# Embeddings & Vector Similarity

**Track:** 3D, Shaders & AI Intuition — Creative Coding — the existing 50
**Framework / surface:** p5.js
**Level:** Advanced
**Prerequisites:** Vectors: Magnitude & Direction, Complexity Intuition

**In one line:** Turn meaning into position — distance measures relatedness.

## Theory, aesthetics & inspiration

An embedding turns meaning into geometry: each word, sentence, or image becomes a point in a high-dimensional space arranged so that related things sit near one another. The premise is the distributional hypothesis — J.R. Firth's 1957 maxim that "you shall know a word by the company it keeps" — operationalized by Tomas Mikolov and colleagues in word2vec (2013), where vector arithmetic could capture analogy. Distance, usually cosine similarity, then measures relatedness directly. The aesthetic is cartographic and quietly strange: concepts laid out as terrain, with neighborhoods of synonyms and axes that, surprisingly often, correspond to human notions of gender, tense, or scale.