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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]
AIMA gives detailed information about the working of algorithms in AI. 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 ...
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]
It is unknown whether human-level artificial intelligence will arrive in a matter of years, later this century, or not until future centuries. Regardless of the initial timescale, once human-level machine intelligence is developed, a "superintelligent" system that "greatly exceeds the cognitive performance of humans in virtually all domains of interest" would most likely follow surprisingly ...
"Strategy is not driven by future intent alone. It is the gap between today’s reality and intent for the future that is critical." [15] Scenario planning is a practical application for incorporating "thinking in time" into strategy making. [36] Hypothesis driven, ensuring that both creative and critical thinking are incorporated into strategy ...
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The Alignment Problem: Machine Learning and Human Values is a 2020 non-fiction book by the American writer Brian Christian.It is based on numerous interviews with experts trying to build artificial intelligence systems, particularly machine learning systems, that are aligned with human values.
The original GAN method is based on the GAN game, a zero-sum game with 2 players: generator and discriminator. The game is defined over a probability space (,,), The generator's strategy set is the set of all probability measures on (,), and the discriminator's strategy set is the set of measurable functions : [,].
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