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Rather than evaluating and interpreting data, generative AI creates entirely new content, such as an image, a piece of literature, or even music. In contrast to more traditional AI models, generative AI models can generate their own data by extrapolating from patterns and examples learned during training. These models, which are often based on deep learning methodologies such as generative adversarial networks (GANs) or variational autoencoders (VAEs), may generate highly realistic and coherent outputs that match the features of the training data.
The capacity of generative AI to broaden the possibilities of what AI can create, allowing for inventive and creative solutions, has improved art, design, narrative, and even scientific study.
