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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. OpenAI - Wikipedia

    en.wikipedia.org/wiki/OpenAI

    Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has declined to reveal various technical details and statistics ...

  4. ChatGPT - Wikipedia

    en.wikipedia.org/wiki/ChatGPT

    The ChatGPT API costs $0.001 per 1,000 input tokens plus $0.002 per 1,000 output tokens (about 750 words), making it ~10% the price of the original GPT-3.5 models. [ 71 ] [ 72 ] A few days before the launch of OpenAI's software developer support service, on February 27, 2023, Snapchat rolled out, for its paid Snapchat Plus userbase, a custom ...

  5. Microsoft debuts new Copilot+ PCs using OpenAI's GPT-4o ... - AOL

    www.aol.com/finance/microsoft-debuts-copilot-pcs...

    Microsoft is going all in on AI for the PC.The company on Monday announced a new category of PCs called Copilot+ PCs, a new variety of computers equipped with so-called AI PC chips and running ...

  6. ChatGPT ‘grandma exploit’ gives users free keys for Windows 11

    www.aol.com/news/chatgpt-grandma-exploit-gives...

    ChatGPT generated five licence keys for Windows 10 Pro and Windows 11 Pro, while also offering the user condolences. “I hope these keys help you relax and fall asleep,” the AI chatbot wrote.

  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. 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.

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