Dix A. Artificial Intelligence. Humans at the Heart of Algorithms 2ed 2025

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Dix A. Artificial Intelligence. Humans at the Heart of Algorithms 2ed 2025

Textbook in PDF format An authoritative and accessible one-stop resource, the first edition of An Introduction to Artificial Intelligence presented one of the first comprehensive examinations of AI. Designed to provide an understanding of the foundations of artificial intelligence, it examined the central computational techniques employed by AI, including knowledge representation, search, reasoning and learning, as well as the principal application domains of expert systems, natural language, vision, robotics, software agents and cognitive modelling. Many of the major philosophical and ethical issues of AI were also introduced. This new edition expands and revises the book throughout, with new material to augment existing chapters, including short case studies, as well as adding new chapters on explainable AI, big data and Deep Learning, temporal and web-scale data, statistical methods and data wrangling. It expands the book’s focus on human-centred AI, covering gender, ethnic and social bias, the need for transparency, intelligent user interfaces, and designing interactions to aid Machine Learning. With detailed, well-illustrated examples and exercises throughout, this book provides a substantial and robust introduction to Artificial Intelligence in a clear and concise coursebook form. This edition, while substantially expanding the material covered, still seeks to follow the same principles as the first edition, providing accessible coverage of the key areas of AI in such a way that it will be understandable to those with only a basic knowledge of mathematics and computer science. The huge growth of data science and the ubiquity of AI mean that today this approach is more important than ever, and I hope that the new material in this new edition has followed this principle as well as the first. The book takes a pragmatic approach to AI, looking at how AI techniques are applied to various application areas, and includes both more traditional symbolic AI and sub-symbolic AI including neural networks and Deep Learning. It covers both general principles such as reasoning and Machine Learning and also more specific techniques for areas such as computer vision, language understanding and the web. Educators can use the book to support a one-semester introductory module spending approximately one week each on Chapters 2 to 8 and selected further chapters. Alternatively it can be used as a longer course covering most of the chapters, again at around one week per xxiichapter. You are encouraged to include some material on social, human or philosophical aspects, both to bring the topic to life and most critically because the questions of how AI fit into wider society are some of the most pressing for everyone. If you are an AI professional, this book will primarily be useful to give you a grandstand view of the area, helping you to understand the field as a whole, and identify the particular topics you need to know about in more detail. Having identified these areas use the recommended reading at the end of each chapter or web resources to dig deeper. It stands as a core text for all students and computer scientists approaching AI. Preface Introduction Knowledge in AI Reasoning Search SECTION II Data and Learning Machine Learning Neural Networks Statistical and Numerical Techniques Going Large: Deep Learning and Big Data Making Sense of Machine Learning Data Preparation SECTION III Specialised Areas Game Playing Computer Vision Natural Language Understanding Time Series and Sequential Data Planning and Robotics Agents Web-scale Reasoning SECTION IV Humans at the Heart Expert and Decision Support Systems AI Working with and for Humans When Things Go Wrong Explainable AI Models of the Mind – Human-like Computing Philosophical, Ethical and Social Issues SECTION V Looking Forward Epilogue: What Next? SUMMARY – FROM HYPE TO HOPE