Multimodal AI refers to systems that process and connect multiple types of data (images, text, audio, video, or sensor readings) within a single model. A text-only chatbot is unimodal. A model that can look at a photo, read a question about it, and speak an answer is multimodal. The key distinction is that multimodal models learn relationships between modalities, not just process them in parallel: they understand that the word dog corresponds to the visual pattern of a dog.

Modern multimodal AI centers on transformer architectures that handle multiple input types through tokenization. Images become patch tokens (via ViT), text becomes word tokens (via BPE), and audio becomes spectrogram tokens. Once tokenized, different modalities can be processed in the same transformer. VLMs are the largest category of multimodal AI in computer vision, but the space also includes text-to-image models (Stable Diffusion, DALL-E), video-language models (Cosmos, VideoLLaMA), audio-visual models, and multimodal agents that combine perception with action.

Multimodal AI powers document processing (OCR + layout + language understanding), video surveillance (visual detection + audio analysis), autonomous driving (camera + LiDAR + radar fusion), medical diagnostics (imaging + clinical notes + lab data), and retail analytics (product images + reviews + sales data). The trend is toward models that handle more modalities and longer contexts, with recent models processing images, video, audio, and text in a single forward pass.