Subject Resources: Artificial Intelligence: EBooks

This book provides theoretical and practical knowledge about swarm and evolutionary approach of generative AI and Large Language Models (LLMs). The development of such tools contributes to better optimizing methodologies with the integration of several machinelearning and deep learning techniques. In particular, it discusses how the "emergence" concept can contribute to the improvement of AI.The book aims to model human cognitive f unction in terms of "emergence" and to explain the feasibility of AI. To this end, it focuses on human perceptions of "utility." It describes the emergence of various cognitive errors, and irrational behaviours in the multiobjective situations. It also reviews the cognitive differences and similarities between humans and LLMs. Such studies are important when applying LLMs to real-world tasks that involve human cognition, e.g., financial engineering and market issues.The book describes the intelligent behaviour of living organisms. This is to clarify how to achieve AI in the direction of artificial life. It describes sexual selection, which is a well-known natural phenomenon that troubled Darwin, i.e., why evolutionarily useless items evolved such as peacock feathers and moose antlers etc. The book shows how sexual selection is extended as "novelty search" for the application of generative AI, i.e., the image generation with diffusion model. Real-world applications are emphasised. Empirical examples from real-world data show how the concept of deep swarm and evolution is successfully applied when addressing tasks from such recent fields as robotics, e-commerce Web Shop and image generation etc.

Parallel and High-Performance Computing in Artificial Intelligence explores high-performance architectures for data-intensive applications as well as efficient analytical strategies to speed up data processing and applications in automation, machine learning, deep learning, healthcare, bioinformatics, natural language processing (NLP), and vision intelligence.The book's two major themes are high-performance computing (HPC) architecture and techniques and their application in artificial intelligence. Highlights include: HPC use cases, application programming interfaces (APIs), and applications Parallelization techniques HPC for machine learning Implementation of parallel computing with AI in big data analytics HPC with AI in healthcare systems AI in industrial automation Coverage of HPC architecture and techniques includes multicore architectures, parallel-computing techniques, and APIs, as well as dependence analysis for parallel computing. The book also covers hardware acceleration techniques, including those for GPU acceleration to power big data systems.As AI is increasingly being integrated into HPC applications, the book explores emerging and practical applications in such domains as healthcare, agriculture, bioinformatics, and industrial automation. It illustrates technologies and methodologies to boost the velocity and scale of AI analysis for fast discovery. Data scientists and researchers can benefit from the book's discussion on AI-based HPC applications that can process higher volumes of data, provide more realistic simulations, and guide more accurate predictions. The book also focuses on deep learning and edge computing methodologies with HPC and presents recent research on methodologies and applications of HPC in AI.

Industry 4.0 and Industry 5.0 applications will revolutionize production, enabling smart manufacturing machines to interact with their environments. These machines will become self-aware, self-learning, and capable of real-time data interpretation for self-diagnosis and prevention of production issues. They will also self-calibrate and prioritize tasks to enhance production quality and efficiency.Computational Intelligence for Analysis of Trends in Industry 4.0 and 5.0 examines the trends in applications that merge three key disciplines: Computational Intelligence (CI), Industry 4.0, and Industry 5.0. It presents solutions using industrial Internet of Things (IIoT) technologies, augmented by CI-based techniques, modeling, controls, estimations, applications, systems, and future scopes. These applications use data from smart sensors, processed through enhanced CI methods, to make smart automation more effective.Industry 4.0 integrates data and intelligent automation into manufacturing, using technologies like CI, Internet of Things (IoT), IIoT, and cloud computing. It transforms data into actionable insights for decision-making and process optimization, essential for modern competitive businesses managing high-speed data integration in production processes. Currently, Industry 4.0 and Industry 5.0 are undergoing significant transformations due to advances in applying artificial intelligence (AI), big data analytics, telecommunication technologies, and control theory. These trends are increasingly multidisciplinary, integrating mechanical, control, and information technologies. However, they face technical challenges such as parametric uncertainties, external disturbances, sensor noise, and mechanical failures. To address these issues, this book examines trends such as CI technologies as fuzzy logic, neural networks, and reinforcement learning and their application to modeling, control, and estimation. It also covers recent advancements in IIoT sensors, microcontrollers, and big data analytics that further enhance CI-based solutions in Industry 4.0 and Industry 5.0 systems.

The book highlights how technologies including artificial intelligence and machine learning are transforming renewable energy technologies and enabling the development of new solutions. It further discusses how smart technologies are employed to optimize energy production and storage, enhance energy efficiency, and improve the overall sustainability of energy systems. This book: Discusses artificial intelligence-based techniques namely neural networks, fuzzy expert systems, optimization techniques, and operational research. Showcases the importance of artificial intelligence and machine learning in the energy market, demand analysis, and forecasting of renewable energy applications. Illustrates strategies for sustainable development using artificial intelligence and machine learning applications. Presents applications of artificial intelligence in the domain of electronics transformation and development, smart cities, and renewable energy utilization. Highlights the role of artificial intelligence in solving problems such as image and signal processing, smart weather monitoring, smart farming, and distributed energy sources. It is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, energy, and environmental engineering.

