How are Foundation Models characterized?

Foundation models are characterized as AI systems that leverage self-supervised learning techniques. This approach allows these models to be pre-trained on vast amounts of unlabeled data, enabling them to learn representations and features from this data without the need for extensive manual labeling or programming.

Self-supervised learning is particularly powerful in that it can capture complex patterns and relationships within the data, making the foundation models versatile and capable of being fine-tuned for various specific applications later on. This capability is what distinguishes foundation models from simpler machine learning approaches, which may require more manual intervention or structured input data. By using self-supervised learning, foundation models can generalize better across a range of tasks, achieving state-of-the-art performance in numerous applications within the field of AI.