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  2. ChatGPT - Wikipedia

    en.wikipedia.org/wiki/ChatGPT

    t. e. ChatGPT is a chatbot and virtual assistant developed by OpenAI and launched on November 30, 2022. Based on large language models (LLMs), it enables users to refine and steer a conversation towards a desired length, format, style, level of detail, and language.

  3. GPT-4o - Wikipedia

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

    Website. openai .com /index /hello-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 ...

  4. OpenAI - Wikipedia

    en.wikipedia.org/wiki/OpenAI

    They said that GPT-4 could also read, analyze or generate up to 25,000 words of text, and write code in all major programming languages. [196] Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the problems with earlier revisions. [197]

  5. Bing Chat vs. ChatGPT: Which AI Can Make You More Money? - AOL

    www.aol.com/bing-chat-vs-chatgpt-ai-195545131.html

    Bing Chat offers search and chat functions, while ChatGPT is only a chatbot. Bing Chat can be used to make money with all of the strategies mentioned in the section above, as you can use...

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

    www.aol.com/finance/money-ai-chat-gpt-earn...

    AI has the potential to speed up tasks like writing or web development. Right now, you're most likely to get rich from AI by programming AI systems. How much money can you make with AI?

  7. Generative pre-trained transformer - Wikipedia

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

    History Initial developments. Generative pretraining (GP) was a long-established concept in machine learning applications. 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.