Top AI books curated by experts

In this richly illustrated book, key neural network learning algorithms are explained informally first, followed by detailed mathematical analyses. Topics include both historically important neural networks (e.g. perceptrons), and modern deep neural networks (e.g. generative adversarial networks…

Artificial Intelligence Basics has arrived to equip you with a fundamental, timely grasp of AI and its impact. Author Tom Taulli provides an engaging, non-technical introduction to important concepts such as machine learning, deep learning, natural language processing (NLP), robotics, and more.<…

The society of mind theory views the human mind and any other naturally evolved cognitive systems as a vast society of individually simple processes known as agents. These processes are the fundamental thinking entities from which minds are built, and together produce the many abilities we attri…

What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? Drawing on more than a decade of research, award‑winning scholar Kate Crawford reveals how AI is a technology of extraction: from the mi…

A historical study of Chile's twin experiments with cybernetics and socialism, and what they tell us about the relationship of technology and politics. In Cybernetic Revolutionaries, Eden Medina tells the history of two intersecting utopian visions, one political and one technological.

AI 2041 by Kai-Fu Lee and Chen Qiufan explores how artificial intelligence will shape the world in the next two decades. They present scenarios where AI could help solve some of our biggest problems, while also acknowledging the challenges and ethical implications of this technology.

Reinforcement learning is an autonomous, self-teaching system that essentially learns by trial and error. It performs actions with the aim of maximizing rewards, or in other words, it is learning by doing in order to achieve the best outcomes.

Knowledge representation and reasoning is the foundation of artificial intelligence, declarative programming, and the design of knowledge-intensive software systems capable of performing intelligent tasks. Using logical and probabilistic formalisms based on answer set programming (ASP) and actio…

The alignment problem describes the problems associated with building powerful artificial intelligence systems that are aligned with their operators. The concept was popularised by Brian Christian in his book The Alignment Problem: Machine Learning and Human Values

Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions.

Trace the development of Machine Learning from the early days of a computer learning how to play checkers, to machines able to beat world masters in chess and go. Understand how large data is so important to Machine Learning, and how the collection of massive amounts of data provides Machine Lea…

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification.

In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the lea…

AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts…