Autonomous ships just took a small but important step forward. Hyundai's Avikus subsidiary says it has completed the world's first autonomous navigation of a large ship across the ocean. The Prism Courage (pictured) left Freeport in the Gulf of Mexico on May 1st, and used Avikus' AI-powered HiNAS 2.0 system to steer the vessel for half of its roughly 12,427-mile journey to the Boryeong LNG Terminal in South Korea's western Chungcheong Province. The Level 2 self-steering tech was good enough to account for other ships, the weather and differing wave heights. The autonomy spared the crew some work, of course, but it may also have helped the planet. Avikus claims HiNAS' optimal route planning improved the Prism Courage's fuel efficiency by about seven percent, and reduced emissions by five percent.

As the superintendent of Vistra Corp's Luminant Martin Lake Power Plant, Wayne Brown is an expert in power generation. Vistra is the largest competitive power producer in the United States, operating power plants in 12 states and producing more than 39 gigawatts of electricity--enough to power nearly 20 million homes. The company has been on a journey to drive operational excellence across its generation portfolio. Launched in 2016, its Operational Performance Initiative has driven a step-change improvement in the efficiency of its assets, generating hundreds of millions in incremental EBITDA along the way. This article is a collaborative effort by Duane S. Boning, Vijay D'Silva, Pete Kimball, Bruce Lawler, Retsef Levi, and Ingrid Millan, representing views from McKinsey's Operations Practice and the Massachusetts Institute of Technology's Machine Intelligence for Manufacturing and Operations program. To maintain and improve its position, Vistra is continually looking for the tools, technologies, and approaches that will help it achieve the next level of performance. Most recently, the company has turned to digital and analytics, including machine intelligence (MI). It measures how much electricity is generated for each ton of fuel consumed by the plant.

The optimal power flow (OPF) problem, as a critical component of power system operations, becomes increasingly difficult to solve due to the variability, intermittency, and unpredictability of renewable energy brought to the power system. Although traditional optimization techniques, such as stochastic and robust optimization approaches, could be used to address the OPF problem in the face of renewable energy uncertainty, their effectiveness in dealing with large-scale problems remains limited. As a result, deep learning techniques, such as neural networks, have recently been developed to improve computational efficiency in solving large-scale OPF problems. However, the feasibility and optimality of the solution may not be guaranteed. In this paper, we propose an optimization model-informed generative adversarial network (MI-GAN) framework to solve OPF under uncertainty. The main contributions are summarized into three aspects: (1) to ensure feasibility and improve optimality of generated solutions, three important layers are proposed: feasibility filter layer, comparison layer, and gradient-guided layer; (2) in the GAN-based framework, an efficient model-informed selector incorporating these three new layers is established; and (3) a new recursive iteration algorithm is also proposed to improve solution optimality. The numerical results on IEEE test systems show that the proposed method is very effective and promising.

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.

In light of growing concern about the energy requirements of large machine learning models, a recent study from MIT Lincoln Laboratory and Northeastern University has investigated the savings that can be made by power-capping GPUs employed in model training and inference, as well as several other techniques and methods of cutting down AI energy usage. The new work also calls for new AI papers to conclude with an'Energy Statement' (similar to the recent trend for'ethical implication' statements in papers from the machine learning research sector). The chief suggestion from the work is that power-capping (limiting the available power to the GPU that's training the model) offers worthwhile energy-saving benefits, particularly for Masked Language Modeling (MLM), and frameworks such as BERT and its derivatives. Constraining power consumption does not constrain training efficiency or accuracy on a 1-1 basis, and offers power savings that are notable at scale. For larger-scale models, which have captured attention in recent years due to hyperscale datasets and new models with billions or trillions of parameters, similar savings can be obtained as a trade-off between training time and energy usage.

The ability of industries to adapt and mitigate climate change depends on the capabilities the industry developed to adapt to change. This includes how well the company has used digitalisation to adapt to market trends. For example, the insurance industry is automating its processes with AI to increase efficiency and improve its abilities to manage risk. The industries that are able to successfully adapt and digitalise their assets also develop capabilities that make them more resilient to change. By expanding their technological abilities (such as investing in AI), the companies that are leaders of these industries can face and deal with risks and adapt to change.

AI is revolutionizing the world we live in--from the way we entertain ourselves, to the products and services that we consume, to the way we care for our bodies, and how we go about our daily work. Organizations are leveraging the power of AI to transform our lives by accelerating superior product innovations, increasing organization competitiveness no matter their size or available resources, and immersing us into more amazing, photo-realistic virtual worlds in movies and games. At Microsoft, our mission is to empower every person and every organization on the planet to achieve more. With the power and scalability available through Microsoft Azure, we provide the compute tools and capabilities for all organizations no matter their size or resources to do more, faster. AI is a key tool to help organizations innovate and create new capabilities, discover new insights and deliver superior products and services.

Google is bringing artificial intelligence to wind energy. The tech giant is providing wind turbine output prediction software to French utility company Engie to improve efficiency and predictability at Engie's German wind farms. Google Cloud's global energy solutions director compared the technology to a trading recommendations tool, and Google said early tests improved the value of wind energy by 20%. Engie is Google's first customer in this area, and Google says it hopes to expand the service beyond the US$33 billion wind power industry.

Electric utilities are taking a course in machine learning to create smarter grids for tough challenges ahead. The winter 2021 megastorm in Texas left millions without power. Grid failures the past two summers sparked devastating wildfires amid California's record drought. "Extreme weather events of 2021 highlighted the risks climate change is introducing, and the importance of investing in more resilient electricity grids," said a May 2021 report from the International Energy Agency, a group with members from more than 30 countries. It called for a net-zero carbon grid by 2050, fueled by hundreds more gigawatts in renewable sources.

Google Cloud and power company Engie announced today the launch of a new partnership aimed at optimizing the value of Engie's wind portfolio and accelerating wind energy development through the development of a new artificial intelligence-based (AI) solution. The new solution aims to address some of the key challenges for wind power developers and operators, driven by the complexity of the short-term power markets and the unpredictable nature of wind production. Utilizing advanced machine learning algorithms applied to vast amounts of data from disparate sources, the solution will predict how much wind power should be sold on which power market and at what price. The companies highlighted the potential far-reaching impact of the new project, with hundreds of gigawatts of wind farms around the world potentially benefiting from improved AI-based forecasting. "At Google Cloud, we believe that more accurate data and predictions of wind power production will be valuable to electricity grids, creating benefits for consumers and making wind more competitive with fossil fuels. We are delighted to work with ENGIE on this project, which can accelerate Europe's clean energy transition, while laying the groundwork for wind farms around the world to benefit from improved forecasting via Artificial Intelligence."