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  2. 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 ...

  3. Prompt engineering - Wikipedia

    en.wikipedia.org/wiki/Prompt_engineering

    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]

  4. Text-to-image model - Wikipedia

    en.wikipedia.org/wiki/Text-to-image_model

    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 ...

  5. These are the countries where workers are most likely to use ...

    www.aol.com/finance/countries-where-workers-most...

    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 ...

  6. Neurosymbolic AI could be a best-of-both-worlds marriage between deep learning and “good old-fashioned AI.” Generative AI can’t shake its reliability problem. Some say ‘neurosymbolic AI ...

  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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