Universal Model

Universal models aim to create single, adaptable machine learning systems capable of handling diverse tasks and datasets without extensive retraining for each new application. Current research focuses on developing architectures like transformers and employing techniques such as prompt engineering, multi-stage decoding, and data augmentation to achieve this universality across various domains, including medical imaging, robotics, and natural language processing. The success of universal models promises significant advancements by reducing the need for task-specific models, improving efficiency, and facilitating knowledge transfer across different applications, ultimately leading to more robust and adaptable AI systems.