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2. Generative artificial intelligence - Wikipedia

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

   Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...

3. Runway (company) - Wikipedia

   en.wikipedia.org/wiki/Runway_(company)

   Runway AI, Inc. (also known as Runway and RunwayML) is an American company headquartered in New York City that specializes in generative artificial intelligence research and technologies. [1] The company is primarily focused on creating products and models for generating videos, images, and various multimedia content.

4. List of programming languages for artificial intelligence

   en.wikipedia.org/wiki/List_of_programming...

   In the context of AI, it is particularly used for embedded systems and robotics. Libraries such as TensorFlow C++, Caffe or Shogun can be used. [1] JavaScript is widely used for web applications and can notably be executed with web browsers. Libraries for AI include TensorFlow.js, Synaptic and Brain.js. [6]

5. 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.

6. Adobe Firefly - Wikipedia

   en.wikipedia.org/wiki/Adobe_Firefly

   Adobe Firefly is a generative machine learning text-to-image model included as part of Adobe Creative Cloud.It is currently being tested in an open beta phase. [1] [2] [3]Adobe Firefly is developed using Adobe's Sensei platform.

7. GPT-2 - Wikipedia

   en.wikipedia.org/wiki/GPT-2

   GPT-2 deployment is resource-intensive; the full version of the model is larger than five gigabytes, making it difficult to embed locally into applications, and consumes large amounts of RAM. In addition, performing a single prediction "can occupy a CPU at 100% utilization for several minutes", and even with GPU processing, "a single prediction ...