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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 ...
to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
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 ]
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.
The government has launched a new trial of an OpenAI-powered chatbot to help people find relevant information on the Gov.UK website. Gov.UK Chat is to be tested by up to 15,000 business users, and ...
English: With the growth in AI generated art, many new AI/ML (Artificial Intelligence / Machine Learning) models have been implemented and connected to each other. This diagram shows the major AI/ML Datasets / Corpora, Classifier / Transformer Models, Generative Models, and End-User Applications as well as how they are related and their dependencies.
Generative AI may not be coming for your job, but it’s certainly coming to your job. Worldwide, 75% of knowledge workers now use gen AI in their work, according to a recent Microsoft/LinkedIn ...
Retrieval Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.
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