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AI Book Recs: Add These to Your Reading List

Our HAI community offered up the best books in AI that they’re reading.

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DALL-E painting of a woman reading from a stack of books

DALL-E

This summer we asked our HAI community across social media channels what books on artificial intelligence they’d recommend. Here are some books to nab the next time you visit your local bookseller, from general interest to deep dives for practitioners and a few from the fiction aisles.

General AI

Viral Justice, by Ruha Benjamin

Benjamin is a groundbreaking scholar focused on race, technology, and justice. In this book, she offers a personal view of small decisions that can make a big difference in our lives and in society.

Genius Makers, by Cade Metz

Metz, who has covered the tech industry for the New York Times and Wired, weaves together the stories of AI researchers and corporate leaders racing to lead this emerging technology, highlighting the conflicts between business incentives and science and national interests and human concerns.

Human Compatible, by Stuart Russell

Computer scientist Russell says conflict between humans and AI is avoidable if we rethink how we build these machines. He suggests a new foundation that would create altruistic AI.

The Alignment Problem, by Brian Cristian

In this book, Cristian details the alignment problem (when the AI systems we train don’t do what we expect), all the terrible things that can and do go wrong, and the movement to fix them.

The Singularity is Nearer, by Ray Kurzweil

A follow-up to his 2005 book, futurist Kurzweil assesses his earlier predictions and tackles topics including radical life extension, nanobots, AI’s impact on unemployment, self-driving cars, and more.

Life 3.0: Being Human in the Age of Artificial Intelligence, by Max Tegmark

MIT’s Tegmark tackles some of the biggest questions in AI. How will it affect jobs? Will there be an AI arms race? How will it impact crime? He asks us to consider what kind of future we want as this technology grows more ubiquitous. 

Artificial Intelligence: A Guide for Thinking Humans, by Melanie Mitchell

Just how intelligent are today’s systems? Computer scientist Mitchell shows readers what they can actually do versus what our imaginations think they can do, offering a useful overview of the technology, its achievements, and its problems.

Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots, by John Markoff

Will machines help or replace us? New York Times reporter and former Stanford HAI fellow Markoff looks at the historical relationship between humans and machines and shows that we are on the verge of a new era of technological revolution, and we must think very carefully about how we want to integrate robots into our lives. 

Cybernetic Revolutionaries: Technology and Politics in Allende’s Chile, by Eden Medina

Indiana University’s Medina writes about two failed revolutions in Chile — one a socialist regime change under Salvador Allende and one known as Project Cybersyn, an attempt to manage the economy through an intelligent computer system.

Prediction Machines, by Ajax Agrawal, Joshua Gans, and Avi Goldfarb

Three economists frame AI as prediction and show how it can be used by companies, policymakers, and investors for strategy, new business structures, and better decision-making tools.

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, by Erik Brynjolfsson and Andrew McAfee

Stanford Digital Economy Lab director Brynjolfsson and MIT scientist McAfee show how digital technology has upended how we work and how we live and is rapidly changing the economy as we know it.

AI and Human Intelligence

The Society of Mind, by Marvin Minsky

What is the mind and how does it work? In this book from 1986, MIT AI Lab co-founder Minsky offers a model of human cognition as a series of interactions with simple parts and delves into topics including language, memory, and consciousness.

Gödel, Escher, Bach: An Eternal Golden Braid, by Douglas R. Hofstadter

This Pulitzer Prize-winning book from 1979 explores mathematics, symmetry, and intelligence and discusses how systems acquire meaningful context when made of meaningless parts. “It can be difficult, but absolutely worth the effort,” says the recommender.

What Is Thought, by Eric Baum

Here scholar Baum proposes a computational explanation of thought and explores what computer scientists can learn from understanding the evolution of human intelligence.

Textbooks/Practitioners

The Political Philosophy of AI: An Introduction, Mark Coeckelbergh

While this book was recommended for our student audience, anyone can find value in this exploration of political challenges related to artificial intelligence, from discrimination and surveillance to a functioning democracy.

Grokking Deep Reinforcement Learning, by Miguel Morales

In this serious dive into deep reinforcement learning, Morales provides an overview of the approach, complete with illustrations, exercises, and real-world applications.

Pattern Recognition and Machine Learning, by Christopher M. Bishop

Bishop, the director of Microsoft Research AI4Science, details the growth of Bayesian methods in this textbook while also offering up an introduction to pattern recognition and machine learning.

Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

The authors explore deep learning’s mathematical background, several research perspectives from representation learning to deep generative models, and the techniques used in industry including NLP, computer vision, and optimization algorithms.

Neural Networks from Scratch, by Harrison Kinsley and Daniel Kukieła

Learn to code neurons, create layers, calculate loss, and do backpropagation, along with some general background on the structure of ML algorithms.

Reinforcement Learning: Industrial Applications of Intelligent Agents, by Phil Winder

This book for professionals explores the state of reinforcement learning, various algorithms and frameworks, and real-life industrial applications.

Trustworthy Machine Learning, by Kush Varshney

Accuracy isn’t enough when you’re training ML systems for important applications, says IBM Distinguished Researcher Varshney. Models must be fair, understandable, transparent, inclusive and can’t fall apart in different conditions.

Fiction

Flowers for Algernon, by Daniel Keyes.

In this novel, a man with a low IQ takes part in an experiment designed to increase his intelligence and finds himself struggling with new reflections on his relationships, his past, and who he is now. “It is one of the most enthralling books exploring the vitality of the human brain through AI,” notes our recommender.

AI 2041: Ten Visions for our Future, by Chen Qiufan and Kai-Fu Lee

AI will bring about incredible change in our lives and society, as well as incredible danger. These 10 short stories imagine the world in 2041 shaped by AI, in both terrifying and mesmerizing ways.

Stanford HAI’s mission is to advance AI research, education, policy and practice to improve the human condition. Learn more.

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