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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.
An example of an AI-generated conversation by NotebookLM (January 2025) NotebookLM (Google NotebookLM) is a research and note-taking online tool developed by Google Labs that uses artificial intelligence (AI), specifically Google Gemini , to assist users in interacting with their documents.
Thankfully, researchers at the Allen Institute for Artificial Intelligence have developed a new model to summarize text from scientific papers, and present it in a few sentences in the form of TL ...
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]
There are dozens of companies online offering AI-book writing services. My book was from BookByAnyone. When I contacted the chief executive Adir Mashiach, based in Israel, he told me he had sold ...
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.
Semantic Scholar is a research tool for scientific literature. It is developed at the Allen Institute for AI and was publicly released in November 2015. [2] Semantic Scholar uses modern techniques in natural language processing to support the research process, for example by providing automatically generated summaries of scholarly papers. [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 ...
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