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Artificial intelligence

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!

Artificial Intelligence Foundations

Artificial Intelligence Foundations

Author: Andrew Lowe, Steve Lawless Format: Paperback / softback Release Date: 05/02/2021

In line with the BCS AI Foundation and Essentials certificates, this book guides you through the complex and ever-evolving world of AI. You will learn how AI is being utilised today to support products, services, science and engineering, and how it is likely to be used in the future to balance the talents of humans and machines. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed.

Strategy, Leadership, and AI in the Cyber Ecosystem

Strategy, Leadership, and AI in the Cyber Ecosystem

Author: Hamid (Northumbria University London, London, UK) Jahankhani Format: Paperback / softback Release Date: 01/02/2021

Strategy, Leadership and AI in the Cyber Ecosystem investigates the restructuring of the way cybersecurity and business leaders engage with the emerging digital revolution towards the development of strategic management, with the aid of AI, and in the context of growing cyber-physical interactions (human/machine co-working relationships). The book explores all aspects of strategic leadership within a digital context. It investigates the interactions from both the firm/organization strategy perspective, including cross-functional actors/stakeholders who are operating within the organization and the various characteristics of operating in a cyber-secure ecosystem. As consumption and reliance by business on the use of vast amounts of data in operations increase, demand for more data governance to minimize the issues of bias, trust, privacy and security may be necessary. The role of management is changing dramatically, with the challenges of Industry 4.0 and the digital revolution. With this intelligence explosion, the influence of artificial intelligence technology and the key themes of machine learning, big data, and digital twin are evolving and creating the need for cyber-physical management professionals.

AI on Trial

AI on Trial

Author: Mark Deem, Peter Warren Format: Paperback / softback Release Date: 31/01/2021

With AI now being used in many walks of our every day life, this book looks at the questions surrounding AI and its future. For example: Who owns AI? Is AI a product in its own right? Should AI have rights and responsibilities? What are the status, capacity and authority issues relating to AI? Is AI racist? What are the issues (legal and ethical) created by implicit bias of coders and data sets? Can AI be used to gain a competitive advantage? If so, is it anti-competitive? What is the role of AI in cybersecurity? Can we trust AI? Written by experts and laid out in the style of a trial, starting with opening submissions, followed by the evidence, closing submissions and finally the judgment, the book takes an innovative approach to the most innovative of technological areas.

Artificial Intelligence Techniques in IoT Sensor Networks

Artificial Intelligence Techniques in IoT Sensor Networks

Author: Mohamed Elhoseny Format: Hardback Release Date: 22/01/2021

Artificial Intelligence Techniques in IoT Sensor Networks is a technical book which can be read by researchers, academicians, students and professionals interested in Artificial Intelligence (AI), Sensor Networks and Internet of Things (IoT). This book intends to develop a shared understanding of applications of AI techniques in the present and near term. The book maps the technical impacts of AI technologies, applications, and their implications on the design of solutions for sensor networks. This book introduces the researchers and aspiring academicians the subject of latest developments and trends in AI applications for sensor networks in a clear and well-organized manner. It is mainly useful for research scholars in sensor networks and AI techniques. In addition, professionals and practitioners' working on the design of real time applications for sensor networks may be benefited directly from this book. Moreover, graduate and master students of any departments related to AI, IoT and sensor networks can find this book fascinating for developing expert systems or real time applications. The book is written in a simple and easy language, discusses the concepts from fundamentals which relieve the requirement of earlier background of the field finds it readable. From this expectation and experience, we believe that every library will be interested to collect copies of this book.

An Introduction to IoT Analytics

An Introduction to IoT Analytics

Author: Harry G. Perros Format: Paperback / softback Release Date: 22/01/2021

An Introduction to IoT Analytics covers techniques that can be used to analyze data from IoT sensors and also addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so that one can learn how to apply these tools in practice with a good understanding of their inner workings. It is an introductory book for readers that have no familiarity with these techniques. The techniques presented in the book come from the areas of Machine Learning, Statistics, and Operations Research. Machine Learning techniques are described that can be used to analyze IoT data generated from sensors for clustering, classification, and regression. The statistical techniques described can be used to carry out regression and forecasting of IoT sensor data, and dimensionality reduction of data sets. Operations Research is concerned with the performance of an IoT system by constructing a model of a system under study, and then carry out what-if analysis. The book also describes simulation techniques. Key features: IoT analytics is not just Machine Learning but it also involves other tools, such as, forecasting and simulation techniques. Many diagrams and examples are given throughout the book to better explain the material presented. At the end of each chapter, there is a project designed to help the reader to better understand the techniques described in the chapter. The material is this book has been class tested over several semesters. Contains practice exercises, with solutions provided online at www.routledge.com/9780367686314

An Introduction to IoT Analytics

An Introduction to IoT Analytics

Author: Harry G. Perros Format: Hardback Release Date: 22/01/2021

An Introduction to IoT Analytics covers techniques that can be used to analyze data from IoT sensors and also addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so that one can learn how to apply these tools in practice with a good understanding of their inner workings. It is an introductory book for readers that have no familiarity with these techniques. The techniques presented in the book come from the areas of Machine Learning, Statistics, and Operations Research. Machine Learning techniques are described that can be used to analyze IoT data generated from sensors for clustering, classification, and regression. The statistical techniques described can be used to carry out regression and forecasting of IoT sensor data, and dimensionality reduction of data sets. Operations Research is concerned with the performance of an IoT system by constructing a model of a system under study, and then carry out what-if analysis. The book also describes simulation techniques. Key features: IoT analytics is not just Machine Learning but it also involves other tools, such as, forecasting and simulation techniques. Many diagrams and examples are given throughout the book to better explain the material presented. At the end of each chapter, there is a project designed to help the reader to better understand the techniques described in the chapter. The material is this book has been class tested over several semesters. Contains practice exercises, with solutions provided online at www.routledge.com/9780367686314

