IN THIS ARTICLE
- Best Books on Artificial Intelligence: Philosophy and Theory
- Best Books on Artificial Intelligence: Practical Application
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Artificial Intelligence (AI) is a hot topic across industries. Avid news consumers will come across a story about Amazon’s Alexa, connected cars, or another AI innovation nearly every day. While this technology was once restricted to science fiction and the far reaches of our imagination, today we interact with smart technology without even realizing it.
There are many books written on the subject, so we’ve curated a list of the best books on artificial intelligence. Whether you’re a tech veteran looking to refresh your skills or you’re transitioning from a military career to machine learning (ML), you can bet that there will be books about AI on this list that will pique your interest.
This list of the best books on artificial intelligence combines both the classics and the most innovative new ideas. It will provide deep insights into where AI came from and where it’s heading.
Best Books on Artificial Intelligence: Philosophy and Theory
Introduction to Artificial Intelligence: Phillip C. Jackson, Jr. provides a comprehensive introduction into the science of reasoning. The ideas presented in this book are backed up by two decades of research and results. Originally published in 1985, this book provides an in-depth look at AI during its infancy and sheds some light into the nature of thought. It’s perfect for laymen and developers as it combines both advanced and introductory material to form an argument for what AI could achieve in the future. Read the reviews.
The Human Use of Human Beings – Cybernetics and Society: Norbert Weiner sets the foundation for thinking about AI in this classic. During his time, the author was widely misunderstood. But today, in the age of automation, his ideas have become the norm. It’s also an excellent examination of the implications of cybernetics across industries. Read the reviews.
Computers and Thought: Edward Feigenbaum and Julian Feldman published a collection of classic papers from AI pioneers such as Marvin Minsky, Allen Newell, Herbert A. Simon, and Alan Turing. It makes the list of the best books on artificial intelligence because it provides an in-depth look into how experts within the field defined and shaped AI over the decades. Read the reviews.
Artificial Intelligence – A Modern Approach: We can’t have a list of the best books on artificial intelligence without Peter Norvig’s bestseller that’s grown into a standard for both undergraduate and graduate-level AI students. The text can be described as a detailed look into the theory and use cases in AI that can be applied both generally or to specific models. Read the reviews.
The Master Algorithm – How the Quest for the Ultimate Learning Machine Will Remake Our World: Pedro Domingos has written what can be described as a comprehensive overview of ML with this thought-provoking book. While exploring the revolutionary developments of our time, he also gives us a peek into the inner workings of the machines that power Amazon and Google. Read the reviews.
Robot Ethics: The Ethical and Social Implications of Robotics: This is a thought-provoking exploration of how we can design and hard-code ethics into robots and other artificial systems. Although AI is still in its infancy, we should address these questions now to avoid potential catastrophic events in the future. The authors also discuss the need for ethics and regulation now rather than later (for not only AI but all game-changing technology). Read the reviews.
How to Create a Mind – The Secret of Human Thought Revealed: Ray Kurzweil is one of the most influential technological visionaries of our time. In this book, he explores ideas such as reverse engineering the brain to better understand how it works. So if you’re interested in AI and the concept of super-intelligence, the ideas presented in this book are an excellent place to start your journey. Read the reviews.
The Emotion Machine – Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind: Marvin Minskey, who has written some of the industry’s best artificial intelligence books, offers a compelling new model of how the mind works. In this offering, he argues that feelings, emotions, and intuitions aren’t distinct things but a different way of thinking. If we approach it as a step-by-process, we have the potential to build AI that’s consciously aware. Read the reviews.
Life 3.0 – Being Human in the Age of Artificial Intelligence: Drawing from his extensive experience, Max Tegmark, an MIT professor, explores how AI can impact crime, jobs, wars, and even society as a whole. It’s the book that inspired Elon Musk to say that AI posed a “fundamental risk to the existence of human civilization.” Read reviews.
The Sentient Machine – The Coming Age of Artificial Intelligence: Amir Husain, unlike some of his colleagues in the industry, takes a positive approach to the age of AI. Rather than the impending doom predicted by the likes of Musk and Tegmark, Husain explains not only how we can survive, but thrive. This makes the list of our favorite artificial intelligence books because the author manages to draw from a variety of cultural and historical references to communicate complex concepts in a natural and accessible language. Read the reviews.
Gödel, Escher, Bach – An Eternal Golden Braid: Douglas Hofstadter explores the links between formal systems and the nature of maps. The author actually argues that the formal system behind all mental activity transcends the system that supports it. If you’re a fan of AI and philosophy, this is one of the best AI books on the subject. Read the reviews.
