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  2. Google debuts powerful Gemini generative AI model in strike ...

    www.aol.com/finance/google-debuts-powerful...

    Google (GOOG, GOOGL) on Wednesday debuted its new Gemini generative AI model. The platform serves as Google’s answer to Microsoft-backed OpenAI’s GPT-4, and according to DeepMind CEO Demis ...

  3. 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.

  4. StyleGAN - Wikipedia

    en.wikipedia.org/wiki/StyleGAN

    The Style Generative Adversarial Network, or StyleGAN for short, is an extension to the GAN architecture introduced by Nvidia researchers in December 2018, [1] and made source available in February 2019.

  5. Text-to-image model - Wikipedia

    en.wikipedia.org/wiki/Text-to-image_model

    A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description. Text-to-image models began to be developed in the mid-2010s during the beginnings of the AI boom, as a result of advances in deep neural networks.

  6. Gemini (language model) - Wikipedia

    en.wikipedia.org/wiki/Gemini_(language_model)

    Multiple publications viewed this as a response to Meta and others open-sourcing their AI models, and a stark reversal from Google's longstanding practice of keeping its AI proprietary. [35] [36] [37] Google announced an additional model, Gemini 1.5 Flash, on May 14th at the 2024 I/O keynote. [38] Gemma 2 was released on June 27, 2024. [39]

  7. DeepDream - Wikipedia

    en.wikipedia.org/wiki/DeepDream

    The software is designed to detect faces and other patterns in images, with the aim of automatically classifying images. [10] However, once trained, the network can also be run in reverse, being asked to adjust the original image slightly so that a given output neuron (e.g. the one for faces or certain animals) yields a higher confidence score.