HP Enterprise has partnered with the National Renewable Energy Laboratory (NREL), a unit of the Department of Energy, to create AI and machine learning-systems for greater data-center energy efficiency. The Department of Energy lab will provide HPE with multiple years' worth of historical data from sensors within its supercomputers and in its Energy Systems Integration Facility (ESIF) High-Performance Computing (HPC) Data Center, one of the world's most efficient data centers. This information will help other organizations to optimize their own operations, said NREL. The project, dubbed "AI Ops R&D collaboration," is expected to run over three years. Already NREL has 16 terabytes of data from the ESIF data center, collected from sensors in NREL's supercomputers, Peregrine and Eagle, and its facility.

Declaring as much is a favorite line of his whenever someone asks what his two-year-old company, Hypergiant, does. What he means is that he doesn't produce anything as uniform and universal as utensils. Were he a purveyor of tableware, he wouldn't have to spend so much of his time customizing products to individual clients or explaining what can be done with them. Everybody knows what spoons are for. Contrast that with the broadest definition of what Hypergiant does in fact sell--artificial intelligence-enabled software and hardware--and you'll appreciate Lamm's problem. Even many people lacking in technological savvy have heard of AI as a force with the potential to shape much of humanity's future--for better or worse.

India and Sweden on Monday announced the launch of a pilot project to convert paddy stubble into green coal in Mohali, Punjab, as Prime Minister Narendra Modi and visiting Swedish King Carl XVI Gustaf inaugurated a bilateral high-level policy dialogue on innovation policy. The dialogue created a platform for key stakeholders from the government, private sector and academia to provide strategic direction for joint innovation policy formulation. The dialogue jointly formulated and implemented short- and long-term projects in strategic areas such as, but not limited to, circular economy, digital health, artificial intelligence, sustainable energy and future mobility, a statement said. The dialogue brought together government officials, prominent industrialists as well as renowned academicians from both Sweden and India. Sweden's Minister for Business, Industry and Innovation Ibrahim Baylan, and Harsh Vardhan, Union Minister for Science and Technology, Earth Sciences, Health and Family Welfare, were also present for the dialogue.

Chemical process industries (CPI) companies are entering a critical stage in the movement toward digitalization (Industry 4.0), in which the majority of organizations are now initiating pilot projects aimed at improving operations with advanced digital tools. This includes a wide range of technologies, including data analytics, cloud computing, machine learning, artificial intelligence and many others. As the digitalization transformation of the CPI gains momentum, it has become clear that the movement is as much about people as it is about technology. The acceptance and involvement of workers is critical to the successful adoption and expansion of digital tools, as they are asked to adapt to new work practices. He emphasizes: "Companies don't adopt new technologies; people do."

A method for machine learning and serving of discrete field theories in physics is developed. The learning algorithm trains a discrete field theory from a set of observational data on a spacetime lattice, and the serving algorithm uses the learned discrete field theory to predict new observations of the field for new boundary and initial conditions. The approach to learn discrete field theories overcomes the difficulties associated with learning continuous theories by artificial intelligence. The serving algorithm of discrete field theories belongs to the family of structure-preserving geometric algorithms, which have been proven to be superior to the conventional algorithms based on discretization of differential equations. The effectiveness of the method and algorithms developed is demonstrated using the examples of nonlinear oscillations and the Kepler problem. In particular, the learning algorithm learns a discrete field theory from a set of data of planetary orbits similar to what Kepler inherited from Tycho Brahe in 1601, and the serving algorithm correctly predicts other planetary orbits, including parabolic and hyperbolic escaping orbits, of the solar system without learning or knowing Newton's laws of motion and universal gravitation. The proposed algorithms are also applicable when effects of special relativity and general relativity are important. The illustrated advantages of discrete field theories relative to continuous theories in terms of machine learning compatibility are consistent with Bostrom's simulation hypothesis.

This paper attempts to design an intelligent simulation model for pedestrian crowd evacuation. For this purpose, the cellular automata (CA) was fully integrated with fuzzy logi c, the k th nearest neighbors ( K NN), and some statistical equations. In this model, each pedestrian was assigned a specific speed, according to his/her physical, biological and emotional features. The emergency behavior and evacuation efficiency of each pedestrian were evaluated by coupling his/her speed with various elements, such as environment, pedestrian distribution and familiarity with the exits. These elements all have great impacts on the ev acuation process. Several experiments were carried out to verify the performance of the model in different emergency scenarios. The results show that the proposed model can predict the evacuation time and emergency behavior in various types of building int eriors and pedestrian distributions. The research provides a good reference to the design of building evacuation systems.

Argonne researchers are using artificial intelligence to speed up the day-ahead electricity market clearing and real-time operations. The electricity market clearing and grid operations rely on the security constrained unit commitment, or SCUC, which helps grid operators set a schedule for daily and hourly power generation. As the SCUC problem is solved multiple times a day, data accumulates that can be used to discover patterns applicable to solving the next round of problems. To that end, Argonne researchers have developed AI that now can solve a SCUC about 12 times faster than conventional methods. Researchers continue to refine the method, an early version of which was used successfully in tests at Midcontinent Independent System Operator (MISO), overseeing electricity market and delivery across 15 states and one Canadian province.

According to a recent press release, "Today, the U.S. Department of Energy's (DOE's) Advanced Research Projects Agency-Energy (ARPA-E) announced $15 million in funding for 23 projects to accelerate the incorporation of machine learning and artificial intelligence into the energy technology and product design processes as part of the Design Intelligence Fostering Formidable Energy Reduction (and) Enabling Novel Totally Impactful Advanced Technology Enhancements (DIFFERENTIATE) program. Launched in April of this year, the DIFFERENTIATE program aims to develop streamlined solutions to next-generation energy challenges. The program identified three general mathematical optimization problems that are common to many design processes. The selected projects then conceptualized machine learning and artificial intelligence-based solutions to help engineers execute and solve these problems in a manner that dramatically accelerates the pace of energy innovation." The release goes on, "Following the initial round of Phase I funding for the DIFFERENTIATE program, additional funding will be available to qualifying awardees at a future date… DIFFERENTIATE projects include: Iowa State University – Ames, Iowa. Iowa State University will develop machine learning tools to accelerate the inverse design of new microstructures in photovoltaics. The team will create a new deep generative model to combat challenges in real-world inverse design problems. The proposed inverse design tools, if successful, will produce novel, manufacturable material microstructures with improved electromagnetic properties relative to existing technology."

Our cities are growing at an uncontrollable rate. The UN estimates that there are now 33 megacities with a population of over 10 million, (five in India and six- or more-in China), and the largest city in the world, Tokyo, has close to 37.5 million people. As cities sprawl into green space and their inhabitants endure increasingly cramped and polluted conditions, accurate planning about how urban spaces function is more important than ever. With the climate crisis looming, data and new technology could be our best option to create more liveable and sustainable cities. Part one of this series will focus on visualizing how cities are growing, how to plan them more accurately and sustainably, and explore how smart technologies can make cities more efficient now and in the future.

New breakthroughs in AI make the headlines everyday. Far from the buzz of customer-facing businesses, the wide adoption and powerful applications of Machine Learning in Finance are less well known. In fact, there are few domains with as much historical, clean and structured data as the financial industry -- making it one of those predestined use cases where'learning machines' made an early mark with tremendous success that still continues. About three years ago, I got involved in developing Machine Learning (ML) models for price predictions and algorithmic trading in Energy markets, specifically for the European market of Carbon emission certificates. In this article, I want to share some of the learnings, approaches and insights which I have found relevant in all my ML projects since.