Best 10 Books on AI for Amateurs and Experts

Artificial intelligence (AI) is reshaping industries, influencing everything from business and health care to art and entertainment. Whether you are a curious beginner looking to grasp AI fundamentals or are an experienced professional aiming to refine your expertise, the right book can be a valuable resource. Fortunately, there are many interesting reads on AI. Some focus on its societal and ethical implications, while others dive into the technical details. This guide breaks it down for you, listing the best books on AI for amateurs and experts.

Best AI Books for Beginners

Getting started with artificial intelligence can be easy with these books, as they break down complex AI concepts into easy-to-understand language.

1. Artificial Intelligence: A Guide for Thinking Humans

Melanie Mitchell’s Artificial Intelligence: A Guide for Thinking Humans is an excellent starting point for beginners. This book takes a conversational approach, making AI concepts accessible to general readers. In A Guide for Thinking Humans, Mitchell explores the strengths and limitations of AI in great depth. It addresses common misconceptions about the technology while also highlighting the challenges researchers face in developing truly intelligent systems.

Through engaging examples and clear explanations, the book easily explains machine learning, neural networks and AI’s real-world applications. This book helps by making sense of how machines learn and perceive their world and discusses the ethicalities of AI systems.

2. Superintelligence: Paths, Dangers, Strategies

Superintelligence is a must-read for anyone interested in the future of AI and its potential risks. While not overly technical, the book takes a philosophical and strategic approach to exploring how AI could surpass human intelligence — and what that could mean for society.

Nick Bostrom examines different scenarios in which AI could evolve, from benevolent assistants to uncontrollable superintelligent entities. He digs into the ethical and existential risks of AI, focusing on the importance of proactive governance and safety measures. This book may be thought-provoking, but it is well-suited for beginners trying to understand the long-term implications of AI.

3. Artificial Intelligence: A Very Short Introduction

For readers looking for a concise yet comprehensive overview of AI, A Very Short Introduction is a perfect choice. As part of Oxford’s Very Short Introduction series, this book distills the key concepts of AI into an easy read for beginners.

In this book, Margaret A. Boden explores the history, fundamentals and ethical implications of AI. It also answers questions like how machines learn and what the future of AI may hold. It even explores whether the technology can think like humans, as a system like the Theory of Mind can offer more emotional responses but is only in the partial stages of development. Therefore, the book touches on whether thinking like humans is possible for artificial intelligence.

4. Atlas of AI

Atlas of AI takes a different approach to artificial intelligence by exploring its social, political and environmental impact. Rather than focusing on technical mechanics, Kate Crawford examines the hidden costs of AI — how it is built, who controls it, and how it affects society.

The book sheds light on the often-overlooked aspects of AI, such as the labor, data extraction and power structures behind its development. With a critical and thoroughly researched perspective, Atlas of AI challenges the common perception of it as purely a technological advancement and encourages readers to consider its broader implications. 

5. Human Compatible: Artificial Intelligence and the Problem of Control

One problem that AI books have is that they focus on what AI can do today. However, Human Compatible can be an interesting read as the author shifts the conversation to the future and explores how people can ensure this technology remains beneficial to humanity.

The book examines the risks of AI systems becoming misaligned with human values and proposes solutions for developing artificial intelligence that prioritizes human well-being. Stuart Russell — a leading AI researcher — presents these ideas in an approachable way. Human Compatible is also essential for anyone interested in the long-term challenges of AI development.

Best AI Books for Experts

For those with a strong foundation in AI, the following books offer deeper insights into machine learning, neural networks and advanced artificial intelligence concepts.

1. Artificial Intelligence: A Modern Approach

Widely regarded as the definitive textbook on AI, A Modern Approach by Stuart Russell and Peter Norvig provides an in-depth look at the field’s core principles and techniques. Now in its fourth edition, the book covers everything from search algorithms and logic to deep learning and reinforcement learning. Therefore, it is an excellent resource for AI researchers, engineers and advanced students.

The latest edition includes updates on deep learning, probabilistic programming, multi-agent systems and more. While highly technical, it offers a full deep dive into AI that can help you master this field.

2. Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning is a fundamental resource that deepens readers’ understanding of statistical pattern recognition and probabilistic machine learning. This book takes a rigorous mathematical approach, covering Bayesian networks, graphical models and kernel methods.

This text also assumes a solid foundation in linear algebra, calculus and probability theory. It provides a thorough yet accessible introduction to machine learning techniques, complete with practical examples and exercises. 

3. Deep Learning: Foundations and Concepts

Deep Learning by Christopher Bishop and Hugh Bishop is a comprehensive guide to one of the most influential areas of modern AI. This book covers the mathematical and theoretical foundations of deep learning, including neural networks, optimization techniques and advanced architectures, such as convolutional and recurrent networks.

Written for AI researchers and engineers, this book offers prior knowledge of machine learning and mathematical principles. It provides in-depth explanations alongside real-world applications. As a result, it can help those looking to push the boundaries of deep learning and artificial intelligence.

4. Reinforcement Learning: An Introduction

Reinforcement Learning: An Introduction is the definitive guide to understanding reinforcement learning (RL), one of the most dynamic areas of AI research. The book offers foundational RL concepts, including Markov decision processes, dynamic programming and deep reinforcement learning. 

With a balance of theory and practical examples, it provides a structured approach to understanding how agents learn through trial and error. For those with a background in machine learning and probability theory, the text is best for experts looking to develop AI systems capable of autonomous decision-making and continuous learning.

5. Transformers for Natural Language Processing and Computer Vision — Third Edition

Transformers for Natural Language Processing and Computer Vision is a cutting-edge resource for AI professionals looking to master transformer models. The book guides readers through transformer architectures, including BERT, GPT and Vision Transformers. It also provides in-depth explanations of how these models process language and visual data.

With practical case studies and hands-on implementations, this book fills the gap between theory and application.

Expand Your Knowledge With the Best Books on AI

Artificial intelligence is a rapidly evolving field, so staying informed is essential. With the best books on AI, experts and beginners can gain deep insights and explanations of AI and foundational concepts. Regardless of your expertise, reading widely about it helps develop a well-rounded understanding of its capabilities, limitations and ethical implications.