As per a CNN report, some of the biggest technology companies are using 200,000 books to train artificial intelligence systems. These books help generative AI learn how to communicate information.
It is interesting to note that while we’re reading books on AI, generative AI, and predictive analytics, these models are reading books written by humans to gain more context.
Within a year, AI transitioned from being a technology of the future to gaining widespread application in our professional lives. Emerging technology companies use AI systems and their generative and thinking capabilities to automate repetitive tasks across departments.
If you want to learn more about AI in detail, go through the list of the best AI books you should read in 2024.
Top 10 Artificial Intelligence (AI) Books to Read in 2024
- Top 10 Books on AI You Must Read This Year
- 1. Human Compatible: Artificial Intelligence and the Problem of Control
- 2. Machine Learning for Beginners
- 3. Artificial Intelligence for Humans
- 4. Cybernetic Revolutionaries: Technology and Politics in Allende’s Chile
- 5. AI Superpowers: China, Silicon Valley, and the New World Order
- 6. The Society of Mind
- 7. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
- 8. Deep Learning
- 9. Life 3.0: Being Human in the Age of Artificial Intelligence
- 10 Neural Networks and Deep Learning
Summarize this article for me pleaseTop 10 Books on AI You Must Read This Year
1. Human Compatible: Artificial Intelligence and the Problem of Control
About the book
- Author: Stuart Russell
- Number of pages: 349
- Estimated reading time: 13 hours
- Year published: 2019
- Recommended level: Basic and intermediate readers
- Reviews and ratings:
- 4.1/5 (Amazon)
- 4.1/5 (Goodreads)
Stuart Russell’s Human Compatible: Artificial Intelligence and the Problem of Control took the world by storm in 2019. The Guardian called it “the most important book on AI in 2019.”
The book provides the necessary context before we start advocating artificial intelligence.
Russell sees the conflict between humans and machines as inevitable, threatening jobs and human values. However, we can avoid this if we rethink AI from the ground up. While questioning our concepts of human comprehension and machine learning, the writer discusses the possibilities of superhuman AI.
He argues that the biggest challenge in designing IQ is in the software that will require several breakthroughs, one of which needs to be the comprehension of language.
“Alas, the human race is not a single, rational entity. It comprises nasty, envy-driven, irrational, inconsistent, unstable, computationally limited, complex, evolving, and heterogeneous entities.” – Stuart Russell
Key takeaways
- The possible dangers of autonomous AI systems include lethal autonomous weapons, automated surveillance, fake news behavior manipulation, and automated blackmail, among others
- As we adopt AI for real-world applications, we must avoid human enfeeblement. It refers to the time when humans will delegate everything to AI and lose autonomy
What readers say
“A must-read: this intellectual tour-de-force by one of AI’s true pioneers not only explains the risks of ever more powerful artificial intelligence in a captivating and persuasive way but also proposes a concrete and promising solution.”
2. Machine Learning for Beginners
About the book
- Author(s): Chris Sebastian
- Number of pages: 163
- Estimated reading time: 2 hours
- Year published: 2019
- Recommended level: Beginners
- Reviews and ratings:
- 3.9/5 (Amazon)
- 3.9/5 (Goodreads)
Chris Sebastian argues in Machine Learning for Beginners that machine learning grew from the human desire for reinforcement learning. For instance, computers initially learned how to play checkers, and then they beat world chess champions.
To provide context, Sebastian picks up historical inventions such as Charles Babbage’s mechanical device that engineers could program with punch cards in 1834 or Alan Turing’s ‘Turing Test’ of machine intelligence in 1950.
This book is for people interested in learning about AI, computer science, ML, and swarm intelligence. You will also understand how large data sets are vital to machine learning by providing AI engineers with information for developing advanced algorithms.
“Machine Learning, Neural Networks, and Swarm Intelligence interact and complement each other as part of the quest to generate machines capable of thinking and reacting to the world.” – Chris Sebastian
Key Takeaways
- Although mathematicians figured out the initial theories of machine learning long back, it took us several decades to convert the theories into real-world examples
What readers say
“This is a good book for a very high-level glimpse into machine learning and the pros and cons of how machine learning might impact our lives.”
3. Artificial Intelligence for Humans
About the book
- Author(s): Jeff Heaton
- Number of pages: 222
- Estimated reading time: 8 hours
- Year published: 2013
- Recommended level: Intermediate and advanced readers
- Reviews and ratings:
- 4/5 (Amazon)
- 3.8/5 (Goodreads)
There are a few popular AI books, but most need basic understanding. Artificial Intelligence for Humans: Volume 1 by Jeff Heaton aims to bridge that gap in a relatively easy-to-follow style.
As a reader, you will understand basic AI algorithms under the machine learning category. The first volume explains learning in the context of computer networks and different types of machine learning. Touching upon supervised and unsupervised learning, the author describes essential techniques such as regression and clustering to develop and train large learning models.
