Here are 100 books that A Brief History of Artificial Intelligence fans have personally recommended if you like
A Brief History of 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…
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.
Artificial intelligence is, of necessity, an academic pursuit, at least initially. McCorduck’s book is her account of the history and development of AI. She was not a historian coming to events after the fact but a living witness. Her circle of friends included all the key figures, the people those of us who fell into AI later didn’t have the opportunity to know.
This book, personal and human, not technical and heavy, reveals the humanness of the process. Yes, artificial intelligence was the goal, but human intelligence (and frailty) were central to its emergence.
In the autumn of 1960, twenty-year-old humanities student Pamela McCorduck encountered both the fringe science of early artificial intelligence, and C. P. Snow's Two Cultures lecture on the chasm between the sciences and the humanities. Each encounter shaped her life. Decades later her lifelong intuition was realized: AI and the humanities are profoundly connected. During that time, she wrote the first modern history of artificial intelligence, Machines Who Think, and spent much time pulling on the sleeves of public intellectuals, trying in futility to suggest that artificial intelligence could be important. Memoir, social history, group biography of the founding fathers…
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?
When I was a doctoral student in historical musicology, I went to Paris to study postwar government budgets for music, but it was really boring. So I started hanging out listening to Parisian songbirds instead. The more I learned about birdsong, the more I realized it raised some really big questions, like why biologists and musicians have completely different standards of evidence. Those questions led me to write my book, which is about what it means to sing if you’re not considered fully human, and most of my work today is about how thinking about animals can help us understand what we value in those who are different.
A friend gave me this when I was almost done writing my book.
I couldn’t figure out how to justify footnoting a science fiction novel written from the point of view of a one-thousand-year-old spaceship in my own book, which was mostly about theories of musical evolution, but I still wish I had. The spaceship-protagonist can’t tell genders apart and refers to every single character as “she,” and it loves (I mean loves) to sing.
The book tackles surprisingly relevant questions about the way power, gender, and difference circulate through a post-colonial society. It’s completely different from the kind of work I do as an academic, but I think it has something just as important to say about music, human identity, and modern science.
Once, she was the Justice of Toren -- a colossal starship with an artificial intelligence linking thousands of soldiers in the service of the Radch, the empire that conquered the galaxy. Now, an act of treachery has ripped it all away, leaving her with one fragile human body, unanswered questions, and a burning desire for vengeance.
We’ve had the privilege to be part of a profession that has developed beyond all recognition. Both of us worked in senior public relations roles and know how difficult leadership can be in this context. A desire to combine what we’d learned with the best research resulted in us becoming professors in a university business school. Our aim is to provide a bridge between practice and academia, an ambition that has led us to work with inspiring practitioners and researchers around the world. We’ve had a great time and as you’ll see from our ‘book picks’ we draw on many perspectives to inform our work as authors, educators, and researchers.
Here we are in the world of the metaverse. So many public relations books are about what the practice looks like now. This one is different. It’s inviting us to look at what the world will look like when just about everything is infused with AI and total sensory immersion is commonplace. It invites us to look at how public relations can harness these technologies and the power they will give us to develop incredible knowledge about people, build deep relationships and emotional connections. If so inclined, we could manipulate people without them even knowing it. For me this was a wake-up call to the profession. We can’t just be seduced by the shiny tools of technology, but need to step back and reflect on their ethical implications.
This concise text provides an accessible introduction to artificial intelligence and intelligent user interfaces (IUIs) and how they are at the heart of a communication revolution for strategic communications and public relations.
IUIs are where users and technology meet - via computers, phones, robots, public displays, etc. They use AI and machine learning methods to control how those systems interact, exchange data, learn from, and develop relations with users. The authors explore research and developments that are already changing human/machine engagement in a wide range of areas from consumer goods, healthcare, and entertainment to community relations, crisis management, and activism.…
I am the co-author of Small Teaching K-8. I hold Massachusetts teacher licensure in English 5-12, Library k-12, and School Administration 5-8 as well as an M.Ed. from Boston College.
Why should we be emphasizing creativity in classrooms? In short order, our students’ careers will require them to augment the work of machines.
ChatGPT and DALI-2 are only the beginning. Du Sautoy explores the implications of artificial intelligence on the future of work. The Creativity Code is a reminder that technology is only as creative as its programmers—at least, for now.
Will a computer ever compose a symphony, write a prize-winning novel, or paint a masterpiece? And if so, would we be able to tell the difference?
As humans, we have an extraordinary ability to create works of art that elevate, expand and transform what it means to be alive.
Yet in many other areas, new developments in AI are shaking up the status quo, as we find out how many of the tasks humans engage in can be done equally well, if not better, by machines. But can machines be creative? Will they soon be able to learn from the…
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…
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…
