Top AI Books to Read to Master Artificial Intelligence (AI): Recommendations for AI Beginners to Advanced Experts
Online AI Sources versus Books
Whether you work in technology or not, artificial intelligence (AI) is in the workspace. One way or another, existing and future work will involve artificial intelligence tools or innovation, requiring all professionals to understand AI. After all, it’s not artificial intelligence that will replace us; the other professionals can understand AI and integrate it into their work streams.
Of course, one could debate that books are optional for learning AI due to the immense amount of AI blogs and social media content out there. However, after ChatGPT, AI became too mainstream, and since everyone seems to have an opinion on it, finding the right article by a true expert can become daunting.
Conversely, AI books can become outdated quickly due to the pace of fast technological development. Nevertheless, having relevant printed resources can become handy if you’d like to dive deep into the topic or have historical knowledge available for research.
Books provide a structured, in-depth exploration of complex topics that are often diluted in shorter online content. They are typically authored by experts who have dedicated years to studying a subject, such as AI, offering well-researched perspectives and comprehensive frameworks.
Unlike online sources, which can vary greatly in quality and accuracy, books are usually peer-reviewed and edited, ensuring higher reliability. Furthermore, books allow for a more reflective, focused learning experience, free from the distractions of the internet.
For leaders, understanding AI isn’t just about knowing the buzzwords or the prompt; it’s about grasping the fundamental principles, ethical considerations, and strategic applications that will shape the future of business.
Let’s look at all the relevant AI books out there, catering to different levels of expertise and interest, recommended by various (human and AI) expert sources, including our in-house experts.
Image: Patrick Tomasso (Unsplash)
AI Books for Beginners
“Artificial Intelligence: A Very Short Introduction (Very Short Introductions)” by Margaret A. Boden
In her Very Short Introduction, Margaret A. Boden reviews the philosophical and technological challenges raised by Artificial Intelligence, considering whether programs could ever be really intelligent, creative, or even conscious, and shows how the pursuit of artificial intelligence has helped us to appreciate how human and animal minds are possible. This guide explains the history, theory, potential, application, and limitations of artificial intelligence. Boden shows how research into AI has shed light on the workings of human and animal minds, and she considers the philosophical challenges AI raises: could programs ever be really intelligent, creative, or even conscious?
"Artificial Intelligence: A Guide for Thinking Humans" by Melanie Mitchell
This book is perfect for leaders who are new to AI. Mitchell, a renowned computer scientist, demystifies the concepts of AI in an accessible and engaging manner. She provides historical context, explains key concepts like neural networks and machine learning, and discusses the potential and limitations of AI. The book offers a balanced view of AI, making it easier for beginners to understand without oversimplifying the subject.
"AI Superpowers: China, Silicon Valley, and the New World Order" by Kai-Fu Lee
Lee, a pioneer in AI and venture capitalism, explores the global AI landscape, focusing on the competition between the U.S. and China. This book is a great entry point for understanding the geopolitical and economic implications of AI. It’s particularly valuable for leaders interested in the global impact of AI and the strategic moves of leading nations in this space.
“The Hundred-Page Machine Learning Book" by Andriy Burkov
In this condensed book, the author offers supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering, and hyperparameter tuning! Math, intuition, and illustrations, all in just a hundred pages.
“The Business Case for AI: A Leader's Guide to AI Strategies, Best Practices & Real-World Applications” by Kavita Ganesan
Leaders who want to take advantage of artificial intelligence often don’t know where to start. The process can feel overwhelming—from analyzing existing processes and software systems and choosing where to apply AI automation to preparing every tier of the organization for the transition. In this practical guide for business leaders, Kavita Ganesan takes the mystery out of implementing AI, showing you how to launch AI initiatives that get results. With real-world AI examples to spark your own ideas, you’ll learn how to identify high-impact AI opportunities, prepare for AI transitions, and measure your AI performance.
