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  2. ChatGPT-4 vs. 3.5: Which Version Offers More Money ... - AOL

    www.aol.com/chatgpt-4-vs-3-5-170413903.html

    For starters, ChatGPT-4 is multimodal (working with both imagers and text) and more “steerable” than ChatGPT-3.5 in that it can take multiple prompts in greater numbers, which allows it to ...

  3. 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]

  4. GPT-4o - Wikipedia

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

    Sam Altman noted on 15 May 2024 that GPT-4o's voice-to-voice capabilities were not yet integrated into ChatGPT, and that the old version was still being used. [9] This new mode, called Advanced Voice Mode, is currently in limited alpha release [10] and is based on the 4o-audio-preview. [11] On 1 October 2024, the Realtime API was introduced. [12]

  5. Microsoft Copilot - Wikipedia

    en.wikipedia.org/wiki/Microsoft_Copilot

    When Microsoft demonstrated Bing Chat to journalists, it produced several hallucinations, including when asked to summarize financial reports. [35] The new Bing was criticized in February 2023 for being more argumentative than ChatGPT, sometimes to an unintentionally humorous extent.

  6. OpenAI - Wikipedia

    en.wikipedia.org/wiki/OpenAI

    The app supports chat history syncing and voice input (using Whisper, OpenAI's speech recognition model). [255] [254] [256] In September 2023, OpenAI announced that ChatGPT "can now see, hear, and speak". ChatGPT Plus users can upload images, while mobile app users can talk to the chatbot. [257] [258]

  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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