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  2. ChatGPT - Wikipedia

    en.wikipedia.org/wiki/ChatGPT

    ChatGPT is a generative artificial intelligence chatbot [2] [3] developed by OpenAI and launched in 2022. It is currently based on the GPT-4o large language model (LLM). ChatGPT can generate human-like conversational responses and enables users to refine and steer a conversation towards a desired length, format, style, level of detail, and language. [4]

  3. Generative artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Generative_artificial...

    Other scholars have disputed that GPT-4 reaches this threshold, calling generative AI "still far from reaching the benchmark of ‘general human intelligence’" as of 2023. [44] In 2023, Meta released an AI model called ImageBind which combines data from text, images, video, thermal data, 3D data, audio, and motion which is expected to allow ...

  4. GPT-4o - Wikipedia

    en.wikipedia.org/wiki/GPT-4o

    GPT-4o ("o" for "omni") is a multilingual, multimodal generative pre-trained transformer developed by OpenAI and released in May 2024. [1] GPT-4o is free, but with a usage limit that is five times higher for ChatGPT Plus subscribers. [ 2 ]

  5. Understanding images is just one way Chat GPT-4 goes ... - AOL

    www.aol.com/news/understanding-images-just-one...

    On the SAT reading and writing section, GPT-4 scored a 710 out of 800, 40 points higher than GPT-3.5. On the SAT math section, GPT-4 scored 700, marking a 110 point increase from GPT-3.5.

  6. Apple adds ChatGPT to iPhone in latest iOS update - AOL

    www.aol.com/news/apple-adds-chatgpt-iphone...

    The rollout is part of a wider deployment of artificial intelligence-powered tools in the latest updates to the operating systems for iPhones, iPads and Mac computers. The ChatGPT integration ...

  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] 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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