What is an example of a major Generative AI model?

Generative Adversarial Networks (GANs) are a prominent example of a major Generative AI model because they are specifically designed for generating new data by using two neural networks in competition with each other: the generator and the discriminator. The generator creates new data samples, while the discriminator evaluates them against real data, providing feedback to the generator to improve its outputs. This framework has led to impressive advancements in the generation of realistic images, audio, and other forms of data, showcasing the capabilities of generative models in AI.

The other models listed serve different purposes. Convolutional Neural Networks (CNNs) are primarily used for image processing tasks, and although they can be a component within a generative framework, they do not inherently generate new data. Recurrent Neural Networks (RNNs) are suited for sequential data analysis, such as time series or natural language processing, but again, are not specifically generative models themselves. Support Vector Machines (SVMs) are mainly used for classification tasks, separating data into different categories rather than generating new data. Therefore, among the options provided, GANs are the most relevant example of a major Generative AI model.