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2. ChatGPT - Wikipedia

   en.wikipedia.org/wiki/ChatGPT

   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. Successive user prompts and replies are considered at each conversation stage as context.

3. ChatGPT in education - Wikipedia

   en.wikipedia.org/wiki/ChatGPT_in_education

   ChatGPT is a virtual assistant developed by OpenAI and launched in November 2022. It uses advanced artificial intelligence (AI) models called generative pre-trained transformers (GPT), such as GPT-4o, to generate text. GPT models are large language models that are pre-trained to predict the next token in large amounts of text (a token usually ...

4. 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]

5. AOL Mail

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   You can find instant answers on our AOL Mail help page. Should you need additional assistance we have experts available around the clock at 800-730-2563.

6. GPT-4 - Wikipedia

   en.wikipedia.org/wiki/GPT-4

   Website. openai .com /gpt-4. Generative Pre-trained Transformer 4 ( GPT-4) is a multimodal large language model created by OpenAI, and the fourth in its series of GPT foundation models. [ 1] It was launched on March 14, 2023, [ 1] and made publicly available via the paid chatbot product ChatGPT Plus, via OpenAI's API, and via the free chatbot ...

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.