Embedding

Embedding is a technique that converts discrete, high-dimensional data—such as text, images, and audio—into a continuous, low-dimensional vector space. This enables machine learning models to handle the similarity and semantic relationships between data points numerically, and is widely used in NLP, recommendation systems, image recognition, and many other AI domains. In natural language processing in particular, "word embedding" is a representative application, converting words and sentences into numerical vectors while preserving their meaning. In this vector space, semantically similar words are positioned close to one another. Representative embedding techniques: • Word2Vec: Learns vectors based on word co-occurrence relationships • GloVe: A model that leverages global word frequency statistics • FastText: Vectorizes at the subword level for better handling of unknown words • Context-aware embeddings from Transformer-based models such as BERT and GPT • Multimodal embeddings (image + text) via models such as CLIP and DINO Advantages of embeddings: • Converts discrete data into a form that machine learning models can directly process • Preserves semantic similarity and relational structure • Applicable to search, classification, clustering, and generation tasks In recent years, "embedding vector search (vector search)" and "similarity computation in embedding space" have become increasingly active areas, and embeddings play an important role in the backend of generative AI and search engines. In short, embeddings are the foundational technology of the AI era—"quantifying meaning and converting it into a form that AI can understand and process."