How do embeddings support multi-modal AI models?

Embeddings enable multi-modal AI models by converting diverse data types—like text, images, or audio—into numerical representations that share a common mathematical space. This allows models to process and relate information across modalities, even if the original data formats are fundamentally different. For example, a text description of a dog and an image of a dog can both be mapped to embedding vectors with similar positions in a high-dimensional space. By aligning these representations, the model learns to associate concepts across modalities, enabling tasks like searching images with text queries or generating captions for videos.

A key way embeddings support multi-modal models is by bridging semantic gaps between data types. Models like CLIP (Contrastive Language-Image Pretraining) train separate encoders for text and images, producing embeddings that are aligned through contrastive learning. During training, pairs of matching images and text (e.g., a photo and its caption) are pushed closer in the embedding space, while mismatched pairs are separated. This creates a shared understanding: the embedding for “a red balloon” becomes geometrically near the embedding of an actual red balloon image. Similarly, audio embeddings can align with text or visual embeddings—like mapping spoken words to their transcriptions or associating sound effects with video scenes.

From a practical standpoint, embeddings simplify complex multi-modal workflows. For instance, in a retrieval system, embeddings allow comparing a user’s text query directly to image or video databases using vector similarity metrics like cosine distance. In generative tasks, embeddings act as intermediaries: text embeddings from a prompt can guide a diffusion model to create corresponding images (e.g., Stable Diffusion). Developers also reuse pre-trained embeddings to bootstrap models with limited data—a speech recognition system might leverage text embeddings trained on large corpora to improve accuracy. By providing a unified way to represent and relate data, embeddings reduce the need for custom architectures per modality, making multi-modal systems more scalable and efficient.