Multimodal AI is a category of models that process multiple data types, text, images, audio, video, in a single system. GPT-4V can analyze images and text together. Gemini is natively multimodal, handling text, code, images, audio, and video in one model. Multimodal systems are more powerful than unimodal systems because they can reason across modalities.
They understand how image content relates to text descriptions. They can extract information from screenshots. They can analyze diagrams. They can watch videos and understand visual sequences. Building multimodal models is harder than building unimodal ones. You need training data that contains multiple modalities. You need architecture that processes different data types.
You need alignment between modalities, the image understanding needs to align with the text understanding. But the capability gains are substantial. A text-only model can read a description of a circuit diagram. A multimodal model can look at the diagram, understand it visually, and reason about it at a deeper level. Multimodal AI is the direction the field is moving.
The future is systems that can reason about text, images, video, audio simultaneously. This mirrors how humans understand the world. We don't think in pure language or pure vision. We integrate all sensory inputs.