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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 ...
YouTube is testing a new AI-powered chatbot that can converse with viewers to let them “dive deeper into the content they are watching.” The new conversational tool on YouTube will provide ...
YouTube will also integrate generative AI text and image output into an “Inspiration” feature for creators, which is intended to feed them suggestions and examples for video content.
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.
Analogously, a classifier based on a generative model is a generative classifier, while a classifier based on a discriminative model is a discriminative classifier, though this term also refers to classifiers that are not based on a model. Standard examples of each, all of which are linear classifiers, are: generative classifiers:
AI in architecture has created a way for architects to create things beyond human understanding. AI implementation of machine learning text-to-render technologies, like DALL-E and stable Diffusion, gives power to visualization complex. [380] AI allows designers to demonstrate their creativity and even invent new ideas while designing.
The rapid speed of change in generative AI (GenAI), where significant advances can happen even month-to-month, means that executives must avoid obsessing over perfecting near-term use cases.