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The Reddit bot "autotldr", [31] created in 2011 summarizes news articles in the comment-section of reddit posts. It was found to be very useful by the reddit community which upvoted its summaries hundreds of thousands of times. [32] The name is reference to TL;DR − Internet slang for "too long; didn't read". [33] [34]
Reading scientific papers is a tough job. Thankfully, researchers at the Allen Institute for Artificial Intelligence have developed a new model to summarize text from scientific papers, and ...
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]
These agents can interact with users, their environment, or other agents. AI agents are used in various applications, including virtual assistants, chatbots, autonomous vehicles, game-playing systems, and industrial robotics. AI agents operate within the constraints of their programming, available computational resources, and hardware limitations.
[6] [7] At the time, the focus of the research was on improving Seq2seq techniques for machine translation, but the authors go further in the paper, foreseeing the technique's potential for other tasks like question answering and what is now known as multimodal Generative AI. [1] The paper's title is a reference to the song "All You Need Is ...
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.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
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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related to: ai that summarizes long articles apa reference