AI & Enterprise
KAIST develops tech to cut sensory confusion and hallucinations in multimodal AI
Researchers in South Korea have developed techniques to reduce cases in which AI confuses multiple sensory inputs or generates nonexistent information. KAIST said a team led by professor Yong-man Ro developed two methods to improve sensor understanding in multimodal large language models and reduce confusion between visual and audio data. The approaches are designed to work with less data or be applied without retraining. KAIST said the technology could be used in autonomous driving, rescue robots, drones and medical AI.
