In machine learning, a foundation model1 (FM) is a model trained using self-supervised learning on a large scale of unlabeled data to mathematically capture salient information from inputs to the model. Once trained, the FM can be adapted to enable specific tasks in new contexts, especially including previously unforeseen contexts and tasks.