Embedding

What is Embedding?

Embedding is technology converting words, images, and other data into numerical vectors (combinations of multiple numbers) that AI systems understand. Using these numerical representations, AI discovers “semantically similar items.” For example, “apple” and “orange” position closely together in vector space.

In a nutshell: Converting words into “map coordinates.” Similar meaning words locate near each other on the map, enabling AI to understand “relationships.”

Key points:

• What it does: Convert text and images into numbers AI understands
• Why it matters: Enable AI to understand “meaning,” enabling more accurate searches and recommendations
• Who uses it: Search engines, recommendation systems, translators, chatbots

Why it Matters

Traditional keyword search finds only exact word matches. However, when users search “affordable accommodation,” we want to find “budget-friendly hotel” and “bargain inn” too. Embedding recognizes similar meaning expressions.

Also, E-Commerce “Recommendations for You” features rely on embedding. Expressing purchaser preferences as vectors discovers other customers with similar tastes, enabling product recommendations.

How it Works

Embedding involves two major stages. First stage is “training.” Large text datasets train neural networks, teaching systems how to convert words into vectors.

For example, training on words “king,” “queen,” “man,” “woman” teaches neural networks that “king - man + woman = queen”—meaning “king minus male plus female equals queen.” This captures meaning relationships.

Second stage is “application.” Using the trained model, convert new text into vectors, calculating similarity, searching, or making recommendations.

Real-World Use Cases

Google Search Relevance Improvement

Three queries—“good movie,” “excellent movie,” “entertaining movie”—are semantically similar. Embedding displays identical relevant articles for all three in “movies” category.

Amazon “Customers Also Viewed”

Vectorizing customer purchase history identifies customer groups with similar preferences, recommending items that group purchased.

ChatGPT and LLM Meaning Understanding

User text input converts to embedding, drawing from similar learned data patterns to generate optimal responses.

Benefits and Considerations

Benefits include automatic semantic similarity recognition without manual rule definition. Once trained, models apply to new words and images.

Considerations include training requiring massive data and computing resources. Additionally, what embedding learned from hundreds or thousands of numbers is often incomprehensible to humans—called the “black box problem.”

Related Terms

• Neural Network — AI structure training embeddings
• Natural Language Processing (NLP) — Overall text processing technology
• Vector Search — Search using embedding similarity
• Large Language Model (LLM) — ChatGPT and similar text-generation AI
• Recommendation System — Recommending products and articles to users

Frequently Asked Questions

Q: Do different languages create different vectors?

A: Traditionally, language-specific models were needed. However, recent multilingual embeddings calculate similarity across languages.

Q: What’s the embedding accuracy level?

A: Depends on training data quality and volume. Models trained on millions of texts are quite accurate, though specialized domains like medical papers may require retraining.

Q: Does ChatGPT use embedding?

A: Yes. User input converts to embedding, with similar learned data referenced before answer generation.