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  2. OpenAI o1 - Wikipedia

    en.wikipedia.org/wiki/OpenAI_o1

    OpenAI o1 is a generative pre-trained transformer (GPT). A preview of o1 was released by OpenAI on September 12, 2024. o1 spends time "thinking" before it answers, making it better at complex reasoning tasks, science and programming than GPT-4o. [1] The full version was released to ChatGPT users on December 5, 2024. [2]

  3. GPT-1 - Wikipedia

    en.wikipedia.org/wiki/GPT-1

    The GPT-1 architecture was a twelve-layer decoder-only transformer, using twelve masked self-attention heads, with 64-dimensional states each (for a total of 768). Rather than simple stochastic gradient descent , the Adam optimization algorithm was used; the learning rate was increased linearly from zero over the first 2,000 updates to a ...

  4. 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]

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

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

    How To Make Money With AI: 4 Ways To Earn $1,000. Of course, ChatGPT is not the only AI platform out there. There are many other ways to make money with AI that don’t involve ChatGPT. Build Websites

  6. 01.AI - Wikipedia

    en.wikipedia.org/wiki/01.AI

    01.AI was founded in March 2023 by former Microsoft and Google executive and Sinovation Ventures co-founder Kai-Fu Lee. [1] In Chinese 01.AI is known as 零一万物 in Chinese, which means "zero-one, everything" and alludes to a passage from the Taoist text Tao Te Ching. [2] External backers of 01.AI include Alibaba Group and Xiaomi. [3]

  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.