Structure and Interpretation of Computer Programs: This book has attracted a cult following—and for good reason. It presents an alternative way of thinking about programming using a version of the LISP programming language. Often described as one of the best artificial intelligence books, it should make your top 10, even if it raises a few eyebrows with its content. Read the reviews.
Superintelligence – Paths, Dangers, Strategies: Nick Bostrom’s New York Times bestseller explores what could possibly happen once machines surpass humans’ intelligence. Will it lead to our downfall or will it be the catalyst that leads us to a better world? By correlating evolutionary processes to smart algorithms, the author introduces the possibility of achieving far greater intelligence than we ever thought possible. Read the reviews.
Our Final Invention – Artificial Intelligence and the End of the Human Era: If you’re into science fiction, this book will make for an exciting read. Will machines have the same drive to survive as humans? Will it become an alien entity to the human race? While the answers to these questions can sometimes seem despondent and even apocalyptic, it’s important to consider them carefully even if you are a passionate supporter of AI innovation. Read the reviews.
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Best Books on Artificial Intelligence: Practical Application
Machine Learning – A Probabilistic Perspective: Kevin P. Murphy’s book includes the fundamental principles behind successful AI models. It has a lot of exercises and covers a wide range of topics that can be useful to both beginners and seasoned veterans. All topics are illustrated extensively, and the book itself is written in an informal and accessible style. Almost all the models presented in the book can also be deployed in a MATLAB software package. Read the reviews.
Deep Learning with R: Francois Chollet, a Google AI researcher and Keras library creator introduces the reader to the fundamentals of deep learning in the R programming language, which is also a very popular language among data scientists to do statistical analysis. It’s also an excellent instruction manual for the Keras library. The book helps readers develop an understanding of deep learning through practical examples and intuitive explanations. Although you don’t need any previous ML experience, you should have an intermediate working knowledge of R programming. Read the reviews.
Deep Learning (Adaptive Computation and Machine Learning Series): This book takes a conceptual and mathematical approach to explore relevant concepts in informational theory, linear algebra, ML, numerical computation, probability theory, and more. The authors discuss the techniques already used by practitioners in the industry and explore theoretical topics such as approximate interference, autoencoders, deep generative models, linear factor models, representation learning, and structured probabilistic models. Read the reviews.
Learning OpenCV 3 – Computer Vision in C++ with the OpenCV Library: Written by the creators of the widely used open-source library OpenCV, this book throws you right into the middle of the ever-expanding field of computer vision. It has all the information you need to build simple or sophisticated applications that can see and make decisions based on data. It’s one of the best books on AI as the information contained within these pages can get both developers and hobbyists quickly up and running with the framework. Read the reviews.
Python Machine Learning – Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow: Sebastian Raschka and Vahid Mirjalili offer a practical framework for data science, deep learning, and ML. As Python has grown more popular within this space, it’s an excellent place to start and increase one’s knowledge about classifying objects, developing learning algorithms, and transforming raw data into useful information. Read the reviews.
Speech and Language Processing: When it comes to books on AI, Daniel Jurafsky and James H. Martin’s offering is up there with the best. In fact, it’s one of the first books to cover all aspects of language technology. Each chapter is dedicated to one or more real-world examples that demonstrate the primary ideas within it. If you’re interested in natural language processing, it will help to read this empirical approach to the subject. Read the reviews.
TensorFlow in 1 Day: Make Your Own Neural Network: Krishna Rungta offers a comprehensive exploration of deep learning practices with highly detailed examples. It’s an excellent read for those working with the popular deep learning library TensorFlow to design and visualize neural networks. Read the reviews.
Reinforcement Learning: An Introduction: Andrew Barto and Richard Sutton provide key ideas and algorithms of reinforcement learning in a simple and accessible manner. Their discussion ranges from the field’s foundations to recent developments and use cases. While it’s often referred to as the “reinforcement learning bible,” you don’t need to have a computer science background to understand the ideas discussed in this book. However, you do need an elementary familiarity with the concept of probability. Read the reviews.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction: This book explores the use of data in new technologies like machine learning and bioinformatics. The authors do an excellent job of explaining important and complex ideas in a common conceptual framework that is easily accessible. If you’re interested in data mining to improve supervised and unsupervised learning, this a good read to formulate a comprehensive view. Read the reviews.
For more AI and machine learning resources, check out these Springboard posts:
- 20 AI Experts You Should Follow
- 20 Must-Read AI, Data Science Newsletters
- 40 Must-Read AI / Machine Learning Blogs
Reading some of the best books on AI can help you get a sense of the origins of the field and where it’s headed. It’s also a great way to get into the minds of some industry pioneers. If you’re thinking about a career in AI, these classics and books related to programming can provide a well-rounded picture of what’s in store for the industry and the human race.
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