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  2. OpenAI - Wikipedia

    en.wikipedia.org/wiki/OpenAI

    As a leading organization in the ongoing AI boom, [7] OpenAI is known for the GPT family of large language models, the DALL-E series of text-to-image models, and a text-to-video model named Sora. [ 8 ] [ 9 ] Its release of ChatGPT in November 2022 has been credited with catalyzing widespread interest in generative AI .

  3. Generative artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Generative_artificial...

    Generative AI systems trained on sets of images with text captions include Imagen, DALL-E, Midjourney, Adobe Firefly, FLUX.1, Stable Diffusion and others (see Artificial intelligence art, Generative art, and Synthetic media). They are commonly used for text-to-image generation and neural style transfer. [54]

  4. GPT-3 - Wikipedia

    en.wikipedia.org/wiki/GPT-3

    Generative Pre-trained Transformer 3 (GPT-3) is a large language model released by OpenAI in 2020.. Like its predecessor, GPT-2, it is a decoder-only [2] transformer model of deep neural network, which supersedes recurrence and convolution-based architectures with a technique known as "attention". [3]

  5. GPT-2 - Wikipedia

    en.wikipedia.org/wiki/GPT-2

    The Gradient published an open letter to OpenAI requesting that they release the model publicly, comparing the threat posed by text-generation AI to the threat posed by the printing press, and giving Photoshop as an example of "a technology that has (thankfully) not destroyed modern society despite its potential for chaos": [22]

  6. OpenAI releases video creation tool

    www.aol.com/openai-releases-video-creation-tool...

    ChatGPT producer OpenAI rolled out a video creation tool this week that lets paying customers create short-length video clips through text prompts. OpenAI introduced Sora, a video generation model ...

  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.

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