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  2. Text-to-image model - Wikipedia

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

    An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.

  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. Adobe Express - Wikipedia

    en.wikipedia.org/wiki/Adobe_Express

    Adobe Voice for iPhone, which let users record their own stories with their voice and then illustrate them with images; In 2016, Adobe unified these apps under the Adobe Spark brand and added a web version. [4] In 2021, the application was relaunched as Adobe Creative Cloud Express, [3] which was simplified to Adobe Express the next year. [5]

  5. AOL Mail

    mail.aol.com

    Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!

  6. LinkedIn - Wikipedia

    en.wikipedia.org/wiki/LinkedIn

    LinkedIn is particularly well-suited for personal branding, which, according to Sandra Long, entails "actively managing one's image and unique value" to position oneself for career opportunities. [124] LinkedIn has evolved from being a mere platform for job searchers into a social network which allows users a chance to create a personal brand ...

  7. Artificial intelligence art - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence_art

    The GAN uses a "generator" to create new images and a "discriminator" to decide which created images are considered successful. [46] Unlike previous algorithmic art that followed hand-coded rules, generative adversarial networks could learn a specific aesthetic by analyzing a dataset of example images. [12]

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