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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.
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 ...
Google AI is a division of Google dedicated to artificial intelligence. [1] It was announced at Google I/O 2017 by CEO Sundar Pichai. [2]This division has expanded its reach with research facilities in various parts of the world such as Zurich, Paris, Israel, and Beijing. [3]
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.
Google has been racing to beat back appearances that it fell behind Microsoft and OpenAI since the two teamed up to release a generative AI-powered version of its Bing search engine and chatbot in ...
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence.The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. [1]
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] 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.
An alternative division defines these symmetrically as: a generative model is a model of the conditional probability of the observable X, given a target y, symbolically, (=) [2]