Dr. UMESHA NAIK, Associate Professor and Chairman of the Department of Library and Information Science at Mangalore University in Mangalagangothri, Karnataka, India, is an eminent academician with a stellar 30-year technical, academic, and professional journey. His career soared during impressive 8-year tenure at the UGC-autonomous INFLIBNET Centre in Ahmedabad, spanning from 1994 to 2003. His academic prowess is evidenced by his impressive qualifications: a B.L.I.Sc, from Mangalore University, an M.L.I.S. from IGNOU, a CIC from IGNOU, and a Ph.D. from Mangalore University. He also serves as a Research Guide. His expertise spans Information and Communication Technology, Networking, Internet, Web Technologies, and Digital and Electronic Libraries, all contributing to his substantial impact on academia, reflected in a portfolio of more than one hundred and fifteen (115) published articles and papers in national and international journals, conferences, and books. Active involvement in international and national conferences, over 20 resource person roles, and participation in 50+ workshops, seminars, and webinars cement his eminent position, and served as Editor for "FROM DEWEY TO DIGITAL: EVOLUTION OF LIBRARIES IN THE DIGITAL AGE." Dr. Naik's membership in prestigious professional associations further underscores his formidable presence in the Library and Information Science domain.

This book reflects the author’s years of hands-on experience as an academic and practitioner. It is primarily intended for executives, managers and practitioners who want to redefine the way they think about artificial intelligence (AI) and other exponential technologies. Accordingly the book, which is structured as a collection of largely self-contained articles, includes both general strategic reflections and detailed sector-specific information. More concretely, it shares insights into what it means to work with AI and how to do it more efficiently; what it means to hire a data scientist and what new roles there are in the field; how to use AI in specific industries such as finance or insurance; how AI interacts with other technologies such as blockchain; and, in closing, a review of the use of AI in venture capital, as well as a snapshot of acceleration programs for AI companies.

"A major contribution to the field..." ." Gordon B. Davis, Honeywell Professor of Management Information Systems, Carlson School of Management, University of Minnesota, USA " "This book is required reading for anyone who wants to understand how and why computers influence organization structure. It established a conceptual foundation for the field, and examines the particular characteristics of the tools computer systems provide and what organizational impacts they can be expected to have. The conclusions are sometimes counter-intuitive, but always convincingly argued." Lee L. Gremillion, Partner, PricewaterhouseCoopers, USA "This is a superb source for people seeking to learn about organizational structure. Groth gives remarkable reasoning and interesting examples throughout the book. An exciting contribution" Kamar Singh, Project Manager, GE Aircraft Engines, Ohio, USA Lars Groth addresses a subject of key importance and takes a fresh and innovative look at the ways to build and develop organizations with the assistance of information technology. Five major examples are used to point out the road ahead for those aiming to improve existing organizations with the help of new technology, as well as explaining some significant properties inherent in organizations and information technology. For the first time, the interplay between organization structure and information technology is thoroughly analysed in the context of established organization theory. Through examination of existing models and taking into account the new possibilities offered by IT, this book will enable practising managers and consultants to look at their organizations and decide where the greatest, and least, opportunities lie. The book will also be highly relevant to MBA, MIS and Executive courses concerned with the relationship between organizations and IT.

The Hidden Pattern presents a novel philosophy of mind, intended to form a coherent conceptual framework within which it is possible to understand the diverse aspects of mind and intelligence in a unified way. The central concept of the philosophy presented is the concept of "pattern" minds and the world they live in and co-create are viewed as patterned systems of patterns, evolving over time, and various aspects of subjective experience and individual and social intelligence are analyzed in detail in this light. Many of the ideas presented are motivated by recent research in artificial intelligence and cognitive science, and the author's own AI research is discussed in moderate detail in one chapter. However, the scope of the book is broader than this, incorporating insights from sources as diverse as Vedantic philosophy, psychedelic psychotherapy, Nietzschean and Peircean metaphysics and quantum theory. One of the unique aspects of the patternist approach is the way it seamlessly fuses the mechanistic, engineering-oriented approach to intelligence and the introspective, experiential approach to intelligence.

This authoritative reference work will provide readers with a complete overview of artificial intelligence (AI), including its historic development and current status, existing and projected AI applications, and present and potential future impact on the United States and the world. Some people believe that artificial intelligence (AI) will revolutionize modern life in ways that improve human existence. Others say that the promise of AI is overblown. Still others contend that AI applications could pose a grave threat to the economic security of millions of people by taking their jobs and otherwise rendering them "obsolete"-or, even worse, that AI could actually spell the end of the human race. This volume will help users understand the reasons AI development has both spirited defenders and alarmed critics; explain theories and innovations like Moore's Law, mindcloning, and Technological Singularity that drive AI research and debate; and give readers the information they need to make their own informed judgment about the promise and peril of this technology. All of this coverage is presented using language and terminology accessible to a lay audience.

Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles. An Introduction to Deep Learning Business Applications for Developers covers some common DL algorithms such as content-based recommendation algorithms and natural language processing. You’ll explore examples, such as video prediction with fully convolutional neural networks (FCNN) and residual neural networks (ResNets). You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. There will also be mention of the powerful set of algorithms called Generative Adversarial Neural networks (GANs) that can be applied for image colorization, image completion, and style transfer. After reading this book you will have an overview of the exciting field of deep neural networks and an understanding of most of the major applications of deep learning. The book contains some coding examples, tricks, and insights on how to train deep learning models using the Keras framework. What You Will Learn Find out about deep learning and why it is so powerful Work with the major algorithms available to train deep learning models See the major breakthroughs in terms of applications of deep learning Run simple examples with a selection of deep learning libraries Discover the areas of impact of deep learning in business Who This Book Is For Data scientists, entrepreneurs, and business developers.

Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.