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  2. How to trust a GenAI agent: four key requirements - AOL

    www.aol.com/finance/trust-genai-agent-four-key...

    When ChatGPT debuted in late 2022, it took the world by storm. Users were stunned by the app’s ability to give helpful, human-like responses to their input. Overnight, generative AI (GenAI) went ...

  3. Generative artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Generative_artificial...

    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 ...

  4. GenAI is being used in New Hanover County schools. Here's how ...

    www.aol.com/genai-being-used-hanover-county...

    Hudson said she personally uses GenAI to make her emails sound nicer and she has used it to make acronyms or songs for lessons to help students learn a topic more efficiently.

  5. How genAI is revolutionizing the field of economics

    www.aol.com/genai-revolutionizing-field...

    Economists already utilize machine learning, a branch of AI, to analyze data and develop economic projections. But genAI is a separate technology. It underpins ChatGPT and similar tools and has ...

  6. Retrieval-augmented generation - Wikipedia

    en.wikipedia.org/wiki/Retrieval-augmented_generation

    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.

  7. Generative pre-trained transformer - Wikipedia

    en.wikipedia.org/wiki/Generative_pre-trained...

    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.

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