The rapid adoption of deep learning models has resulted in many business services becoming model services, yet most AI systems lack the necessary automation and industrialization capabilities. This leads to heavy reliance on manual operation and maintenance, which not only consumes power but also causes resource wastage and stability issues during system mutations. The inadequate self-adaptation of AI systems poses significant challenges in terms of cost-effectiveness and operational stability. Principles and Applications of Adaptive Artificial Intelligence, edited by Zhihan Lv from Uppsala University, Sweden, offers a comprehensive solution to the self-adaptation problem in AI systems. It explores the latest concepts, technologies, and applications of Adaptive AI, equipping academic scholars and professionals with the necessary knowledge to overcome the challenges faced by traditional business logic transformed into model services. With its problem-solving approach, real-world case studies, and thorough analysis, the Handbook provides practitioners with practical ideas and solutions, while also serving as a valuable teaching material and reference guide for students and educators in AI-related disciplines. By emphasizing self-adaptation, continuous model iteration, and dynamic learning based on real-time feedback, the book empowers readers to significantly enhance the cost-effectiveness and operational stability of AI systems, making it an indispensable resource for researchers, professionals, and students seeking to revolutionize their research and applications in the field of Adaptive AI.
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Zhihan Lv, PhD Ocean University of China, is an IEEE Senior Member, British Computer Society Fellow, ACM Distinguished Speaker, Careerlong Scientific Influence Rankings of Stanford's Top 2% Scientists, Marie Skłodowska-Curie Fellow, Clarivate Highly Cited Researcher, Elsevier Highly Cited Chinese Researcher. They have contributed 300 papers including more than 90 papers on IEEE/ACM Transactions. I'm Editor-in-Chief of Internet of Things and Cyber-Physical Systems(KeAi), an Associate Editor of ACM Transactions on Multimedia Computing, Communications, and Applications, IEEE Transactions on Intelligent Transportation System, IEEE Transactions on Network and Service Management, IEEE Transaction on Computational Social Systems, IEEE Transactions on Network Science and Engineering, IEEE Consumer Electronics Magazine, IEEE Technology Policy and Ethics Newsletter, Early Career Advisory Board member of IEEE/CAA Journal of Automatica Sinica. They are General Chair, Co-Chair or TPC of 50 conferences including Area Chair of ACM MM 2021-2022, Workshop Chair of ACM MM 2023, Online Experience Chair of IEEE VR 2023, Sponsorship Chair of MobileHCI 2023, Program Committee member of ACMIUI 2015-2023. They have reviewed 400 papers, and have received more than 20 awards from China, Europe, IEEE. They have supervised students to get more than 20 awards, and has won 7 Best Paper awards. They also have given 80 invited talks for universities and companies in Europe and China, 20 keynote talks for International conferences. They have studied at the French National Centre for Scientific Research(CNRS)-UPR9080 at Paris in France, at Umea University at Umea in Sweden, at Virtual Environments and Computer Graphics(VECG) group at University College London(UCL) at London in UK, and at Event Lab at University of Barcelona at Barcelona in Spain.
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Paperback. Etat : new. Paperback. The rapid adoption of deep learning models has resulted in many business services becoming model services, yet most AI systems lack the necessary automation and industrialization capabilities. This leads to heavy reliance on manual operation and maintenance, which not only consumes power but also causes resource wastage and stability issues during system mutations. The inadequate self-adaptation of AI systems poses significant challenges in terms of cost-effectiveness and operational stability. Principles and Applications of Adaptive Artificial Intelligence, edited by Zhihan Lv from Uppsala University, Sweden, offers a comprehensive solution to the self-adaptation problem in AI systems. It explores the latest concepts, technologies, and applications of Adaptive AI, equipping academic scholars and professionals with the necessary knowledge to overcome the challenges faced by traditional business logic transformed into model services. With its problem-solving approach, real-world case studies, and thorough analysis, the Handbook provides practitioners with practical ideas and solutions, while also serving as a valuable teaching material and reference guide for students and educators in AI-related disciplines. By emphasizing self-adaptation, continuous model iteration, and dynamic learning based on real-time feedback, the book empowers readers to significantly enhance the cost-effectiveness and operational stability of AI systems, making it an indispensable resource for researchers, professionals, and students seeking to revolutionize their research and applications in the field of Adaptive AI. "This book covers the latest and cutting-edge application technology of Adaptive AI in various fields. It can provide relevant practitioners with ideas to solve problems and have a deeper understanding of Adaptive AI. At the same time, guide and help Adaptive AI and related industries to deepen their understanding of the industry and enhance their professional knowledge and skills"-- Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798369302316
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