| 4 days until Christmas! 10 days until New Year! Even though the AI labs keep shipping, we urge you to slow down a little and spend some time with a good book this holiday season. | We don’t want to repeat ourselves, so here’s the link to last year’s recommendation (it’s very good). But life moves on, and with the rise of the agentic discourse, we’ll recommend more books on the topic, plus those about AI and ML published this year. | Star this collection in your inbox – it might come in handy throughout the next year! And please, share it with your friends, colleagues, and social networks. Keeping each other educated about AI and ML is a great way to stay in control. | Table of Contents | | Highly recommended | The MANIAC (2023) by Benjamin Labatut I read a lot this year – mostly about AI in one form or another. Among many brilliant works, this book stands out as the one you simply can’t put down. It’s a thrilling, disorienting dive into the lives of geniuses and the chaos they leave in their wake. From the heartbreak of Paul Ehrenfest to the brilliance of John von Neumann and the triumph of AlphaGo, Labatut weaves a narrative that’s both haunting and exhilarating. With a kaleidoscope of voices and a whirlwind of ideas, this book takes you on an unforgettable journey through humanity’s ambitions, fears, and the enigma of artificial intelligence.
| Classics | The Art of Computer Programming, authored by Donald Knuth, stands as a monumental work in computer science, offering an in-depth exploration of algorithms and programming techniques. This comprehensive collection encompasses volumes that dive into fundamental algorithms, seminumerical algorithms, sorting and searching, and combinatorial algorithms, providing both theoretical insights and practical applications. His meticulous approach and dedication to the field have made this series an essential resource for computer scientists and programmers alike. I haven’t read this one but I’m saving it in my list to go through next year. Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell offers an accessible and insightful overview of AI, demystifying complex concepts for general readers. It pays attention to the quest for common sense in AI systems, providing a clear-eyed view of what AI has accomplished and the hurdles that remain. Mitchell’s engaging writing style, flavored with personal stories and a twist of humor, makes this book a valuable resource for anyone interested in understanding the realities and future prospects of artificial intelligence. →She also has a newsletter “AI: A Guide for Thinking Humans” Probabilistic machine learning a book series by Kevin Murphy. His latest addition from 2023 builds on his earlier work, offering a clear and structured approach to advanced ML concepts. Covering topics like Bayesian deep learning and diffusion models, it connects foundational ideas with recent research. Praised by experts like Geoffrey Hinton and Yoshua Bengio for its clarity and practical value, this book is a solid resource for researchers and practitioners aiming to deepen their understanding of probabilistic methods in machine learning. {free} Dive into Deep Learning This open-source book offers a hands-on introduction to deep learning, combining interactive code, practical examples, and clear explanations. Developed by the D2L team and adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge, it covers core topics like neural networks, optimization, and modern architectures, making it ideal for learners who want to experiment while they learn. With its seamless integration of theory and implementation, Dive into Deep Learning is a comprehensive guide for anyone stepping into the field of AI.
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| Agents related (some of them are already classics as well) | Artificial Intelligence: A Modern Approach (4th US edition, 2020) by Stuart Russell and Peter Norvig is a foundational AI textbook, widely used in over 1,500 schools worldwide. The book offers a thorough introduction to AI's core principles and applications, covering topics like problem-solving, knowledge representation, reasoning, planning, and machine learning. It also provides essential insights into the development and functioning of intelligent agents, making it a cornerstone for understanding agent-based AI systems. (We use it a lot working on our agentic series). The authors bring deep expertise: Stuart Russell is a computer science professor at UC Berkeley, and Peter Norvig is a director of research at Google and Education Fellow at Stanford HAI. Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence by Gerhard Weiss (Editor) (1999) is a foundational guide to the world of multiagent systems and distributed artificial intelligence. This book covers key topics like intelligent agents, distributed problem-solving, and decision-making, with contributions from leading experts. It’s an essential resource for students and professionals alike, offering practical insights and theoretical depth. {free} Artificial Intelligence: Foundations of Computational Agents (3rd Edition, 2023) by David L. Poole and Alan K. Mackworth is a comprehensive resource for understanding AI, this book presents a unified framework for designing intelligent computational agents. The third edition introduces new chapters on neural networks, deep learning, and the social impacts of AI. Notably, the complete text is freely accessible online, providing an invaluable resource for students and professionals alike. (You can also buy this book here.) Michael Wooldridge is a legend in the world of multi-agent systems. As a professor at Oxford, he’s spent decades shaping how we understand intelligent systems and their interactions. He’s written the go-to books for understanding how agents think, work together, and solve problems. His work is clear, insightful, and packed with examples that make even complex ideas click. If you’re serious about AI agents – or just curious – Wooldridge is your starting point for grasping the big ideas driving the field forward. Here is a list we recommend:
| | 2024 - NEW Practical Guides to AI | Build a Large Language Model (From Scratch) by Sebastian Raschka
Raschka walks you through building an LLM from the ground up, focusing on clear explanations and hands-on coding. You’ll see how theoretical concepts map to real-world implementations, making it a go-to resource for anyone ready to roll up their sleeves and learn the internal workings of language models. Immediately became an Amazon’s bestseller. LLM Engineer's Handbook: Master the art of engineering large language models from concept to production by Paul Iusztin and Maxime Labonne
This handbook lays out a practical path for anyone keen on taking an LLM from an idea to a fully functional product. You’ll find insights on project structure, data handling, and deployment strategies—all aimed at helping you build and deliver production-grade language models. Another Amazon’s bestseller! AI Engineering: Building Applications with Foundation Models by Chip Huyen. Chip was on our last year’s list with her book “Designing ML systems”. This year, Huyen offers a pragmatic approach to integrating foundation models into your projects. From design considerations to deployment best practices, this book helps you navigate the complexities of AI engineering, ensuring your systems are not just cutting-edge, but also maintainable and efficient. Hands-On Large Language Models by Jay Alammar and Maarten Grootendorst provides a practical deep dive into training, fine-tuning, and deploying large language models. Whether you’re exploring fundamental concepts or advanced techniques, you’ll gain a balanced view that equips you to build impactful LLM-based solutions from the ground up. Why Machines Learn: The Elegant Math Behind Modern AI by Anil Ananthaswamy A fellow journalist! This book is a captivating journey through the history and mathematics that underpin modern machine learning. Ananthaswamy masterfully combines clear explanations of concepts like Bayes' Rule, neural networks, and statistical learning with stories of the pioneers who shaped AI. Whether you’re dusting off old math skills or exploring generative AI, it balances historical insight with technical depth, making complex ideas engaging and accessible.
