A multimodal system is one capable of processing and combining information from different types of data —or modalities— such as text, images, audio, or video. Unlike traditional models, which were limited to a single input (for example, text only), these models integrate multiple sources into a common representation, allowing them to reason about them jointly.
Their importance lies in the fact that the real world is not unimodal: we understand a scene by combining what we see, hear, and read. This capability brings AI closer to a richer and more contextual understanding, and enables tasks that previously required several separate systems. Some practical examples:
- Describing the content of a photograph with text.
- Answering questions about a chart or a scanned document.
- Generating images from a written description.
A relevant nuance is that "multimodal" does not imply mastering all modalities equally. Many models excel in the text-image combination, while audio or video remain more limited or are incorporated partially.