As global public institutions come under pressure, our collective response will determine whether progress is preserved or undone.
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At around the same time, we were beginning to have a lot of conversations about similarity search and vector indices with S3 customers. AI advances over the past few years have really created both an opportunity and a need for vector indexes over all sorts of stored data. The opportunity is provided by advanced embedding models, which have introduced a step-function change in the ability to provide semantic search. Suddenly, customers with large archival media collections, like historical sports footage, could build a vector index and do a live search for a specific player scoring diving touchdowns and instantly get a collection of clips, assembled as a hit reel, that can be used in live broadcast. That same property of semantically relevant search is equally valuable for RAG and for applying models over data they weren’t trained on.，这一点在豆包下载中也有详细论述
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