The Alignment Problem

The Alignment Problem

Author: Brian Christian Format: Paperback / softback Release Date: 21/01/2021

'Vital reading. This is the book on artificial intelligence we need right now.' Mike Krieger, cofounder of Instagram Artificial intelligence is rapidly dominating every aspect of our modern lives influencing the news we consume, whether we get a mortgage, and even which friends wish us happy birthday. But as algorithms make ever more decisions on our behalf, how do we ensure they do what we want? And fairly? This conundrum - dubbed 'The Alignment Problem' by experts - is the subject of this timely and important book. From the AI program which cheats at computer games to the sexist algorithm behind Google Translate, bestselling author Brian Christian explains how, as AI develops, we rapidly approach a collision between artificial intelligence and ethics. If we stand by, we face a future with unregulated algorithms that propagate our biases - and worse - violate our most sacred values. Urgent and fascinating, this is an accessible primer to the most important issue facing AI researchers today.

Multi-Agent Coordination

Multi-Agent Coordination

Author: Arup Kumar Sadhu, Amit Konar Format: Hardback Release Date: 21/01/2021

This book explores the usage of Reinforcement Learning for Multi-Agent Coordination. Chapter 1 introduces fundamentals of the multi-robot coordination. Chapter 2 offers two useful properties, which have been developed to speed-up the convergence of traditional multi-agent Q-learning (MAQL) algorithms in view of the team-goal exploration, where team-goal exploration refers to simultaneous exploration of individual goals. Chapter 3 proposes the novel consensus Q-learning (CoQL), which addresses the equilibrium selection problem. Chapter 4 introduces a new dimension in the literature of the traditional correlated Q-learning (CQL), in which correlated equilibrium (CE) is computed partly in the learning and the rest in the planning phases, thereby requiring CE computation once only. Chapter 5 proposes an alternative solution to the multi-agent planning problem using meta-heuristic optimization algorithms. Chapter 6 provides the concluding remarks based on the principles and experimental results acquired in the previous chapters. Possible future directions of research are also examined briefly at the end of the chapter.

Content-Based Image Classification

Content-Based Image Classification

Author: Rik Das Format: Hardback Release Date: 19/01/2021

Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques is a comprehensive guide to research with invaluable image data. Social Science Research Network has revealed that 65% of people are visual learners. Research data provided by Hyerle (2000) has clearly shown 90% of information in the human brain is visual. Thus, it is no wonder that visual information processing in the brain is 60,000 times faster than text-based information (3M Corporation, 2001). Recent times have witnessed a significant surge in conversing with images due to the popularity of social networking platforms. The other reason for embracing usage of image data is the mass availability of high resolution cellphone cameras. Wide usage of image data in diversified application areas including medical science, media, sports, remote sensing and so on has spurred the need of further research in optimizing archival, maintenance and retrieval of appropriate image content to leverage data-driven decision-making. This book demonstrates several techniques of image processing to represent image data in desired format for information identification. It discusses the application of machine learning and deep learning for identifying and categorizing appropriate image data helpful in designing automated decision support systems. The book offers comprehensive coverage of the most essential topics, including: Image feature extraction with novel handcrafted techniques (traditional feature extraction) Image feature extraction with automated techniques (representation learning with CNNs) Significance of fusion-based approaches in enhancing classification accuracy MATLAB (R) codes for implementing the techniques Use of Open Access data mining tool Weka for multiple tasks The book is intended for budding researchers, technocrats, engineering students and machine learning / deep learning enthusiasts who are willing to start their computer vision journey with content-based image recognition. The readers will get a clear picture of the essentials for transforming the image data into valuable means of insight generation. Readers will learn coding tricks necessary to propose novel mechanisms and disruptive approaches. The Weka guide provided is beneficial for those who are not comfortable coding for machine learning algorithms. The Weka tool assists the learner to implement machine learning algorithms with the click of a button. Thus, this book will be a stepping stone for your machine learning journey. Please visit the author's website for any further guidance at https://www.rikdas.com/

The Rationalist's Guide to the Galaxy

The Rationalist's Guide to the Galaxy

Author: Tom Chivers Format: Paperback / softback Release Date: 07/01/2021

'A fascinating and delightfully written book about some very smart people who may not, or may, be about to transform humanity forever' JON RONSON 'Beautifully written, and with wonderful humour, this is a thrilling adventure story of our own future' LEWIS DARTNELL, author of THE KNOWLEDGE and ORIGINS Are paperclips going to destroy life as we know it? What can Mickey Mouse teach us about how to programme AI? Could a more rational approach to life be what saves us all? This is a book about about a community of people who are trying to think rationally about intelligence and what insight they can and can't give us about the future of the human race. It explains why these people are worried about an AI apocalypse, why they might be right, and why they might be wrong. It is a book about the cutting edge of our thinking on intelligence and rationality right now by the people who stay up all night worrying about it.

Practical Fairness

Practical Fairness

Author: Aileen Nielsen Format: Paperback / softback Release Date: 31/12/2020

Fairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we've been trying to fight in the real world. But what does fairness mean when it comes to code? This practical book covers basic concerns related to data security and privacy to help AI and data professionals use code that's fair and free of bias. Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to black box model audits. Author Aileen Nielsen guides you through the technical, legal, and ethical aspects of making code fair and secure while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms. Write data processing and modeling code that follows fair machine learning best practices Understand complex interrelationships between fairness, privacy, and data security Use preventive measures to minimize bias when developing data modeling pipelines Identify opportunities for bias and discrimination in current data scientist models Detect data pipeline aspects that implicate security and privacy concerns