Quick answer: Represent text/images/audio as dense vectors — the foundation of search + clustering.
Embeddings are numerical representations of text, images, or audio that capture semantic meaning in dense vector form. Instead of treating words as isolated tokens, embeddings map them into a high-dimensional space where similar concepts cluster together. This transformation is foundational to modern AI — it powers search engines that understand intent, recommendation systems that find relevant content, and clustering algorithms that group similar data without explicit labels.
You can build search engines that find documents by meaning rather than keywords, recommendation systems that surface relevant products or content, and clustering systems that automatically group customer feedback or research papers. Embeddings are the bridge between human language and machine learning models.