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Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
Co-founders of the company Kidaptive, Dylan Arena, (left) chief learning officer and P.J. Gunsangar, CEO with the Leo's Pad app dispayed on an iPad at their headquarters on Wednesday Nov. 21, 2012 ...
Leading textbook company Pearson has outfitted its digital textbooks—specifically 50 science titles, such as Intro to Biology and Intro to Chemistry—with generative AI study tools. As of this ...
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 ...
This table lists (just about) every topic that appears in the title of a section or in a chapter summary of Russell & Norvig (2003), the most popular AI textbook. Information for the other textbooks is based on their tables of contents, available online. Several topics appear more than once, in different contexts.
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]
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]
Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. The resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents.
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