1. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig:
A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
2. The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind by Marvin Minsky
In this mind-expanding book, scientific pioneer Marvin Minsky continues his groundbreaking research, offering a fascinating new model for how our minds work. He argues persuasively that emotions, intuitions, and feelings are not distinct things, but different ways of thinking.
3. Introduction to Artificial Intelligence by Philip C Jackson
Introduction to Artificial Intelligence presents an introduction to the science of reasoning processes in computers, and the research approaches and results of the past two decades. You’ll find lucid, easy-to-read coverage of problem-solving methods, representation and models, game playing, automated understanding of natural languages, heuristic search theory, robot systems, heuristic scene analysis and specific artificial-intelligence accomplishments. Related subjects are also included: predicate-calculus theorem proving, machine architecture, psychological simulation, automatic programming, novel software techniques, industrial automation and much more.
4. The Master Algorithm by Pedro Domingos
If data-ism is today’s rising philosophy, this book will be its bible. The quest for universal learning is one of the most significant, fascinating, and revolutionary intellectual developments of all time. A groundbreaking book, The Master Algorithm is the essential guide for anyone and everyone wanting to understand not just how the revolution will happen, but how to be at its forefront.
5. Machine Learning by Tom M. Mitchell
This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.
6. The Singularity is Near by Ray Kurzweil
One of CBS News’s Best Fall Books of 2005 • Among St Louis Post-Dispatch’s Best Nonfiction Books of 2005 • One of Amazon.com’s Best Science Books of 2005
A radical and optimistic view of the future course of human development from the bestselling author of How to Create a Mind and The Age of Spiritual Machines who Bill Gates calls “the best person I know at predicting the future of artificial intelligence”
7. The Hundred-Page Machine Learning Book by Andriy Burkov
This book is an excellent and brief overview of various ML techniques for solving supervised and unsupervised learning problems. The author covers every topic by providing just enough detail in a clear and concise way. Highly recommended for readers who want a solid understanding of ML in only 100-150 pages
8. How to Create a Mind: The Secret of Human Thought Revealed by Ray Kurzweil
Ray Kurzweil, in his much-anticipated How to Create a Mind, he takes this exploration to the next step: reverse-engineering the brain to understand precisely how it works, then applying that knowledge to create vastly intelligent machines.
9. Human + Machine: Reimagining Work in the Age of AIby Paul R. Daugherty
In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization–whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly–or to completely reimagine them. AI is changing all the rules of how companies operate.
10. Superintelligence: Paths, Dangers, Strategies by Nick Bostrom
This profoundly ambitious and original book breaks down a vast track of difficult intellectual terrain. After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom’s work nothing less than a reconceptualization of the essential task of our time.
11. Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
12. The Sentient Machine: The Coming Age of Artificial Intelligence by Amir Husain
“In The Sentient Machine, Husain prepares us for a brighter future; not with hyperbole about right and wrong, but with serious arguments about risk and potential” (Dr. Greg Hyslop, Chief Technology Officer, The Boeing Company). He addresses broad existential questions surrounding the coming of AI: Why are we valuable? What can we create in this world? How are we intelligent? What constitutes progress for us? And how might we fail to progress? Husain boils down complex computer science and AI concepts into clear, plainspoken language and draws from a wide variety of cultural and historical references to illustrate his points. Ultimately, Husain challenges many of our societal norms and upends assumptions we hold about “the good life.”
13. Artificial Intelligence: What Everyone Needs to Knowby Jerry Kaplan
The emergence of systems capable of independent reasoning and action raises serious questions about just whose interests they are permitted to serve, and what limits our society should place on their creation and use. Deep ethical questions that have bedeviled philosophers for ages will suddenly arrive on the steps of our courthouses. Can a machine be held accountable for its actions? Should intelligent systems enjoy independent rights and responsibilities, or are they simple property? Who should be held responsible when a self-driving car kills a pedestrian? Can your personal robot hold your place in line, or be compelled to testify against you? If it turns out to be possible to upload your mind into a machine, is that still you? The answers may surprise you.
14. Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat
Through profiles of tech visionaries, industry watchdogs, and groundbreaking AI systems, James Barrat’s Our Final Inventionexplores the perils of the heedless pursuit of advanced AI. Until now, human intelligence has had no rival. Can we coexist with beings whose intelligence dwarfs our own? And will they allow us to?
15. The AI Advantage: How to Put the Artificial Intelligence Revolution to Work by Thomas H. Davenport
In The AI Advantage, Thomas Davenport offers a guide to using artificial intelligence in business. He describes what technologies are available and how companies can use them for business benefits and competitive advantage. He cuts through the hype of the AI craze—remember when it seemed plausible that IBM’s Watson could cure cancer?—to explain how businesses can put artificial intelligence to work now, in the real world
16. Reinforcement Learning: An Introduction by Andrew Barto and Richard Sutton
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field’s intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.
17.The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity by Byron Reese
In The Fourth Age, Byron Reese makes the case that technology has reshaped humanity just three times in history:
– 100,000 years ago, we harnessed fire, which led to language.
– 10,000 years ago, we developed agriculture, which led to cities and warfare.
– 5,000 years ago, we invented the wheel and writing, which lead to the nation state.
18. AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee
In AI Superpowers, Kai-fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power.
Note: The above list of AI books are selected on the basis of their reviews on Amazon, social media influence, popularity and online mentions in AI domains
Please note: This is not a ranking article.
If you have any question or suggestion or if you want to suggest any book that we missed in this list then please email us at asif@marktechpost.com
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June 28, 2020 at 06:12AM
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Top Artificial Intelligence Books to Read in 2020 - MarkTechPost
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