New invention hailed as the "Holy Grail" of solar energy and "game changer" for the planet A Bill Gates-backed startup just announced a technological breakthrough in solar energy that could finally put fossil fuel to rest. The clean energy company Heliogen has developed a solar oven that reach temperatures a quarter as hot as the surface of the sun. That means solar energy just became powerful enough to fuel industrial processes it couldn't touch before. Until now, the extreme heat required to make cement, steel, glass and other industrial materials could not be generated efficiently by solar. But now, the scientists at Heliogen have figured out how to create a 1000-degree-celcius solar oven with a field of mirrors all aimed at a single point.

Lightning is one of the most unpredictable phenomena in nature. It regularly kills people and animals and sets fire to homes and forests. It keeps aircraft grounded and damages power lines, wind turbines and solar-panel installations. However, little is known about what triggers lightning, and there is no simple technology for predicting when and where lightning will strike the ground. At EPFL's School of Engineering, researchers in the Electromagnetic Compatibility Laboratory, led by Farhad Rachidi, have developed a simple and inexpensive system that can predict when lightning will strike to the nearest 10 to 30 minutes, within a 30-kilometer radius.

Designing the best molecular building blocks for battery components is like trying to create a recipe for a new kind of cake, when you have billions of potential ingredients. The challenge involves determining which ingredients work best together--or, more simply, produce an edible (or, in the case of batteries, a safe) product. But even with state-of-the-art supercomputers, scientists cannot precisely model the chemical characteristics of every molecule that could prove to be the basis of a next-generation battery material. Instead, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have turned to the power of machine learning and artificial intelligence to dramatically accelerate the process of battery discovery. As described in two new papers, Argonne researchers first created a highly accurate database of roughly 133,000 small organic molecules that could form the basis of battery electrolytes.

On a sturdy workbench in seismologist Chris Marone's lab on the fifth floor of the geosciences building at Pennsylvania State University (Penn State) sits a large steel-framed machine with thick hydraulic pistons that force metal blocks and plates to grind past each other under extreme pressure. When the device is running, Marone sometimes closes the door to the lab so the loud bangs of "laboratory earthquakes" do not disrupt people across the hall. Lately, however, it has been the quieter sounds emanating from the machine that have caused a disruption in the field of seismology. In a recent spate of studies, researchers applied machine learning to acoustic emission data from Marone's earthquake machine, as well as from natural faults. The work led to the discovery of a new relationship between a fault's acoustic emissions and its physical characteristics, including its frictional state, its displacement rate, and the timing and magnitude of its next failure.

Most companies report measurable benefits from AI where it has been deployed; however, much work remains to scale impact, manage risks, and retrain the workforce. A group of high performers shows the way. Adoption of artificial intelligence (AI) continues to increase, and the technology is generating returns. The findings of the latest McKinsey Global Survey on the subject show a nearly 25 percent year-over-year increase in the use of AI in standard business processes, with a sizable jump from the past year in companies using AI across multiple areas of their business. A majority of executives whose companies have adopted AI report that it has provided an uptick in revenue in the business areas where it is used, and 44 percent say AI has reduced costs.

Helping to locate new oil and gas sources. Another large AI project is aimed at helping the company find new sources of oil and gas by cleaning up data from seismic surveys, which are used to create images of rock formations that in turn help scientists locate oil deposits below the ocean floor. The problem, historically, has been that these surveys don't paint a clear picture of what rock formations look like. Underwater currents and other factors produce noisy data that affects the images. Shell created machine-learning algorithms, based on images the company has cleaned, to filter out that noise.

Hetal Yagnik Associate Director – Strategy & Operations Polymerupdate Abstract The presentation will cover how artificial intelligence has gained prominence in petrochemical industry. The increased volatility in polymer prices increases the difficulty of forecasting accurately, making the simple methods less reliable. To overcome these limitations, machine learning (ML) models were used as an alternative to conventional models. He has over 18 years of experience in Sales & Marketing, Market Research & Analytics, Competitive Intelligence, Consulting & General Management area. He has experience in delivering clients with actionable research reports in various industries including pharmaceuticals, chemical, petrochemical, automobile, agriculture equipments.

For too long, we have drawn an artificial line between content and data, as well as between unstructured and structured information. This approach has the unfortunate effect of relegating content to a secondary status – a business area that represents a complex problem to solve (or live with) rather than an opportunity to exploit. By most estimates, content – or, if you will, unstructured information – represents more than 80 percent of all information. More importantly, almost all human-generated information is content. Content literally provides the basis for how the modern enterprise conducts its work. Content is how we communicate, and collaborate with one another.

Abstract--In this letter, an age of information (AoI)-aware transmission power and resource block (RB) allocation tech nique for vehicular communication networks is proposed. Due to the highly dynamic nature of vehicular networks, gaining a prior knowledge about the network dynamics, i.e., wireless channels and interference, in order to allocate resources, is challenging. Therefore, to effectively allocate power and RBs, the proposed approach allows the network to actively learn its dynamics by balancing a tradeoff between minimizing the probability that the vehicles' AoI exceeds a predefined thre shold and maximizing the knowledge about the network dynamics. In this regard, using a Gaussian process regression (GPR) approach, an online decentralized strategy is proposed to a ctively learn the network dynamics, estimate the vehicles' future A oI, and proactively allocate resources. Simulation results sh ow a significant improvement in terms of AoI violation probabili ty, compared to several baselines, with a reduction of at least 50%.

The automated nature of such attacks means that they can be launched at speeds far in excess of what humans are capable of, he said, suggesting that attacks could happen on a microsecond-by-microsecond level. "We're going to have to understand the implications of, not people-to-machine attacks, but machine-to-machine attacks," said Mr. Fanning. Some security teams are using AI defensively, but cybersecurity leaders across sectors worry that the same technology could propel sophisticated attacks that will be difficult to fend off. A congressional report published last year raised the possibility of AI-based attacks overwhelming grid defenses. Utilities need to invest in defenses and do so quickly, said Mark James, an adjunct professor of law at Vermont Law School and a co-author of a report on state power utilities' cybersecurity practices, published this month.