Vector Embeddings
- [Cornell University]
- Overview
Vector embeddings are lists of numbers that represent complex data like words, sentences, images, or audio in a high-dimensional mathematical space. They capture the semantic meaning and context of the data, allowing artificial intelligence and machine learning models to compare, sort, and find similarities using simple math.
1. How Vector Embeddings Work:
- Data Translation: An AI model reads input data like text or photos and translates it into a long array of numbers (a vector).
- Semantic Proximity: Items with similar meanings or features are placed close together in the vector space. For example, the vector for "dog" will sit close to the vector for "puppy," but far from "airplane."
- Mathematical Distance: Computers measure how close two points are using math rules like cosine similarity or Euclidean distance to determine if concepts match.
2. Common Types and Uses:
- Word and Sentence Embeddings: Models like BERT or Word2Vec map text meaning for search engines and chatbots.
- Image Embeddings: Models like CLIP turn visual features into numbers to power reverse-image search and visual tagging.
- Recommendation Systems: Platforms use embeddings to group user preferences and suggest products or shows you might like.
[More to come ...]
