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The book is intended to explain how computers reason and perceive, and introduce the field of artificial intelligence. It describes the field, both as a branch of engineering and as a science, providing a computational perspective. Ideas for representing knowledge, using knowledge, and building practical systems are provided.
Artificial Intelligence: A Guide for Thinking Humans is a 2019 nonfiction book by Santa Fe Institute professor Melanie Mitchell. [1] The book provides an overview of artificial intelligence (AI) technology, and argues that people tend to overestimate the abilities of artificial intelligence. [2] [3]
The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and computer vision.
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
Knowledge representation goes hand in hand with automated reasoning because one of the main purposes of explicitly representing knowledge is to be able to reason about that knowledge, to make inferences, assert new knowledge, etc. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system.
The book begins by positing a scenario in which AI has exceeded human intelligence and become pervasive in society. Tegmark refers to different stages of human life since its inception: Life 1.0 referring to biological origins, Life 2.0 referring to cultural developments in humanity, and Life 3.0 referring to the technological age of humans.
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This book is considered by some to mark the beginning of the AI winter of the 1970s, a failure of confidence and funding for AI. However, by the time the book came out, methods for training multilayer perceptrons by deep learning were already known (Alexey Ivakhnenko and Valentin Lapa, 1965; Shun'ichi Amari, 1967). [9]