Bootstrapping AI refers to building or improving an artificial intelligence system with limited initial data, resources, or infrastructure, then using the system’s early results to generate additional useful data, feedback, or capabilities. A common approach is to start with a small labeled dataset, train an initial model, and use its predictions to identify examples that can be reviewed and added to the training process.
This approach can be valuable when manually labeling large datasets is expensive or slow. In computer vision, for example, model-generated annotations can help prioritize images for human review, gradually expanding the training dataset while reducing repetitive labeling work.