This comprehensive guide to the emerging areas and synergistic relationships among the domains of 6G, machine learning, and embedded systems offers readers a detailed analysis of their converging paths and contributions to the development of intelligent wireless systems. Readers will gain a solid understanding of the principles and technologies behind 6G, machine learning, and embedded systems. They will learn how these three areas intertwine and why this intersection is pivotal for the next generation of wireless technologies.The contributors to this volume present a thorough and detailed analysis of this technology, highlighting its promising features, underlying technologies, and potential applications. The book first explores various applications of machine learning algorithms in areas such as network optimization, resource allocation, interference management, and intelligent data processing and analysis. Design considerations and challenges are presented, and case studies of innovative applications, such as smart cities, autonomous vehicles, healthcare, and industrial automation, are examined. The book concludes with a discussion of future trends and opportunities in this rapidly evolving field. Readers will benefit from the theoretical foundations and practical insights presented within and will be prepared to address future challenges and opportunities in these three fields.This book is a valuable resource for academic researchers and industry professionals working in the fields of wireless communication, machine learning, embedded systems, and artificial intelligence.

The book will help the readers in the field of the Internet of Things, and especially its convergence with artificial intelligence, which has given rise to the new paradigm of artificial intelligence of things (AIoT). It covers important concepts such as intelligent space and human-centered robotics and its effect on human wellbeing, and human-centered aviation automation.This book: Supports the advancement in artificial intelligence and the Internet of Things used in societal applications. Discusses the role of modeling human factors in designing smart systems as highlighted in Industry 4.0. Covers big data scheduling and the global standard method applied to smart maintenance. Presents human-centered aviation automation, human-centered processes, and decision support systems. Highlights the importance of data privacy and secure communication in society 5.0. The text is primarily written for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

The aim of this book is to highlight the most promising lines of research, using new enabling technologies and methods based on AI/ML techniques to solve issues and challenges related to intelligent and computing systems. Intelligent computing easily collects data using smart technological applications like IoT-based wireless networks, digital healthcare, transportation, blockchain, 5.0 industry and deep learning for better decision making. AI enabled networks will be integrated in smart cities concept for interconnectivity. Wireless networks will play an important role. The digital era of computational intelligence will change the dynamics and lifestyle of human beings. Future networks will be introduced with the help of AI technology to implement cognition in real-world applications. Cyber threats are dangerous to encode information from network. Therefore, AI-Intrusion detection systems need to be designed for identification of unwanted data traffic. This book: Provides a better understanding of artificial intelligence-based applications for future smart cities. Presents a detailed understanding of artificial intelligence tools for intelligent technologies. Showcases intelligent computing technologies in obtaining optimal solutions using artificial intelligence. Discusses energy-efficient routing protocols using artificial intelligence for Flying ad-hoc networks (FANETs). Covers machine learning-based Intrusion detection system (IDS) for smart grid. It is primarily written for Senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

Point clouds from LiDAR and photogrammetry are vital and vast sources of geospatial information. This book provides the latest theory and methodology for point cloud processing with AI to better serve earth observation, 3D vision, autonomous driving, smart city, and geospatial information applications. It addresses various aspects of 3D geospatial information including data capturing, fusing, geo-computing, modeling, and applications with the latest AI point cloud methods. With the numerous illustrations, diagrams, and practical applications, readers will better understand the point cloud, and its technical challenges, and learn how to utilize point cloud in different fields.

The Future of Labour: How AI, Technological Disruption and Practice will Change the Way we Work is an anthology that offers a forward-looking exploration of how artificial intelligence (AI), digitalisation and technological transformation are reshaping the future of work. Through a series of studies conducted by scientists and industry professionals, this volume takes a deep dive into many of the issues related to new policies, AI and the digital transformation's anticipated impact on the labour market. Balancing speculative foresight with scientific rigor, the authors ground their discussions in empirical data, theoretical frameworks, and comprehensive literature studies. The book delves into the evolving labour landscape through three key themes: The Future of Society and Economy - Examining the socio-economic implications of emerging technologies and their transformative impact on work. The Future Way of Working - Investigating how AI and digital tools are redefining the nature of work and workplace conditions. New Uses of AI and Technology in Labour - Highlighting innovative applications of AI and technology that are reshaping job roles and industries. Each chapter provides unique insights, blending rigorous analysis with predictive insights, to illuminate the challenges and opportunities of a rapidly technologically advancing labour landscape.

Artificial intelligence (AI) in the form of machine learning and nature-inspired optimization algorithms are vastly used in material science. These techniques improve many quality metrics, such as reliability and ergonomics.

This book highlights the recent challenges in this field and helps readers to understand the subject and develop future works. It reviews the latest methods and applications of AI in material science. It covers a wide range of topics, including Material processing; Properties prediction; Conventional machining, such as turning, boring, grinding, and milling; non-conventional machining, such as electrical discharge machining, electrochemical machining, laser machining, plasma machining, ultrasonic machining, chemical machining, and water-jet machining; Machine tools, such as programming, design, and maintenance. AI techniques reviewed in the book include Machine learning, Fuzzy logic, Genetic algorithms, Particle swarm optimization, Cuckoo search, Grey wolf optimizer, and Ant colony optimization.

This book heralds a new era in instrumentation and measurements. It combines artificial intelligence (AI) and wireless communications technologies with instrumentation and measurement systems to function as a single unit. AI has advanced considerably due to deep learning utilizing artificial neural networks, availability of large and curated datasets, implementation of a new generation of fast processors having millions of transistors in chips, advanced algorithms, competitive commercial interests, and interests of governments to gain advantages. At the same time, new and highly advanced wireless technologies open new frontiers in communication systems, both technologically and in terms of applications aspects. Advanced technologies such as 5G and 6G networks enable easy use of communication systems by billions of people as well as by billions of machine-to-machine systems. In this book, the communication principles are explained and the implementation of AI on wireless networks is discussed. Many examples are provided. The author discusses instruments and instrumentation networks, modern sensors, and transducers in detail. AI is the technology humans have created where the machines do not only assist us but also think for us creatively in some cases, excelling humans thinking and reasoning. This book includes a chapter explaining how this is done, backed up with more than 50 figures. The security issues, fairness, efficiency, and social impact and acceptance of AI are highlighted. As explained in this book, AI and wireless communications are changing our lives in many ways, including entertainment, games, social interactions, medicine and healthcare, R&D, automated living, intelligent transport systems, finance and economy, and the Internet of Things.

