Vision Embedding Space Travelling (VEST)
Take a three-dimensional journey through the space of visual embeddings.
Embeddings are the internal representation AI models use to process, compare, and organize information. You can think of them as a common representation for text and images.
Take a three-dimensional journey through the space of visual embeddings.
Prefer a softer start? Plant lilies and explore embeddings in a calm 2D world. Available soon.
Both applications demonstrate how AI translates content into numerical vectors (embeddings) and uses them to identify similarities and underlying structures. Similar content is located closer together, while different content is spaced farther apart.
This illustrates how AI translates meaning into numerical representations, identifies similarities between pieces of content, and uncovers the connections between them.
An embedding is a compact numerical representation of meaning. Rather than comparing raw words or pixels, AI models compare these representations to determine how similar different pieces of content are. Because mathematical vectors can be processed efficiently by AI models and capture semantic similarities more effectively than raw text or pixels.