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  2. Automatic summarization - Wikipedia

    en.wikipedia.org/wiki/Automatic_summarization

    Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data.

  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. Artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence

    Generative artificial intelligence (generative AI, GenAI, [167] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 168 ] [ 169 ] [ 170 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 171 ...

  5. Applications of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Applications_of_artificial...

    Artificial intelligence is used in astronomy to analyze increasing amounts of available data [160] [161] and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights" for example for discovering exoplanets, forecasting solar activity, and distinguishing ...

  6. Generative adversarial network - Wikipedia

    en.wikipedia.org/wiki/Generative_adversarial_network

    The generator aims to minimize the objective, and the discriminator aims to maximize the objective. The generator's task is to approach , that is, to match its own output distribution as closely as possible to the reference distribution. The discriminator's task is to output a value close to 1 when the input appears to be from the reference ...

  7. Foundation model - Wikipedia

    en.wikipedia.org/wiki/Foundation_model

    The Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM) coined the term "foundation model" in August 2021 [16] to mean "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks". [17]

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