Multimodal foundation models process and understand information across multiple modalities—text, images, audio, video, and potentially actions or internal states—within a unified representation space. These models learn to map different types of information into shared embedding spaces, enabling them to understand relationships between modalities, translate between them, and reason about concepts that span multiple sensory channels.
This innovation addresses the limitation of single-modality AI systems, which can only process one type of information and struggle with tasks requiring understanding across modalities. By learning unified representations, multimodal models can understand that a picture of a cat, the word "cat," and the sound of meowing all refer to the same concept, enabling more sophisticated understanding and interaction. Models like GPT-4V, Claude, and Gemini demonstrate multimodal capabilities, processing text and images together.
The technology is fundamental to creating AI agents that can interact with the real world, which inherently involves multiple sensory modalities. As AI systems become more capable and are deployed in applications requiring real-world interaction—from robotics to virtual assistants to content creation—multimodal understanding becomes essential. The technology continues to evolve, with research exploring how to incorporate additional modalities like audio, video, actions, and potentially even internal states or proprioception, moving toward more comprehensive world understanding.
