Pamela McCorduck revisits the artificial intelligence arena and updates this look at the history and future of artificial intelligence with over 100 pages of new material. This book should inspire anyone interested in human intelligence and the future of machine intelligence. The reader should not be surprised to find out that there is much more to AI than simple robots and manufacturing automation.

This book explores the theoretical as well as the technical research outcomes on all the aspects of Agile Software Development by using various methods, principles, artificial intelligence, soft computing, machine learning.

Sensing a future beyond work lurking in an age of crisis, the 'post-capitalist' utopias of today spread the idea of a permanent escape from work aided by the automation of production, a universal basic income and the reduction of working hours to zero. By skilfully unpicking the political economy of contemporary work and its futures, this book mounts a forceful critique of the post-work society vision. Dinerstein and Pitts reveal that transitional measures towards a world beyond work do not do enough to break away from the key features of capitalist society, and instead potentially stifle the capacity for transformative social change. Proposing an innovative alternative, the authors envision the construction of 'concrete utopias' that shape and anticipate non-capitalist futures.

Introduces design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt and adapt. Free from endless derivations equations are presented and explained strategically, explaining why it is imperative to use them and how they will help in your task at hand. Illustrations and simple explanation help readers visualize and absorb easily, difficult to understand concepts.

AI AND MACHINE LEARNING FOR NETWORK AND SECURITY MANAGEMENT Extensive Resource for Understanding Key Tasks of Network and Security Management AI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly detection, and security operations. In addition, the authors introduce their large-scale intelligent network management and operation system and elaborate on how the aforementioned areas can be integrated into this system, plus how the network service can benefit. Sample ideas covered in this thought-provoking work include: How cognitive means, e.g., knowledge transfer, can help with network and security management How different advanced AI and machine learning techniques can be useful and helpful to facilitate network automation How the introduced techniques can be applied to many other related network and security management tasks Network engineers, content service providers, and cybersecurity service providers can use AI and Machine Learning for Network and Security Management to make better and more informed decisions in their areas of specialization. Students in a variety of related study programs will also derive value from the work by gaining a base understanding of historical foundational knowledge and seeing the key recent developments that have been made in the field.

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today's most popular machine learning methods. This book serves as a practitioner's guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R's machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: * Offers a practical and applied introduction to the most popular machine learning methods. * Topics covered include feature engineering, resampling, deep learning and more. * Uses a hands-on approach and real world data.

"CSCL 2: Carrying Forward the Conversation" is a thorough and up-to-date survey of recent developments in Computer Supported Collaborative Learning, one of the fastest growing areas of research in the learning sciences. A follow-up to "CSCL: Theory and Practice of an Emerging Paradigm" (1996), this volume both documents how the field has grown and fosters a meaningful discussion of how the research program might be advanced in substantive ways.
Recognizing the long-standing traditions of CSCL work in Europe and Japan, the editors sought to broaden and expand the conversation both geographically and topically. The 45 participating authors represent a range of disciplinary backgrounds, including anthropology, communication studies, computer science, education, psychology, and philosophy, and offer international perspectives on the field. For each chapter, the goal was not only to show how it connects to past and future work in CSCL, but also how it contributes to the interests of other research communities. Toward this end, the volume features a "conversational structure" consisting of target chapters, invited commentaries, and author responses. The commentaries on each chapter were solicited from a diverse collection of writers, including prominent scholars in anthropology of education, social studies of science, CSCW, argumentation, activity theory, language and social interaction, ecological psychology, and other areas.
The volume is divided into three sections:
*Part I explores four case studies of technology transfer involving CSILE, one of the most prominent CSCL projects.
*Part II focuses on empirical studies of learning in collaborative settings.
*Part III describes novel CSCL technologies and the theories underlying their design.
Historically, there has been a certain amount of controversy as to what the second "C" in CSCL should represent. The conventional meaning is "collaborative" but there are many C-words that can be seen as relevant. With the publication of this volume, "conversational" might be added to the list and, in this spirit, the book might be viewed as an invitation to join a conversation in progress and to carry it forward.

