Here are 100 books that This Could Be Important fans have personally recommended if you like
This Could Be Important.
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My passion for generative AI first ignited in 2016 when I spoke about it at a conference, and ever since then, I can’t stop! I've created an online course, a newsletter and even wrote a book to spread knowledge on this groundbreaking technology. As an instructor, I empower others to explore the boundless potential of generative AI applications. Day in day out, I assist clients in crafting their own generative AI solutions, tailoring them to their unique needs.
I truly believe that this is the book that brought my generation of AI experts into the fold. Despite having studied AI and ML, this book took me by the hand and grounded me in the fundamentals. I love the fact that it covers everything from mathematical basics to industry-level techniques.
Written by the OGs of deep learning, it's an absolute must-read for anyone serious about the field. Highly recommend for students and engineers alike.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all…
As a child of the microcomputer revolution in the late 1970s, I’ve always been fascinated by the concept of a general-purpose machine that I could control. The deep learning revolution of 2010 or so, followed most recently by the advent of large language models like ChatGPT, has completely altered the landscape. It is now difficult to interpret the behavior of these systems in a way that doesn’t argue for intelligence of some kind. I’m passionate about AI because, decades after the initial heady claims made in the 1950s, AI has reached a point where the lofty promise is genuinely beginning to be kept. And we’re just getting started.
Goodfellow’s Deep Learning is a must in the field because it was the first. Prince’s new book is an essential follow-up to be up-to-date with the latest model types, including diffusion models (think Stable Diffusion or DALL-E), transformers (the heart of large language models), graph networks (reasoning over relationships), and reinforcement learning.
The math level is similar to what you’ll find in Goodfellow’s book.
An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.
Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced…
As a child of the microcomputer revolution in the late 1970s, I’ve always been fascinated by the concept of a general-purpose machine that I could control. The deep learning revolution of 2010 or so, followed most recently by the advent of large language models like ChatGPT, has completely altered the landscape. It is now difficult to interpret the behavior of these systems in a way that doesn’t argue for intelligence of some kind. I’m passionate about AI because, decades after the initial heady claims made in the 1950s, AI has reached a point where the lofty promise is genuinely beginning to be kept. And we’re just getting started.
Woolridge presents the history of artificial intelligence from the point of view of an insider. This book is one of the few accounts of AI history presenting a measured perspective, one that has weathered more than one boom and bust cycle.
The book is nicely complemented by his recent series of lectures, which can be easily found on YouTube. I read Woolridge as saying, “Yes, something new has happened with the advent of large language models, but much work remains.”
From Oxford's leading AI researcher comes a fun and accessible tour through the history and future of one of the most cutting edge and misunderstood field in science: Artificial Intelligence
The somewhat ill-defined long-term aim of AI is to build machines that are conscious, self-aware, and sentient; machines capable of the kind of intelligent autonomous action that currently only people are capable of. As an AI researcher with 25 years of experience, professor Mike Wooldridge has learned to be obsessively cautious about such claims, while still promoting an intense optimism about the future of the field. There have been genuine…
As a child of the microcomputer revolution in the late 1970s, I’ve always been fascinated by the concept of a general-purpose machine that I could control. The deep learning revolution of 2010 or so, followed most recently by the advent of large language models like ChatGPT, has completely altered the landscape. It is now difficult to interpret the behavior of these systems in a way that doesn’t argue for intelligence of some kind. I’m passionate about AI because, decades after the initial heady claims made in the 1950s, AI has reached a point where the lofty promise is genuinely beginning to be kept. And we’re just getting started.
Alan Turing’s 1936 paper “On Computable Numbers, with an Application to the Entscheidungsproblem” was foundational to the development of computer science. To this day, Turing machines, the theoretical computational devices imagined in Turing’s paper, are a research cornerstone as they embody the concept of “computable.” If a programming language can implement a Turing machine, then the language is deemed Turing complete and is, therefore, general-purpose enough to implement any algorithm.
