The Most Important Books on Artificial Intelligence
Why should we be reading books on artificial intelligence? Well, if you’re anything like me, then you’re equally fascinated and terrified by the development of artificial intelligence. It is an area of study that cannot be ignored. Since 2000, the number of start-ups in the subject area has increased by 1400%, with investment into them increasing sixfold. Whether you fall into the category of those who are encouraged and excited by the speed of development, or you’re on the half of people who are fearful that it’s developing too quickly with not enough time to consider the rules and morals that should govern such intelligence – we should all be learning about it. There have been some incredible books on artificial intelligence; many you may have already read. However, this panel of experts has agreed to help me put together a reading list of the most important books on artificial intelligence. Each of them has their own unique viewpoint; which has made for a fascinating list of books on artificial intelligence. Please meet our expert panel who will help us discover some of the most important books on artificial intelligence.
Michael Wooldridge is a Professor of Computer Science and Head of Department of Computer Science at the University of Oxford. He has been an AI researcher for more than 25 years and has published more than 350 scientific articles on the subject. He is a Fellow of the Association for Computing Machinery (ACM), the Association for the Advancement of AI (AAAI), and the European Association for AI (EurAI).
Roger Schank is the Chairman and CEO of Socratic Arts and the Executive Director and founder of Engines for Education. He was the founder of the Institute for the Learning Sciences at Northwestern University where he was John Evans Professor of Computer Science, Education, and Psychology. Prior to that, he was Professor of Computer Science and Psychology at Yale University and Director of the Yale Artificial Intelligence Project.
Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University. Her research focuses on perception for autonomous robotic manipulation and grasping. Before joining the Autonomous Motion lab in January 2012, Jeannette was a PhD student at the Computer Vision and Active Perception lab (CVAP) at KTH in Stockholm. Her thesis was on Multi-modal scene understanding for Robotic Grasping.
Paolo Turrini is an Assistant Professor at the Department of Computer Science of the University of Warwick. The areas that Paolo works in are; game theory, artificial intelligence, logic, social choice theory and mechanism design. Paolo was awarded an Honorary Lectureship at Department of Computing, Imperial College London in 2017 and in 2016, a Fellowship of the Global Future Councils at the World Economic Forum.
Tim Rocktäschel is a Lecturer at the Department of Computer Science of the University College London. He did his Ph.D in Machine reading group at University College London, was the recipient of a Google Ph.D. Fellowship in Natural Language Processing and also worked as a Research Intern at Google DeepMind. His research focus is on machine learning models that learn reusable abstractions.
You’ve met the panel and now it is time to discover their nominations for the most important books on A.I.
Artificial Intelligence: A Modern Approach is what I think about when I think about an AI book. It is by far the most comprehensive introduction on the subject I’ve come across and what I like about it is that it really tries hard to embrace the huge number of approaches and problems that we call Artificial Intelligence within a unifying logical framework. It is not as technically deep as specialised books in, say, reinforcement learning or constraints satisfaction, but it does give the reader a great overview of all those. It is particularly good for students as well as for lecturers, as it comes with plenty of exercises, and the authors have taken care of writing down solutions and even slides.
I can vividly remember the ripples of excitement this book caused when it was first published. There had been many introductions to AI published before, but this book heralded the emergence of AI as a mature scientific discipline. The scope was astonishing: AI is a huge, sprawling field, and no other text has come close to this book in terms of putting the entire field into context. The third edition remains the standard undergraduate text on AI today and is unlikely to be surpassed any time soon. I use it as a reference work on a weekly basis. And if you really want to understand the science of AI, there is no better place to start.
The Human Use of Human Beings is the foundational book for thinking about AI in general.
There is a cult around this book and with good reason. It presents a mind-expanding way of thinking about programs and programming, using a version of the LISP programming language. LISP was invented in the 1950s by John McCarthy – the man who gave the name to the field of AI. LISP was the AI programming language of choice for decades. Structure and Interpretation of Computer Programs requires some work, but if you persevere, then it gives you a whole new way of thinking about programs and programming. It is a beautiful and important book, and if everyone had read it and followed its lessons, the world would be a better place. Some AI colleagues might raise eyebrows at this choice, because it isn’t, strictly speaking, an AI book – but they miss the point. AI programs require mind-bending ways of thinking about programs, and if you “get” this book, then you are well-placed to build beautiful AI systems, whatever your programming language of choice.
John McCarthy, the founder of AI, had a hugely influential vision for the field he named. He believed that logical reasoning was the key to AI. His dream was that the whole process of intelligent action could be reduced to logical deduction – an intelligent robot would be one that reasoned logically about what to do. This book is a gloriously pure articulation of McCarthy’s vision. The book had a huge influence on me as I began my PhD studies at the end of the 1980s, although McCarthy’s vision for logical AI fell out of favour not long after that – logic turned out to be a powerful tool for some problems in AI, but some seemingly trivial problems proved to be impossible for logic-based AI to handle. So this book is perhaps important mainly as a historical document, but it still has things to teach us, and it is worth reading simply to gain an understanding of one important thread in the tapestry of AI.
Somewhat surprisingly, I choose the Selfish Gene by Richard Dawkins. This truly seminal and inspiring manuscript is claimed by a number of scientific disciplines – so why not AI as well?- and has fundamentally contributed to our understanding of genetic and cultural evolution as the product of the interaction of self-replicating processes. This might still seem far from artificial intelligence as we know it, but there is a twist. Recently published results in multi-agent reinforcement learning have established a beautiful mathematical connection between the evolutionary processes, as we know them from biology and game theory, and the learning algorithms, as we know them from artificial intelligence: genetic and cultural evolution can now be seen as reinforcement learning by a number of repeatedly interacting computational processes.