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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 ...
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.
A generative model is a statistical model of the joint probability distribution (,) on a given observable variable X and target variable Y; [1] A generative model can be used to "generate" random instances of an observation x.
[7] [8] [5] [9] It is argued that construction grammar is not an original model of cultural evolution, but for essential part the same as memetics. [10] Construction grammar is associated with concepts from cognitive linguistics that aim to show in various ways how human rational and creative behaviour is automatic and not planned. [11] [6]
Generative Pre-trained Transformer 2 (GPT-2) is a large language model by OpenAI and the second in their foundational series of GPT models. GPT-2 was pre-trained on a dataset of 8 million web pages. [2] It was partially released in February 2019, followed by full release of the 1.5-billion-parameter model on November 5, 2019. [3] [4] [5]
Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus. Context-free models such as word2vec or GloVe generate a single word embedding representation for each word in the vocabulary, whereas BERT takes into account the context for each occurrence of a given word ...
Generative Pre-trained Transformer 1 (GPT-1) was the first of OpenAI's large language models following Google's invention of the transformer architecture in 2017. [2] In June 2018, OpenAI released a paper entitled "Improving Language Understanding by Generative Pre-Training", [ 3 ] in which they introduced that initial model along with the ...
[2] [31] [18] Their most traditional application was dimensionality reduction or feature learning, but the concept became widely used for learning generative models of data. [ 32 ] [ 33 ] Some of the most powerful AIs in the 2010s involved autoencoder modules as a component of larger AI systems, such as VAE in Stable Diffusion , discrete VAE in ...