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An Introduction To Artificial Intelligence
An authoritative and accessible one-stop resource, An Introduction to Artificial Intelligence presents the first full examination of AI. Designed to provide an understanding of the foundations of artificial intelligence, it examines 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 modeling. Many of the major philosophical and ethical issues of AI are also introduced.
Throughout the volume, the authors provide detailed, well-illustrated treatments of each topic with abundant examples and exercises. The authors bring this exciting field to life by presenting a substantial and robust introduction to artificial intelligence in a clear and concise coursebook form. This book stands as a core text for all computer scientists approaching AI for the first time.
Throughout the volume, the authors provide detailed, well-illustrated treatments of each topic with abundant examples and exercises. The authors bring this exciting field to life by presenting a substantial and robust introduction to artificial intelligence in a clear and concise coursebook form. This book stands as a core text for all computer scientists approaching AI for the first time.
288 pages, Hardcover
Published September 30, 2017
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November 16, 2017
The most comprehensible, math-free, explanation of artificial intelligence problems and solutions I could find to date. Some parts (like the chapter about Search or Computer Vision) are more challenging than others, but overall perfectly understandable.
This is an 11 years old book. As such, it barely touches today's hot topics like supervised and unsupervised learning and it doesn't mention at all reinforcement or transfer learning. It's also a bit outdated on some examples (like the capability for an AI to beat humans at Chess or Go), but if you plan to read this book, you are probably already aware of research progress in those areas and you can easily compensate the inaccurate information in the book.
Nonetheless, it's a great read to have a basic understanding of the complexities underneath the problems we try to solve today with AI.
The book also is an inspiring read if you come from neuroscience studies. You'll be tempted to draw parallels with modern theories like the Hierarchical temporal memory (HTM) one and think about the differences between how AI works and how the human brain is supposed to work according to those theories.
This is an 11 years old book. As such, it barely touches today's hot topics like supervised and unsupervised learning and it doesn't mention at all reinforcement or transfer learning. It's also a bit outdated on some examples (like the capability for an AI to beat humans at Chess or Go), but if you plan to read this book, you are probably already aware of research progress in those areas and you can easily compensate the inaccurate information in the book.
Nonetheless, it's a great read to have a basic understanding of the complexities underneath the problems we try to solve today with AI.
The book also is an inspiring read if you come from neuroscience studies. You'll be tempted to draw parallels with modern theories like the Hierarchical temporal memory (HTM) one and think about the differences between how AI works and how the human brain is supposed to work according to those theories.
Displaying 1 of 1 review