Generative AI models can be used to develop machine learning models, such as Generative Adversarial Networks (GANs) and Transformer-based models.
These models are capable of creating artifacts from real-world content, such as text-to-image translation, face identification and verification systems, healthcare, marketing, and more.
To train a generative model, a large amount of data in some domain is collected (e.g. millions of images, sentences, or sounds) and then used to generate data like it.
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Generative AI is a type of machine learning algorithm that uses existing content like text, audio and video files, images, and even code to create new possible content. It is used to create artifacts that look like the real deal, such as text-to-image translation, face identification and verification systems, healthcare, marketing, and more. GANs and Transformer-based models are two of the most widely used generative AI models, and they are modeled to make them capable of creating artifacts from real-world content.
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