introduce an atomistic structure learning algorithm (ASLA) that utilizes a convolutional neural network to build 2D compounds and layered structures atom by atom. The algorithm takes no prior data or knowledge on atomic interactions but inquires a first-principles quantum mechanical program for physical properties. Using reinforcement learning, the algorithm accumulates knowledge of chemical compound space for a given number and type of atoms and stores this in the neural network, ultimately learning the blueprint for the optimal structural arrangement of the atoms for a given target property. ASLA is demonstrated to work on diverse problems, including grain boundaries in graphene sheets, organic compound formation and a surface oxide structure. This approach to structure prediction is a first step toward direct manipulation of atoms with artificially intelligent first principles computer codes.

Emerging technologies such as artificial intelligence (AI) and machine learning are rapidly proving their value across many industries. Today's infographic comes from GoldSpot Discoveries, and it shows that when this tech is applied to massive geological data sets, that there is growing potential to unlock the next wave of mineral discoveries. Discovering new sources of minerals, such as copper, gold, or even cobalt, can be notoriously difficult but also very rewarding. According to Goldspot, the chance of finding a new deposit is around 0.5%, with odds improving to 5% if exploration takes place near a known resource. On the whole, mineral exploration has not been a winning prospect if you compare the total dollar spend and the actual value of the resulting discoveries.

Artificial intelligence (AI) is no longer the stuff of science fiction. The technology is already disrupting multiple industries, many of which impact you on a daily basis. Own an iPhone X? Its facial recognition system is powered by AI. Ever been redirected by Google Maps because of an accident or construction ahead? And those are just a couple of small examples.

Real-world project scheduling often requires flexibility in terms of the selection and the exact length of alternative production activities. Moreover, the simultaneous scheduling of multiple lots is mandatory in many production planning applications. To meet these requirements, a new flexible resource-constrained multi-project scheduling problem is introduced where both decisions (activity selection flexibility and time flexibility) are integrated. Besides the minimization of makespan, two alternative objectives inspired by a steel industry application case are presented: maximization of balanced length of selected activities (time balance) and maximization of balanced resource utilization (resource balance). New mixed integer and constraint programming (CP) models are proposed for the developed integrated flexible project scheduling problem. The real-world applicability of the suggested CP models is shown by solving large steel industry instances with the CP Optimizer of IBM ILOG CPLEX. Furthermore, benchmark instances on flexible resource-constrained project scheduling problems (RCPSP) are solved to optimality.

Like early mammals scuttering between the legs of tyrannosaurs, a lot of little companies are trying to weave around--and maybe even outlast--the big boys of self-driving technology. One such example is Perrone Robotics, a small Virginia company that has developed a self-driving package that it says can be quickly adapted to any vehicle. This Swiss Army knife of an AI can give smarts to an existing car, shuttle bus, or truck--even the gargantuan trucks used in mining. Tiny shuttles and behemoth trucks sell in small numbers, and equipping them to drive themselves is beneath the dignity of major players, like Alphabet's Waymo and General Motors' Cruise Automation. "What we're doing, certainly Waymo and GM Cruise could do, but they are focused on their own agenda. This is our niche, and we are going where we can add real value," says David Hofert, the chief marketing officer at Perrone Robotics.