What model type is commonly associated with generative AI?

Generative AI is primarily associated with models that have the capability to produce new content or data rather than just classifying or making predictions based on input data. GANs, or Generative Adversarial Networks, are a prominent type of model within generative AI that utilize a two-network architecture consisting of a generator and a discriminator. The generator creates data samples, while the discriminator evaluates them, leading to improved performance over iterations.

Transformer-based large language models (LLMs) also belong to this category as they excel at generating human-like text based on given prompts, effectively creating coherent and contextually relevant content.

The other model types mentioned do not align with the principles of generative AI. Support Vector Machines and Random Forests are generally used for classification and regression tasks, focusing on analyzing and predicting outcomes based on input data rather than generating new data. Linear Regression specifically models the relationship between variables and provides predictions, but it does not create new content, which is a core characteristic of generative models.