“Artificial Intelligence in Short” by Ryan Richardson Barrett
Artificial Intelligence in Short is a poignant book about the fundamental concepts of AI and machine learning. Written clearly and accompanied by numerous practical examples, this book enables any capable reader to understand concepts such as how computer vision and large language models are created and used while remaining free of mathematical formulas or other highly technical details. The book discusses the most up-to-date research in AI and computer science but also elaborates on how machines have come to learn and the historical origins of AI. The concepts of AI are outlined in relation to everyday life—just as AI has become a tool integrated into devices used daily by many people.
Fundamental Resources for Advanced AI Studies
This category includes books that reveal artificial intelligence's practical applications and strategic insights. They are recommended for executives or anyone who might be required to apply artificial intelligence to existing or future business units.
"Prediction Machines: The Simple Economics of Artificial Intelligence" and “Power and Prediction: The Disruptive Economics of Artificial Intelligence” by Ajay Agrawal, Joshua Gans, and Avi Goldfarb
This book breaks down the economic implications of AI, framing it as a "prediction machine" that changes the cost and nature of decision-making. The authors discuss how AI can be integrated into business strategy and operations. Leaders will gain practical insights into how AI can drive business value and improve decision-making processes.
"The AI Book: The Artificial Intelligence handbook for investors, entrepreneurs and fintech visionaries" by Editors Susanne Chishti, Ivana Bartoletti, Anne Leslie, Shân M. Millie
The AI Book by Wiley and the Fintech Circle is one of the first of its kind, compiling bite-sized expertise and insights from experts worldwide. The book was published in 2020, and since the co-authors went through a diligent acceptance process, their opinions and relevance were verified by expert editors. This book also includes a surprise: Contextual Solutions Founder, Elif Kocaoglu Ulbrich, is one of the co-authors of the AI Book and has contributed a piece on ethical AI before AI was not a thing.
S. Elif Kocaoglu Ulbrich, is one of the co-authors of the AI Book and has contributed a piece on ethical AI back in 2020.
Image: Wiley
"Human + Machine: Reimagining Work in the Age of AI" by Paul R. Daugherty and H. James Wilson
Daugherty and Wilson discuss the concept of the "AI-powered enterprise" where humans and machines collaborate to enhance productivity and innovation. The book is filled with case studies that show how companies are already leveraging AI to transform their operations. This book is essential for leaders looking to understand how AI can be implemented in their organizations to augment human capabilities
“Competing in the Age of AI: How machine intelligence changes the rules of business” by Marco Iansiti and Karim R. Lakhani
This book defines the profound implications of artificial intelligence for business. Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have restricted business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, allow massive scope increase, enable companies to straddle industry boundaries, and create powerful opportunities for learning—to drive ever more accurate, complex, and sophisticated predictions.
Ford’s book compiles interviews with over 20 of the world’s leading AI experts, offering a diverse range of insights into the development and future of AI. The book covers everything from technical advances to societal impacts. It provides leaders with a multifaceted understanding of AI from the perspectives of those who are at the forefront of its development.
“Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking” by Foster Provost, Tom Fawcett
Data Science for Business introduces the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. You’ll not only learn how to improve communication between business stakeholders and data scientists but also how to participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically and fully appreciate how data science methods can support business decision-making.
"Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom
Bostrom’s book is a deep dive into the future of AI, exploring the concept of superintelligence and the existential risks it poses. He discusses various scenarios for how AI might evolve and the ethical challenges that come with it. For leaders interested in the long-term implications and ethical dilemmas of AI, this book provides a thought-provoking analysis that goes beyond the technical aspects.
"Life 3.0: Being Human in the Age of Artificial Intelligence" by Max Tegmark
Tegmark, a physicist, and AI researcher explores the philosophical and ethical dimensions of AI. He discusses how AI will reshape society and the economy and what it means to be human. This book challenges readers to think about the broader impact of AI on humanity and the ethical responsibilities of those who lead AI-driven transformations.
“Superforecasting: The Art and Science of Prediction” by Philip E. Tetlock (Autor), Dan Gardner
This New York Times bestseller offers a masterwork on prediction, drawing on decades of research and the results of a massive, government-funded forecasting tournament. The Good Judgment Project involves tens of thousands of ordinary people—including a Brooklyn filmmaker, a retired pipe installer, and a former ballroom dancer—who set out to forecast global events. Some of the volunteers have turned out to be astonishingly good. They’ve beaten other benchmarks, competitors, and prediction markets. They’ve even beaten the collective judgment of intelligence analysts with access to classified information. They are "superforecasters."
