Vector Embedding

Vector embeddings address the core challenge of representing words and concepts as numbers while preserving their meaning. The process involves mapping text into a high-dimensional numerical space where similar concepts are positioned closely together. 

For example, words like "car" and "automobile" are neighbors, while "car" and "happiness" are distant, reflecting their semantic difference. This approach allows AI systems to grasp synonyms, related concepts, and complex semantic relationships without needing to be explicitly programmed with every possible connection.

The use of embeddings has been a core part of Google Search's evolution. Google continues to build upon this technology, using it in various products to power things like recommendations and language processing.

This technology has profound implications for applications like search and content discovery. It enables semantic search, where systems move beyond matching exact keywords to finding content based on its underlying meaning. 

Instead of just looking for the word "recipe," an AI can understand that a user searching for "how to cook lasagna" is looking for similar information. 

This allows AI to comprehend a user's intent more accurately and retrieve highly relevant information, even when the query's specific terms don't match the content's vocabulary. This shift from keyword matching to conceptual understanding is a cornerstone of modern AI.