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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!
This book engages with the title question: what is artificial intelligence (AI)? Instead of reiterating received definitions or surveying the field from a disciplinary perspective, the question is engaged here by putting two standpoints into conversation. The standpoints are different in their disciplinary groundings - i.e. technology and the humanities - and also in their approaches - i.e. applied and conceptual. Peter is an AI engineer: his approach is in terms of how to make AI work. Suman is a humanities researcher: his approach is in terms of what people and academics mean when they say 'AI'.A coherent argument, if not a consensus, develops by putting the two standpoints into conversation. The conversation is presented in 32 short chapters, in turn by Suman and Peter. There are two parts: Part 1, Questioning AI, and Part 2, AI and Government Policy. The first part covers issues such as the meaning of intelligence, automation, evolution, artificial and language. It outlines some of the processes through which these concepts may be technologically grounded as AI. The second part addresses policy considerations that underpin the development of AI and responds to the consequences. Themes taken up here include: rights and responsibilities; data usage and state-level strategies in the USA, UK and China; unemployment and policy futures.
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.
Is it possible to design robots and other machines that can reproduce and evolve? And, if so, what are the implications: for the machines, for ourselves, for our environment, and for the future of life on Earth and elsewhere? In this book the authors provide a chronological survey and comprehensive archive of the early history of thought about machine self-reproduction and evolution. They discuss contributions from philosophy, science fiction, science and engineering, and uncover many examples that have never been discussed in the Artificial Intelligence and Artificial Life literature before now. In the final chapter they provide a synthesis of the concepts discussed, offer their views on the field's future directions, and call for a broad community discussion about the significant implications of intelligent evolving machines. The book will be of interest to general readers, and a valuable resource for researchers, practitioners, and historians engaged with ideas in artificial intelligence, artificial life, robotics, and evolutionary computing.
This book highlights cutting-edge applications of machine learning techniques for disaster management by monitoring, analyzing, and forecasting hydro-meteorological variables. Predictive modelling is a consolidated discipline used to forewarn the possibility of natural hazards. In this book, experts from numerical weather forecast, meteorology, hydrology, engineering, agriculture, economics, and disaster policy-making contribute towards an interdisciplinary framework to construct potent models for hazard risk mitigation. The book will help advance the state of knowledge of artificial intelligence in decision systems to aid disaster management and policy-making. This book can be a useful reference for graduate student, academics, practicing scientists and professionals of disaster management, artificial intelligence, and environmental sciences.
This book brings together past insights, current research and promising future trends associated with distributed computing, artificial intelligence and their application in order to provide efficient solutions to real-world problems. The book is based on the International Conference on Distributed Computing and Artificial Intelligence 2020 (DCAI 2020), which provided a forum to present applications of innovative techniques for studying and solving complex problems in artificial intelligence and computing areas. It includes contributions on well-established and evolving areas of research, by authors from 26 countries, representing a truly wide area network of research activity
This research-oriented book presents key contributions on architecting the digital transformation. It includes the following main sections covering 20 chapters: * Digital Transformation * Digital Business * Digital Architecture * Decision Support * Digital Applications Focusing on digital architectures for smart digital products and services, it is a valuable resource for researchers, doctoral students, postgraduates, graduates, undergraduates, academics and practitioners interested in digital transformation.
This book intends to bring together researchers and developers from industry, the education field, and the academic world to report on the latest scientific research, technical advances, and methodologies. The 10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning is hosted by the University of L'Aquila and is going to be held in L'Aquila (Italy). Initially planned on the 17th to the 19th of June 2020, it was postponed to the 7th to the 9th of October 2020, due to the COVID-19 outbreak. The 10th edition of this conference and its related workshops expand the topics of the evidence-based TEL workshops series in order to provide an open forum for discussing intelligent systems for TEL, their roots in novel learning theories, empirical methodologies for their design or evaluation, stand-alone solutions, or web-based ones. This bridge has been realized also thanks to the sponsor of this edition of MIS4TEL: the Armundia Group https://www.armundia.com, the support from national associations (AEPIA, APPIA, CINI, and EurAI), and organizers (UNIVAQ, UNIROMA1, UNIBZ, UCV, UFSC, USAL, AIR institute, UNC, and UNIBA)
Ascend AI Processor Architecture and Programming: Principles and Applications of CANN offers in-depth AI applications using Huawei's Ascend chip, presenting and analyzing the unique performance and attributes of this processor. The title introduces the fundamental theory of AI, the software and hardware architecture of the Ascend AI processor, related tools and programming technology, and typical application cases. It demonstrates internal software and hardware design principles, system tools and programming techniques for the processor, laying out the elements of AI programming technology needed by researchers developing AI applications. Chapters cover the theoretical fundamentals of AI and deep learning, the state of the industry, including the current state of Neural Network Processors, deep learning frameworks, and a deep learning compilation framework, the hardware architecture of the Ascend AI processor, programming methods and practices for developing the processor, and finally, detailed case studies on data and algorithms for AI.
This book includes 46 scientific papers presented at the conference and reflecting the latest research in the fields of data mining, machine learning and decision-making. The international scientific conference Intellectual Systems of Decision-Making and Problems of Computational Intelligence was held in the Kherson region, Ukraine, from May 25 to 29, 2020. The papers are divided into three sections: Analysis and Modeling of Complex Systems and Processes, Theoretical and Applied Aspects of Decision-Making Systems and Computational Intelligence and Inductive Modeling. The book will be of interest to scientists and developers specialized in the fields of data mining, machine learning and decision-making systems.
This book is the first volume in a collection of contributions arising from a multidisciplinary project developed in the field of agri-food value chain analysis. The respective papers combine a range of disciplines to analyse major agri-food challenges in Europe and South America, offering readers a practical understanding of how risk and uncertainties can be managed by means of validated data and results from agri-food systems analysis. Experts from agronomy, information communication and technology, operations and supply chain management share their findings and propose novel approaches. Given its scope, the book will be of interest to a broad readership who want to learn about current agri-food challenges and requirements, and to professionals whose work involves real-life industry requirements, food and/or farming.
This book is a compendium of the proceedings of the International Conference on Big-Data and Cloud Computing. The papers discuss the recent advances in the areas of big data analytics, data analytics in cloud, smart cities and grid, etc. This volume primarily focuses on the application of knowledge which promotes ideas for solving problems of the society through cutting-edge big-data technologies. The essays featured in this proceeding provide novel ideas that contribute for the growth of world class research and development. It will be useful to researchers in the area of advanced engineering sciences.
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.