“The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World” by Pedro Domingos
In The Master Algorithm, Pedro Domingos reveals how machine learning is remaking business, politics, science, and war. And he takes us on an awe-inspiring quest to find 'The Master Algorithm' - a universal learner capable of deriving all knowledge from data.
In 2016, Bill Gates recommended the book, alongside Nick Bostrom's Superintelligence, as one of two books everyone should read to understand AI. According to Wikipedia, the book was spotted on Chinese Communist Party general secretary Xi Jinping's bookshelf in 2018.
“Artificial Unintelligence: How Computers Misunderstand the World (Mit Press)” by Meredith Broussard
This controversial book serves as a guide to understanding the inner workings and outer limits of technology and why we should never assume that computers always get it right.
When researching artificial intelligence, it is important to mix early publications with up-to-date research to understand the tech development and how the understanding changed.
Image: Igor Omilaev (Unsplash)
Mastering Artificial Intelligence
To master artificial intelligence, an interest in math is crucial, and linear algebra, calculus, and probability maths skills are required. The book choices for this category reflect these skill needs in addition to the deep and detailed artificial intelligence research that exists as of August 2024.
“Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
The book offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology, and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and is said to remain available online for free.
“Artificial Intelligence: A Guide to Intelligent Systems” by Michael Negnevitsky
Negnevitsky shows students how to build intelligent systems by drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation, and intelligent agents. The principles behind these techniques are explained without resorting to complex mathematics, showing how the various techniques are implemented, when they are useful, and when they are not. No particular programming language is assumed, and the book does not tie itself to any of the software tools that are available. However, available tools and their uses are described, and program examples are given in Java.
This book was printed in three editions, while the second edition is available online via this link, free of charge.
“Probabilistic Machine Learning,” book series by Kevin Murphy
This book series, published in 2012, 2022, and 2023, offers a deep dive into machine learning concepts for advanced artificial intelligence researchers and developers.
“Natural Language Processing with Transformers” by Lewis Tunstall, Leandro von Werra, Thomas Wolf
If you're a data scientist or coder, this practical book explains how to train and scale large models using Hugging Face Transformers, a Python-based deep-learning library.
“Machine Learning for Asset Managers” by Marcos M López de Prado
Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this publication is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories.
“The Little Book of Deep Learning” by François Fleuret
This book was written by François Fleuret, Professor and Head of Machine Learning at the University of Geneva. Fleuret designed the book as a short introduction to deep learning for readers with a STEM background and updated it in May 2024. The book serves as a guide to deep learning, Machine Learning, and AI and is recommended by Richard Saldanha, a Machine Learning expert and Guest Lecturer at the Queen Mary University of London, LSE, University of Oxford and Co-Head of specialist Management Consultancy Oxquant. You can download "The Little Book of Deep Learning" free of charge via this link.
This list reflects expert-recommended printed artificial intelligence sources as of August 2024 and will be updated as necessary. Are there any other AI books you love? Let us know!
How can you integrate artificial intelligence into your daily work? How can you innovate?
Understanding AI through books is the first step towards leveraging this powerful technology in your business. But knowledge alone isn’t enough. To truly capitalize on AI’s potential, you need a strategic approach tailored to your unique business needs and goals. That’s where we come in. We specialize in helping leaders like you integrate AI into your service offerings, operations, and overall business strategy. Whether you’re looking to digitize existing processes, create new AI-driven products, or enhance your decision-making with data-driven insights, we have the expertise and tools to guide you every step of the way.
Don’t just read about AI—make it a driving force in your business.
Contact us today to learn how we can help you harness the power of AI for sustainable growth and innovation, or book a workshop to explore the automation and productivity possibilities. Together, we can turn the insights from these books into actionable strategies that set your business apart in the AI-driven world.
