Multimodal AI represents the convergence of vision, language, and other sensory modalities within unified machine learning systems, enabling models to process and understand diverse data types—text, images, audio, video, and more—simultaneously. Unlike unimodal approaches, multimodal models learn cross-modal representations that capture semantic relationships across modalities, unlocking capabilities like visual question answering, image captioning, zero-shot classification, and visual reasoning. The key architectural insight is that alignment in a shared embedding space—achieved through contrastive learning or cross-attention mechanisms—allows models to ground language in visual context and vice versa, creating systems that truly "see" and "understand" rather than merely pattern-match. This represents a fundamental shift: where traditional computer vision required task-specific training, multimodal AI leverages natural language supervision to generalize across tasks, making it both more capable and more accessible.