“Computer-based neural networks are not like the human brain in that they are not general-purpose computation devices. They carry out tiny specific tasks.” – Jeff Heaton
Key takeaways
- Most AI algorithms accept an input array of numbers and produce an output array. Engineers often model the problems that AI would solve in this form
What readers say
“I have found the information in this book presented extremely clearly and concisely. Very useful in understanding the basic workings of the topics at hand.”
4. Cybernetic Revolutionaries: Technology and Politics in Allende’s Chile
About the book
- Author(s): Eden Medina
- Number of pages: 326
- Reading time: 11 hours
- Year published: 2011
- Recommended level: Intermediate and advanced readers
- Reviews and ratings:
- 4.3 (Goodreads)
Cybernetic Revolutionaries: Technology and Politics in Allende’s Chile is one of the only two AI books in this list to provide political and technological intersection of artificial intelligence. The author covers two real-time projects exposing AI’s dangers.
The first was Chile’s ambitious experiment with peaceful socialist change. Another example was its attempt to build a computer system known as Project Cybersyn for managing the country’s economy.
The results were dangerous.
Chile’s government, helmed by Salvador Allende, got caught up in a military coup and never implemented the other project.
The book details the cybernetic system of the Chilean government that was to bring a holistic design system, human-computer interaction, decentralized management, a national telex network, and modelling of the behavior of dynamic systems.
Interviews, photographs, and vivid descriptions of the network’s Star Trek-like operations room make this book compelling.
“Pursuing a technological solution for the problem of economic management conformed to the ideas of economic progress found in dependency theory, but only to a point.” – Eden Medina
Key takeaways
- Chile’s Project Cybersyn stands for Cybernetics-Synergy, which was an attempt to manage nationalized factories using cybernetics
- Taking this political context, the author offers lessons about the relationship of technology with politics and human values
What readers say
“The story and research here are fascinating and right up my alley– cybernetics, management, mainframe computers!!! Flowcharts!!! I am happy that I read this book.”
5. AI Superpowers: China, Silicon Valley, and the New World Order
About the book
- Author(s): Kai-Fu Lee
- Listening length: 9 hours 17 minutes
- Year published: 2018
- Recommended level: Beginners, intermediate, and advanced readers
- Reviews and ratings:
- 4.4 (Audible)
AI Superpowers: China, Silicon Valley, and the New World Order is a riveting audiobook that shocks listeners with the unexpected consequences of AI development.
Through some interesting actual AI events, Dr Lee touches upon the fierce competition between the United States and China over AI inventions. The book dwells on the New World Order conspiracy theory and whether some AI innovations are leading to an actual world government.
The author sheds light on the jobs affected and those that artificial intelligence would enhance. Plus, he predicts we’re on the verge of an AI economy.
“AI never allows us to understand ourselves truly; it will not be because these algorithms captured the mechanical essence of the human intelligence. It will be because they liberated us to forget about optimizations and to instead focus on what truly makes us human: loving and being loved.” – Kai-Fu Lee
Key takeaways
- China has a unique AI ecosystem marked by fierce competition, a high level of entrepreneurial spirit, a large pool of talented engineers, a supportive government, and a willingness to take risks
What readers say
“This is one of the most important books of 2018. You should read this if you are engaged in any business that is or will use machine learning (intense learning).”
6. The Society of Mind
About the book
- Author(s): Marvin Minsky
- Number of pages: 336
- Estimated reading time: 11 hours
- Year published: 1988
- Recommended level: Advanced readers
- Reviews and ratings:
- 4.7/5 210 reviews
The Society of Mind makes an engaging inquiry into the human mind through one-page essays, each introducing a new idea.
The book covers in-depth concepts of computer vision, ML networks, prediction machines, robotic manipulation, and commonsense reasoning.
We cover this AI book because it has implications for the field of artificial intelligence, encouraging readers to think about building systems with modular and hierarchical structures that mimic the diverse functions of the human mind in the modern society.
“The ‘chunks’ of reasoning, language, memory, and ‘perception’ ought to be more extensive and more structured, and their factual and procedural contents must be more intimately connected to explain the apparent power and speed of mental activities.” – Marvin Minsky
Key takeaways
- Intelligence emerges from the interactions and cooperations among the myriad agents in the mind. It is not confined to a single centralized model but rather arises from the distributed efforts of these agents
What readers say
“The author, one of the undisputed fathers of AI, sets out to provide an abstract model of how the human mind works. His thesis is that our minds consist of a huge aggregation of tiny mini-minds or agents that have evolved to perform particular tasks.”
7. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
About the book
- Author(s): Pedro Domingos
- Number of pages: 352
- Estimated reading time: 11 hours
- Year published: 2018
- Recommended level: Advanced and intermediate readers
- Reviews and ratings:
- 4.3/5 (Amazon)
- 3.7/5 (Goodreads)
One of the best books on AI, The Master Algorithm, explains how ML networks work by learning from data clusters in digital technology. These algorithms observe our actions online, imitate us, and experiment with the available information.
