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  2. ChatGPT - Wikipedia

    en.wikipedia.org/wiki/ChatGPT

    GPT-4o is twice as fast and costs half as much as GPT-4 Turbo. GPT-4o is free to all users within a usage limit, despite being more capable than the older model GPT-4, which is only available through paid subscriptions. The usage limit is five times higher for ChatGPT Plus subscribers than for free users. [95]

  3. GPT-4o - Wikipedia

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

    GPT-4o ( GPT-4 Omni) is a multilingual, multimodal generative pre-trained transformer designed by OpenAI. It was announced by OpenAI's CTO Mira Murati during a live-streamed demo on 13 May 2024 and released the same day. [ 1] GPT-4o is free, but with a usage limit that is 5 times higher for ChatGPT Plus subscribers. [ 2]

  4. Sora (text-to-video model) - Wikipedia

    en.wikipedia.org/wiki/Sora_(text-to-video_model)

    e. Sora is an upcoming generative artificial intelligence model developed by OpenAI, that specializes in text-to-video generation. The model generates short video clips corresponding to prompts from users. Sora can also extend existing short videos. As of August 2024 it is unreleased and not yet available to the public.

  5. GPT-J - Wikipedia

    en.wikipedia.org/wiki/GPT-J

    GPT-J. GPT-J or GPT-J-6B is an open-source large language model (LLM) developed by EleutherAI in 2021. [1] As the name suggests, it is a generative pre-trained transformer model designed to produce human-like text that continues from a prompt. The optional "6B" in the name refers to the fact that it has 6 billion parameters.

  6. Make Money With AI and ChatGPT: How To Earn $1,000 a Month - AOL

    www.aol.com/money-ai-chat-gpt-earn-195918119.html

    AI and ChatGPT do not offer get-rich-quick schemes. But if you are willing to put in the time and couple ChatGPT with your other skills, you can easily earn $1,000 per month or more. Here’s a ...

  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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