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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 ]

  3. 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 ]

  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. OpenAI o3 - Wikipedia

    en.wikipedia.org/wiki/OpenAI_o3

    OpenAI o3 is a generative pre-trained transformer (GPT) model developed by OpenAI as a successor to OpenAI o1. It is designed to devote additional deliberation time when addressing questions that require step-by-step logical reasoning.

  6. List of chatbots - Wikipedia

    en.wikipedia.org/wiki/List_of_chatbots

    A chatbot is a software application or web interface that is designed to mimic human conversation through text or voice interactions. [1] [2] [3] Modern chatbots are typically online and use generative artificial intelligence systems that are capable of maintaining a conversation with a user in natural language and simulating the way a human would behave as a conversational partner.

  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.