Top 10 Artificial Intelligence AI Books in 2025
The rapid advancements in artificial intelligence (AI) are reshaping industries and transforming the global workforce. According to a McKinsey report, approximately 15 percent of the global workforce, or about 400 million workers, could be displaced by automation between 2016 and 2030. This monumental shift underscores the importance of understanding AI and its implications. Whether you're a seasoned professional or a curious newcomer, delving into the intricacies of AI is essential. To help you navigate this evolving landscape, we've curated a list of the top 10 books on AI. These insightful reads will equip you with the knowledge to stay ahead in an increasingly automated world.
1. "Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom
via Amazon
"Superintelligence" delves into the intriguing realm of what occurs when machines surpass human intelligence. Nick Bostrom raises pivotal questions: Will artificial agents become humanity's saviors or lead to its demise? Through his exploration, Bostrom lays a profound foundation for comprehending the trajectory of intelligent life and the future of humanity.
The human brain possesses unique capabilities that set our species apart from others, granting us a dominant position in the world. However, if machine intelligence were to surpass human intelligence, the resulting superintelligence could wield unprecedented power, potentially beyond our ability to control. Similar to how the fate of gorillas now hinges more on human actions than on the species itself, the destiny of humankind would be intricately tied to the actions of this machine superintelligence.
Yet, humans possess a crucial advantage: we have the opportunity to take the initial steps. Is it feasible to create a seed Artificial Intelligence, engineering conditions to ensure a survivable intelligence explosion? How can we orchestrate a controlled transition?
Bostrom's work embarks on a profoundly ambitious and original journey, navigating complex intellectual terrain. Through an enthralling exploration that pushes the boundaries of our understanding of humanity and intelligent life's future, Bostrom offers nothing short of a reimagining of our era's most essential task.
2. "An Introduction to Universal Artificial Intelligence (Chapman & Hall/CRC Artificial Intelligence and Robotics Series) 1st Edition" by Marcus Hutter
via Amazon
An Introduction to Universal Artificial Intelligence provides the formal underpinning of what it means for an agent to act intelligently in an unknown environment. First presented in Universal Algorithmic Intelligence (Hutter, 2000), UAI offers a framework in which virtually all AI problems can be formulated, and a theory of how to solve them. UAI unifies ideas from sequential decision theory, Bayesian inference, and algorithmic information theory to construct AIXI, an optimal reinforcement learning agent that learns to act optimally in unknown environments. AIXI is the theoretical gold standard for intelligent behavior.
The book covers both the theoretical and practical aspects of UAI. Bayesian updating can be done efficiently with context tree weighting, and planning can be approximated by sampling with Monte Carlo tree search. It provides algorithms for the reader to implement, and experimental results to compare against. These algorithms are used to approximate AIXI. The book ends with a philosophical discussion of Artificial General Intelligence: Can super-intelligent agents even be constructed? Is it inevitable that they will be constructed, and what are the potential consequences?
This text is suitable for late undergraduate students. It provides an extensive chapter to fill in the required mathematics, probability, information, and computability theory background.
3. "Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)" by Kevin P. Murphy
via Amazon
This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.
Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
4. "Artificial Intelligence in Higher Education and Scientific Research: Future Development (Bridging Human and Machine: Future Education with Intelligence) 2023rd Edition" by Fatima Roumate
via Amazon
This book explains the interaction between artificial intelligence and higher education. It explores artificial intelligence’s tangible and intangible impact on higher education and scientific research and discusses how higher education and scientific research enhance the progress of artificial intelligence technologies. Based on systematic analysis with a multidisciplinary approach and a combination of theory and practice, the book brings original perspectives from the massive use of artificial intelligence in higher education and scientific research since the appearance of COVID-19. This book also discusses ethics in artificial intelligence, taking into consideration the recommendation on ethics of artificial intelligence adopted by UNESCO. This book explains the importance of technological sovereignty and new strategies to face current and future challenges related to e-learning, deep learning, and machine learning.
5. "AI Literacy Fundamentals: Helping You Join the AI Conversation" by Ben Jones
via Amazon
Feeling overwhelmed by AI? It's not you—it's the breakneck speed of technological progress. To quickly get into the AI conversation, you need a clear and simple foundation of knowledge to build on. This book is a friendly primer on the basic concepts of AI, how it's already snuck into our daily lives, and what we need to know to prepare for the future.
Ben Jones, an expert at breaking down technical concepts from teaching thousands of people the basics of data literacy, lays out everything you need to know to join the AI conversation, from the history of AI to the deep learning revolution happening today. This technology is here to stay. Time for you to pull a seat up to the table.
