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  2. Audio deepfake - Wikipedia

    en.wikipedia.org/wiki/Audio_deepfake

    An audio deepfake (also known as voice cloning or deepfake audio) is a product of artificial intelligence [1] used to create convincing speech sentences that sound like specific people saying things they did not say.

  3. StyleGAN - Wikipedia

    en.wikipedia.org/wiki/StyleGAN

    A direct predecessor of the StyleGAN series is the Progressive GAN, published in 2017. [9]In December 2018, Nvidia researchers distributed a preprint with accompanying software introducing StyleGAN, a GAN for producing an unlimited number of (often convincing) portraits of fake human faces.

  4. 15.ai - Wikipedia

    en.wikipedia.org/wiki/15.ai

    15.ai was designed and created by an anonymous research scientist affiliated with the Massachusetts Institute of Technology known by the alias 15. [38] According to posts made by its developer on Hacker News, 15.ai costs several thousands of dollars per month to operate; they are able to support the project due to a successful startup exit. [39]

  5. Generative artificial intelligence - Wikipedia

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

    Generative AI features have been integrated into a variety of existing commercially available products such as Microsoft Office (Microsoft Copilot), [69] Google Photos, [70] and the Adobe Suite (Adobe Firefly). [71] Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA [72] language model.

  6. Electric generator - Wikipedia

    en.wikipedia.org/wiki/Electric_generator

    It is also known as a unipolar generator, acyclic generator, disk dynamo, or Faraday disc. The voltage is typically low, on the order of a few volts in the case of small demonstration models, but large research generators can produce hundreds of volts, and some systems have multiple generators in series to produce an even larger voltage. [ 17 ]

  7. DALL-E - Wikipedia

    en.wikipedia.org/wiki/DALL-E

    DALL·E's model is a multimodal implementation of GPT-3 [22] with 12 billion parameters [5] which "swaps text for pixels," trained on text–image pairs from the Internet. [23] In detail, the input to the Transformer model is a sequence of tokenized image caption followed by tokenized image patches.

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