Foundational Model

Foundational models (FMs) are large-scale, pre-trained machine learning models designed to learn generalized patterns from massive datasets, enabling adaptation to diverse downstream tasks with minimal additional training. Current research emphasizes applying FMs across various data modalities (text, images, tabular data, molecular structures, brain signals) and exploring efficient fine-tuning techniques like parameter-efficient fine-tuning and prompting. This approach promises to improve the efficiency and generalizability of AI systems, impacting fields like medical imaging, drug discovery, and manufacturing through improved accuracy and reduced reliance on extensive labeled data for specific tasks.