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2. Generative artificial intelligence - Wikipedia

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

   In March 2020, 15.ai, created by an anonymous MIT researcher, was a free web application that could generate convincing character voices using minimal training data. [42] The platform is credited as the first mainstream service to popularize AI voice cloning ( audio deepfakes ) in memes and content creation , influencing subsequent developments ...

3. Text-to-image model - Wikipedia

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

   An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.

4. 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]

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   Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!

6. Prompt engineering - Wikipedia

   en.wikipedia.org/wiki/Prompt_engineering

   Prompt engineering is the process of structuring or crafting an instruction in order to produce the best possible output from a generative artificial intelligence (AI) model. [ 1 ] A prompt is natural language text describing the task that an AI should perform. [ 2 ]

7. Generative adversarial network - Wikipedia

   en.wikipedia.org/wiki/Generative_adversarial_network

   The idea of InfoGAN is to decree that every latent vector in the latent space can be decomposed as (,): an incompressible noise part , and an informative label part , and encourage the generator to comply with the decree, by encouraging it to maximize (, (,)), the mutual information between and (,), while making no demands on the mutual ...