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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] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 3 ] [ 4 ] based on the input ...

  3. 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 ...

  4. This Nonprofit Uses AI to Aid African Teachers and Farmers - AOL

    www.aol.com/news/nonprofit-uses-ai-aid-african...

    Opportunity International is leading the creation of 3 generative-AI based apps for high-poverty communities

  5. From design to photography: How various creative tools are ...

    www.aol.com/design-photography-various-creative...

    The future of generative AI is uncertain, but it's here to stay. There is legal uncertainty around AI and its role in licensing and copyrights. In January 2023, three artists filed a lawsuit ...

  6. Applications of artificial intelligence - Wikipedia

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

    Aircraft simulators use AI for training aviators. Flight conditions can be simulated that allow pilots to make mistakes without risking themselves or expensive aircraft. Air combat can also be simulated. AI can also be used to operate planes analogously to their control of ground vehicles. Autonomous drones can fly independently or in swarms. [338]

  7. Text-to-image model - Wikipedia

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

    In 2016, Reed, Akata, Yan et al. became the first to use generative adversarial networks for the text-to-image task. [5] [7] With models trained on narrow, domain-specific datasets, they were able to generate "visually plausible" images of birds and flowers from text captions like "an all black bird with a distinct thick, rounded bill".

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