"CSCL 2: Carrying Forward the Conversation" is a thorough and up-to-date survey of recent developments in Computer Supported Collaborative Learning, one of the fastest growing areas of research in the learning sciences. A follow-up to "CSCL: Theory and Practice of an Emerging Paradigm" (1996), this volume both documents how the field has grown and fosters a meaningful discussion of how the research program might be advanced in substantive ways.
Recognizing the long-standing traditions of CSCL work in Europe and Japan, the editors sought to broaden and expand the conversation both geographically and topically. The 45 participating authors represent a range of disciplinary backgrounds, including anthropology, communication studies, computer science, education, psychology, and philosophy, and offer international perspectives on the field. For each chapter, the goal was not only to show how it connects to past and future work in CSCL, but also how it contributes to the interests of other research communities. Toward this end, the volume features a "conversational structure" consisting of target chapters, invited commentaries, and author responses. The commentaries on each chapter were solicited from a diverse collection of writers, including prominent scholars in anthropology of education, social studies of science, CSCW, argumentation, activity theory, language and social interaction, ecological psychology, and other areas.
The volume is divided into three sections:
*Part I explores four case studies of technology transfer involving CSILE, one of the most prominent CSCL projects.
*Part II focuses on empirical studies of learning in collaborative settings.
*Part III describes novel CSCL technologies and the theories underlying their design.
Historically, there has been a certain amount of controversy as to what the second "C" in CSCL should represent. The conventional meaning is "collaborative" but there are many C-words that can be seen as relevant. With the publication of this volume, "conversational" might be added to the list and, in this spirit, the book might be viewed as an invitation to join a conversation in progress and to carry it forward.

This book presents cutting-edge research on various ways to bridge the semantic gap in image and video analysis. The respective chapters address different stages of image processing, revealing that the first step is a future extraction, the second is a segmentation process, the third is object recognition, and the fourth and last involve the semantic interpretation of the image. The semantic gap is a challenging area of research, and describes the difference between low-level features extracted from the image and the high-level semantic meanings that people can derive from the image. The result greatly depends on lower level vision techniques, such as feature selection, segmentation, object recognition, and so on. The use of deep models has freed humans from manually selecting and extracting the set of features. Deep learning does this automatically, developing more abstract features at the successive levels. The book offers a valuable resource for researchers, practitioners, students and professors in Computer Engineering, Computer Science and related fields whose work involves images, video analysis, image interpretation and so on.

On the one side, Industrial competitiveness today means shorter product lifecycles, increased product variety, and shorter times to market and customized tangible products and services. To face these challenges, the manufacturing industry is forced to move from traditional management, control, and automation approaches towards industrial cyber-physical systems. On the other side, several emergent engineering approaches and related Information-Communication-Control-Technologies, such as Multi-Agent-Systems, Service-Oriented Architecture, Plug-and-Produce Systems, Cloud and Fog Technologies, Big Data and Analytics, among others, have been researched during the last years. The confluence of those results with the latest developments in Industrial Digitalization, Systems-of-Cyber-Physical-Systems Engineering, Internet-of-Things, Internet-of-Services, and Industry 4.0 is opening a new broad spectrum of innovation possibilities. The PERFoRM (Production-harmonizEd-Reconfiguration of Flexible Robots and Machinery) approach is one of them. It teaches the reader what it means when production machines and systems are digitalized and migrated into Industrial Cyber-Physical Systems and what happens when they are networked and start collaborating with each other and with the human, using the internet. After a Technology Trend Screening and beyond a comprehensive state-of-the-art analysis about Industrial Digitalization and Industry 4.0-compliant solutions, the book introduces methods, architectures, and technologies applicable in real industrial use cases, explained for a broad audience of researchers, practitioners, and industrialists.

