How does AI image search work?
A lot of the excitement around generative AI is the capability of these models in generating text, images, and sounds. However, the underlying technology can be used for many other applications. One interesting field that is being redefined is image search.
Generative models are changing the image search experience in ways that were previously impossible. They enable us to blur the lines between discovery and creation. Read all about AI image search in my latest post on TechTalks.
Key findings:
One of the key features of generative models are embeddings
Embeddings are numerical vectors that represent the features of text, image, sound, etc.
When used on a single type of data, embedding models can perform interesting tasks such as search, similarity comparison, etc.
But when an embedding model is trained on multiple modalities (e.g., text+image) it becomes much more interesting
Joint embeddings can link from one data type to the other
AI image search uses these joint embeddings to find images whose embeddings match the embedding of your search query
This enables you to type in much richer search queries that are not explicitly included in the text description of images
Beyond discovery, generative models can help you create the image you desire when it is not in the search results
Read the full article on TechTalks.
For more on generative AI:
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