The book’s premise is that most AI research labs and universities are trying to invent a new foundation of a learning algorithm to discover any knowledge from data and do what we want. The author argues that no single master algorithm can predict any problem domain.
You learn more about the learning machines that power Amazon, Google, and other technology companies.
“If you’re a lazy and not-too-bright computer scientist, machine learning is ideal because learning algorithms do all the work but let you take all the credit.” – Pedro Domingos
Key takeaways
- The book introduces the idea of a single, overarching learning algorithm called the Master Algorithm, capable of incorporating different approaches to machine learning
- You can categorize machine learning approaches into five tribes, each representing a different philosophy. They include symbolic logic, connectionist neural networks, evolutionary algorithms, Bayesian probability, and analogical reasoning. The Master Algorithm should cover the strength of each tribe
What readers say
“Pedro Domingos demystifies ML and shows how wondrous and exciting the future will be.”
8. Deep Learning
About the book
- Author(s): Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Number of pages: 800
- Estimated reading time: 23 hours
- Year published: 2016
- Recommended level: Advanced and intermediate readers
- Ratings:
- 4.3/5 (Amazon)
- 4.4/5 (Goodreads)
For undergraduate and graduate students planning a career in computation and machine learning, Deep Learning is a legitimate resource for learning complex concepts.
The book offers mathematical and conceptual background covering various subjects, including linear algebra, probability theory and information theory, numerical computation, and ML.
You will enjoy reading about how practitioners use ML learning in the industry, such as optimization, convolutional networks, sequence modelling, regularization, practical methodology, and deep feedforward.
“Neural networks can be much more expressive than most other models, but that expressiveness does not automatically result in an ability to learn the true underlying structure of the data.” – Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Key takeaways
- Deep feedforward networks, called multilayer perceptrons (MLPs), are the quintessential deep learning models. These networks define a mapping and learn the value of the parameters, resulting in the best function approximation
What readers say
“The AI bible… the text should be mandatory reading by all data computer scientists and ML practitioners to get a proper foothold in this rapidly growing area of next-gen technology.”
9. Life 3.0: Being Human in the Age of Artificial Intelligence
About the book
- Author(s): Max Tegmark
- Number of pages: 364
- Estimated reading time: 11 hours
- Year published: 2018
- Recommended level: Beginners, intermediate, and advanced readers
- Reviews and ratings:
- 4.4/5 (Amazon)
- 4/5 (Goodreads)
Among the Times Books of The Year, Life 3.0: Being Human in the Age of Artificial Intelligence asks whether superhuman intelligence will be our tool or god. The author takes you to the heart of the latest thinking about AI and helps separate myths from realities and utopias from dystopias.
Tegmark explains how automation can help us grow our prosperity without humankind losing purpose or income. He explores ways to ensure that future artificial intelligence systems perform tasks without malfunctioning or getting hacked.
“The alignment problem is the key challenge in building superintelligent AI – how to get a machine to understand what we want and help us achieve it, even if we don’t know how to specify that goal ourselves.” – Max Tegmark
Key takeaways
- The first stage of Life, Life 1.0, is biological; the second stage (Life 2.0) is cultural; the third stage (Life 3.0) is a form of technological life with the ability to design its software and hardware
- The role of consciousness in artificial intelligence and ethical implications of creating conscious machines
What readers say
“The fictional prologue of this non-fiction book frames the importance of managing the progress towards Artificial General Intelligence.”
10 Neural Networks and Deep Learning
About the book
- Author(s): Charu C. Aggarwal
- Number of pages: 553
- Estimated Reading time: 14.8 hours
- Year published: 2023
- Recommended level: Beginners, intermediate, and advanced readers
- Reviews and ratings:
- 4.1/5 13 reviews
Neural Networks and Deep Learning takes a modern approach to deep learning while touching on classical models. The author argues that the theory and blueprint of neural networks are essential for understanding complex subjects such as predictive analysis and neural architectures in different case studies.
What happens when neural network models perform better than off-the-shelf machine-learning models, and why is training these networks difficult?
You will learn how engineers create neural architectures for solving other problems. The author focuses on modern ML learning ideas like transformers, mechanisms, and pre-trained language models.
“An important aspect of neural networks is tightly integrated data storage and computations. For example, the states in a neural network are a type of transient memory, which behaves much like the ever-changing registers in the central processing unit of a computer.” – Charu C. Aggarwal
Key takeaways
- The strength of neural network models is also their biggest weakness because they often overfit the training data unless we design the learning process carefully
- Conventional ML methods use optimization and gradient-descent methods for learning parameterized models. Neural network systems are not different
What readers say
“This is one of the few academic-style books on deep learning, and it focuses on the fundamentals of the subject, including the theory and applications that power deep learning.”
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