Multiple Sens

Multiple senses research explores how to effectively integrate and interpret information from diverse sources, aiming to improve the performance and understanding of artificial intelligence systems. Current research focuses on enhancing large language models with semantic information, modeling the evolution of word meanings, and developing robust methods for sensemaking in various contexts, including healthcare and robotics, often employing machine learning models like ElasticNet regression and masked autoencoders. This work is significant for advancing AI capabilities, improving the interpretability of complex systems, and enabling more efficient and effective applications across numerous domains.