What Are Embeddings? How AI Converts Meaning Into Numbers

Embeddings are numerical representations of information, such as words, sentences, or images, which enable computers to understand and compare their meaning. To illustrate, consider these two sentences: "I want to buy a phone with a long-lasting battery" and "Which smartphone should I choose if I don't want to charge it frequently?" Though they share few words, the reader intuitively grasps they describe the same concept—a phone that doesn't require constant charging. Computers can achieve this through embeddings.

An embedding is a learned representation built from patterns the model picked up during training, capturing relationships between different pieces of information. Unlike simple word IDs, embeddings are not straightforward numerical assignments.

AI needs embeddings to overcome the literal nature of computers. Traditional exact text matching methods fail to recognize meaningful connections between phrases like "car repair" and "fixing an automobile." Embeddings bridge this gap by converting text into numerical vectors, enabling mathematical comparisons of meaning rather than just text strings.

Text becomes an embedding through a straightforward process: Text → Embedding Model → Numerical Vector. The embedding model, a machine learning model trained for this task, transforms input like a sentence into a vector—a list of many dimensions. The model learns, from vast training data, how to position semantically similar inputs close together in this numerical space, while dissimilar inputs remain far apart.

A vector might look like [0.12, -0.43, 0.78, ...] for the word "coffee," and [0.15, -0.39, 0.74, ...] for "tea"—similar but not identical, reflecting their related but distinct meanings. Real embeddings consist of hundreds or thousands of dimensions, not hand-designed by engineers.

Embeddings capture meaning through statistical patterns of association rather than genuine understanding. Words with similar meanings cluster near each other in the vector space, but the dimensions don't correspond to human-readable labels. The space itself has as many dimensions as the embedding model uses.

Measuring similarity between embeddings can be done using methods like cosine similarity, which looks at the angle between vectors, or Euclidean distance, measuring the straight-line distance between points. These techniques provide a consistent way to gauge semantic similarity, akin to finding the nearest neighbors within the vector space.