How does CLIP work, and why is it important for multimodal AI?

CLIP trains two encoders — one for images, one for text — so that matching image-caption pairs land close together in a shared embedding space and mismatched pairs land far apart. It learns this with a contrastive objective over hundreds of millions of image-text pairs from the web: for each batch, pull the correct pairs together and push the wrong ones apart. The payoff is a shared space where you can compare images and text directly. That enables zero-shot classification (embed the image, embed candidate label phrases, pick the nearest), and it's the backbone of cross-modal search and of image-generation guidance. CLIP mattered because it showed you can learn broadly useful visual-text alignment from noisy web data without task-specific labels — many multimodal systems still build on that idea.