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Top rated artificial intelligence and semantics books

Here are some top-rated artificial intelligence and semantics books:

Artificial Intelligence:

"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: A comprehensive guide to deep learning techniques and their applications.
"Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig: A widely used textbook that covers the basics of AI, including machine learning, natural language processing, and computer vision.
"Life 3.0: Being Human in the Age of Artificial Intelligence" by Max Tegmark: A book that explores the possibilities and challenges of AI and its potential impact on human society.
"Human + Machine: Reimagining Work in the Age of AI" by Paul R. Daugherty and H. James Wilson: A book that focuses on the practical applications of AI in business and industry.
"The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World" by Pedro Domingos: A book that explores the concept of a "master algorithm" that can learn from data and apply it to a wide range of problems.

Semantics:

"Semantics: An Introduction to Meaning in Language" by Adrian Akmajian: A textbook that introduces the basics of linguistic semantics, including meaning, reference, and compositionality.
"The Cambridge Handbook of Semantics" edited by Maria Aloni and Paul Dekker: A comprehensive handbook that covers various topics in semantics, including lexical semantics, compositional semantics, and semantic theory.
"Word Meaning and Montague Grammar" by David Dowty: A book that applies Montague grammar to the study of word meaning and semantic composition.
"Semantics in Generative Grammar" by Irene Heim and Angelika Kratzer: A book that explores the relationship between semantics and generative grammar.
"Formal Semantics: The Essential Readings" edited by Paul Portner and Barbara H. Partee: A collection of classic papers on formal semantics, including works by Richard Montague, Hans Kamp, and Barbara Partee.

Natural Language Processing and AI:

"Speech and Language Processing" by Dan Jurafsky and James H. Martin: A textbook that covers the basics of natural language processing, including speech recognition, language modeling, and machine translation.
"Natural Language Processing (almost) from Scratch" by Collobert et al.: A paper that presents a neural network-based approach to natural language processing.
"Deep Learning for Natural Language Processing" by Yoav Goldberg: A book that applies deep learning techniques to natural language processing tasks.
"Introduction to Information Retrieval" by Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze: A textbook that covers the basics of information retrieval, including search engines and text retrieval.
"Natural Language Understanding" by James Allen: A book that explores the challenges and opportunities of natural language understanding, including question answering, text summarization, and dialogue systems.

Note: The ratings of these books can vary depending on the source and the audience. These books are generally well-regarded in the field, but it's always a good idea to read reviews and summaries to determine which ones are the best fit for your interests and needs.

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