11 Books on Artificial Intelligence That Will Make You Think

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Introduction to Artificial Intelligence

Author: Philip C Jackson

Philip Jackson’s book is an exploration into the computer’s mind, how it works now, and how it will work as AI developments surface. In this book, you will find accessible prose discussing the capabilities and limits of computer intelligence covering technical topics including problem-solving methods, automated understanding of natural languages, robot systems, and specific artificial-intelligence accomplishments. Join the discussion on machine architecture, psychological simulation, and automatic programming as you read. Jackson seeks to discover how computers can be made to act intelligently, bridging the gap from current computer intelligence to future advancements in AI.

Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies

Author: Steven Finlay

Steven Finlay provides a clear and comprehensive text designed for managers and businesses to understand the complicated world of AI and machine learning. You’ll learn to understand and are utilizing the concepts behind these technologies and how to merge them with your business. Managers will learn to maximize profits with lower costs with the aid of AI and machine learning. This book is the newest guide to the practical application of AI concepts to increase the profitability of your company in association with new technologies. Finlay’s writing is accessible to readers who don’t understand AI’s technical language or dense theories behind machine learning but is written so we readers can understand how it all works and how these complex technologies can work for us.

Thinking Machines: The Quest for Artificial Intelligence–and Where It’s Taking Us Next

Author: Luke Dormehl

In this book, Luke Dormehl has a unique take on AI and begins by discussing the genesis of AI technologies developed in the Cold War. Dormehl points out the present existence of AI in our everyday lives—cell phones navigating us to the nearest grocery store, or even telling us what we need to buy at the store—using our comfortability with AI as the foundation for his exploration into its future advancements. You will become aware of the present impact of machine intelligence and where AI can advance. Dormehl wants to discuss the fascinating and possible fearful future of AI in our day-to-day lives.

Superintelligence: Paths, Dangers, Strategies

Author: Nick Bostrom

The creation of the first super-intelligent computer in Nick Bostrom’s book produces an abundance of questions. What will happen when humans are no longer the most intelligent beings? Will superintelligence benefit or harm us? You and Bostrom will wrestle with thoughts on how our world would change if machines possessed dominant intelligence over us as we have enjoyed over animals. With machine learning on the rise and the competitive presence of AI looming, where will we fit on the grand scale of power? Tackle these questions and more as Bostrom explores the possible effects of AI on our future humanity and evolving intelligence. Recommended by Bill Gates.

Life 3.0: Being Human in the Age of Artificial Intelligence

Author: Max Tegmark

As technological advancements gain momentum, Artificial Intelligence becomes a hot topic of inquiry. With empowering prose, Max Tegmark pushes the conventional conversation from machines taking over the world to how our world will function with the eminent changes AI will bring. “Life 3.0” holds nothing back as Tegmark explores all the possibilities of AI to benefit our lives, without deflecting questions like, “How do we advance the abilities of AI without making human labor and intelligence obsolete?”. Tegmark takes a practical approach to questions like these and guides us through the uncertainty of the human experience existing with intelligent machines. Recommended by Elon Musk.

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Author: Pedro Domingos

In this book, Pedro Domingos unveils an ambitious drive behind his hypothesis to create a universal learning algorithm capable of learning all knowledge from all time: past, present, and future. He calls it “The Master Algorithm.” You will learn the essentials about the origins of machine learning before taking a metaphoric journey with Domingos as he explains both the possible creation and functionality of the Master Algorithm. With this achievement, all learning would become obtainable and all knowledge accessible.  Explore how learning machines work to power our smartphones and from here generates conversation on how a universal algorithm could use data to learn all knowledge from our history to our future. Recommended by Bill Gates.

Pattern Recognition and Machine Learning

Author: Chris Bishop

Chris Bishop’s new textbook is the first of its kind helping describe machine learning effectively with the application of graphic models. Learn about approximate inference algorithms, probability distribution models, and an introduction to fundamental probability theory. It is not necessary for the reader to have previous knowledge of pattern recognition or machine learning concepts. However, a comprehensive understanding of multivariate calculus and basic linear algebra is needed. Be the first to experience the Bayesian viewpoint applied to pattern recognition.

Deep Learning

Authors: Ian Goodfellow, Yoshua Bengio, & Aaron Courville

In their book, “Deep Learning,” Goodfellow, Bengio, and Courville introduce us to an innovative machine learning where computers learn through experience, rather than knowledge given. Discover how after first learning simple concepts, these computers learn to expand upon what they have learned to understand complicated concepts. In this text, you’ll find relevant concepts in linear algebra, probability theory, informational theory, machine learning, and more. The authors explain and provide deep learning methods used by different businesses in a variety of industries. Gain perspective on the evolving advancements of machine intelligence starting with your comprehensive understanding of “Deep Learning.”

Learning From Data

Authors: Yaser S. Abu-Mostafa,‎ Malik Magdon-Ismail,‎ Hsuan-Tien Lin

This book is a must-read for students or anyone wanting to know more about machine learning. These professors have collaborated to produce a complete introduction to machine learning designed to act as a short course.  Learn the essential fundamentals and core topics that every student must know as you progress through “Learning from Data”. Easily comprehend complex theories and data as you read theoretical and practical examples provided in a clear story like prose. The authors strived to make this text instructive and applicable. Gain free access to online materials on the newest advancements in machine intelligence technology constantly updated by the authors.
Additional resource: https://amlbook.com/

Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain

Author: Amit Konar

Konar’s book, “Artificial Intelligence and Soft Computing” is a comprehensive text accessible to a broad scope of readers. In this book, you’ll learn both traditional and modern aspects of AI and soft computing as you read clear, in-depth, and comprehensive text beginners can understand while exploring complicated and technical concepts. This book clearly articulates the progressing abilities of machines in knowledge acquisition, ability to reason, and more. Discover how computer brains are approaching the abilities of human brains and how advancements in AI could enable machines knowledge to surpass human intellect.

Reinforcement Learning: An Introduction

Authors: Richard S. Sutton & Andrew G. Barto

This book is a clear and accessible approach to AI gaining knowledge through reinforcement learning. Sutton and Barto introduce the fundamental aspects and algorithms behind reinforcement learning. A complete understanding of the basic concepts of probability is required of the reader. Learn about the history of reinforcement learning as you trace its origins to present-day applications and advancements. Explore current case studies on machine learnings process and consider the future of reinforcement learning on AI.