
Deep learning is a subfield of artificial intelligence and machine learning that uses multilayer artificial neural networks to learn hierarchical representations from data. It underpins major advances in computer vision, speech recognition, natural language processing, and decision-making systems since the early 2010s.

Embedding models are machine learning techniques that transform high-dimensional data into lower-dimensional vector spaces, preserving semantic relationships and enabling efficient processing across various data types.

Foundation models are large‑scale AI systems trained on broad, predominantly self‑supervised data and adapted to many downstream tasks. Coined in 2021 by Stanford’s Center for Research on Foundation Models, they exhibit emergent capabilities and drive homogenization across AI applications.