AI Term:Data Annotation

·

·

« Back to Glossary Index

Data Annotation is the process of labeling or adding meta-information to raw data. This could be as simple as tagging a photo, categorizing a blog post, or as complex as pinpointing specific pixels in an image or specific words or phrases within a sentence.

In the context of machine learning and AI, data annotation is used to create training data for algorithms. By providing examples with correct answers (labels), the algorithm learns to predict the correct output for new, unseen data. This is analogous to a teacher providing a student with practice problems and correct answers to study from.

For instance, if we want to train a machine learning model to identify dogs in images, we’d first need a collection of images that are annotated to indicate whether or not a dog is present, and possibly where the dog is located in the image. This annotated data set would then be used to train the model.

So, in a nutshell, data annotation is a crucial step in training machine learning models, as it provides the models with the correct answers that they use to learn and make predictions.

« Back to Glossary Index