Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. Like all AI, generative AI is powered by ML models—very large models that are pre-trained on vast amounts of data and commonly referred to as foundation models (FMs). Recent advancements in ML (specifically the invention of the transformer-based neural network architecture) have led to the rise of models that contain billions of parameters or variables. FMs can perform so many more tasks because they contain many parameters that make them capable of learning complex concepts.

The size and general-purpose nature of FMs make them different from traditional ML models, which typically perform specific tasks, like analyzing text for sentiment, classifying images, and forecasting trends.

To achieve each task, for each ML model, customers need to gather labeled data, train a model, and deploy that model. With foundation models, instead of gathering labeled data and training multiple models, you use the same pretrained FM to adapt several tasks. FMs can also be customized to perform domain-specific functions that are differentiating to their businesses, using only a small fraction of the data and compute required to train a model from scratch.