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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 ...
Throughout the book, it is suggested that each different tribe has the potential to contribute to a unifying "master algorithm". Towards the end of the book the author pictures a "master algorithm" in the near future, where machine learning algorithms asymptotically grow to a perfect understanding of how the world and people in it work. [1]
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.
ML involves the study and construction of algorithms that can learn from and make predictions on data. [3] These algorithms operate by building a model from a training set of example observations to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.
A Human Algorithm: How Artificial Intelligence Is Redefining Who We Are is a 2019 non-fiction book by American international human rights attorney Flynn Coleman. It argues that, in order to manage the power shift from humans to increasingly advanced artificial intelligence, it will be necessary to instill human values into artificial intelligence, and to proactively develop oversight mechanisms.
Artificial Intelligence (AI) is a university textbook on artificial intelligence, written by Patrick Henry Winston. It was first published in 1977, and the third edition of the book was released in 1992. [1] It was used as the course textbook for MIT course 6.034. [2]
Lazy evaluation and the list and LogicT monads make it easy to express non-deterministic algorithms, which is often the case. Infinite data structures are useful for search trees. The language's features enable a compositional way to express algorithms. Working with graphs is however a bit harder at first because of functional purity.