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  2. GitHub Copilot - Wikipedia

    en.wikipedia.org/wiki/GitHub_Copilot

    GitHub Copilot. GitHub Copilot is a code completion tool developed by GitHub and OpenAI that assists users of Visual Studio Code, Visual Studio, Neovim, and JetBrains integrated development environments (IDEs) by autocompleting code. [ 1] Currently available by subscription to individual developers and to businesses, the generative artificial ...

  3. OpenAI Codex - Wikipedia

    en.wikipedia.org/wiki/OpenAI_Codex

    OpenAI Codex is an artificial intelligence model developed by OpenAI. It parses natural language and generates code in response. It powers GitHub Copilot, a programming autocompletion tool for select IDEs, like Visual Studio Code and Neovim. [ 1] Codex is a descendant of OpenAI's GPT-3 model, fine-tuned for use in programming applications.

  4. Comparison of video converters - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_video_converters

    Comparison of video converters. Video converters are computer programs that can change the storage format of digital video. They may recompress the video to another format in a process called transcoding, or may simply change the container format without changing the video format. The disadvantages of transcoding are that there is quality loss ...

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    mail.aol.com

    You can find instant answers on our AOL Mail help page. Should you need additional assistance we have experts available around the clock at 800-730-2563.

  6. Applications of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Applications_of_artificial...

    Artificial intelligence supported design of aircraft, [ 362] or AIDA, is used to help designers in the process of creating conceptual designs of aircraft. This program allows the designers to focus more on the design itself and less on the design process. The software also allows the user to focus less on the software tools.

  7. Generative pre-trained transformer - Wikipedia

    en.wikipedia.org/wiki/Generative_pre-trained...

    Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] [18] 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.

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