AI Foundation Model

AI foundation models are large, general-purpose AI models trained on massive datasets to perform a wide range of tasks across various modalities, including text, images, and code. Current research emphasizes developing more efficient and adaptable models, often using transformer architectures and techniques like retrieval-augmented instruction tuning and parameter-efficient fine-tuning, to improve performance and address issues like bias and dual-use potential. These models are proving impactful across diverse fields, from medical imaging and climate modeling to process engineering and document processing, by enabling more robust and versatile AI applications.