Turing’s paper is readable, but Petzold’s book breaks it down in minute detail to explain the nomenclature and meaning behind Turing’s words. I believe all computer science students should study this paper, and you’ll be hard-pressed to find a more thorough review than the one presented in this book.
Programming Legend Charles Petzold unlocks the secrets of the extraordinary and prescient 1936 paper by Alan M. Turing
Mathematician Alan Turing invented an imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be computable, creating the field of computability theory in the process, a foundation of present-day computer programming.
The book expands Turing's original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing's statements, making the original difficult-to-read document accessible to present day programmers, computer science majors, math geeks,…
I spent over forty years developing complex, software-intensive systems, and the Association of Computing Machinery honored me with the title of distinguished engineer. AI and robotics have been my main technical focus for the last 5 years. For the last couple of years, I have been binge-watching videos on advances in AI and robotics and binge-reading books on the topic. I am also a multi-award-winning author of science fiction novels and short stories. Most of the short stories in my coming book involve AI and robots.
I really loved this book because of its wide breadth. If you are only going to read a single book on AI and its ramifications, this would be my top recommendation.
It covers everything from the history of artificial intelligence to what the short- and mid-term future will look like. This book provides a solid foundation to help you prepare for a future dominated by AIs and robots.
"Machines of Tomorrow offers a fascinating journey into the future of AI, providing a unique perspective that combines technology, economics, geopolitics, and history." — PASCAL BORNET, Technology Influencer, 2 million followers.
How AI Will Shape Our World.
Written in an easy-to-read style accessible to everyone, "Machines of Tomorrow" grounds the reader in a thorough history and description of Artificial Intelligence, enabling an appreciation of its real trajectory and ultimately a deeper level of engagement with the core concept of the book: what should societies expect from AI in the coming decades?
Software developers love to question the assumptions that underpin their practice. Some of the most exciting phases of my career have come about as a result of such questions. Often they are revolutionary in the literal sense that they ask you to turn your thinking upside down – to design systems from the bottom up rather than the top down, for example, or to write your tests before your components. I may not adopt every practice, but each challenge enriches the conceptual world in which I work. Over the years, I have come to look for similar shifts and inversions across other subject areas. Here are some recommendations from my reading.
As a coder and a lifelong SF reader, I am fascinated by AI. I've even written a LLM chat client named Shelley. Fascination, though, is not the same as uncritical fanboyism.
It is tempting to treat AI as a natural or magical apparition. Crawford's book turns this illusion on its head and explores AI literally from the ground up, beginning with its vast hunger for natural resources. She describes a similar hunger for training data as well as the implicit (or disguised) biases underlying the systems of classification that drive an AI's "understanding" of the world.
This is not a book about the future of AI so much as a particular map of the state of the project – a look into the wizard's booth. It offers an essential first step in considering what is to come and how we might negotiate it.
The hidden costs of artificial intelligence-from natural resources and labor to privacy, equality, and freedom
"This study argues that [artificial intelligence] is neither artificial nor particularly intelligent. . . . A fascinating history of the data on which machine-learning systems are trained."-New Yorker
"A valuable corrective to much of the hype surrounding AI and a useful instruction manual for the future."-John Thornhill, Financial Times
"It's a masterpiece, and I haven't been able to stop thinking about it."-Karen Hao, senior editor, MIT Tech Review
What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our…
Coming from two very different backgrounds gives Dean and I a unique ‘view’ of a topic that we are both hugely passionate about: artificial intelligence. Our work together has gifted us a broader perspective in terms of understanding the development of and the philosophy beneath what is coined as artificial intelligence today and where we truly stand in terms of its potential for good – and evil. Our book list is intended to provide a great starting point from where you can jump into this incredibly absorbing topic and draw your own conclusions about where the future might take us.
A fantastic, non-techy read that cuts through the AI fear-mongering in a way that is both hopeful and practical.
The authors work well together to craft a story of how we arrived at this point in time, where our ever-evolving research could lead us, and the practical solutions needed to enable us to move forward in a world governed by the complex and rapidly changing landscape of AI.
