Principles And Applications Of Adaptive Artificial Intelligence

"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"--

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 of Uppsala University, Sweden, offers a comprehensive solution to the self-adaptation issues 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, the handbook presents real-world cases, along with thorough analysis and relatable examples, making it an invaluable resource for practitioners seeking practical ideas and solutions in the field. Additionally, the book serves as a teaching material and reference guide for students and educators in AI-related disciplines, ensuring a deep understanding and exploration of the emerging discipline of Adaptive AI. 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. In a rapidly changing landscape, Principles and Applications of Adaptive Artificial Intelligence becomes the ultimate guide for researchers, professionals, and students, enabling them to unleash the full potential of Adaptive AI and revolutionize their research and applications.

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.

These original contributions provide a unique opportunity for researchers and computing professionals, engineers, and managers to explore both the principles underlying basic AI research and their application in practice. The first part of the book describes work in five areas of AI research that is currently at the stage where it can be implemented in practical programs. These areas include blackboard architectures and systems, learning algorithms and strategies, neural networks, adaptive learning using pattern recognition, and signal processing. The second part describes six systems, designed for a wide variety of applications, that are now either in operation or at an advanced stage of development; intelligent techniques for spectral estimation, expert systems applied to antenatal assessment of fetal well-being, AI in the processing of underwater acoustic data, automatic speech recognition using neural networks, fault diagnosis of microwave digital radio, and waveguide filter alignment using adaptive learning techniques. A. R. Mirzai is a Research Fellow in the Department of Electrical Engineering at the University of Edinburgh. Artificial Intelligence: Concepts and Applications is included in the Artificial Intelligence series, edited by Michael Brady, Daniel Bobrow, and Randall Davis.

The rapid proliferation of connected devices in our daily lives, from smart homes to industrial sensors, has led to an explosion of data that requires processing before it is useful to experts. However, modern devices often have limited resources, making it challenging to decode and utilize this data effectively. Additionally, the need for real-time decision-making further complicates this issue, as traditional data processing methods take far too long to be able to keep up with the required volume and speed. Developments Towards Next Generation Intelligent Systems for Sustainable Development offers a comprehensive solution to these challenges by integrating novel technologies such as AI, edge computing, federated learning, quantum computing, and more. The book shows how intelligent systems can maximize computing power by leveraging these technologies to process large volumes of data efficiently and autonomously and make real-time decisions. The proposed architectures and frameworks focus on real-time analysis, faster decision-making, enhanced privacy, and efficient data processing.

In the digital age, the relentless growth of data centers and cloud computing has given rise to a pressing dilemma. The power consumption of these facilities is spiraling out of control, emitting massive amounts of carbon dioxide, and contributing to the ever-increasing threat of global warming. Studies show that data centers alone are responsible for nearly eighty million metric tons of CO2 emissions worldwide, and this figure is poised to skyrocket to a staggering 8000 TWh by 2030 unless we revolutionize our approach to computing resource management. The root of this problem lies in inefficient resource allocation within cloud environments, as service providers often over-provision computing resources to avoid Service Level Agreement (SLA) violations, leading to both underutilization of resources and a significant increase in energy consumption. Computational Intelligence for Green Cloud Computing and Digital Waste Management stands as a beacon of hope in the face of the environmental and technological challenges we face. It introduces the concept of green computing, dedicated to creating an eco-friendly computing environment. The book explores innovative, intelligent resource management methods that can significantly reduce the power consumption of data centers. From machine learning and deep learning solutions to green virtualization technologies, this comprehensive guide explores innovative approaches to address the pressing challenges of green computing. Whether you are an educator teaching about green computing, an environmentalist seeking sustainability solutions, an industry professional navigating the digital landscape, a resolute researcher, or simply someone intrigued by the intersection of technology and sustainability, this book offers an indispensable resource.

In the advancing fields of artificial intelligence (AI) and data science, a pressing ethical dilemma arises. As technology continues its relentless march forward, ethical considerations within these domains become increasingly complex and critical. Bias in algorithms, lack of transparency, data privacy breaches, and the broader societal repercussions of AI applications are demanding urgent attention. This ethical quandary poses a formidable challenge for researchers, academics, and industry professionals alike, threatening the very foundation of responsible technological innovation. Navigating this ethical minefield requires a comprehensive understanding of the multifaceted issues at hand. The Ethical Frontier of AI and Data Analysis is an indispensable resource crafted to address the ethical challenges that define the future of AI and data science. Researchers and academics who find themselves at the forefront of this challenge are grappling with the evolving landscape of AI and data science ethics. Underscoring the need for this book is the current lack of clarity on ethical frameworks, bias mitigation strategies, and the broader societal implications, which hinder progress and leave a void in the discourse. As the demand for responsible AI solutions intensifies, the imperative for this reliable guide that consolidates, explores, and advances the dialogue on ethical considerations grows exponentially.