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The best books on artificial intelligence (AI)
Hunting for the best books on artificial intelligence? Look no further; our curated list of must-reads covers essentials for everyone – from beginners to seasoned AI pros.
We’ve compiled books spanning 10 topics exploring AI's history, ethical considerations, future, and more. If you're looking to really understand AI's impact (and its key issues), each title listed here acts as a great resource packed with valuable insight and expert analysis. But only for genuine humans.
Jump to the good stuff
- Broad overviews like ‘Artificial Intelligence: A Modern Approach’ and ‘Life 3.0’ provide foundational knowledge in AI and its societal implications.
- Get to know machine learning in ‘Pattern Recognition and Machine Learning’ and ‘The Hundred-Page Machine Learning Book’, with essential principles for understanding and applying AI techniques.
- Looking for big answers to even bigger questions? ‘Weapons of Math Destruction,’ highlights the importance of accountability and transparency in AI development.
1. Best books on artificial intelligence for business leaders
Artificial intelligence is here to stay – and it’s going to be pretty transformative for any department that adopts it. For business leaders, that incoming change will require understanding AI’s real-world use cases, its pitfalls, and where its applications will yield the strongest ROI.
The following three books will give you a great head start, but we also have a bunch more tailor-made reads designed specifically for business leaders and decision-makers in our brand new guide:
Free ebook: 10 Essential AI books for business leaders in 2025
“The Business Case for AI: A Leader’s Guide to AI Strategies, Best Practices & Real-World Applications” by Kavita Ganesan
Kavita Ganesan takes the mystery out of implementing AI here, showing you how to thoroughly identify, launch, and measure AI initiatives that get real results. It’s a straightforward framework designed specifically for business leaders looking to jumpstart their AI strategy and initiatives.
“Prediction Machines: The Simple Economics of Artificial Intelligence” by Ajay Agrawal, Joshua Gans, and Avi Goldfarb
The authors of this book, three renowned economists, reframe AI as (inexpensive) prediction tools, explain how prediction fits into personal and business decision-making processes, and explore how foundational technologies will impact business choices. It’s a great place to start if you’re looking to demystify AI’s potential for streamlining decision-making, problem solving, creating new – and optimizing existing – business strategies, and much more.
“The AI-Savvy Leader: Nine Ways to Take Back Control and Make AI Work” by David De Cremer
Discover the nine actions leaders must take to successfully shepherd an organization towards a more AI-centric future – and avoid the common mistakes many early adopters make. Instead of focusing on the ins and outs of digital technology itself, this book highlights how the skills leaders already possess – communication and strategic vision – can be applied to responsibly guide organizations through the age of artificial intelligence.
2. AI overviews
These books provide a rounded understanding of all things artificial intelligence, including its past, core principles, and prospects. That’s alongside examining the broader societal impact and philosophical questions surrounding AI, and business guidance that bridges the gap between academic knowledge and real-world application.
"Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig
Start here if you’re looking for a full-on breakdown of all things artificial intelligence, including detailed explanations of the fundamentals, like search algorithms, knowledge representation, and machine learning. You’ll also learn how AI is being applied to transform real-world scenarios through automated reasoning, robotics, and natural language processing – and how these innovative technologies are reshaping a host of industries.
"Life 3.0: Being Human in the Age of Artificial Intelligence" by Max Tegmark
What does it mean to be a human being in an era where we’re not the only smart guys on planet Earth? Here, author Max Tegmark engages with meaty, existential questions around our identity as humans within a landscape dominated by artificial intelligence, urging his audience to reflect on how profoundly this emergent tech could alter human life. That includes the consequences AI implementation will have across work, education, our moral principles, and potential existential threats.
3. Machine learning fundamentals
Artificial intelligence is really an umbrella term for a bunch of different technologies, of which machine learning is a massively important puzzle piece. That makes grasping the basics of machine learning super important for anyone aiming to gain a deeper insight into artificial intelligence as a whole. These next two books make for an ideal introduction to what machine learning is, does, and enables.
"Pattern Recognition and Machine Learning" by Christopher Bishop
By concentrating on ML’s basis in Bayesian methods – whereby new information is added to existing datasets – this book acts as a one-stop shop for understanding what makes machine learning tick and how it’s been deployed in real-world settings.
Machine learning can be a tricky thing to get to grips with, but Pattern Recognition and Machine Learning offers an extensive overview of its underpinnings and inner workings.
"The Hundred-Page Machine Learning Book" by Andriy Burkov
Here we have an expansive overview of machine learning – for novices and seasoned experts alike – in a pretty digestible package. 100 pages, to be precise.
Using clear examples, this pint-size resource delivers real-world insight into what machine learning can do and how to implement it successfully. Among often impenetrable competitors, it stands as a really accessible option for anyone seeking a concise introduction to the vital components of machine learning.
4. Specialized AI systems
Artificial intelligence systems tailored to particular use cases – like neural networks, computer vision, and reinforcement learning – require a deep understanding of AI’s various facets. These systems have a bunch of real-world applications across various industries, so you’ll want to get familiar with the concepts and science going on behind the scenes.
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Recognized as a fundamental piece of literature within the AI sector, this book encompasses the basic principles, historical progressions, and pivotal algorithms that enable deep learning models. The focus here is on deep learning’s mathematical underpinnings and algorithms – including neural networks and their architecture – and it serves as an indispensable guide as a result.
"Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto
Reinforcement learning is a research area within artificial intelligence that explores how models attempt to maximize their reward from prompts within untested environments. Did the model do well? Then it’ll use that information to fine-tune things next time.
It’s fascinating stuff, and this book explores the basics, the key ideas, and the algorithms that influence reinforcement learning’s role as a ‘trial and error’ approach to AI modeling.
