In a deal that made few ripples outside the energy industry, two very large but relatively obscure companies, Rockwell Automation and Schlumberger Limited, announced a joint venture called Sensia. The new company will "sell equipment and services to advance digital technology and automation in the oilfield," according to the Houston Chronicle. Yet the partnership has ramifications far beyond Houston's energy corridor: It's part of a growing trend that sees major tech companies teaming with oil giants to use automation, AI, and big data services to enhance oil exploration, extraction, and production. Rockwell is the world's largest company that is dedicated to industrial automation, and Schlumberger, a competitor of Halliburton, is the world's largest oilfield services firm. Sensia will be, according to the press release, "the first fully integrated digital oilfield automation solutions provider."

Enel and C3 have been working together since 2013. Two of Enel's enterprise-wide digital transformation efforts with C3 are fraud detection and predictive maintenance of distribution assets. With C3, Enel transformed its approach to identifying and prioritizing electricity theft (non-technical loss), with a goal to double the recovery of unbilled energy while improving productivity. The effort required building AI/machine learning algorithms to match the performance delivered by Enel experts using a process honed over 30 years of experience. To accomplish this, the teams worked together to replace traditional non-technical loss identification processes with the C3 Fraud Detection application.

Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output LC filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Though it is an intuitive controller easy to understand and implement, it has the significant disadvantage of requiring a large number of online calculations for solving the optimization problem. On the other hand, the application of model-free approaches such as artificial neural network-based (ANN-based) approaches is currently growing rapidly in the area of power electronics and drives. This paper presents a new control scheme for a two-level converter based on combining MPC with feed-forward ANN, with the aim of getting lower THD and improving the steady and dynamic performance of the system for different types of loads. First, MPC is used, as an expert, in the training phase to generate data required for training the proposed neural network. Then, once the neural network is fine-tuned, it can be successfully used online for voltage tracking purpose, without the need of using MPC. The proposed ANN-based control strategy is validated through simulation, using MATLAB/Simulink tools, taking into account different loads conditions. Moreover, the performance of the ANN-based controller is evaluated, on several samples of linear and non-linear loads under various operating conditions, and compared to that of MPC, demonstrating the excellent steady-state and dynamic performance of the proposed ANN-based control strategy.

Stochastic differential equations are an important modeling class in many disciplines. Consequently, there exist many methods relying on various discretization and numerical integration schemes. In this paper, we propose a novel, probabilistic model for estimating the drift and diffusion given noisy observations of the underlying stochastic system. Using state-of-the-art adversarial and moment matching inference techniques, we circumvent the use of the discretization schemes as seen in classical approaches. This yields significant improvements in parameter estimation accuracy and robustness given random initial guesses. On four commonly used benchmark systems, we demonstrate the performance of our algorithms compared to state-of-the-art solutions based on extended Kalman filtering and Gaussian processes.

Newly leaked concept images have revealed the first real glimpse into Alphabet's plans for an Orwellian smart city on the Toronto waterfront. Sidewalk Labs, an offshoot of Google's parent company, reached an agreement with the city back in 2017 to develop a futuristic community known as Quayside, complete with robotic waste-sorting systems, sensor-lined pavement, digital infrastructure, and wireless 5G connectivity all throughout. The plans have sparked both concerns and curiosity from the public, fueled further by a lack of information on how it will ultimately come to fruition, aside from a series of simple sketches. The new images published by Sidewalk Labs this month and leaked by Toronto Star now reveal stunning plans for a dozen timber towers and modular pavement in the development, allowing it to evolve to the city's changing needs. Sidewalk Labs also detailed a system of underground tunnels where robots can transport waste and freight out of the public's sight.

Modern technologies are enabling increased automation across multiple markets and enhancing everyday life. Artificial intelligence and machine learning are reshaping the way we live through the advent of automated and autonomous vehicles, smart cities, smart factories and much more. Modern technologies are enabling increased automation across multiple markets with the help with rugged, robust, reliable systems from Crystal Group. Early adopters and continued investors in AI and ML, military organizations and defense contractors helped to pioneer autonomous vehicles, which rely upon AI and ML capabilities. Critical infrastructure sectors – including power, oil and gas, telecommunications, and more – are undergoing modernization and digitization, and in turn, increasingly relying on AI, ML, and rugged, reliable systems to increase automation, efficiency, safety, and security.

Neither humans nor chatbots have the time for small talk or casual banter, specially when it's time to look for bookings for a vacation. Engati will help you build a chatbot that will understand and interpret the user's language correctly so that the bot is able to respond with the appropriate answer. Like for example, famous restaurants and food joints, like Taco Bell, KFC, and others have incorporated chatbots in their website so that customers can add or remove the items of their choice with just one click. And that's what customers want and are looking for – ease and convenience at their fingertips and freedom to maneuver their choices. Further, chatbots that are trained to have contextual conversations with the users are much in demand.

For decades, those four words--not to be confused with a hit Daft Punk song--have both driven fear into developers and driven sales. However, as the energy burdens for the Internet of Things (IoT), cloud computing, crypto currencies and artificial intelligence (AI) increase, a fifth word is necessary: greener. Xnor.ai (Xnor) isn't scared of the "greener" challenges facing industries today, and the unveiling of its new application-specific integrated circuit (ASIC) technologies proves so. "Power will become the biggest bottleneck to scaling AI," said Ali Farhadi, co-founder of Xnor. "What Xnor has proved today is that it is now possible to run AI inference at such low power that you don't even need a battery. This will change not only the way products are built in the future, but how entire cities and countries deploy AI solutions at scale."

Boaty McBoatface may be better known for its name than for its oceangoing prowess. But the autonomous underwater vehicle and darling of the internet is headed to greater things: embarking on the longest journey of an AUV by far, with an uninterrupted, roughly 2,000-mile crossing of the Arctic Ocean. The submersible robot got its moniker when it became the consolation prize in a 2016 publicity stunt. The United Kingdom's Natural Environmental Research Council had created an online poll to name the country's new polar research ship. The public picked "Boaty McBoatface" (suggested by a BBC radio announcer), but the British government nixed the idea and named the ship after naturalist David Attenborough.

In this paper, we demonstrate a computationally efficient new approach based on deep learning (DL) techniques for analysis, design, and optimization of electromagnetic (EM) nanostructures. We use the strong correlation among features of a generic EM problem to considerably reduce the dimensionality of the problem and thus, the computational complexity, without imposing considerable errors. By employing the dimensionality reduction concept using the more recently demonstrated autoencoder technique, we redefine the conventional many-to-one design problem in EM nanostructures into a one-to-one problem plus a much simpler many-to-one problem, which can be simply solved using an analytic formulation. This approach reduces the computational complexity in solving both the forward problem (i.e., analysis) and the inverse problem (i.e., design) by orders of magnitude compared to conventional approaches. In addition, it provides analytic formulations that, despite their complexity, can be used to obtain intuitive understanding of the physics and dynamics of EM wave interaction with nanostructures with minimal computation requirements. As a proof-of-concept, we applied such an efficacious method to design a new class of on-demand reconfigurable optical metasurfaces based on phase-change materials (PCM). We envision that the integration of such a DL-based technique with full-wave commercial software packages offers a powerful toolkit to facilitate the analysis, design, and optimization of the EM nanostructures as well as explaining, understanding, and predicting the observed responses in such structures.