Foundation Model refers to a large AI model trained on vast amounts of data that serves as a foundation for various specialized applications. It can be thought of as a base upon which other AI applications are built.
These models are initially trained on a broad spectrum of data – from texts and images to programming code – enabling them to understand fundamental patterns and relationships. What makes Foundation Models special is their versatility: a single model can be adapted for different tasks without requiring complete retraining. This is achieved through “fine-tuning,” where the model is further trained with additional task-specific data.
Foundation Models have revolutionized AI development by making it possible to create advanced AI applications with significantly less effort and fewer resources than before. They are like versatile building blocks that can be customized for specific purposes while maintaining their fundamental understanding of language, images, or other data types. However, they also come with challenges, such as the high energy consumption during training or the risk of inheriting biased or incorrect information from their training data.