This book presents state-of-the-art data warehousing research and practice from an integrated business and computer science perspective - the first monograph to do so - and broadens the scope of data mining by discussing it in terms of data warehousing. The material, rooted in database management systems and artificial intelligence, brings the intelligent techniques associated with AI to the entire process of data warehousing, from preparing data and building data warehousing to analyzing data stored in the data warehouses using data mining.

Machine Learning Algorithms for Signal and Image Processing Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as: Speech recognition, image reconstruction, object classification and detection, and text processing Healthcare monitoring, biomedical systems, and green energy How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.

Discover how data, analytics, and AI will transform public services for the better In AI and the Future of the Public Sector: The Creation of Public Sector 4.0, renowned executive and consultant Tony Boobier delivers a comprehensive reference of the most relevant and central issues regarding the adoption and implementation of AI in the public sector. In the book, you'll find out why data and analytics are the solution to significant and ongoing problems in the public service relating to its ability to effectively provide services in an environment of reduced funding. You'll also discover the likely impact of future technological developments, like 5G and quantum computing, as well as explore the future of healthcare and the effective digitalization of the healthcare industry. The book also offers: Discussions of policing 4.0 and how data and analytics will transform public safety Explorations of the future of education and how ai can dramatically enhance educational standards while reducing costs Treatments of the internationalization of public services and its impact on agencies and departments everywhere A can't-miss resource for public sector employees at the managerial and professional levels, AI and the Future of the Public Sector is an insightful and timely blueprint to the effective use of artificial intelligence that belongs in the bookshelves of policy makers, academics, and public servants around the world.

This book offers a detailed description of the histogram probabilistic multi-hypothesis tracker (H-PMHT), providing an accessible and intuitive introduction to the mathematical mechanics of H-PMHT as well as a definitive reference source for the existing literature on the method. Beginning with basic concepts, the authors then move on to address extensions of the method to a broad class of tracking problems. The latter chapters present applications using recorded data from experimental radar, sonar and video sensor systems. The book is supplemented with software that both furthers readers' understanding and acts as a toolkit for those who wish to apply the methods to their own problems.

In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have provided groundbreaking outcomes in fields such as multimedia mining or voice recognition. Machine learning is now used in virtually every domain and deep learning algorithms are present in many devices such as smartphones, cars, drones, healthcare equipment, or smart home devices. The Internet, cloud computing and the Internet of Things produce a tsunami of data and machine learning provides the methods to effectively analyze the data and discover actionable knowledge. This book describes the most common machine learning techniques such as Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first gives an introduction into the principles of machine learning. It then covers the basic methods including the mathematical foundations. The biggest part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possible new research areas of machine learning and artificial intelligence in general. This book is meant to be an introduction into machine learning. It does not require prior knowledge in this area. It covers some of the basic mathematical principle but intends to be understandable even without a background in mathematics. It can be read chapter wise and intends to be comprehensible, even when not starting in the beginning. Finally, it also intends to be a reference book. Key Features: Describes real world problems that can be solved using Machine Learning Provides methods for directly applying Machine Learning techniques to concrete real world problems Demonstrates how to apply Machine Learning techniques using different frameworks such as TensorFlow, MALLET, R

In this short introduction, David J. Gunkel examines the shifting world of artificial intelligence, mapping it onto everyday twenty-first century life and probing the consequences of this ever-growing industry and movement. The book investigates the significance and consequences of the robot invasion in an effort to map the increasingly complicated social terrain of the twenty-first century. Whether we recognize it as such or not, we are in the midst of a robot invasion. What matters most in the face of this machine incursion is not resistance, but how we decide to make sense of and respond to the social opportunities and challenges that autonomous machines make available. How to Survive a Robot Invasion is a fascinating and accessible volume for students and researchers of new media, philosophy of technology, and their many related fields. It aims both to assist readers' efforts to understand a changing world and to provide readers with the critical insight necessary for grappling with our science fiction-like future.