In the past decade, Artificial Intelligence (AI) has made significant advancements in various sectors of society, such as education, health, e-commerce, media and entertainment, banking and finance, transportation, and defense, among others. Its application has permeated every sector, leaving no area untouched. However, the utilization of AI brings forth crucial legal, ethical, and technical concerns and obstacles that must be appropriately addressed through thoughtful deliberation, discussions, and the implementation of effective regulations. The book serves as an invaluable resource for anyone seeking to understand and navigate the complex world of AI. By offering a comprehensive exploration of its applications, ethical considerations, and data protection techniques, it provides researchers and scholars, graduate students, software engineers along with data scientists the necessary insights to harness the full potential of AI while ensuring its responsible and ethical use.

he reference text will enable the readers to design, optimize, and control complex systems with greater precision and efficiency. It further provides practical insights and presents case studies for readers interested in exploring the intersections between artificial intelligence and industry. The book discusses important topics such as algorithmic design, mathematical modeling, natural language processing, machine learning, and computer vision.

Design Optimization Using Artificial Intelligence serves as an insightful resource for understanding the transformative role of Artificial Intelligence (AI) in optimizing design processes across a variety of fields. This book explores foundational concepts, advanced methodologies, and real-world applications, offering a comprehensive guide to leveraging AI for innovative design solutions.Covering a broad spectrum of topics, the book examines algorithmic advancements, machine learning techniques, and optimization strategies that enhance design efficiency and accuracy. It delves into AI's applications in sustainable energy systems, biomedical innovations, image processing, cyber-security, and other critical domains, showcasing its ability to solve complex challenges with precision and creativity.The content bridges theoretical foundations with practical implementations, incorporating case studies that highlight successful applications of AI-driven design optimization. Readers will gain valuable insights into state-of-the-art tools, programming techniques, and frameworks that drive advancements in intelligent design systems.Features: Explores AI-driven methodologies for enhancing design accuracy and efficiency Discusses advanced machine learning techniques and optimization algorithms Highlights AI's applications in diverse fields such as energy systems, healthcare, image processing, and cyber-security Includes in-depth case studies to illustrate real-world implementations of AI in design optimization Investigates future trends and the potential of AI to transform traditional design processes Targeted at researchers, engineers, and students in the fields of artificial intelligence, computational science, and engineering, this book is an essential reference for anyone seeking to understand and utilize AI in the realm of design optimization. It provides the tools and knowledge necessary to harness AI's power for innovative, efficient, and intelligent design solutions.

AIoT: Artificial Intelligence of Things explores the integration of Artificial Intelligence (AI) into everyday objects and devices to allow them to analyze data, make decisions, and communicate with each other, which leads to improved efficiency, convenience, and new possibilities in various aspects of daily life.With a wide-ranging scope, the book covers various aspects of Artificial Intelligence of Things (AIoT) from fundamental concepts to advanced applications. It begins with an introduction to AI, Internet of Things (IoT), and their synergistic relationship and explains how AI technologies enhance the capabilities of IoT devices. The book then explores the key components of AIoT, including sensors, connectivity, data processing, and machine learning algorithms. Highlights include the following: Edge computing and AIoT Privacy and security in AIoT AIoT applications in such domains as healthcare, finance, and manufacturing. Smart cities and AIoT AIoT and wearables Ethical considerations concerning AIoT AIoT and augmented reality and virtual reality AIoT and blockchain AIoT and 5G technology. The book presents AIoT architectures, such as cloud-based, edge-based, and hybrid systems, and discusses their advantages and challenges. Exploring the role of big data analytics in AIoT, it highlights how large-scale data collection and analysis can drive intelligent decision-making and predictive insights. It delves into such AI techniques employed in AIoT systems as natural language processing, computer vision, deep learning, and reinforcement learning.The book concludes with a discussion of emerging trends and future directions in AIoT, which includes the integration of blockchain technology, edge computing, and swarm intelligence. It also discusses the societal implications of AIoT and its effect on the job market, privacy, and ethics.

Artificial Intelligence and Evaluation: Emerging Technologies and their Implications for Evaluation is a groundbreaking exploration of how the landscape of program evaluation will be redefined by artificial intelligence and other emerging digital technologies. In an era where digital technologies and artificial intelligence (AI) are rapidly evolving, this book presents a pivotal resource for evaluators navigating the transformative intersection of their practice and cutting-edge technology. Addressing the dual dimensions of how evaluations are conducted and what is evaluated, a roster of distinguished contributors illuminate the impact of AI on program evaluation methodologies. Offering a discerning overview of various digital technologies, their promises and perils, they carefully dissect the implications for evaluative processes, and debates how evaluators must be equipped with the requisite skills to harness the full potential of AI tools. Further, the book includes a number of compelling use cases, demonstrating the tangible applications of AI in diverse evaluation scenarios. The use cases range from the application of GIS data to advanced text analytics. As such this book provides evaluators with inspirational cases on how to apply AI in their practice as well as what pitfalls one must look out for. Artificial Intelligence and Evaluation is an indispensable guide for evaluators seeking to not only adapt to but thrive in the dynamic landscape of evaluation practices reshaped by the advent of artificial intelligence.

