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Artificial intelligence. Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is artificial intelligence capable of generating text, images, videos, or other data using generative models, [2] often in response to prompts. [3][4] Generative AI models learn the patterns and structure of their input training data and then generate ...
Postmodernism Generator. An example of a randomly generated title. The Postmodernism Generator is a computer program that automatically produces "close imitations" of postmodernist writing. It was written in 1996 by Andrew C. Bulhak of Monash University using the Dada Engine, a system for generating random text from recursive grammars. [1]
Natural language generation (NLG) is a software process that produces natural language output. A widely-cited survey of NLG methods describes NLG as "the subfield of artificial intelligence and computational linguistics that is concerned with the construction of computer systems that can produce understandable texts in English or other human languages from some underlying non-linguistic ...
Creators will input text prompts to create AI images, which can then become the basis of the six-second clips. Mohan teased it with an AI-generated video of a dog and a sheep becoming friends ...
t. e. DALL·E, DALL·E 2, and DALL·E 3 (pronounced DOLL-E) are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural language descriptions known as "prompts". The first version of DALL-E was announced in January 2021. In the following year, its successor DALL-E 2 was released.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] [18] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.