AI Termcirca 2013· Added May 29, 2026
Embeddings
Embeddings are numerical representations of data, often used to capture semantic meaning in text or images.
Embeddings convert textual or visual information into fixed-length vectors. These vectors can represent complex ideas, contexts, and relations by mapping semantically similar inputs to nearby points in vector space. This concept is fundamental in NLP tasks like word similarity, where models like Word2Vec and BERT generate embeddings that preserve contextual relationships across languages. In image processing, embeddings can help identify similarities between pictures or objects by analyzing pixel patterns.
Examples
- Word2Vec converts words into vectors based on context.
- BERT provides embeddings for sentence-level tasks.
- Image embeddings identify similar photos based on pixel patterns.
Common misconceptions
- Embeddings are not limited to text; they apply to images and audio.
- They do not inherently preserve the original structure of data but focus on meaning.
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