Artificial Intelligence Assisted Structural Optimization explores the use of machine learning and correlation analysis within the forward design and inverse design frameworks to design and optimize lightweight load bearing structures as well as mechanical metamaterials. Discussing both machine learning and design analysis in detail, this book enables readers to optimize their designs using a data driven approach. This book discusses the basics of the materials utilized, for example shape memory polymers, and the manufacturing approach employed, such as 3D or 4D printing. Additionally, the book discusses the use of forward design and inverse design frameworks to discover novel lattice unit cells and thin-walled cellular unit cells with enhanced mechanical and functional properties such as increased mechanical strength, heightened natural frequency, strengthened impact tolerance, and improved recovery stress. Inverse design methodologies using generative adversarial networks are proposed to further investigate and improve these structures. Detailed discussions on fingerprinting approaches, machine learning models, structure screening techniques and typical Python codes are provided in the book. The book provides detailed guidance for both students and industry engineers to optimize their structural designs using machine learning.

This book deals with the emerging 6G networking and services, which are expected to be implemented in 2030. With 6G, there will be a deep convergence of computing and communication with AI. This book provides a unified framework for the convergence of computing and communications that can be optimized as a single integrated system using AI.

Artificial Intelligence in Heat Transfer shows how AI tools and techniques, such as artificial neural networks, machine learning algorithms, genetic algorithms, etc., provide practical benefits specific to thermal sciences. It presents case studies involving heat and mass transfer, multi-objective optimization, conjugate heat transfer, nano-fluids, thermal radiation, heat transfer through porous media (metal foam), and more. Drawing on the collective expertise of leading researchers and experts in multiple fields, the book provides an in-depth understanding of the possibilities that emerge when these tools are applied to problems related to thermal sciences. Artificial Intelligence (AI) is an ever-evolving discipline that has created new and groundbreaking opportunities to advance the mechanical engineering field, particularly in the area of numerical heat transfer. This volume of Advances in Numerical Heat Transfer explores various ways AI is used in heat transfer to solve engineering problems. This book will serve as an important resource for upper-level undergraduate students, researchers, engineers, and professionals, equipping them with the knowledge and inspiration to push the boundaries of the thermal sciences through AI-driven tools and techniques.

The Routledge Handbook of Artificial Intelligence and Philanthropy acts as a catalyst for the dialogue between two ecosystems with much to gain from collaboration: artificial intelligence (AI) and philanthropy. Bringing together leading academics, AI specialists, and philanthropy professionals, it offers a robust academic foundation for studying both how AI can be used and implemented within philanthropy and how philanthropy can guide the future development of AI in a responsible way. The contributors to this handbook explore various facets of the AI-philanthropy dynamic, critically assess hurdles to increased AI adoption and integration in philanthropy, map the application of AI within the philanthropic sector, and evaluate how philanthropy can and should promote an AI that is ethical, inclusive, and responsible, and identify the landscape of risk strategies for their limitations and/or potential mitigation. These theoretical perspectives are complemented by several case studies that offer a pragmatic perspective on diverse successful and effective AI-philanthropy synergies. As a result, this handbook stands as a valuable academic reference capable of enriching the interactions of AI and philanthropy, uniting the perspectives of scholars and practitioners, thus building bridges between research and implementation, and setting the foundations for future research endeavors on this topic.

The aim of this book is to delineate the challenges faced by new generation mobile networks, such as 5G and forthcoming 6G, and introduce the concept of addressing these challenges through the development of a novel Distributed AI (DAI) framework. The book proposes a distributed AI approach to handle these complexities effectively. Distributed Artificial Intelligence for 5G/6G Communications: Frameworks with Machine Learning aims to provide a comprehensive understanding of the proposed DAI framework, its architecture, implementation, and application in the context of D2D communication in 5G and beyond networks. It showcases how the framework can enhance decision-making, control, and communication performance while considering both static and dynamic network environments. The book delves into the advantages of D2D communication, highlighting how it transcends licensed frequency bands and bypasses the cellular network, leading to improved network metrics such as spectral efficiency, energy efficiency, data rates, and interference management. It concludes by emphasizing that the DAI framework can offer enhanced network control, reduced signaling overhead, and efficient decision-making while capitalizing on existing implementations to tackle various challenges across the 5G and 6G landscape. This book is intended for professionals that specialize in designing, implementing, and maintaining communication networks such as telecommunications/ communication engineers, wireless engineers. The book's emphasis on incorporating AI and ML techniques in the proposed framework also makes it of interest to computer and software engineers working in artificial intelligence and machine learning engineers.

Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications discusses the theory behind Machine Learning and shows how Mathematics can be used in Artificial Intelligence. The book illustrates how to improve existing algorithms by using advanced Mathematics and offers cutting-edge Artificial Intelligence technologies. The book goes on to discuss how Machine Learning can support mathematical modeling and how to simulate data by using artificial neural networks. Future integration between Machine Learning and complex mathematical techniques is also highlighted within the book.

The new age space value chain is a complex interconnected system with diverse actors, which involves cross-sector and cross-border collaborations. This book helps to enrich the knowledge of Artificial Intelligence (AI) across the value chain in the space-related domains. Advancements of AI and Machine Learning have impactfully supported the space sector transformation as it is shown in the book. "This book embarks on a journey through the fascinating realm of AI in space, exploring its profound implications, emerging trends, and transformative potential." Prof. mult. Dr.med. Dr.rer.nat. Oliver Ullrich Director Innovation Cluster Space and Aviaton (UZH Space Hub) University of Zurich, Switzerland Aimed at space engineers, risk analysts, policy makers, technical experts and non-specialists, this book demonstrates insights into the implementation of AI in the space sector, alongside its limitations and use-case examples. It covers diverse AI-related topics applicable to space technologies or space big data such as AI-based technologies for improving Earth Observation big data, AI for space robotics exploration, AI for astrophysics, AI for emerging in-orbit servicing market, and AI for space tourism safety improvement.

