Vectors gave us AI search, tensors are going to make it smarter

Vectors have powered AI search by converting data into numerical embeddings for semantic search and RAG, but their one-dimensional nature limits them. Tensors, which can have multiple axes, offer richer data representation that improves relevance scoring, multimodal search, and handling of longer documents. A Vespa.ai-sponsored webinar hosted by The New Stack on May 5 will cover the differences between vectors and tensors, real-world tensor applications in e-commerce and life sciences, and how to future-proof AI data strategies.