How do AI systems understand meaning?
Explore how text becomes numerical vectors that capture semantic relationships in AI applications.
Vector Space Explorer
Understanding Semantic Clustering
Notice how semantically related words form distinct clusters in the vector space:
Words vs. Concepts
Embeddings capture meaning, not just spelling. "Dog" and "puppy" cluster together because they represent similar concepts, not because the words look similar.
Hierarchy of Meaning
More specific concepts ("kitten") appear closer to their parent category ("cat") than to other categories ("technology"). This reflects real-world taxonomy.
Geographic Relationships
"Paris" is closer to "France" than to "Japan", showing how embeddings capture real-world relationships between places and countries.
Concept Distance
The distance between "food" and "animal" clusters shows semantic separation, while terms like "sushi" might show some relationship to "japan" across categories.
Explore how embeddings position words in a multi-dimensional space, where related concepts naturally cluster together.
Select a Phrase to Explore
Choose from the following phrases to see how they are positioned in the vector space: