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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. You can now use ChatGPT for free without a login - AOL

   www.aol.com/lifestyle/now-chatgpt-free-without...

   There’s no need to register for a login and password, and it’s totally free of charge. They made the announcement on X: We’re rolling out the ability to start using ChatGPT instantly ...

4. 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 ...

5. 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]

6. Understanding images is just one way Chat GPT-4 goes ... - AOL

   www.aol.com/news/understanding-images-just-one...

   The creators behind the increasingly popular ChatGPT tool unveiled a new version of the generative artificial intelligence (AI) tool, known as GPT-4, Tuesday. The updated version of OpenAI’s ...

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.