6. "LLM Prompt Engineering For Developers: The Art and Science of Unlocking LLMs' True Potential" by Aymen El Amri
via Amazon
A practical approach to Prompt Engineering for developers. Dive into the world of Prompt Engineering agility, optimizing your prompts for dynamic LLM interactions. Learn with hands-on examples from the real world and elevate your developer experience with LLMs. Discover how the right prompts can revolutionize your interactions with LLMs.
In "LLM Prompt Engineering For Developers," we take an exhaustive journey into the world of LLMs and the art of crafting effective prompts for them.
The guide starts by laying the foundation, exploring the evolution of Natural Language Processing (NLP) from its early days to the sophisticated LLMs we interact with today. You will dive deep into the complexities of models such as GPT models, understanding their architecture, capabilities, and nuances.
As we progress, this guide emphasizes the importance of effective prompt engineering and its best practices. While LLMs like ChatGPT (GPT-3.5 and GPT-4) are powerful, their full potential is only realized when they are communicated with effectively. This is where prompt engineering comes into play. It's not simply about asking the model a question; it's about phrasing, context, and understanding the model's logic.
7. "Artificial Intelligence: A Modern Approach (Pearson Series in Artifical Intelligence) 4th Edition" by Stuart Russell and Peter Norvig
via Amazon
The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence
The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
8. "Cambrian Intelligence: The Early History of the New AI 1st Edition" by Rodney A Brooks
via Amazon
Until the mid-1980s, AI researchers assumed that an intelligent system doing high-level reasoning was necessary for the coupling of perception and action. In this traditional model, cognition mediates between perception and plans of action. Realizing that this core AI, as it was known, was illusory, Rodney A. Brooks turned the field of AI on its head by introducing the behavior-based approach to robotics. The cornerstone of behavior-based robotics is the realization that the coupling of perception and action gives rise to all the power of intelligence and that cognition is only in the eye of an observer. Behavior-based robotics has been the basis of successful applications in entertainment, service industries, agriculture, mining, and the home. It has given rise to both autonomous mobile robots and more recent humanoid robots such as Brooks' Cog.
This book represents Brooks' initial formulation of and contributions to the development of the behavior-based approach to robotics. It presents all of the key philosophical and technical ideas that put this "bottom-up" approach at the forefront of current research in not only AI but all of cognitive science.
9. "Introduction to Deep Learning for Healthcare 1st ed. 2021 Edition" by Cao Xiao and Jimeng Sun
via Amazon
This comprehensive textbook delves into the realm of deep learning models and their significant applications within the healthcare sector. It places a strong emphasis on leveraging extensive health data and employing deep learning models proficiently for effective data modeling.
Healthcare data, particularly electronic health records (EHRs), have witnessed widespread adoption and have significantly transformed healthcare delivery in recent times. EHRs offer a pivotal advantage by capturing comprehensive patient encounters encompassing rich multi-modality data. This data comprises both structured information, such as medical codes for diagnoses, lab results, and medication details, as well as unstructured data, including clinical notes, medical imaging data like X-rays and MRIs, and time-series data such as ECGs and EEGs. Moreover, the proliferation of wearable sensors has led to the accumulation of self-generated patient data, further enriching healthcare datasets. The authors explore various deep learning case studies across these diverse data types.
Deep learning models, a subset of neural network methods with numerous layers, have demonstrated prowess in extracting intricate features from raw data. Their application in healthcare represents a natural and promising progression, yielding numerous successful outcomes. The textbook delves into a plethora of deep learning architectures, including deep neural networks, convolutional neural networks, recurrent neural networks, embedding methods, autoencoders, attention models, graph neural networks, memory networks, and generative models. Each model is illustrated through concrete healthcare case studies, ranging from clinical predictive modeling and readmission prediction to x-ray classification, ECG diagnosis, and medication recommendation, among others.
Tailored for graduate-level students with a focus on deep learning methodologies and their healthcare applications, this textbook serves as an invaluable resource for understanding core concepts in deep learning and their practical implementations. Additionally, researchers in the field will find it immensely beneficial for advancing their investigations and contributing to the evolving landscape of deep learning in healthcare.
10. "Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms" by Jeff Heaton
via Amazon
Several AI books are available, but many require a foundational understanding. Jeff Heaton's "Artificial Intelligence for Humans: Volume 1" aims to fill this gap in an accessible manner.
Within its pages, readers gain insight into fundamental AI algorithms, particularly within the realm of machine learning. Volume 1 introduces concepts such as learning within computer networks and various types of machine learning. From supervised to unsupervised learning, the author navigates through essential techniques like regression and clustering, empowering readers to construct and train expansive learning models.
As Jeff Heaton notes, "Computer-based neural networks differ from the human brain in their lack of general-purpose computation. They are specialized for specific tasks."