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| Free and great | {Free} Alice’s Adventures in a Differentiable Wonderland: A Primer on Designing Neural Networks by Simone Scardapane invites readers into the fascinating world of neural networks, or as he calls them, "differentiable models." Blending theory with practical insights, this book explores the foundational components of neural networks, tracing their evolution from early concepts to modern applications like generative AI. With a focus on design and a balance of historical context and recent advancements, it’s an engaging resource for anyone looking to deepen their understanding of these transformative models. {Free} The Little Book of Deep Learning by François Fleuret is a concise introduction to deep learning, tailored for readers with a STEM background. Designed for readability on phone screens, it covers foundational concepts and modern advancements in the field. The book is freely available under a Creative Commons license and has been downloaded over 600,000 times. You can download the latest version from Fleuret's website. {Free} The Basics of Reinforcement Learning from Human Feedback by Nathan Lambert provides a clear and structured introduction to RLHF, blending technical depth with accessibility for those with a quantitative background. Covering topics from preference data and reward modeling to optimization techniques like instruction tuning, it also explores advanced frontiers like Constitutional AI and evaluation challenges. The book offers both theoretical insights and practical applications, making it a useful resource for anyone exploring RLHF's role in modern ML systems. Nathan also writes one of our favorite newsletters Interconnects.
| Now, from ML practitioners to everybody else: What to read about AI in general | DOs and DON’Ts | Definitely Do | AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference (2024) by Arvind Narayanan and Sayash Kapoor In "AI Snake Oil," Princeton computer scientists Arvind Narayanan and Sayash Kapoor cut through the AI hype, offering a clear-eyed look at what AI can truly achieve and where it falls short. They expose the overblown claims surrounding predictive AI – tools that promise to forecast human behavior – and highlight the real-world consequences of misplaced trust in these technologies. By demystifying AI's capabilities, Narayanan and Kapoor empower readers to discern genuine innovation from digital snake oil. Very well written book.
| Co-Intelligence: Living and Working with AI (2024) by Ethan Mollick redefines AI not as a tool, but as a partner. In Co-Intelligence, he explores how AI becomes a co-worker, coach, and creative collaborator, reshaping how we work and learn. With practical examples and sharp insights, Mollick reveals the balance between leveraging AI’s potential and preserving human agency. This isn’t just a guide; it’s a call to rethink our relationship with intelligent systems—where the “co-” in collaboration truly matters. It’s sn easy and pleasant read. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence by Kate Crawford. This book is not new but we will be thinking about these questions more in 2025. You can also check our 5 Must-Read Books on AI Ethics.
| Do if you like picking into the future | Genesis: Artificial Intelligence, Hope, and the Human Spirit by Henry Kissinger, Eric Schmidt, and Craig Mundie explores the profound potential of artificial intelligence, blending deep philosophical inquiry with global pragmatism. Kissinger, Schmidt, and Mundie unpack AI’s promise to tackle humanity’s greatest challenges—like inequality and climate change—while grappling with its existential questions. It’s an ambitious call for ethical frameworks and international cooperation. If you’re ready to think beyond the algorithm, this book is an invitation to reimagine the relationship between AI and the human spirit. The Singularity Is Nearer: When We Merge with AI by Ray Kurzweil is a vision of humanity’s AI-powered transformation. Nanobots in your veins? Check. Immortality? Almost there. This book dives into a future where AI solves scarcity, links our brains to the cloud, and redefines art and life itself. With bold predictions and technocratic optimism, Kurzweil both excites and unnerves, pushing readers to imagine a world where intelligence and biology converge – whether for better or for blood bots. If you want to play predictions game about our life with AI, that will be fun for you. Haven’t read this one by really liked the title Dancing with Qubits - 2nd edition: From qubits to algorithms, embark on the quantum computing journey shaping our future by Robert S Sutor.
| DONT’S | We don’t recommend Nexus: A Brief History of Information Networks from the Stone Age to AI by Yuval Harari. It’s too long and one-sided. It feels like it surfs the wave of hype without depth. And in that sense it’s misleading. For a global intellectual with such influence, the book’s lack of focus and inability to offer meaningful, actionable perspectives on AI feels irresponsible. This quote from The New York Times review says it all: “It doesn’t feel controlled, or even particularly expert – and the effect is a little like a flight where the person sitting next to you is well-read, hyper-caffeinated, and determined to tell you his Theory of Everything.”
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