10 Must-Read AI Books
Data Literacy CEO Ben Jones recommends 10 of his favorite AI books that have been published in the past five years. If you feel overwhelmed by the breadth of information on AI and unsure where to start, this curated selection is a great jumping off point.
Each book brings a unique lens to the AI discussion, whether it’s through technical insights, ethical considerations, or personal stories from the front lines of AI research and application. As AI continues to shape our world, staying informed through these readings will not only enhance our understanding but also prepare us to engage responsibly with the technology that is reshaping our collective future.
Watch the full video or read the recap below!
1. BEST OVERVIEW
Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell
What it’s about: A critical exploration of AI’s history, current capabilities, limitations, and ethical implications and concerns, accessible to general readers.
What I like about it: This is a true insider’s take, since Mitchell has been in the field of AI through multiple rounds of the AI hype cycle. She shares a balanced perspective about various hopes, fears, and debates about AI.
2. BEST MEMOIR
The Worlds I See by Dr. Fei Fei Li
What it’s about: A memoir of an AI trailblazer, blending her personal story of immigration, her first-hand account of the deep learning revolution, and the historical underpinnings of the field.
What I like about it: This is an incredibly inspirational and intimate account of someone who overcame the odds as a Chinese immigrant and as a female in a historically dominated field.
3. BEST EXPOSE
Unmasking AI by Joy Buolamwini
What it’s about: An award-winning examination of the biases embedded in artificial intelligence technologies, and the formation of the Algorithmic Justice League, focusing on the social impacts and ethical considerations of AI.
What I like about it: Buolamwini relates her own early experiences with biased algorithms and tells the story of how those experiences led her to become a champion for change within AI.
4. BEST HISTORY
A Brief History of Artificial Intelligence by Michael Wooldridge
What it’s about: A comprehensive overview from a leading AI researcher that traces the evolution of AI from its inception to its current state and into its future possibilities, reflecting on its societal implications.
What I like about it: Another great resource from a long-time industry insider and leader, this book tells the full backstory to the field of AI, and also gives a real and at times humorous view into its current dynamics and factions.
5. MOST CONCERNING
The Coming Wave by Mustafa Suleyman
What it’s about: An urgent exploration of the transformative and potentially perilous impacts of emerging technologies like AI, synthetic biology, and quantum computing on global order.
What I like about it: Suleyman rings a serious warning bell while somehow also remaining optimistic about the future and our ability to harness multiple technologies that could potentially mix together in explosive ways.
6. MOST CURRENT
Co-Intelligence: Living and Working with AI by Ethan Mollick
What it’s about: An insightful exploration into the integration of generative AI in daily life and work, emphasizing the need for us to adapt and coexist with these transformative systems while cautioning us about harmful forms of use.
What I like about it: Mollick is a very experienced “super user” of generative AI applications himself, and he is up-to-date on studies about their use, so he is able to give advice on how to use them and how not to use them.
7. MOST HELPFUL
Artificial Negligence by James Wilson
What it’s about: Covers the potential and risks of AI, using analogies to prepare non-technical readers for a future increasingly shaped by AI and addressing common questions and concerns about AI’s impact on jobs, lifestyle, and ethics.
What I like about it: This book is incredibly useful for a layperson who is looking for a non-technical perspective about all the fear and hype.
8. MOST PHILOSOPHICAL
AI Ethics by Mark Coeckelbergh
What it’s about: The ethical challenges of AI, providing a synthesis of key issues, exploring topics like privacy and the moral status of AI, and touching on deeper philosophical debates about the relationship between humans and machines.
What I like about it: This book is very thorough, and covers both the philosophical as well as the practical aspects of the ethical, moral, and legal challenges facing us during the rise of AI.
9. MOST SUCCINCT
Machine Learning by Ethem Alpaydin
What it’s about: A succinct introduction for general readers to the fundamentals of machine learning, the backbone of many modern applications, covering its evolution, key algorithms, and their real-world applications.
What I like about it: This overview of machine learning technologies and applications is very easy to read and understand, and touches on various approaches like supervised, unsupervised, and reinforcement learning.
10. MOST TECHNICAL
Deep Learning by John D. Kelleher
What it’s about: An introduction to deep learning’s foundational concepts, historical advancements, significant architectures such as neural networks, and the core algorithms of gradient descent and backpropagation.
What I like about it: Kelleher isn’t afraid to dive into the mathematical underpinnings of deep neural networks, doing a good job covering these very challenging aspects for the more advanced reader.
BONUS BOOK
AI Literacy Fundamentals by Ben Jones
What it’s about: A friendly primer on the basic concepts of AI, how it’s already snuck into our daily lives, and what we need to know to prepare for the future. Everything you need to know to join the AI conversation, from the history of AI to the deep learning revolution happening today.
BONUS RESOURCE
Are you spearheading the search for the right AI training for your team? It’s tough to get up-to-speed on this tech yourself, and evaluate training resources. To make this task easier, we’ve compiled a list of questions you and your team should consider as you explore your AI training options called the AI Training Navigator.
Download the free ebook by submitting the form below, browse through the 33 questions, and mark any you haven’t thought about yet. Chances are, you’ve already tackled some of these challenges, but a quick review could uncover overlooked aspects of your planning. Revisit these questions and follow the flowchart in the ebook to plan out your next steps. We hope this is useful in assessing the AI training landscape, and please reach out if you have any questions: https://dataliteracy.com/contact