This book covers the features of modern Field Programmable Gate Arrays (FPGA) devices, design techniques and successful implementations pertaining to AI applications. It describes various hardware options available for AI applications, key advantages of FPGAs, contemporary FPGA ICs with software support. The focus is on exploiting parallelism offered by FPGA to meet heavy computation requirements of AI as complete hardware implementation or customized hardware accelerators. This is a comprehensive text book on the subject covering broad array of topics like technological platforms for implementation of AI, capabilities of FPGA, suppliers' software tools and hardware boards and discussion of implementations done by researchers to encourage the AI community to use and experiment with FPGA.

This book offers the latest advances and results in the fields of Machine Learning and Deep Learning for Wireless Communication and provides positive and critical discussions on the challenges and prospects. It provides a broad spectrum in understanding the improvements in Machine Learning and Deep Learning that are motivating by the specific constraints posed by wireless networking systems. The book offers an extensive overview on intelligent Wireless Communication systems and its underlying technologies, research challenges, solutions, and case studies. It provides information on intelligent wireless communication systems and its models, algorithms and applications. The book is written as a reference that offers the latest technologies and research results to various industry problems.

While mathematical software packages are commercially successful and widely used, the use of formal methods in hardware and software development is also becoming more and more important and necessary. This has made deduction systems indispensable because of the complexity and sheer size of the reasoning tasks involved. This volume is devoted to the integration of computer algebra systems and deduction systems and the results presented will improve the automated design of hardware and software systems. The articles in this collection, presented at the 8th Symposium on the Integration of Symbolic Computation and Mechanized Reasoning, held August 6--7 in St. Andrews, Scotland, address all aspects relating deduction and computer algebra systems.

Robots, autonomous vehicles, unmanned aerial vehicles, and smart factory, will significantly change human living style in digital society. Artificial Intelligence in Wireless Robotics introduces how wireless communications and networking technology enhances facilitation of artificial intelligence in robotics, which bridges basic multi-disciplinary knowledge among artificial intelligence, wireless communications, computing, and control in robotics. A unique aspect of the book is to introduce applying communication and signal processing techniques to enhance traditional artificial intelligence in robotics and multi-agent systems. The technical contents of this book include fundamental knowledge in robotics, cyber-physical systems, artificial intelligence, statistical decision and Markov decision process, reinforcement learning, state estimation, localization, computer vision and multi-modal data fusion, robot planning, multi-agent systems, networked multi-agent systems, security and robustness of networked robots, and ultra-reliable and low-latency machine-to-machine networking. Examples and exercises are provided for easy and effective comprehension. Engineers wishing to extend knowledge in the robotics, AI, and wireless communications, would be benefited from this book. In the meantime, the book is ready as a textbook for senior undergraduate students or first-year graduate students in electrical engineering, computer engineering, computer science, and general engineering students. The readers of this book shall have basic knowledge in undergraduate probability and linear algebra, and basic programming capability, in order to enjoy deep reading.

Mobile Edge Artificial Intelligence: Opportunities and Challenges presents recent advances in wireless technologies and nonconvex optimization techniques for designing efficient edge AI systems. The book includes comprehensive coverage on modeling, algorithm design and theoretical analysis. Through typical examples, the powerfulness of this set of systems and algorithms is demonstrated, along with their abilities to make low-latency, reliable and private intelligent decisions at network edge. With the availability of massive datasets, high performance computing platforms, sophisticated algorithms and software toolkits, AI has achieved remarkable success in many application domains. As such, intelligent wireless networks will be designed to leverage advanced wireless communications and mobile computing technologies to support AI-enabled applications at various edge mobile devices with limited communication, computation, hardware and energy resources.

