Meanwhile, business units focus on simplicity and effectiveness, deploying robotics and edge computing to meet site-specific needs and ensure safety. "From a digital transformation standpoint, what I have learned is that you have to tie your technology to what outcomes drive results for both areas, but you have to allow yourself to be flexible, to be nimble, and to understand that change is constant," he says. Central to this transformation is the rise of industrial AI. Unlike consumer applications, industrial AI operates in high-stakes environments where the cost of errors can be severe. "The wealth of potential information needs to be contextualized, modeled, and governed because of the safety of those underlying processes," says Niño. "If a machine reacts in ways you don't expect, people could get hurt, and so there's an extra level of care that needs to happen and that we need to think about as we deploy these technologies."

Folding is an unique structural technique to enable planer materials with motion or 3D mechanical properties. Textile-based capacitive sensing has shown to be sensitive to the geometry deformation and relative motion of conductive textiles. In this work, we propose a novel self-tracking foldable smart textile by combining folded fabric structures and capacitive sensing to detect the structural motions using state-of-the-art sensing circuits and deep learning technologies. We created two folding patterns, Accordion and Chevron, each with two layouts of capacitive sensors in the form of thermobonded conductive textile patches. In an experiment of manually moving patches of the folding patterns, we developed deep neural network to learn and reconstruct the vision-tracked shape of the patches. Through our approach, the geometry primitives defining the patch shape can be reconstructed from the capacitive signals with R-squared value of up to 95\% and tracking error of 1cm for 22.5cm long patches. With mechanical, electrical and sensing properties, Capafoldable could enable a new range of smart textile applications.

However, a separate company document does mention investments in 5 startups that use data analytics, computer vision, natural language processing, and machine learning technologies. We will begin by taking a closer look at Chevron's partnership with Microsoft in leveraging AI capabilities to streamline workflows in extracting data from drilling and completion reports. We will begin by taking a closer look at Chevron's partnership with Microsoft in leveraging AI capabilities to streamline workflows in extracting data from drilling and completion reports. Microsoft states that this process was error-prone and suffered from being unable to take advantage of valuable data that rig managers could use to improve their drilling performance and decision-making abilities. Microsoft implemented its document processing solution, Form Recognizer, to achieve Chevron Canada's goal of more efficient, insight-driven data extraction.

Nick DeNittis writes and edits AI industry trends and use-cases for Emerj's editorial and client content. Nick holds an MS in Management from Troy University and has earned several professional analytics certificates, including from the Wharton School. Chevron is the second largest producer of oil in the United States (Exxon Mobil). The company is traded on the NYSE (symbol: CVX) and has a market capitalization of approximately $322 billion. In its 2021 annual report, the company reported approximately $15.6 billion in net income on $155.6 billion in revenue.

At the SIS Global Forum 2019, Chevron, Microsoft and Schlumberger, announced the industry's first three-party collaboration to accelerate the creation of innovative digital and petro-technical technologies. The three companies will work together to build Azure-native applications in the DELFI cognitive E&P environment initially for Chevron, which will enable companies to process, visualize, interpret and eventually obtain meaningful and vital insights from numerous data sources. The collaboration will be completed in three phases, starting with the deployment of the Petrotechnical Suite in the DELFI environment. This will be followed by the development of cloud-native applications on Azure and the co-innovation of a suite of cognitive computing native compatibilities across the E&P value chain tailored Chevron's objectives. "Combining the expertise of these three global enterprises creates vastly improved and digitally-enabled Petrotechnical workflows. Never before has our industry seen a collaboration of this kind, and of this scale. Working together will accelerate faster innovation with better results, marking the beginning of a new era in our industry that will enable us to elevate performance across our industry's value chain," said Olivier Le Peuch, Chief Executive Officer, Schlumberger.

The oil price crash of 2014 and the global'digitalization and disruption' drive coincided in a rather bizarre way to push the oil industry to seek cost cuts through innovation and new technologies. Big Tech was only too pleased to help Big Oil, seeing a new revenue stream in an industry long thought to be of the'dinosaur' type that was too slow to embrace new ways of doing things. Many oil and gas firms, especially the world's biggest, are already using data analytics, cloud computing, digital oil fields, digital twins, robotics, automation, predictive maintenance, machine learning, and even AI. The technology giants have seized the opportunity to sell such services to Big Oil, and top managers at Amazon Web Services, Microsoft Azure, and ABB Group, to name a few, flocked to this week's top energy industry event CERAWeek by IHS Markit in Houston to pitch their solutions to a wider audience. "A great wave of innovation and technology is transforming the industry and reshaping the energy future," said Daniel Yergin, conference chair and vice chairman of IHS Markit.

At Next '18 this past July, we announced a range of updates to our AI and machine learning offerings aimed at making AI more accessible to developers. With the excitement of Next behind us, we thought we'd share a little more on these updates and how they can help you quickly and easily inject AI into your applications. Cloud AutoML is a suite of machine learning products that leverages Google's state-of-the-art transfer learning and neural architecture search (NAS) technology so you can easily train high quality custom models, even if you have limited experience with machine learning. This delivers the best of both worlds: high model quality and ease of use. This new suite of products aligns with our mission to democratize AI, and make it easy, fast and useful for all developers and enterprises.

HOUSTON--A Google executive wearing white jeans and a navy T-shirt stood before a roomful of suit-clad oil executives here last month and delivered a blunt sales pitch: We can manage your data better than you. Darryl Willis, part of a new group Google has created to court the oil and gas industry, said energy companies have reams of data but only use 5% of it, a serious problem in the digital economy. Signing a cloud deal with Google, part of Alphabet Inc., GOOGL 3.89% could solve that, he argued. "Companies in the oil and gas industry will either be a catalyst for change or they will be a casualty of change," he said during a presentation at the Unify Conference, an industry forum on digital technology put on by Baker Hughes, a part of General Electric Co. Silicon Valley has come to Houston, as tech companies push to sign oil and gas companies to lucrative cloud and artificial intelligence deals. In recent months, companies including Chevron Corp. CVX 2.08%, Equinor AS EQNR 1.00% A, Total SA TOT 1.14% and Repsol SA REP -0.06% have entered into contracts with companies such as Google and Microsoft Corp. MSFT -0.29% collectively worth billions of dollars.

Boone will be contributing the insights and experience of her position to the board's role in defining and assisting the strategy of the Energistics Consortium in the coming years. "We are very pleased to welcome Kim Boone to the Energistics board," said Ross Philo, president and CEO of Energistics, "Her unique cross-discipline view of Exploration & Production computing will be of great support to the organization as we accelerate the deployment of the new integrated data exchange standards first made available in 2016." Her biography as well as those of the other board members can be found at www.energistics.org/board-of-directors. About Energistics Energistics is the leading upstream oil and gas industry's data standards body. We are a global, non-profit consortium established over 25 years ago to bring together industry professionals in a neutral and collaborative environment to develop and deploy open data exchange standards and to address oil and gas information sharing challenges.

Tech giants Apple (AAPL), Alphabet (GOOGL), Facebook (FB), and Microsoft (MSFT) have raced to apply artificial intelligence to their businesses, and the oil industry is starting to seize on AI's benefits too. The reason interest is surging now is because artificial intelligence is "actually doable," he said in an interview with IBD at CERAWeek, explaining that advancements in cloud computing and infrastructure have made AI more affordable and accessible. "The industrial world is waking up to best practices," he said. "They are all waking up to it." Several heavyweights in the energy industry are already investors in his company, including General Electric (GE), Chevron (CVX), Royal Dutch Shell (RDSA) and Saudi Aramco.