How are embeddings best described in AI and machine learning contexts?

Embeddings are numerical representations of data, such as words, images, or entire documents, transformed into a lower-dimensional vector space. This transformation captures semantic relationships and similarities between data points. For example, in natural language processing, words with similar meanings will have embedding vectors that are close to each other in this space. This makes it easier for machine learning models to process and understand complex data, improving tasks like search, recommendation systems, and classification. While embeddings can reduce data dimensionality, their primary purpose isn't compression (A) or encryption (B). They can aid in visualization (C) but are fundamentally about numerical representation for machine learning.