5. Ethical considerations in AI
Artificial intelligence advancement brings with it a raft of ethical questions to think about. This next book examines these moral quandaries and principal concerns, with a focus on ensuring transparency, equity, and human supervision throughout the creation and implementation phases.
"Weapons of Math Destruction" by Cathy O'Neil
Great title, right? This book looks at how AI systems tend to reinforce pre-existing biases, with examples where data-driven algorithms have delivered discriminatory results across industries like finance and employment. What we get here is a powerful warning about the ethical considerations associated with AI – one that stresses the vital necessity for responsible development and application.
6. AI and human cognition
Studying the interconnection between our own cognitive abilities and AI systems presents an opportunity to focus on how AI might replicate or augment human reasoning in its problem solving. Should AI be a 1:1 reproduction of human intelligence? Or should it become its own, separate beast?
"The Society of Mind" by Marvin Minsky
If you want to understand human thought, start here. “The Society of Mind” proposes a concept where human intelligence emerges from the collective operation of millions of uncomplicated, unintelligent entities. It’s a hypothesis that likens the processes governing our own cognition to those found within AI systems – suggesting that complex intelligence is rooted in simple procedures.
"Gödel, Escher, Bach: An Eternal Golden Braid" by Douglas Hofstadter
Pulitzer Prize-winning and deep in its thinking, this is a sophisticated look at the interplay of human thought and inventiveness as seen through the lens of mathematics, symmetry, and artistic expression. It posits that “if consciousness can emerge out of a formal system of firing neurons, then so too will computers attain human intelligence.”
7. Practical guides for AI professionals
Practical manuals, actionable strategies, and machine learning techniques. Here’s our recommendations for the best books on artificial intelligence aimed at those with boots on the ground. Essential ‘nuts and bolts’ reading for anyone looking to apply AI within real-world contexts.
"Machine Learning Yearning" by Andrew Ng
How do project management skills and techniques come into play when developing AI systems? Here we have a grounded, pragmatic approach for organizing machine learning projects, concentrating on the real-world application aspect. Expect precise, practical implementation recommendations aimed at assisting AI deployment from the ground up.
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
This is a robust, instructional guide to the practical application of machine learning algorithms through well-known platforms. Forget AI's history, theory, and philosophy – here we have real-world applications, Python libraries, and exercises designed to solidify your ML understanding.
8. Philosophical and theoretical perspectives
Working and living with artificial intelligence raises a bunch of interesting philosophical and theoretical questions. The next two books in our list probe some of the ethical dilemmas AI brings to the table, including how this digital technology can align with human values as it continues to get smarter and more powerful.
"Artificial Intelligence: A Guide for Thinking Humans" by Melanie Mitchell
Here, Melanie Mitchell offers a pretty nuanced view of the excitement, fears, and discussions that encompass AI in general. That means zeroing in on its present status and hurdles while pondering what the future may hold – all with an accessibility that hides deep insight under approachable storytelling.
"The Alignment Problem: Machine Learning and Human Values" by Brian Christian
How can we guarantee that AI systems function in harmony with our most basic principles and values? This book shines a light on the importance of baking ethical standards into AI – and what might happen if we don’t. If you’re looking for in-depth discussions on ethical AI infrastructure and security protocols, look no further.
9. Historical context and evolution of AI
Let’s go back a step. Being able to position AI development in relation to the wider history of computing is a great way to deliver insights the future direction of the field. These books chronicle artificial intelligence’s development, emphasizing its significant advancements as well as its ethical considerations.
"Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots" by John Markoff
The shifting dynamic between humans and robots goes under the microscope here, in a book that charts a course from their collaborative past to potential future discord. Through his analysis, Markoff sheds light on how humans and machines have coexisted thus far, to try and decipher how we might do the same in an increasingly automated world.
"Architects of Intelligence" by Martin Ford
Comprising interviews with prominent experts in the AI sphere, Architects of Intelligence covers topics like AI’s profound influence, the ethical implications involved in its development, and what we might expect from future progress. It’s an essential guide for anyone wanting to understand past developments and anticipate future revelations.
10. AI in everyday life
Our last book focuses on the practical influence of artificial intelligence in everyday scenarios, and the social consequences it’ll have in every facet of our lives. These books look at the ways in which AI-driven tech will redefine global industries and individual lifestyles alike.
"Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom
What happens if we create genuinely superintelligent AI? Are we all doomed, or will society flourish like never before? Consider this your practical guide to the tactics required if we’re to mitigate the perils associated with truly self-aware machine thinking.
Best books on artificial intelligence: FAQs
What is the best book about artificial intelligence for beginners?
“Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig is the best book for beginners, offering a thorough introduction to artificial intelligence, its history, and practical implementation as a knowledge representation and generation tool. This foundational text will help you build a solid understanding of the field.
Which book should I read to understand the ethical implications of AI?
To understand the ethical implications of artificial intelligence, read “Weapons of Math Destruction” by Cathy O’Neil. It effectively addresses biases in AI systems and emphasizes the necessity of transparency and fairness.
What is a good book about deep learning?
A great book for learning about deep learning is “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, as it provides a comprehensive overview of foundational concepts and core algorithms in the field. This text is highly recommended for both beginners and experienced practitioners.
Are there any practical guides for AI professionals?
“Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron provides an exceptional guide filled with step-by-step directives for efficient execution of machine learning algorithms. It comes highly recommended for professionals in AI who are on the lookout for hands-on advice.
What book explores the future impact of AI on society?
Max Tegmark’s “Life 3.0: Being Human in the Age of Artificial Intelligence” explores the significant societal changes and philosophical considerations that artificial intelligence could bring about. The book is an essential resource for gaining insight into how our lives may be shaped by artificial intelligence technologies in the future.
Free ebook: 10 Essential AI books for business leaders in 2025