Artificial Intelligence (AI) is defined as the simulation of human intelligence through the mimicking of the human brain for analysis, modeling, and decision making. Science and engineering problem solving requires modeling of physical phenomena and humans approach the solution of scientific and engineering problems differently from other problems. Artificial Intelligence for Science and Engineering Applications addresses the unique differences in how AI should be developed and used in science and engineering. Through inclusion of definitions and detailed examples, this book covers the actual and realistic requirements as well as avoidance of certain characteristics for the correct and successful science and engineering application of AI. This book: -Offers a brief history of AI and covers science and engineering applications. -Explores modeling of physical phenomena using AI. -Discusses explainable AI (XAI) applications. -Covers ethics of AI in science and engineering. -Features real-world case studies. Offering a probing view into the unique nature of scientific and engineering exploration, this book will be of interest to generalists and experts looking to expand their understanding of how AI can better tackle and advance technology and developments in the scientific and engineering disciplines.

Artificial Intelligence and Knowledge Processing play a vital role in various automation industries and their functioning in converting traditional industries to AI-based factories. This book acts as a guide and blends the basics of Artificial Intelligence in various domains, which include Machine Learning, Deep Learning, Artificial Neural Networks, and Expert Systems, and extends their application in all sectors. Artificial Intelligence and Knowledge Processing: Improved Decision-Making and Prediction, discusses the designing of new AI algorithms used to convert general applications to AI-based applications. It highlights different Machine Learning and Deep Learning models for various applications used in healthcare and wellness, agriculture, and automobiles. The book offers an overview of the rapidly growing and developing field of AI applications, along with Knowledge of Engineering, and Business Analytics. Real-time case studies are included across several different fields such as Image Processing, Text Mining, Healthcare, Finance, Digital Marketing, and HR Analytics. The book also introduces a statistical background and probabilistic framework to enhance the understanding of continuous distributions. Topics such as Ensemble Models, Deep Learning Models, Artificial Neural Networks, Expert Systems, and Decision-Based Systems round out the offerings of this book. This multi-contributed book is a valuable source for researchers, academics, technologists, industrialists, practitioners, and all those who wish to explore the applications of AI, Knowledge Processing, Deep Learning, and Machine Learning.

Neuromorphic Photonic Devices and Applications synthesizes in one volume the most critical advances in photonic neuromorphic models, photonic material platforms, and accelerators for neuromorphic computing. It discusses fields and applications that can leverage these new platforms. A brief review of the historical development of the field is provided followed by a discussion of the emerging 2D photonic materials platforms and recent work in implementing neuromorphic models and 3D neuromorphic systems. The application of artificial intelligence such as neuromorphic models to inverse design neuromorphic materials and devices and predict performance challenges is discussed throughout. The book includes a comprehensive overview of the applications of neuromorphic photonic technologies and the challenges, opportunities, and future prospects facing the field. Neuromorphic Photonic Devices and Applications is suitable for researchers and practitioners in academia and R&D in the multi-disciplinary field of photonics.

This book highlights the importance of data-driven techniques to solve wireless communication problems. It presents a number of problems (e.g., related to performance, security, and social networking), and provides solutions using various data-driven techniques, including machine learning, deep learning, federated learning, and AI. The target audience of this book are professionals, researchers, professors, and students working in the field of networking, communications, machine learning, and related fields.

The text provides sustainable energy solutions using smart technologies such as artificial intelligence, blockchain technology, and the internet of things. It further presents several case studies on applications of the internet of things, artificial intelligence, and blockchain technology in the field of sustainable energy.

Artificial intelligence (AI) and digital engineering have become prevalent in business, industry, government, and academia. However, the workforce still has a lot to learn on how to leverage them. This handbook presents the preparatory and operational foundations for the efficacy, applicability, risk, and how to take advantage of these tools and techniques. Handbook of Mathematical and Digital Engineering Foundations for Artificial Intelligence: A Systems Methodology provides a guide for using digital engineering platforms for advancing AI applications. The book discusses an interface of education and research in the pursuit of AI developments and highlights the facilitation of advanced education through AI and digital engineering systems. It presents an integration of soft and hard skills in developing and using AI and offers a rigorous systems approach to understanding and using AI. This handbook will be the go-to resource for practitioners and students on applying systems methodology to the body of knowledge of understanding, embracing, and using digital engineering tools and techniques. The recent developments and emergence of Chatbots (AI tools) all have mathematical foundations for their efficacy. Such AI tools include ChatGPT, GPT-4, Bard, Tidio Support Bot, Kuki AI Companion, Meena, BlenderBot, Rose AI Chatbot, Replika: AI Friend, Eviebot, and Tay. This handbook highlights the importance of mathematical and digital foundations for AI developments. The handbook will enhance the understanding and appreciation of readers about the prevailing wave of artificial intelligence products, and, thereby, fitting the current market needs.

