What is a Multimodal Model?
A multimodal model is an artificial intelligence system that can perceive, understand, and generate multiple different types of data (modalities) simultaneously. While traditional models are restricted to a single domain (like text-only or image-only), multimodal models bridge these domains.
How does it work?
Instead of having one separate model for vision and one separate model for text, a true multimodal model is trained on a combined dataset (for instance, an image paired with its text description). This allows the model to build an internal mathematical representation that maps visual features directly to linguistic concepts.
What is a simple example?
An older, text-only model could read a description of a receipt and extract the total. A multimodal model allows you to point a camera at the physical receipt and ask the system "How much did I spend on coffee?" The AI natively processes the visual image and outputs a text answer.
Why does it matter?
The real world is multimodal. Humans do not process information purely in text; we see, hear, and read simultaneously to gather context. Multimodal models allow AI applications to interact with the physical world in a much more natural, robust, and human-like way.