I keep hearing about Generative AI in the context of ChatGPT, Stable Diffusion, and other tools. How exactly does Generative AI differ from traditional AI models like classifiers or recommendation systems?
Generative AI refers to models that can create new data resembling their training data—text, images, audio, or even code.
Traditional AI models are usually discriminative: they classify, predict, or recommend based on existing data. For example, a spam filter is trained to classify emails as spam or not spam.
Generative models, on the other hand, learn the underlying distribution of data and can produce novel outputs.
GPT learns language patterns and generates coherent text; Stable Diffusion learns visual features and generates images.
The key difference lies in output type: traditional AI outputs labels or predictions, while Generative AI outputs entirely new content. This makes Generative AI powerful for creativity, simulation, and personalization—but it also introduces risks like hallucination and bias.