Artificial intelligence and Internet of Things (IoT) are growing rapidly in today's business world. In today's era, 25 billion devices, including machines, sensors and cameras, are connected and continue to grow steadily. It is assumed that in 2025, 41.6 billion IoT devices will be connected, generating around 79.4 zettabytes of data. Internet of Things and Artificial Intelligence are intersecting each other in various scenarios. IoT-enabled devices are generating a huge amount of data, with the help of artificial intelligence huge amount of generated data is used to build various intelligent models. These intelligent models are helpful in our daily life and make the world smarter. Internet of Things is the most powerful technology that connects things together to make the world smarter. Connected devices such as sensors, mobile phones, etc. utilize every bit of data that we use in our daily life to complete the task. A huge volume of data is collected through connected devices, Artificial Intelligence algorithms can analyze this data and act accordingly to create values in our daily life. This book addresses issues related to system safety, security, reliability, and deployment strategies in healthcare, military, transport, energy, infrastructure, smart cities, smart home, etc.

This book focuses on the research and development in the field of self-aware robots. Its theme is artificial consciousness, a field that covers both artificial intelligence and robotics, and includes philosophy, psychology, the study of biological evolution, physiology, and medicine, especially brain neuroscience and neuropsychiatry.

This book looks at industry change patterns and innovations (such as artificial intelligence, machine learning, big data analysis, and blockchain support and efficiency technology) that are speeding up industrial transformation, industrial infrastructure, biodiversity, and productivity. This bookfocuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturing.It also offers insights into manufacturing, logistics, and supply chain, wheresystems have undergone an industrial transformation. It discusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposure.It goes on to cover the effects that the Fourth Industrial Revolution has on industrial infrastructures and looks at the current industry change patterns and innovations that are accelerating industrial transformation activities. Researchers, scholars, and students from different countries will appreciate this book for itsreal-world applications and knowledge acquisition. This book targets manufacturers, industry owners, product developers, scientists, logistics, and supply chain engineers. Focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturing Offers insights into manufacturing, logistics, and supply chain where systems have undergone an industrial transformation Discusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposure Covers the effects that the 4th Industrial Revolution has on industrial infrastructures Looks at industry change patterns and innovations that are speeding up industrial transformation activities Om Prakash Jena is currently working as an associate professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. Sabyasachi Pramanik is an assistant professor in the Department of Computer Science and Engineering, Haldia Institute of Technology, India. Ahmed A. Elngar is an associate professor in the Faculty of Computers & Artificial Intelligence, Beni-Suef University, Egypt. He is also an associate professor in the College of Computer Information Technology, chair of the Scientific Innovation Research Group (SIRG), and director of the Technological and Informatics Studies Center (TISC), American University in the Emirates, United Arab Emirates.

A glimpse into the future of intelligent machines, and a journey through the laboratories and researchers that are building them. The book offers a mix of fiction and nonfiction narrative: readers can "see" a world, a few decades away, where intelligent machines have become reality, and learn about the science brewing in today's labs and the technical and socioeconomic challenges, often through the words of scientists and entrepreneurs that make it happen.

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications.

This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, etc. and how all countries around the world are feeling this impact. The book aims at low cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and post graduate students, researchers, academicians, institutions, and professionals that are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science.

The resurgence of artificial intelligence has been fueled by the availability of the present generation of high-performance computational tools and techniques. This book is designed to provide introductory guidance to artificial intelligence, particularly from the perspective of digital systems engineering. Artificial Intelligence and Digital Systems Engineering provides a general introduction to the origin of AI and covers the wide application areas and software and hardware interfaces. It will prove to be instrumental in helping new users expand their knowledge horizon to the growing market of AI tools, as well as showing how AI is applicable to the development of games, simulation, and consumer products, particularly using artificial neural networks. This book is for the general reader, university students, and instructors of industrial, production, civil, mechanical, and manufacturing engineering. It will also be of interest to managers of technology, projects, business, plants, and operations.

Technology has created innovative new prospects for manufacturing industries with Industry 4.0 and has helped further the growth of the manufacturing sector. This book focuses on the next stage which is Industry 5.0 and the steps in taking automation to that next level by increasing processes, operational efficiency, and reducing workforce size. Industry 5.0: The Future of the Industrial Economy discusses the integration of product, process, machine, software, and industrial robots in realizing Industrial 5.0. It covers the dual integration of human intelligence with machine intelligence and talks about the results of making use of Industrial Internet of Things (IIoT) and Artificial Intelligence (AI). The creation of a new category of robots named Collaborative Robots (Cobots) specifically designed to speed up the manufacturing process and profitability is explored. The book also provides how to reduce waste in product design through the manufacturing process and offers more personalized and customized products for customers. Manufacturing, design, industrial and mechanical engineers, as well as practicing professionals will find this book of interest. Management executives, CIO, CEO, IT professionals, and academics, will also find something of value in this book that takes Industry 4.0 to Industry 5.0 and beyond.

Artificial intelligence (AI) and data mining is the fastest growing field in computer science. AI and data mining algorithms and techniques are found to be useful in different areas like pattern recognition, automatic threat detection, automatic problem solving, visual recognition, fraud detection, detecting developmental delay in children, and many other applications. However, applying AI and data mining techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to Artificial Intelligence. Successful application of security frameworks to enable meaningful, cost effective, personalize security service is a primary aim of engineers and researchers today. However realizing this goal requires effective understanding, application and amalgamation of AI and Data Mining and several other computing technologies to deploy such system in an effective manner. This book provides state of the art approaches of artificial intelligence and data mining in these areas. It includes areas of detection, prediction, as well as future framework identification, development, building service systems and analytical aspects. In all these topics, applications of AI and data mining, such as artificial neural networks, fuzzy logic, genetic algorithm and hybrid mechanisms, are explained and explored. This book is aimed at the modeling and performance prediction of efficient security framework systems, bringing to light a new dimension in the theory and practice.

