AI doesn't understand words the way you do — it understands numbers. Every word in an AI's vocabulary gets converted into a long list of numbers called an embedding (or vector). These numbers place each word as a point in a massive multi-dimensional space. The magic: words with similar meanings end up near each other. 'Cat' and 'dog' are close. 'King' and 'queen' are close. And the direction between words encodes relationships — the direction from 'man' to 'woman' is the same as the direction from 'king' to 'queen'. Click any word below to explore its neighborhood, then try the vector math demo.
Click any word to see its nearest neighbors.
Why This Matters
Word embeddings are the foundation that lets AI understand meaning rather than just matching keywords. They're why a search for "car problems" also returns results about "vehicle issues" — the AI knows those mean the same thing. They're why translation works across languages, why recommendations ("you liked X, you might like Y") are accurate, and why AI can answer questions it's never seen word-for-word before. Every modern language AI — from Google Search to ChatGPT — relies on embeddings at its core.