The public debut of ChatGPT was a watershed moment. It awakened us to the fact that general artificial intelligence—a technology that had long been a source of fantasy and fiction—was finally here and operating among us. For the first time in history, ordinary citizens could converse directly with an AI chatbot. The thrill of this 'first encounter' with artificial general intelligence, coupled with the astonishing capabilities that AI chatbots displayed, gave rise to concerns about whether AI was sentient or self-aware. Although we have been using AI-assisted applications for years in such forms as facial recognition and language translation, our…
My passion for generative AI first ignited in 2016 when I spoke about it at a conference, and ever since then, I can’t stop! I've created an online course, a newsletter and even wrote a book to spread knowledge on this groundbreaking technology. As an instructor, I empower others to explore the boundless potential of generative AI applications. Day in day out, I assist clients in crafting their own generative AI solutions, tailoring them to their unique needs.
I have a deep appreciation for Stuart Russell's book because it brilliantly balances theoretical foundations with practical applications in AI. This book is not just a textbook; it’s a comprehensive guide that covers everything from problem-solving and knowledge representation to machine learning and ethics.
Russell's clear explanations and engaging examples make complex concepts accessible, which resonates with my own passion for demystifying AI for readers. I recommend it to anyone interested in understanding AI's potential and challenges, as it equips you with the knowledge to navigate this rapidly evolving field responsibly and thoughtfully.
I’m a mathematics professor who ended up writing the internationally bestselling novel The Death of Vishnu (along with two follow-ups) and became better known as an author. For the past decade and a half, I’ve been using my storytelling skills to make mathematics more accessible (and enjoyable!) to a broad audience. Being a novelist has helped me look at mathematics in a new light, and realize the subject is not so much about the calculations feared by so many, but rather, about ideas. We can all enjoy such ideas, and thereby learn to understand, appreciate, and even love math.
A primary reason to love math is because of its usefulness. This book shows two sides of math’s applicability, since it is so ubiquitously used in various algorithms.
On the one hand, such usage can be good, because statistical inferences can make our life easier and enrich it. On the other, when these are not properly designed or monitored, it can lead to catastrophic consequences. Mathematics is a powerful force, as powerful as wind or fire, and needs to be harnessed just as carefully.
Cathy O’Neil’s message in this book spoke deeply to me, reminding me that I need to be always vigilant about the subject I love not being deployed carelessly.
'A manual for the 21st-century citizen... accessible, refreshingly critical, relevant and urgent' - Financial Times
'Fascinating and deeply disturbing' - Yuval Noah Harari, Guardian Books of the Year
In this New York Times bestseller, Cathy O'Neil, one of the first champions of algorithmic accountability, sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric.
We live in the age of the algorithm. Increasingly, the decisions that affect our lives - where we go to school, whether we get a loan, how much we pay for insurance - are being made…
I was stimulated by Norbert Wiener’s “Cybernetics” to study circuits in the brain that control behavior. For my graduate studies, I chose the olfactory bulb for its experimental advantages, which led to constructing the first computer models of brain neurons and microcircuits. Then I got interested in how the smell patterns are activated when we eat food, which led to a new field called Neurogastronomy, which is the neuroscience of the circuits that create the perception of food flavor. Finally, because all animals use their brains to find and eat food, the olfactory system has provided new insights into the evolution of the mammalian brain and the basic organization of the cerebral cortex.
The other books in this series are mostly about the real brain. But artificial intelligence promises us a new enhanced brain. What does the future hold? Terrence Sejnowski is a neuroscientist who was one of the first to realize the potential of AI. Since he has been there from the start, in this book he gives the reader an exciting inside story on the people and the advances that are reshaping our lives.
Early attempts at AI were limited, but once computational power took off big computers running multilayer neural nets began proving that they could defeat humans at the most demanding games, enhance human capabilities such as pattern recognition, text recognition, language translation, and driverless vehicles, and work to obtain rewards, just like a human. While these advances are dramatic, it is well to remember that the networks are built not from representations of real neurons, but rather from…
How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy.
The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.