In Artificial Intelligence Hardware Design: Challenges and Solutions, distinguished researchers and authors Drs. Albert Chun Chen Liu and Oscar Ming Kin Law deliver a rigorous and practical treatment of the design applications of specific circuits and systems for accelerating neural network processing. Beginning with a discussion and explanation of neural networks and their developmental history, the book goes on to describe parallel architectures, streaming graphs for massive parallel computation, and convolution optimization.

This comprehensive reference text discusses the fundamental concepts of artificial intelligence and its applications in a single volume. Artificial Intelligence: Fundamentals and Applications presents a detailed discussion of basic aspects and ethics in the field of artificial intelligence and its applications in areas, including electronic devices and systems, consumer electronics, automobile engineering, manufacturing, robotics and automation, agriculture, banking, and predictive analysis. Aimed at senior undergraduate and graduate students in the field of electrical engineering, electronics engineering, manufacturing engineering, pharmacy, and healthcare, this text: Discusses advances in artificial intelligence and its applications. Presents the predictive analysis and data analysis using artificial intelligence. Covers the algorithms and pseudo-codes for different domains. Discusses the latest development of artificial intelligence in the field of practical speech recognition, machine translation, autonomous vehicles, and household robotics. Covers the applications of artificial intelligence in fields, including pharmacy and healthcare, electronic devices and systems, manufacturing, consumer electronics, and robotics.

What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? In this book Kate Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequality. Drawing on more than a decade of research, award-winning science, and technology, Crawford reveals how AI is a technology of extraction: from the energy and minerals needed to build and sustain its infrastructure, to the exploited workers behind 'automated' services, to the data AI collects from us. Rather than taking a narrow focus on code and algorithms, Crawford offers us a political and a material perspective on what it takes to make artificial intelligence and where it goes wrong. While technical systems present a veneer of objectivity, they are always systems of power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world.

This textbook covers the broader field of artificial intelligence. The chapters for this textbook span within three categories: Deductive reasoning methods: These methods start with pre-defined hypotheses and reason with them in order to arrive at logically sound conclusions. The underlying methods include search and logic-based methods. These methods are discussed in Chapters 1through 5. Inductive Learning Methods: These methods start with examples and use statistical methods in order to arrive at hypotheses. Examples include regression modeling, support vector machines, neural networks, reinforcement learning, unsupervised learning, and probabilistic graphical models. These methods are discussed in Chapters~6 through 11. Integrating Reasoning and Learning: Chapters~11 and 12 discuss techniques for integrating reasoning and learning. Examples include the use of knowledge graphs and neuro-symbolic artificial intelligence. The primary audience for this textbook are professors and advanced-level students in computer science. It is also possible to use this textbook for the mathematics requirements for an undergraduate data science course. Professionals working in this related field many also find this textbook useful as a reference.

This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems.

An accessible synthesis of ethical issues raised by artificial intelligence that moves beyond hype and nightmare scenarios to address concrete questions. Artificial intelligence powers Google's search engine, enables Facebook to target advertising, and allows Alexa and Siri to do their jobs. AI is also behind self-driving cars, predictive policing, and autonomous weapons that can kill without human intervention. These and other AI applications raise complex ethical issues that are the subject of ongoing debate. This volume in the MIT Press Essential Knowledge series offers an accessible synthesis of these issues. Written by a philosopher of technology, AI Ethics goes beyond the usual hype and nightmare scenarios to address concrete questions. Mark Coeckelbergh describes influential AI narratives, ranging from Frankenstein's monster to transhumanism and the technological singularity. He surveys relevant philosophical discussions: questions about the fundamental differences between humans and machines and debates over the moral status of AI. He explains the technology of AI, describing different approaches and focusing on machine learning and data science. He offers an overview of important ethical issues, including privacy concerns, responsibility and the delegation of decision making, transparency, and bias as it arises at all stages of data science processes. He also considers the future of work in an AI economy. Finally, he analyzes a range of policy proposals and discusses challenges for policymakers. He argues for ethical practices that embed values in design, translate democratic values into practices and include a vision of the good life and the good society.

This exciting resource identifies technical challenges, benefits, and directions of Deep Learning (DL) based object classification using radar data (i.e., Synthetic Aperture Radar / SAR and High range resolution Radar / HRR data). An overview of machine learning (ML) theory to include a history, background primer, and example and performance of ML algorithm (i.e., DL method) on video imagery is provided. Radar data with issues of collection, application, and examples for SAR/HRR data and communication signals analysis is also discussed. Practical considerations of deploying such techniques, including performance evaluation, hardware issues, and the future unresolved issues are presented.

The first book to analyze the consequences of the political economy of artificial intelligence for global sustainability.

The holy grail of artificial intelligence research has been the achievement of artificial general intelligence. Since the inception of artificial intelligence, machines that can perform any task that a human might have been predicted to be imminent. Some people have been enthusiastic about this prospect, but others have been terrified. Both have been disappointed. In fact, despite all of the progress in solving individual tasks, this research has not been on a road that could ever lead to general intelligence. To paraphrase the Ancient Greek poet, Archilochus, we have been building hedgehogs, when what we are after is a Fox. The fox, he said, knows many things, but the hedgehog knows one big thing. Even a stack of hedgehogs, however, cannot duplicate the intelligence of a fox. This book describes a roadmap for designing a generally intelligent fox that solves the problem of general intelligence. It brings to bear wide swaths of cognitive science, including psychology, philosophy, and history to debunk the barriers to general intelligence by identifying the essential features of intelligence that would be needed to achieve general artificial intelligence. Along the way, it makes it apparent that fears of an imminent explosion of uncontrollable computational intelligence (the so-called "singularity, ") are completely unfounded.

