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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 ...
The term "PowerPoint karaoke" is also sometimes derisively used to refer to presenters who face the screen where their PowerPoint slides are being projected and proceed to read them, boring and effectively ignoring their audience. Spanish conceptual artist Rubén Grilo used "PowerPoint Karaoke" as a title for a show at MARCO in June 2011.
A slide is a single page of a presentation. A group of slides is called a slide deck. A slide show is an exposition of a series of slides or images in an electronic device or on a projection screen. Before personal computers, they were 35 mm slides viewed with a slide projector [1] or transparencies viewed with an overhead projector.
Generative image models are adept at imitating the visual style of particular artists in their training set, prompting a backlash from some artists who object to having imitations of their style generated on a massive scale without their permission. Around the 2020s, generative AI models learned to imitate the distinct style of particular authors.
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.
Generative design is an iterative design process that uses software to generate outputs that fulfill a set of constraints iteratively adjusted by a designer. Whether a human, test program, or artificial intelligence , the designer algorithmically or manually refines the feasible region of the program's inputs and outputs with each iteration to ...
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