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  2. Automatic summarization - Wikipedia

    en.wikipedia.org/wiki/Automatic_summarization

    Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data.

  3. TL;DR: This AI summarizes research papers so you don ... - AOL

    www.aol.com/tl-dr-ai-summarizes-research...

    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 ...

  4. Meet the content creators harnessing AI —and how they ... - AOL

    www.aol.com/meet-content-creators-harnessing-ai...

    Helping in this regard is New York and London based Synthesia, a company whose AI tech is so advanced they can produce AI-generated versions of their clients that look, sound, and gesticulate just ...

  5. You.com - Wikipedia

    en.wikipedia.org/wiki/You.com

    On December 23, 2022, You.com was the first search engine to launch a ChatGPT-style chatbot with live web results alongside its responses. [25] [26] [12] Initially known as YouChat, [27] the chatbot was primarily based on the GPT-3.5 large language model and could answer questions, suggest ideas, [28] translate text, [29] summarize articles, compose emails, and write code snippets, while ...

  6. Multi-document summarization - Wikipedia

    en.wikipedia.org/wiki/Multi-document_summarization

    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.

  7. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...

  8. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Data covering the nonlinear relationships observed in a servo-amplifier circuit. Levels of various components as a function of other components are given. 167 Text Regression 1993 [160] [161] K. Ullrich UJIIndoorLoc-Mag Dataset Indoor localization database to test indoor positioning systems. Data is magnetic field based. Train and test splits ...

  9. 43 startups to bet your career on in 2025 - AOL

    www.aol.com/43-startups-bet-career-2025...

    The company has attracted major clients, including Fortune 500 firms, by focusing on data privacy, compliance, and domain-specific AI solutions, and its recently released AI model emphasizes ...