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  2. NovelAI - Wikipedia

    en.wikipedia.org/wiki/NovelAI

    NovelAI is an online cloud-based, SaaS model, and a paid subscription service for AI-assisted storywriting [2] [3] [4] and text-to-image synthesis, [5] originally launched in beta on June 15, 2021, [6] with the image generation feature being implemented later on October 3, 2022.

  3. Universal Paperclips - Wikipedia

    en.wikipedia.org/wiki/Universal_Paperclips

    All the while, the AI develops more upgrades to quicken the transformation of Earth's remaining matter. After it has all has been converted to paperclips, the AI sets its sights on all available matter in the universe. In the final act, the player launches self-replicating probes into the cosmos to consume and convert all matter into paperclips.

  4. 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), [85] Google Photos, [86] and the Adobe Suite (Adobe Firefly). [87] Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA [88] language model.

  5. Sora (text-to-video model) - Wikipedia

    en.wikipedia.org/wiki/Sora_(text-to-video_model)

    Several other text-to-video generating models had been created prior to Sora, including Meta's Make-A-Video, Runway's Gen-2, and Google's Lumiere, the last of which, as of February 2024, is also still in its research phase. [3]

  6. Attention Is All You Need - Wikipedia

    en.wikipedia.org/wiki/Attention_Is_All_You_Need

    [6] [7] At the time, the focus of the research was on improving Seq2seq techniques for machine translation, but the authors go further in the paper, foreseeing the technique's potential for other tasks like question answering and what is now known as multimodal Generative AI. [1] The paper's title is a reference to the song "All You Need Is ...

  7. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    The original Transformer paper reported using a learned positional encoding, [70] but finding it not superior to the sinusoidal one. [1] Later, [ 71 ] found that causal masking itself provides enough signal to a Transformer decoder that it can learn to implicitly perform absolute positional encoding without the positional encoding module.