| Christmas is almost here. Kwanzaa is around the corner. And it’s less than two weeks until the New Year. This is an a good time to wrap up your 2023 tasks, reflect on the year gone by, and relax on the couch with an interesting book. To help you choose your next read, we put together a list of books loved by the Turing Post Team and and endorsed by leading AI&ML experts. Star this collection in your inbox – might be useful throughout the whole next year! |  | Part of Ksenia’s AI&ML&CS collection. Spot a few books that are not on the list – we still highly recommend them! |
| Basically Classics | Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville A handbook for anyone who wants to refresh their knowledge or dive deeper into the fascinating world of deep learning. Written by the inventor of Generative Adversarial Networks (GANs) Ian Goodfellow, a pioneer in neural networks and deep learning Yoshua Bengio (check out his priorities for AI in 2024 here, it’s a must read with a lot of insights from him and other prominent AI thinkers about the upcoming 2024), and a significant contributor to the academic community Aaron Courville. The Book of Why: The New Science of Cause and Effect by Judea Pearl, Dana Mackenzie Judea Pearl, a Turing Award winner, introduces a new framework for understanding causality that challenges traditional statistical approaches. Co-authored with science writer Dana Mackenzie, the book is known for making complex ideas in causality accessible to a broader audience. Reading it feels like talking to Judea Pearl. Designing Machine Learning Systems by Chip Huyen A comprehensive guide to designing reliable, scalable, maintainable, and adaptable machine learning (ML) systems. Huyen provides a holistic approach to system design, considering the myriad components, stakeholders, and the data-dependent nature of these systems. This book helped us structure the FMOps series. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingo Domingos presents the idea of a 'Master Algorithm' - a universal learner that can extract all knowledge from data. He navigates the reader through the five principal schools of machine learning: Symbolists, Connectionists, Evolutionaries, Bayesians, and Analogizers, discussing their potential integration into this singular, formidable algorithm.
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| Fresh look (books published in 2023) | The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI by Fei-Fei Li A deeply engaging and insightful book that chronicles the remarkable journey of Dr. Fei-Fei Li, one of the pivotal scientists responsible for advances in AI. Her contribution to the field is immense, particularly through the creation of ImageNet, which has been a key catalyst in the development of modern AI. She is now a computer science professor at Stanford University, founding director of the Stanford Institute for Human-Centered AI (HAI), and a founder and chairperson of the board of the nonprofit AI4ALL. The Coming Wave by Mustafa Suleyman and Michael Bhaskar Authored by Mustafa Suleyman, co-founder of DeepMind and a co-founder and CEO of Inflection AI (check the Inflection profile here), the book is an exploration of the transformative impact of technologies like AI, synthetic biology, and quantum computing on our future. Suleyman provides an insider's perspective on how these technologies will reshape everyday life, from personal management to government operations, while also highlighting the dual nature of their potential. Understanding Deep Learning by Simon J. D. Prince Another book on deep learning in our list. This book stands out for its up-to-date coverage of the subject, including cutting-edge topics like transformers and diffusion models. Prince's pragmatic approach bridges the gap between theory and practice, equipping readers with the necessary details to implement naive versions of models. Additionally, the book is complemented by programming exercises offered in Python Notebooks.
| | History of Artificial Intelligence and Machine Learning | We love history of AI and ML! As Haohan Wang and Bhiksha Raj put it in the paper On the Origin of Deep Learning, “To fundamentally push the deep learning research frontier forward, one needs to thoroughly understand what has been attempted in history and why current models exist in present forms.” History empowers us with the knowledge and frees us from reinventing the wheel. Before moving to the list, we invite you to read our historical series covering the origins of large language models (LLMs). It’s one of our most popular series and it’s a fascinating read. To the books: | The Turing Guide by Jack Copeland, Jonathan Bowen, Mark Sprevak, Robin Wilson A real treasure chest for anyone who wants to learn about the life and heritage of Alan Turing. The book has eight parts each covering a dedicated part of the history of Alan Turing's life and work. One of the authors of this book, Jack Copeland, is the Director of the Turing Archive for the History of Computing, an extensive online archive about Alan Turing. The Quest for Artificial Intelligence: A History of Ideas and Achievements by Nils. J Nilsson This book provides a comprehensive and engaging journey through the history of Artificial Intelligence (AI). Tracing its origins back to the dreams of eighteenth-century pioneers, the book chronicles the evolution of AI from its early conceptual stages to the advanced work of today's engineers. It’s a thorough and insightful read connecting the historical dots of AI's fascinating journey. Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence by Pamela McCorduck The book is known for its detailed account of the key figures, breakthroughs, and pivotal moments in AI history. You will hear voices of Marvin Minsky, John McCarthy, Alan Newell, Herbert Simon and others. It poses profound questions about the relationship between humans and intelligent machines, making it an essential read for anyone intrigued by the past, present, and future of AI.
