AI Glossary
Embedding
A numerical representation that helps AI models understand relationships between words, concepts, or objects.
Overview
Computers do not naturally understand words, images, or concepts the way humans do.
Before an AI model can work with information, that information must be converted into a numerical form the model can process.
This is where embeddings become important.
An embedding is a numerical representation of information that captures meaningful relationships between concepts. Rather than simply assigning random numbers, embeddings are designed so that related concepts appear closer together in a mathematical space.
A helpful way to think about embeddings is a map.
On a map, locations that are physically close appear near one another. Similarly, embeddings place related concepts closer together while placing unrelated concepts farther apart.
This allows AI models to recognize similarities, relationships, and patterns that would otherwise be difficult to identify.
Embeddings play a critical role in modern AI systems, particularly in search, recommendation systems, language models, and semantic understanding.
Without embeddings, many of the language capabilities people associate with modern AI would be far less effective.
Why It Matters
Embeddings help AI models understand relationships between words, concepts, and other forms of information.
Real-World Example
A search engine may use embeddings to understand that searches for “car” and “automobile” are closely related even though the words are different.