In the world of machine learning, there used to be a limit on models — they could only handle one type of data at a time. However, the ultimate aspiration of machine learning is to rival the cognitive prowess of the human mind, which effortlessly comprehends various data modalities simultaneously. Recent breakthroughs, exemplified by models like GPT-4V, have now demonstrated the remarkable ability to concurrently handle multiple data modalities. This opens up exciting possibilities for developers to craft AI applications capable of seamlessly managing diverse types of data, which are known as multi-modal applications.

One compelling use case that has gained immense popularity is multi-modal image search. It lets users find similar images by analyzing features or visual content. Thanks to the rapid advancements in computer vision and deep learning, image search has become incredibly powerful.

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