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The Future is Visual: The Image Search Revolution
- The article discusses the shift towards visual search, using images instead of keywords for search queries.
- Image embeddings and vector search are highlighted as revolutionary methods enhancing image search and analysis.
- Vector search involves generating embeddings for images and finding visually similar images by calculating distances between vectors.
- Applications in e-commerce, content moderation, medical imaging, and image retrieval are mentioned as practical uses of vector search.
- The article provides a real-world example of using image embeddings and vector search in SQL on BigQuery for finding similar images.
- Steps include creating a BigQueryML model, generating embeddings, and using VECTOR_SEARCH() for search.
- The result of the search illustrates the effectiveness of vector search in finding visually similar images.
- Image embeddings and vector search are transforming how visual data is interacted with, enabling more nuanced and efficient searches.
- The article concludes by emphasizing the growing importance and impact of visual search powered by vectors.
- The future of search is envisioned to be more visual-centric and driven by advancements in image embeddings and vector search.
- The article recommends exploring image embeddings in BigQuery and Vector Search on BigQuery for further insights.
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