No catches, no fine print just unadulterated book loving, with your favourite books saved to your own digital bookshelf.
New members get entered into our monthly draw to win £100 to spend in your local bookshop Plus lots lots more…Find out more
See below for a selection of the latest books from Artificial intelligence category. Presented with a red border are the Artificial intelligence books that have been lovingly read and reviewed by the experts at Lovereading. With expert reading recommendations made by people with a passion for books and some unique features Lovereading will help you find great Artificial intelligence books and those from many more genres to read that will keep you inspired and entertained. And it's all free!
Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
Written by a senior and well-known member of the Quantitative Finance community who currently runs a research group at a major investment bank, the book will demonstrate the use of machine learning techniques to tackle traditional data science type problems - time-series analysis and the prediction of realised volatility but will also look at novel applications. For example, the Universal Approximation Theorem of Neural Networks shows that a neural network can be used to approximate any function (subject to a number of weak conditions), although how the network is trained is not given. This will be explored within the book. Specific applications will include using a trained neural network to represent market-standard volatility smile models (such as SABR) as well as complex derivative pricing. The book will also potentially look at training a network via reinforcement learning to risk manage a derivatives portfolio. Readers will be attracted by a comprehensive presentation of the techniques available, with the historical perspective providing intuitive understanding of their development, combined with a range of practical examples from the trading floor. Key features: Describes modern machine learning techniques including deep neural networks, reinforcement learning, long-short term memory networks, etc. Provides applications of these techniques to problems within Quantitative Finance (including applications to derivatives modelling) Presents the historical development of the subject from MENACE to Alpha Go Zero and AlphaZero
This book constitutes the refereed proceedings of the 13th International Conference on Intelligent Computer Mathematics, CICM 2020, held in Bertinoro, Italy, in July 2020*.The 15 full papers, 1 invited paper and 2 abstracts of invited papers presented were carefully reviewed and selected from a total of 35 submissions. The papers focus on advances in automated theorem provers and formalization, computer algebra systems and their libraries, and applications of machine learning, among other topics. * The conference was held virtually due to the COVID-19 pandemic.
The smart-machines revolution is reshaping our lives and our societies. Here, Sir Nigel Shadbolt, one of the world's leading authorities on artificial intelligence, and Roger Hampson dispel terror, confusion, and misconception. We are not about to be elbowed aside by a rebel army of super-intelligent robots of our own creation. We were using tools before we became Homo sapiens, and will continue to build and master them, no matter how complicated they become. How we exercise that control-in our private lives, in employment, in politics-and make the best of the wonderful opportunities, will determine our collective future well-being. Chapter by chapter, The Digital Ape outline how our choices and the use and adaptation of the tools we've created can lead to opportunities for the environment (both built and natural), health, and our security. Shadbolt and Hampson are uniquely well-suited to draw on historical precedent and technical know-how to offer a vision of the future that is exciting, rather than nerve-wracking, to contemplate.
This unique compendium presents an introduction to problem solving, information theory, statistical machine learning, stochastic methods and quantum computation. It indicates how to apply quantum computation to problem solving, machine learning and quantum-like models to decision making - the core disciplines of artificial intelligence.Most of the chapters were rewritten and extensive new materials were updated. New topics include quantum machine learning, quantum-like Bayesian networks and mind in Everett many-worlds.
Artificial Intelligence (AI) is an emerging discipline of computer science. It deals with the concepts and methodologies required by computer to perform an intelligent activity. The spectrum of computer science is very wide and it enables the computer to handle almost every activity, which human beings can. It deals with defining the basic problem from viewpoint of solving it through computer, finding out the total possibilities of solution, representing the problem from computational orientation, selecting data structures, finding the solution through searching the goal in search space dealing with the real world uncertain situations, etc. It also develops the techniques for learning and understanding, which make the computer able to exhibit an intelligent behavior. The list is exhaustive and is applied nowadays in almost every field of technology. This book presents almost all the components of AI like problem solving, search techniques, knowledge concepts, expert system and many more in a very simple language. One of the unique features of this book is inclusion of number of solved examples; in between the chapters and also at the end of many chapters. Real life examples have been discussed to make the reader conversant with the intricate phenomenon of computer science in general, and artificial intelligence in particular. The book is primarily developed for undergraduate and postgraduate engineering students.