The book offers major recommendations for actions that governments, businesses, and individuals can take to promote trustworthy and responsible artificial intelligence. Their recommendations include: creation of ethical principles, strengthening government oversight, defining corporate culpability, establishment of advisory boards at federal agencies, using third-party audits to reduce biases inherent in algorithms, tightening personal privacy requirements, using insurance to mitigate exposure to AI risks, broadening decision-making about AI uses and procedures, penalizing malicious uses of new technologies, and taking pro-active steps to address how artificial intelligence affects the workforce.

A fascinating look at the gendering of smart homes, how they came to be so, and how modern households can and should be domains of equality.

The first book of its kind on the detailed steps for creating an autonomous vehicle or robot, this book provides an overview of the technology and introduction of the key elements involved in developing autonomous vehicles, and offers an excellent introduction to the basics for someone new to the topic of autonomous vehicles and the innovative, modular-based engineering approach called DragonFly. Engineering Autonomous Vehicles and Robots: The DragonFly Modular-based Approach covers everything that technical professionals need to know about: CAN bus; chassis; sonars; radars; GNSS; computer vision; localization; perception; motion planning etc. Particularly, it covers Computer Vision for active perceptionandlocalization, as well as mapping and motion planning. The book offers several case studies on the building of an autonomous passenger pod, bus, and vending robot etc. It features a large amount of supplementary material, including the standard protocol and sample codes for chassis, sonar, and radar. GPSD protocol/NMEA protocol and GPS deployment methods are also provided. Most importantly, readers will learn the philosophy behind the DragonFly modular-based design approach, which empowers readers to design and build their own autonomous vehicles and robots with flexibility and affordability.

Mark Lee considers that the current gains in machine learning and deep learning will not produce robots that can interact effectively with humans. The book then explores how robots can become more human-like, more general-purpose, and more social. The book introduces us to the core ideas in Developmental Robotics - showing how this new approach can "grow" robots through (their own) experience rather than building them from design. Original aspects include demonstrating that social robots must be embodied, that embodiment will be necessary for general artificial intelligence, and that threats from advanced technology are not inevitable but avoidable by involving human, social, and ethical issues. The material covers a wide scope; from simple robots to advanced AI. This gives an overview of this area and an appreciation of the main advances, problems, and issues. The scope [of] the readership is intended to be wide: aimed at a general, educated but not specialist audience. For this reason, an engineering viewpoint is adopted; technical details and philosophical aspects are minimized, thus promoting a practical perspective. The aim is to present the fundamental ideas behind AI and robotics in a clear, accessible form, appealing to common sense, so as to encourage the general reader to build their own informed assessment of these technologies. The hope is to reach a wide public readership - reaching anyone who wishes to know what robotics is about, where it is going, and what its limitations are.

From the telegraph and the two-way radio to high-frequency satellites and free-space optical communications, media technologies have changed the way in which troops are organized and deployed, the constitution of armies, and the nature and definitions of warfare itself. Focusing in particular on the rise of artificial intelligence technologies, KILLER APPS shows how media helps to produce enemies and enable war. Co-authors Jeremy Packer and Joshua Reeves offer what they call a polemocentric theory of media escalation, demonstrating that media will never usher in world peace. Quite the opposite-when animated by struggle, media will always escalate towards greater chaos and creative destruction. Each chapter begins with a Department of Defense definition of a military term, and draws from it to theorize the recent escalation of technologies of violence and surveillance. In one chapter, Packer and Reeves critique liberal ideologies of artificial intelligence that suggest it could offer us a feminist and egalitarian future: instead, they demonstrate, AI technologies developed by military contractors serve only to reproduce global capitalism and militarism. Another chapter takes up the historical interpenetration of climate knowledge and warfare to show how global climate catastrophe has enabled and inspired new forms of military and surveillance media technologies. Packer and Reeves trace the history of unmanned military aircraft from explosive balloons used by the Austrian military in 1849, to aerial remote-controlled torpedoes deployed in World War 1, to CIA drone attacks in Afghanistan, Yemen, and Pakistan. Finally, they turn to science fiction visions: while authors and filmmakers have often imaged post-apocalyptic moments as a setting for building a liberal future of global harmony, freedom, and unfettered capitalism, Packer and Reeves turn to science fictional visions of robot mutiny and AI-driven nuclear annihilation to consider the role militarized media will have to play in catastrophe. KILLER APPS will interest scholars of media and communication studies, technology studies, and critical studies of militarism and surveillance.

Dramatic statements about the promise and peril of artificial intelligence for humanity abound, as an industry of experts claims that AI is poised to reshape nearly every sphere of life. Who profits from the idea that the age of AI has arrived? Why do ideas of AI's transformative potential keep reappearing in social and political discourse, and how are they linked to broader political agendas? Yarden Katz reveals the ideology embedded in the concept of artificial intelligence, contending that it both serves and mimics the logic of white supremacy. He demonstrates that understandings of AI, as a field and a technology, have shifted dramatically over time based on the needs of its funders and the professional class that formed around it. From its origins in the Cold War military-industrial complex through its present-day Silicon Valley proselytizers and eager policy analysts, AI has never been simply a technical project enabled by larger data and better computing. Drawing on intimate familiarity with the field and its practices, Katz instead asks us to see how AI reinforces models of knowledge that assume white male superiority and an imperialist worldview. Only by seeing the connection between artificial intelligence and whiteness can we prioritize alternatives to the conception of AI as an all-encompassing technological force. Bringing together theories of whiteness and race in the humanities and social sciences with a deep understanding of the history and practice of science and computing, Artificial Whiteness is an incisive, urgent critique of the uses of AI as a political tool to uphold social hierarchies.