Here are 100 books that Artificial Intelligence fans have personally recommended if you like
Artificial Intelligence.
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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…
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 absolutely love Nick Bostrom's book because it dives deep into the fascinating yet daunting future of artificial intelligence, a topic that resonates with my own work. Bostrom's exploration of how superintelligent AI could emerge and the profound risks it poses is both thought-provoking and essential reading for anyone curious about technology's trajectory.
His insights on the challenges of control and alignment really struck a chord with me, as they highlight the importance of designing AI systems that prioritize human values. This book not only raises critical questions but also inspires a sense of urgency to navigate the future responsibly, making it a personal favorite and a vital resource for anyone interested in the intersection of AI and ethics.
The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but we have cleverer brains.
If machine brains one day come to surpass human brains in general intelligence, then this new superintelligence could become very powerful. As the fate of the gorillas now depends more on us humans than on the gorillas themselves, so the fate of our species then would come to depend on the actions of the machine superintelligence.
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.
While it’s not the newest tech, I love that it covers the essential groundwork that sparked the modern AI revolution. I personally think its perfect for engineers and data scientists. It's also a great precursor to my book, giving you the strong foundation you need before diving into the next wave of AI advancements.
Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models.
Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll…
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.
Bishop’s book laid the mathematical groundwork for me, making it a solid foundation for anyone venturing into Generative AI.
I love how it covers Bayesian inference, graphical models, and machine learning fundamentals in a clear, approachable way. I also think, in my personal opinion, that reading my book after this one would be a natural progression to understand where AI is heading, building on the core concepts that Bishop established.
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models…
My personal passion behind ethical AI started early in my life. I was raised by someone who had a personality disorder, and grew up being gaslit and manipulated. It was hard for me personally to understand what was reality and what was made up. Being a nerdy kid, I spent most of my time studying computers and math to escape it all. And while I have made my own life writing books on machine learning, and programming for a living, I also care deeply about how what I do affects others. Being thoughtful is deep within me, and I sit with a Zen group and volunteer with the Mankind Project.
Peter Flach’s book on machine learning had a profound impact on me. The book is simple to understand, and highly visual. But beyond that Peter himself is a lovely person who obviously cares about all his students. I believe for getting started in machine learning and wanting to understand the algorithms that power many models, this is a great place to start.
But most importantly it’s a great way to understand the power and gain more intention behind what we are doing.
As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role…
Tim Harford is the author of nine books, including The Undercover Economist and The Data Detective, and the host of the Cautionary Tales podcast. He presents the BBC Radio programs More or Less, Fifty Things That Made The Modern Economy, and How To Vaccinate The World. Tim is a senior columnist for the Financial Times, a member of Nuffield College, Oxford, and the only journalist to have been made an honorary fellow of the Royal Statistical Society.
This is a clever and highly readable guide to the brave new world of algorithms: what they are, how they work, and their strengths and weaknesses. It’s packed with stories and vivid examples, but Dr Fry is a serious mathematician and when it comes to the crunch she is well able to show it with clear and rigorous analysis.
When it comes to artificial intelligence, we either hear of a paradise on earth or of our imminent extinction. It's time we stand face-to-digital-face with the true powers and limitations of the algorithms that already automate important decisions in healthcare, transportation, crime, and commerce. Hello World is indispensable preparation for the moral quandaries of a world run by code, and with the unfailingly entertaining Hannah Fry as our guide, we'll be discussing these issues long after the last page is turned.
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.
What sets this book apart for us is its focus on rethinking the very foundations of AI. The author, Russell, thoughtfully examines the concept of intelligence itself, comparing humans and machines and outlining the necessary milestones for reaching superhuman AI.
He also doesn't shy away from the current dangers of AI misuse, providing some really great examples.
We found the book very hard to put down, and it raised so many ‘What if?” questions for us!
