Data annotation is a critical process in AI learning where human annotators label or tag data to create training sets for machine learning models. It involves adding metadata, labels, or tags to raw data, helping algorithms understand and learn from the information provided. This process is essential for various AI applications, such as image recognition, natural language processing, and object detection.

In image annotation, annotators may outline and label objects within images, providing details about their shapes, sizes, and categories. Common techniques include bounding boxes, polygons, keypoints, and semantic segmentation. These annotations guide AI models in recognizing and distinguishing objects within images.

For natural language processing, text annotation involves labeling entities, sentiments, parts of speech, or relationships between words. Sentences are often annotated to highlight specific elements, enabling AI models to comprehend and generate human-like responses.