In line with last year's forecasts, the AI market continues to grow steadily, and in addition to qualitative improvement in technologies, there is a further expansion of the areas in which Artificial Intelligence is being implemented, including such traditional industries as engineering, mining, and agriculture. The spread of AI is due to the fact that the technology has matured enough while continuing to evolve. Above all, we can expect a significant increase in the production of specialized computer chips. Market leaders like NVIDIA, AMD, ARM, and Qualcomm have already begun manufacturing processors optimized for speech recognition and computer vision. According to the experts, the AI chip market will grow by 30-40% this year, while research company Allied Market Research forecasts that the global market could grow to $91.185 billion by 2025.

The energy industry is undergoing a rapid transformation in recent past owing to the enhanced role of renewables and enhanced data-driven models making the value chain smarter. In the context of the primary constituents of this sector comprising of coal, power, renewables, solar energy, oil, and gas, there is a huge role AI can play. The biggest disruption in power in recent times is in the smart grid which is quite flexible in comparison to the traditional grid. AI can be a huge enabler in the form of providing optimal configurations etc to create a really smart and efficient grid. By thorough analysis of data related to losses AI can help prevent transmission and distribution losses.

This paper introduces a novel framework to construct the region of attraction (ROA) of a power system centered around a stable equilibrium by using stable state trajectories of system dynamics. Most existing works on estimating ROA rely on analytical Lyapunov functions, which are subject to two limitations: the analytic Lyapunov functions may not be always readily available, and the resulting ROA may be overly conservative. This work overcomes these two limitations by leveraging the converse Lyapunov theorem in control theory to eliminate the need of an analytic Lyapunov function and learning the unknown Lyapunov function with the Gaussian Process (GP) approach. In addition, a Gaussian Process Upper Confidence Bound (GP-UCB) based sampling algorithm is designed to reconcile the trade-off between the exploitation for enlarging the ROA and the exploration for reducing the uncertainty of sampling region. Within the constructed ROA, it is guaranteed in probability that the system state will converge to the stable equilibrium with a confidence level. Numerical simulations are also conducted to validate the assessment approach for the ROA of the single machine infinite bus system and the New England $39$-bus system. Numerical results demonstrate that our approach can significantly enlarge the estimated ROA compared to that of the analytic Lyapunov counterpart.

Since 1950, when Alan Turing proposed what has since come to be called the Turing test, the ability of a machine to pass this test has established itself as the primary hallmark of general AI. To pass the test, a machine would have to be able to engage in dialogue in such a way that a human interrogator could not distinguish its behaviour from that of a human being. AI researchers have attempted to build machines that could meet this requirement, but they have so far failed. To pass the test, a machine would have to meet two conditions: (i) react appropriately to the variance in human dialogue and (ii) display a human-like personality and intentions. We argue, first, that it is for mathematical reasons impossible to program a machine which can master the enormously complex and constantly evolving pattern of variance which human dialogues contain. And second, that we do not know how to make machines that possess personality and intentions of the sort we find in humans. Since a Turing machine cannot master human dialogue behaviour, we conclude that a Turing machine also cannot possess what is called ``general'' Artificial Intelligence. We do, however, acknowledge the potential of Turing machines to master dialogue behaviour in highly restricted contexts, where what is called ``narrow'' AI can still be of considerable utility.

Why are coal plants in the U.S. and Europe closing at an accelerating rate, while in Asia, coal consumption went up and helped fuel an overall 1.7% year-on-year increase in global carbon emissions? Part of the reason coal continues to grow in countries like China and India is that in these areas, unlike in the U.S., emissions data can be shoddy or hard to acquire. Without accurate information it is harder to hold facilities accountable and keep them in line with meeting emission reduction targets. To address this situation, we are partnering with WattTime and the World Resources Institute (WRI), to launch a new project which will use satellite imagery to quantify carbon emissions from every major power plant across the world. This effort is being funded as one of 20 projects in the Google AI Impact Challenge.

According to a recent investigation, an estimated 33-50% of the world's coral reefs have undergone degradation, believed to be as a result of climate change. A strong driver of climate change and the subsequent environmental impact are greenhouse gases such as methane. However, the exact relation climate change has to the environmental condition cannot be easily established. Remote sensing methods are increasingly being used to quantify and draw connections between rapidly changing climatic conditions and environmental impact. A crucial part of this analysis is processing spectroscopy data using radiative transfer models (RTMs) which is a computationally expensive process and limits their use with high volume imaging spectrometers. This work presents an algorithm that can efficiently emulate RTMs using neural networks leading to a multifold speedup in processing time, and yielding multiple downstream benefits.

This post was written by Katharina Brown, a FSI Global Policy Intern on NTI's Scientific and Technical Affairs team. Katharina is a senior at Stanford University studying Computer Science with a focus on Artificial Intelligence. Today's popular discussion of artificial intelligence (AI) issues reflects the need to debate the implications of new AI-driven technologies, like lethal autonomous weapons systems or self-driving cars, before they become widely adopted. Although there hasn't been as much public debate on AI and machine learning (ML) in the nuclear world, new ML research has the potential to disrupt the nuclear field, and the security implications deserve discussion. These innovations are currently academic projects, not yet solutions that have been adopted by industry or government entities.

Machine learning, Big Data, and automation are revolutionizing global industry – and the energy sector is no exception. Innovation is driving technological progress, boosting economic efficiency, creating smarter business operations, and leading to more resilient infrastructure. It's why businesses and governments around the world are making advanced technology – including artificial intelligence – a top economic and national security priority. Energy companies are implementation big data and AI in versatile ways – and the sector is growing. The market for AI software in the oil and gas industry is expected to reach a whopping $2.85 billion by 2022.

A tiny crack in a nuclear reactor, skyscraper, bridge or dam can cause catastrophic consequences. The Minneapolis bridge collapse, which killed 13 people in 2007, is just one example of what can happen when structural integrity is compromised. Unidentified or under-identified structural damage in nuclear reactors can be cataclysmic. Inspection of critical systems such as nuclear reactors is complicated and time-consuming. Videos captured by an automatic crack detection system can easily misidentify small scratches or welds as cracks, so technicians must review videos frame by frame.

Native palms, such as the Sabal palmetto, play an important role in maintaining the ecological balance in Florida. As a potential side-effect of modern globalization, new phytopathogens like Texas Phoenix Palm Decline (TPPD) have been introduced into forest systems that threaten native palms. This presents new challenges for forestry managers and geographers. Advances in remote sensing have assisted the practice of forestry by providing spatial metrics regarding the type, quantity, location, and the state of health for trees for many years. Spatial details regarding the general palm decline in Florida were elucidated by using the tools found in Hexagon's ERDAS IMAGINE software in conjunction with R classification programing packages.