The universe is full of different kinds of knowledge like tangible, intangible, conceptual, static, dynamic and many more. Knowledge Engineering is an advancement of Artificial Intelligence (AI). The present book describes various concepts of artificial intelligence, and other technical aspects of Knowledge Engineering and Computer Science. Knowledge representation is a key aspect of problem formulation from AI viewpoint. In the light of importance of knowledge representation and its analysis, it has emerged as a fullfledged engineering discipline. The book focuses on the concepts and issues of Knowledge Engineering that have impact on business management strategies, productivity, and the key elements of any business and its people. It also discusses the skills required from the persons working in this area.
It will soon be impossible to tell what is real and what is fake. Recent advances in AI mean that by scanning images of a person (for example using Facebook), a powerful machine learning system can create new video images and place them in scenarios and situations which never actually happened. When combined with powerful voice AI, the results are utterly convincing. So-called 'Deep Fakes' are not only a real threat for democracy but they take the manipulation of voters to new levels. They will also affect ordinary people. This crisis of misinformation we are facing has been dubbed the 'Infocalypse'. Using her expertise from working in the field, Nina Schick reveals shocking examples of Deep Fakery and explains the dangerous political consequences of the Infocalypse, both in terms of national security and what it means for public trust in politics. She also unveils what it means for us as individuals, how Deep Fakes will be used to intimidate and to silence, for revenge and fraud, and how unprepared governments and tech companies are. As a political advisor to select technology firms, Schick tells us what we need to do to prepare and protect ourselves. Too often we build the cool technology and ignore what bad guys can do with it before we start playing catch-up. But when it comes to Deep Fakes, we urgently need to be on the front foot.
This book is the first to examine the history of imaginative thinking about intelligent machines. As real Artificial Intelligence (AI) begins to touch on all aspects of our lives, this long narrative history shapes how the technology is developed, deployed and regulated. It is therefore a crucial social and ethical issue. Part I of this book provides a historical overview from ancient Greece to the start of modernity. These chapters explore the revealing pre-history of key concerns of contemporary AI discourse, from the nature of mind and creativity to issues of power and rights, from the tension between fascination and ambivalence to investigations into artificial voices and technophobia. Part II focuses on the twentieth and twenty-first-centuries in which a greater density of narratives emerge alongside rapid developments in AI technology. These chapters reveal not only how AI narratives have consistently been entangled with the emergence of real robotics and AI, but also how they offer a rich source of insight into how we might live with these revolutionary machines. Through their close textual engagements, these chapters explore the relationship between imaginative narratives and contemporary debates about AI's social, ethical and philosophical consequences, including questions of dehumanization, automation, anthropomorphisation, cybernetics, cyberpunk, immortality, slavery, and governance. The contributions, from leading humanities and social science scholars, show that narratives about AI offer a crucial epistemic site for exploring contemporary debates about these powerful new technologies.
Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications. Deep learning is quickly integrating itself into the technology landscape. Its applications range from applicable data science to deep fakes and so much more. It is crucial for aspiring data scientists or those who want to enter the field of AI to understand deep learning concepts. The best way to learn is by doing. You'll develop a working knowledge of not only TensorFlow, but also related technologies such as Python and Keras. You'll also work with Neural Networks and other deep learning concepts. By the end of the book, you'll have a collection of unique projects that you can add to your GitHub profiles and expand on for professional application. What You'll Learn Grasp the basic process of neural networks through projects, such as creating music Restore and colorize black and white images with deep learning processes Who This Book Is For Beginners new to TensorFlow and Python.
With the emergence of smart technology and automated systems in today's world, artificial intelligence (AI) is being incorporated into an array of professions. The aviation and aerospace industry, specifically, is a field that has seen the successful implementation of early stages of automation in daily flight operations through flight management systems and autopilot. However, the effectiveness of aviation systems and the provision of flight safety still depend primarily upon the reliability of aviation specialists and human decision making. Artificial Intelligence Applications in the Aviation and Aerospace Industries is a pivotal reference source that explores best practices for AI implementation in aviation to enhance security and the ability to learn, improve, and predict. While highlighting topics such as computer-aided design, automated systems, and human factors, this publication explores the enhancement of global aviation security as well as the methods of modern information systems in the aeronautics industry. This book is ideally designed for pilots, scientists, engineers, aviation operators, air crash investigators, teachers, academicians, researchers, and students seeking current research on the application of AI in the field of aviation.