Embeddings: The Reason Machines Finally “Get” Language

Before 2013, computers processed language by simply counting words, making it impossible to understand meaning or context. Word2Vec changed everything by introducing embeddings — converting words into points on a mathematical map where similar meanings cluster together. This allows machines to recognize that "happy" and "joyful" are related without being told, powering modern LLMs, search engines, and social media recommendation systems that match meaning rather than just matching exact words.