Used in systems like ChatGPT with memory, embeddings help retrieve the most relevant chunks from a knowledge base before generating a response. When you search “best budget laptop,” embedding-based search engines won’t just match exact keywords, they’ll find documents semantically similar, like “top affordable notebooks.” This leads to more relevant results. Video embedding models tackle both spatial (frame-level visuals) and temporal (motion across frames) information. This gives large language models up-to-date and grounded context. This helps in understanding data structure, spotting patterns, or finding anomalies. Both users and items (like videos or books) are embedded in the same space. The model gradually adjusts the vector representations so that related inputs end up closer together. Likewise, the word “dimension” has different implications in ML, depending on its context. “Vector” itself, for…