| | AI and Future Studies | The Year in Tech, 2023: The Insights You Need from Harvard Business Review (HBR Insights Series) by Harvard Business Review, Beena Ammanath, Andrew Ng, Michael Luca, and Bhaskar Ghosh A comprehensive and timely resource that encapsulates a year's worth of critical thinking on technology. This book is designed to help business leaders, professionals, and organizations navigate the rapidly evolving technological landscape and its impact on various aspects of business operations and strategy. From the dynamics of the hybrid office and changes on factory floors to the strategic decisions in the C-suite, it provides valuable insights into the opportunities and challenges presented by these technological advancements. AI 2041: Ten Visions for Our Future by Kai-Fu Lee and Chen Qiufan It’s a thought-provoking exploration of the future of AI and its impact on human life, society, and the global order. Co-authored by Kai-Fu Lee, the former president of Google China and a founder of the recently successful unicorn company 01.AI, and novelist Chen Qiufan, this book blends scientific insight with creative storytelling to imagine our world with AI in 2041. It also leaves readers with the impression that we can’t even begin to imagine what will happen by 2041, as much from this book has already become a reality or is very close to it. The Age of AI: And Our Human Future by Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher A powerful trio of the Former US Secretary of State, the former CEO of Google, and the inaugural dean of the MIT Schwarzman College of Computing gathered to explore what AI means for our present and our future. As Daniel Huttenlocher describes the book: it “aims to engage people in more in-depth conversation about how AI is changing the nature of what it means to be human.”
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| AI ethics collection | The significance and relevance of AI ethics are increasingly becoming more apparent and crucial as humans continue to engage and interact with a wide range of AI-based applications. We discussed this rapprochement of a human and AI focusing on one of its consequences: the act of anthropomorphizing AI. Here is an essential read if you are concerned about AI ethics. | AI: Its nature and future by Margaret Boden Margaret Boden examines AI’s impact across various fields, such as biology and linguistics, and its contribution to understanding memory, learning, and language. The book also delves into the philosophical debates surrounding AI, questioning the potential of programs to achieve true intelligence, creativity, and consciousness. AI Ethics by Mark Coeckelbergh An accessible synthesis of ethical issues raised by AI addressing concrete questions. Written by philosopher of technology Mark Coeckelbergh, the book examines AI’s influence in various applications, from search engines to autonomous weapons, and discusses ethical challenges including privacy, responsibility, transparency, and bias. The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism by Nick Couldry, Ulises Mejias Learn about the concept of "data colonialism," highlighting the hidden costs of our increasingly digitized lives. It argues that the convenience of digital connections comes at the price of extensive personal data collection, which corporations exploit for profit. The book is a call to action to decolonize the internet and reclaim autonomy in the digital age. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence by Kate Crawford Kate Crawford, one of the leading scholars of the social implications of artificial intelligence, examines the hidden costs of this technology. From the consumption of natural resources and labor exploitation to the erosion of privacy and freedom. The book reveals how AI is intertwined with undemocratic governance and social inequality.
| | Lastly, a fire-side list of books written completely using AI | | Happy Holidays! | If you want to thank us – upgrade to become Premium. We appreciate it! Or forward this newsletter to your friends and colleagues. In the next couple of weeks, we are announcing our referral program 🤍 | | |
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