Artificial Intelligence
Multimodality
The ability of an AI system to process, understand, and generate information from multiple types of data inputs or 'modes,' such as text, images, audio, and video.
Explanation
Multimodality in artificial intelligence refers to models that can integrate and relate information from different sensory or data sources. Unlike unimodal models, which are restricted to a single type of input (like text-only LLMs), multimodal models use shared embedding spaces to align features from diverse inputs. This allows the system to perform complex tasks such as image captioning, visual question answering, or text-to-video generation. This approach more closely mimics human perception, which relies on the simultaneous processing of sight, sound, and language to understand the world. Key architectures in this field include CLIP (Contrastive Language-Image Pre-training) and various Large Multimodal Models (LMMs).