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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 ...
Prompt engineering is the process of structuring an instruction that can be interpreted and understood by a generative artificial intelligence (AI) model. [1] [2] A prompt is natural language text describing the task that an AI should perform. [3]
This is a documentation subpage for Template:Generative AI editnotice. It may contain usage information, categories and other content that is not part of the original template page. This is an editnotice template for use on articles or talk pages about generative artificial intelligence , in order to deter users from mistakenly entering prompts ...
Text-to-image models began to be developed in the mid-2010s during the beginnings of the AI boom, as a result of advances in deep neural networks. In 2022, the output of state-of-the-art text-to-image models—such as OpenAI's DALL-E 2 , Google Brain 's Imagen , Stability AI's Stable Diffusion , and Midjourney —began to be considered to ...
While OpenAI did not release the fully-trained model or the corpora it was trained on, description of their methods in prior publications (and the free availability of underlying technology) made it possible for GPT-2 to be replicated by others as free software; one such replication, OpenGPT-2, was released in August 2019, in conjunction with a ...
Worldwide, 75% of knowledge workers now use gen AI in their work, according to a recent Microsoft/LinkedIn survey. Often it’s employees, not companies, leading the way: In most countries, and to ...
Transformer architecture is now used in many generative models that contribute to the ongoing AI boom. In language modelling, ELMo (2018) was a bi-directional LSTM that produces contextualized word embeddings, improving upon the line of research from bag of words and word2vec. It was followed by BERT (2018), an encoder-only Transformer model. [33]
It is important to strike a balance between accuracy – how faithfully the explanation reflects the process of the AI system – and explainability – how well end users understand the process. This is a difficult balance to strike, since the complexity of machine learning makes it difficult for even ML engineers to fully understand, let ...
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