Embeddings are a foundational concept in machine learning, particularly within the realm of neural networks, that enables complex, high-dimensional data to be represented in a lower-dimensional space. This representation preserves semantic relationships, meaning that similar items are placed closer together in the vector space. The significance of embeddings lies in their ability to transform abstract, discrete entities into a form that algorithms can efficiently process for tasks such as natural language processing, recommendation systems, and image recognition. By doing so, embeddings allow for nuanced understanding and operations on data, including similarity measurement, clustering, and interpolation. They are crucial in models where the input space is vast and not explicitly numerical, enabling machines to grasp subtleties and context.
The concept of embeddings became prominent in the 2000s, particularly with the advent of Word2Vec in 2013, a technique for learning word embeddings from text data. However, the underlying idea of mapping entities to points in a vector space has roots in earlier works on dimensionality reduction and distributed representations.
Significant contributors to the development and popularization of embeddings include Tomas Mikolov, who was instrumental in the creation of Word2Vec while at Google. Other notable contributions have come from researchers working on similar embedding techniques like GloVe and fastText, expanding the application of embeddings beyond text to other domains such as graphs, user behavior, and images.