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2. 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 ...

3. Microsoft Copilot - Wikipedia

   en.wikipedia.org/wiki/Microsoft_Copilot

   Copilot key (at center) on a Lenovo Legion 7i laptop. Starting in 2024, this key replaces the menu key for licensed Windows-compatible keyboards. Microsoft Copilot is a generative artificial intelligence chatbot developed by Microsoft. Based on a large language model, it was launched in February 2023 as Microsoft's primary replacement for the ...

4. Microsoft Bing - Wikipedia

   en.wikipedia.org/wiki/Microsoft_Bing

   Microsoft Bing, commonly referred to as Bing, is a search engine owned and operated by Microsoft. The service traces its roots back to Microsoft's earlier search engines, including MSN Search, Windows Live Search, and Live Search. Bing offers a broad spectrum of search services, encompassing web, video, image, and map search products, all ...

5. 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.

6. AOL Mail

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   Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!

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.