A leading artificial intelligence researcher lays out a new approach to AI that will enable us to coexist successfully with increasingly intelligent machines
In the popular imagination, superhuman artificial intelligence is an approaching tidal wave that threatens not just jobs and human relationships, but civilization itself. Conflict between humans and machines is seen as inevitable and its outcome all too predictable.
In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the idea of intelligence in humans and in machines.…
We’re a nonprofit that aims to help people have a positive social impact with their careers. Since you have, on average, 80,000 hours in your career, what you decide to do with that time might be your biggest opportunity to make a difference. Over the past ten years, we’ve conducted careful research into high-impact careers, and have helped thousands of people plan a career that has a high positive impact.
One example of an especially pressing threat facing humanity is the rapid development of artificial intelligence. If we want this new technology to go well, it needs to be ‘aligned’ – that is, it should have or act on the same values as us.
In this book, Brian sets out why aligning artificial intelligence is an extremely tricky issue and one which deserves more attention from talented and dedicated people.
Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us-and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem.
Systems cull resumes until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole-and appear to assess Black…
I’m a storyteller writing on business and technology. I specialize in clear views of complex systems. When Juliette showed me her research on tech companies and AI responsibility, I saw the power of a book – the book that ultimately became The AI Dilemma. The core dilemma is that in the right hands the technology is needed, and in the wrong hands it’s dangerous. When Juliette asked me to coauthor it, I jumped at the chance. As we worked, I realized that the topic brought into focus all the research and thinking I’d ever done about human, organizational, and machine behavior.
If ever a subject deserved the sweeping hand of a highly skilled journalist/historian, it’s generative AI and machine learning. The field is shaped by its founders’ idiosyncratic and fascinating personalities.
NYTimes reporter Cade Metz observed many events first-hand. We read about Go Grandmaster Lee Sedol recovering from losing to Google’s AI by mastering the machine’s logic. We see Geoffrey Hinton flying supine because of his back problems, and the origins of Joy Buolamwini’s famous Gender Shades project.
We get the backstory to the most serious issues: like how well can AI developers be trusted to manage risk? As a journalist-historian myself, I deeply appreciate being immersed in contemporary history.
'This colourful page-turner puts artificial intelligence into a human perspective . . . Metz explains this transformative technology and makes the quest thrilling.' Walter Isaacson, author of Steve Jobs ____________________________________________________
This is the inside story of a small group of mavericks, eccentrics and geniuses who turned Artificial Intelligence from a fringe enthusiasm into a transformative technology. It's the story of how that technology became big business, creating vast fortunes and sparking intense rivalries. And it's the story of breakneck advances that will shape our lives for many decades to come - both for good and for ill. ________________________________________________
I’ve been a geeky kid all my life. (I don’t think I’ve quite grown up yet.) Born in the 1970s, my childhood was a wonderful playground of building robots and software. I was awarded one of the early degrees in AI, and a PhD in genetic algorithms. I’ve since spent 25 years exploring how to make computers think, build, invent, compose… and I’ve also spent 20 years writing popular science books. I’m lucky enough to be a Professor in one of the world’s best universities for Computer Science and Machine Learning: UCL, and I guess I’ve written two or three hundred scientific papers over the years. I still think I know nothing at all about real or artificial intelligence, but then does anyone?
I’ve not met Harry, but he seems to have a logical and sensible head on his shoulders. His writing is considered and grounded, which is exactly what you need when discussing the hype that forever seems to surround AI. This book is another look at this topic and finds yet more ways to explain to readers the difference between human intelligence and our algorithmic attempts at intelligence – which are frequently pretty stupid.
Recent startling successes in machine intelligence using a technique called 'deep learning' seem to blur the line between human and machine as never before. Are computers on the cusp of becoming so intelligent that they will render humans obsolete? Harry Collins argues we are getting ahead of ourselves, caught up in images of a fantastical future dreamt up in fictional portrayals. The greater present danger is that we lose sight of the very real limitations of artificial intelligence and readily enslave ourselves to stupid computers: the 'Surrender'.
By dissecting the intricacies of language use and meaning, Collins shows how far…
