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

If you’ve paid AI any mind in the last few years, you’ve heard of vectors. They are easy to understand: A vector is a single, ordered list of numbers that represent something. You can turn a paragraph into a vector, or a dataset into vectors. 

Vectors convert information that humans can understand (text, data, etc) into a numerical form that AI can process. Once you’ve transformed your data into vector embeddings, you can compare them to estimate how similar – or not! – the underlying items are. Vectorized data can power semantic search, generate recommendations, and even help pull data for retrieval-augmented generation (RAG).

Vectors are a crucial part of today’s AI market. But as useful as vectors are for many AI-related tasks, they have a critical weakness.

While vectors are aces at turning information into numerical strings, their inherent flatness is limiting. 

What do we mean by flatness? A vector is a one-dimensional tensor. That means each number is positioned along a single axis. Tensors, in contrast, can have multiple axes. (All vectors are tensors; not all tensors are vectors.) This means tensors can represent the same quantum of information with more context.

That’s a mouthful, but what matters is that tensors make AI search better. Much better. How so? Tesnors improve vector search’s multimodality and ranking ability. Superior ranking means your search tools become smarter at understanding relationships inside of your data, can find more precise matches, and are able handle longer documents with ease. 

Vectors and vector databases got us this far. But it’s time to think bigger. Especially as the amount of data that businesses bring to AI scales. You’re going to need better tools.

To help us understand this better, I’ll be chatting with Vespa.ai‘s Bonnie Chase, Director of Product Marketing, and Zohar Nissare-Houssen, Strategic Presales Lead Engineer, at 12 p.m. Eastern/9 a.m. Pacific on Tuesday, May 5.

We’ll discuss the difference between vectors and tensors, where tensors can impact search capability, and what companies need to understand today to future-proof their AI work.

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During the conversation you’ll learn:

• You’ll learn how vectors and tensors differ, and their relative strengths and weaknesses for search and retrieval. 
• You’ll learn how tensors improve relevance scoring and make multimodal search possible, allowing for more intelligent AI search results.
• You’ll learn what role tensors play for real-time AI applications, critical in today’s AI market as companies work to lever their data for rapid-fire decision-making.
• And we’ll go over real-world examples regarding where tensors could play a critical role in the next era of AI, including e-commerce and the life sciences.