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A generative AI system is constructed by applying unsupervised machine learning (invoking for instance neural network architectures such as GANs, VAE, Transformer, ...) or self-supervised machine learning to a data set. The capabilities of a generative AI system depend on the modality or type of the data set used.
Generative AI has both strengths and weaknesses. For example, it's great at writing, Vartak says. It can draft a tweet, an email or create an elaborate, fantastical story. Sometimes it can break ...
A generative pre-trained transformer (GPT) is a type of large language model (LLM) [1][2][3] and a prominent framework for generative artificial intelligence. [4][5] It is an artificial neural network that is used in natural language processing by machines. [6] It is based on the transformer deep learning architecture, pre-trained on large data ...
e. 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]
The plain transformer architecture had difficulty converging. In the original paper [1] the authors recommended using learning rate warmup. That is, the learning rate should linearly scale up from 0 to maximal value for the first part of the training (usually recommended to be 2% of the total number of training steps), before decaying again.
P ( X , Y ) {\displaystyle P (X,Y)} on a given observable variable X and target variable Y; [1] A generative model can be used to "generate" random instances (outcomes) of an observation x. [2] A discriminative model is a model of the conditional probability. P ( Y ∣ X = x ) {\displaystyle P (Y\mid X=x)} of the target Y, given an observation x.
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