As the United States wildfire season continues to lengthen, electric utilities could find new value from drones backed by advanced analytics to help prevent disasters. Also known as unmanned aerial vehicles (UAVs), drones can deliver literal birds-eye views of potential problems – encroaching vegetation, damaged equipment, nearby hazards – when there is still plenty of time to fix things. This year, during the COVID-19 pandemic, drones can also help keep people safe, going into the field to gather data while human experts stay safely inside and receive high-quality data for better business decisions. "Drones can quickly and efficiently gather information from power poles across vast expanses of the landscape," said Ron Gray, a senior solution engineer at SAP. "With analytical insights on where the biggest potential hazards are, electric companies can develop a prioritized schedule of inspections and maintenance plans, including outage management timeframes. This would also help utilities correct missing or inaccurate information on equipment with fact-based mapping data and prove compliance with regulatory reporting mandates."

The robot possesses an infrared thermal imager and a visual light camera, thereby giving them the ability to replace 24-hour manual inspection. Artificial intelligence is about to trigger explosive changes in our lives, work, and leisure, but few understand what the technology can do beyond Amazon AMZN's Alexa or Apple AAPL's Siri. These are examples of virtual assistant or'weak AI' technology -- the most common example of AI application. But in the data-driven energy sector, sophisticated machine learning is paving the way for'strong AI' to improve efficiency, forecasting, trading, and user accessibility. Electricity is a commodity that can be bought, sold, and traded in open markets.

Join our XPotential Community, future proof yourself with courses from our XPotential Academy, connect, watch a keynote, or browse my blog. The human brain operates on roughly 20 watts of power, or to put it another way, on a third of a 60 watt light bulb, in a space the size of, well, a human head. Meanwhile, the biggest machine learning algorithms use closer to a nuclear power plant's worth of electricity and racks of chips to learn. That's not to slander machine learning, but nature may have a tip or two to improve the situation. Luckily, there's a branch of computer chip design heeding that call right now and recently researchers in the UK spun up a million core computer that gets us closer to our goal of mimicking the human brain with all its energy efficiencies, in computer form. I am, of course, talking about revolutionary neuromorphic computers.

In the summer of 2018, a hobby drone dropped a small package near the lip of Stromboli, a volcano off the coast of Sicily that has been erupting almost constantly for the past century. As one of the most active volcanoes on the planet, Stromboli is a source of fascination for geologists, but collecting data near the roiling vent is fraught with peril. So a team of researchers from the University of Bristol built a robot volcanologist and used a drone to ferry it to the top of the volcano where it could passively monitor its every quake and quiver until it was inevitably destroyed by an eruption. The robot was a softball-sized sensor pod powered by microdoses of nuclear energy from a radioactive battery the size of a square of chocolate. The researchers called their creation a dragon egg. Dragon eggs can help scientists study violent natural processes in unprecedented detail, but for Tom Scott, a materials scientist at Bristol, volcanoes were just the beginning.

Relying on last year's weather to predict this year's power outages is an increasingly risky proposition. Climate change is shifting weather patterns in every region, increasing the frequency and severity of storms, wind, and drought. For example, in the wake of the recent tropical storm Isaias, Con Edison suffered its second-largest outage ever, mainly due to damage from trees in high winds. According to Con Ed: "The storm's gusting winds shoved trees and branches onto power lines, bringing those lines and other equipment down and leaving 257,000 customers out of power. The destruction surpassed Hurricane Irene, which caused 204,000 customer outages in August 2011."

Reinforcement Learning is one of the most in demand research topics whose popularity is only growing day by day. An RL expert learns from experience, rather than being explicitly taught, which is essentially trial and error learning. To understand RL, Analytics Insight compiles the Top 10 Reinforcement Learning Courses and Certifications in 2020. The reinforcement learning specialization consists of four courses that explore the power of adaptive learning systems and artificial intelligence (AI). On this MOOC course, you will learn how Reinforcement Learning (RL) solutions help to solve real-world problems through trial-and-error interaction by implementing a complete RL solution.

Domain translation is the task of finding correspondence between two domains. Several Deep Neural Network (DNN) models, e.g., CycleGAN and cross-lingual language models, have shown remarkable successes on this task under the unsupervised setting---the mappings between the domains are learned from two independent sets of training data in both domains (without paired samples). However, those methods typically do not perform well on a significant proportion of test samples. In this paper, we hypothesize that many of such unsuccessful samples lie at the fringe---relatively low-density areas---of data distribution, where the DNN was not trained very well, and propose to perform Langevin dynamics to bring such fringe samples towards high density areas. We demonstrate qualitatively and quantitatively that our strategy, called Langevin Cooling (L-Cool), enhances state-of-the-art methods in image translation and language translation tasks.

We develop a computational method to learn a molecular Hamiltonian matrix from matrix-valued time series of the electron density. As we demonstrate for three small molecules, the resulting Hamiltonians can be used for electron density evolution, producing highly accurate results even when propagating 1000 time steps beyond the training data. As a more rigorous test, we use the learned Hamiltonians to simulate electron dynamics in the presence of an applied electric field, extrapolating to a problem that is beyond the field-free training data. We find that the resulting electron dynamics predicted by our learned Hamiltonian are in close quantitative agreement with the ground truth. Our method relies on combining a reduced-dimensional, linear statistical model of the Hamiltonian with a time-discretization of the quantum Liouville equation within time-dependent Hartree Fock theory. We train the model using a least-squares solver, avoiding numerous, CPU-intensive optimization steps. For both field-free and field-on problems, we quantify training and propagation errors, highlighting areas for future development.

In nuclear engineering, modeling and simulations (M&Ss) are widely applied to support risk-informed safety analysis. Since nuclear safety analysis has important implications, a convincing validation process is needed to assess simulation adequacy, i.e., the degree to which M&S tools can adequately represent the system quantities of interest. However, due to data gaps, validation becomes a decision-making process under uncertainties. Expert knowledge and judgments are required to collect, choose, characterize, and integrate evidence toward the final adequacy decision. However, in validation frameworks CSAU: Code Scaling, Applicability, and Uncertainty (NUREG/CR-5249) and EMDAP: Evaluation Model Development and Assessment Process (RG 1.203), such a decision-making process is largely implicit and obscure. When scenarios are complex, knowledge biases and unreliable judgments can be overlooked, which could increase uncertainty in the simulation adequacy result and the corresponding risks. Therefore, a framework is required to formalize the decision-making process for simulation adequacy in a practical, transparent, and consistent manner. This paper suggests a framework "Predictive Capability Maturity Quantification using Bayesian network (PCMQBN)" as a quantified framework for assessing simulation adequacy based on information collected from validation activities. A case study is prepared for evaluating the adequacy of a Smoothed Particle Hydrodynamic simulation in predicting the hydrodynamic forces onto static structures during an external flooding scenario. Comparing to the qualitative and implicit adequacy assessment, PCMQBN is able to improve confidence in the simulation adequacy result and to reduce expected loss in the risk-informed safety analysis.

This week we speak to Christian Kroll, the founder and chief executive of internet search engine Ecosia. Christian Kroll wants nothing less than to change the world. "I want to make the world a greener, better place," he says. "I also want to prove that there is a more ethical alternative to the kind of greedy capitalism that is coming close to destroying the planet." The 35-year-old German is the boss of search engine Ecosia, which has an unusual but very environmentally friendly business model - it gives away most of its profits to enable trees to be planted around the world. Founded by Christian in 2009, Ecosia makes its money in the same way as Google - from advertising revenues.