Entry level AI reading recommendations
Interested in learning more about AI and considering implementing AI into your course curriculum? Dawson AI’s Robert Stephens (Profile Coordinator of Arts & Culture; Faculty, Humanities and Philosophy) has some book recommendations to get you started.
The Most Human Human, by Brian Christian (2012). Publisher Penguin Random House describes it as: “a provocative, exuberant, and profound exploration of the ways in which computers are reshaping our ideas of what it means to be human. Its starting point is the annual Turing Test, which pits artificial intelligence programs against people to determine if computers can “think.” This book is a great non-technical introduction to the idea of artificial general intelligence and the challenges facing designers of computer chatbots, as well as a meditation on how human communication works. The book is well-suited to College classroom use – I have assigning it in my own Humanities and Philosophy classes. Helpfully, there is also a freely accessible article by Brian Christian in The Atlantic magazine outlining the main ideas of the book: https://www.theatlantic.com/magazine/archive/2011/03/mind-vs-machine/308386/
Another great, highly excerptable book on a number of facets of A.I. is Possible Minds: 25 Ways of Looking at A.I., ed. John Brockman (2019). Science literary agent John Brockman has assembled twenty-five of the most influential philosophers, social scientists, computer scientists, neuroscientists, and “futurists” working in and around the field of artificial intelligence, each of whom offers a short essay on what they see as the current state of the field, the challenges faced by designers, and the threats and/or opportunities A.I. presents to humanity. There are many short, engaging contributions across the disciplines in this book, highly adaptable to any course touching on A.I.
Finally, for a slightly more technical and in-depth explanation of how A.I. and machine learning actually works: How Smart Machines Think, by Sean Gerrish (2018), offers an effective and engaging overview of the most recent breakthroughs in machine learning across the spectrum, from self-driving cars to Netflix recommendations to medical diagnostics. This book has a bit more mathematical and technical detail, though it is still aimed at the lay, non-STEM reader, and could be helpful as supplemental reading in any class discussing machine learning and algorithmic decision-making.