The "scientific field" of Artificial Intelligence (AI) began in the 1950s but the "concept" of artificial intelligence, the idea of something with mind-like attributes, predates it by centuries. This historically rich concept has served as a blueprint for the research into intelligent machines. But it also has staggering implications for our notions of who we are: our psychology, biology, philosophy, technology and society. This reference work provides scholars in both the humanities and the sciences with the material essential for charting the development of this concept.
The set brings together;
*primary texts from antiquity to the present, including the crucial foundational texts which defined the field of AI
* historical accounts, including both comprehensive overviews and detailed snapshots of key periods
* secondary material discussing the intellectual issues and he "scientific field" of Artificial Intelligence (AI)

The unprecedented Covid-19 crisis revealed the scale and scope of a new type of economy taking shape in front of our very eyes: the digital economy. This book presents a concise theoretical and conceptual framework for a more nuanced analysis of the economic and sociological impacts of the technological disruption that is taking place in the markets of goods and services, labour markets, and the global economy more generally. This interdisciplinary work is a must for researchers and students from economics, business, and other social science majors who seek an overview of the main digital economy concepts and research. Its down-to-earth approach and communicative style will also speak to businesses practitioners who want to understand the ongoing digital disruption of the market rules and emergence of the new digital business models. The book refers to academic insights from economics and sociology while giving numerous empirical examples drawn from basic and applied research and business. It addresses several burning issues: how are digital processes transforming traditional business models? Does intelligent automation threaten our jobs? Are we reaching the end of globalisation as we know it? How can we best prepare ourselves and our children for the digitally transformed world? The book will help the reader gain a better understanding of the mechanisms behind the digital transformation, something that is essential in order to not only reap the plentiful opportunities being created by the digital economy but also to avoid its many pitfalls. Chapters 1, 3 and 5 of this book are available for free in PDF format as Open Access from the individual product page at www.routledge.com. They have been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.

Summarizes and illuminates two decades of research
Gathering important papers by both philosophers and scientists, this collection illuminates the central themes that have arisen during the last two decades of work on the conceptual foundations of artificial intelligence and cognitive science. Each volume begins with a comprehensive introduction that places the coverage in a broader perspective and links it with material in the companion volumes. The collection is of interest in many disciplines including computer science, linguistics, biology, information science, psychology, neuroscience, iconography, and philosophy.
Examines initial efforts and the latest controversies
The topics covered range from the bedrock assumptions of the computational approach to understanding the mind, to the more recent debates concerning cognitive architectures, all the way to the latest developments in robotics, artificial life, and dynamical systems theory. The collection first examines the lineageof major research programs, beginning with the basic idea of machine intelligence itself, then focuses on specific aspects of thought and intelligence, highlighting the much-discussed issue of consciousness, the equally important, but less densely researched issue of emotional response, and the more traditionally philosophical topic of language and meaning.
Provides a gamut of perspectives
The editors have included several articles that challenge crucial elements of the familiar research program of cognitive science, as well as important writings whose previous circulation has been limited. Within each volume the papers are organized to reflect a variety of research programs and issues. Thesubstantive introductions that accompany each volume further organize the material and provide readers with a working sense of the issues and the connection between articles.

Summarizes and illuminates two decades of research
Gathering important papers by both philosophers and scientists, this collection illuminates the central themes that have arisen during the last two decades of work on the conceptual foundations of artificial intelligence and cognitive science. Each volume begins with a comprehensive introduction that places the coverage in a broader perspective and links it with material in the companion volumes. The collection is of interest in many disciplines including computer science, linguistics, biology, information science, psychology, neuroscience, iconography, and philosophy.
Examines initial efforts and the latest controversies
The topics covered range from the bedrock assumptions of the computational approach to understanding the mind, to the more recent debates concerning cognitive architectures, all the way to the latest developments in robotics, artificial life, and dynamical systems theory. The collection first examines the lineageof major research programs, beginning with the basic idea of machine intelligence itself, then focuses on specific aspects of thought and intelligence, highlighting the much-discussed issue of consciousness, the equally important, but less densely researched issue of emotional response, and the more traditionally philosophical topic of language and meaning.
Provides a gamut of perspectives
The editors have included several articles that challenge crucial elements of the familiar research program of cognitive science, as well as important writings whose previous circulation has been limited. Within each volume the papers are organized to reflect a variety of research programs and issues. Thesubstantive introductions that accompany each volume further organize the material and provide readers with a working sense of the issues and the connection between articles.