Vector Embeddings: AI Doesn't Understand Words. It Understands Math (medium.com)

Recent discussions in the AI/ML community have highlighted a fundamental shift in understanding how artificial intelligence processes language. Instead of interpreting words in a traditional linguistic sense, AI models like GPT and BERT utilize mathematical representations known as vector embeddings. This approach allows them to encode words as multi-dimensional points in a high-dimensional space, where the relationships between words can be represented mathematically. This means AI doesn't "understand" words in a human way; it calculates similarities and associations through geometric relationships. The significance of this realization lies in enhancing how developers and researchers harness AI capabilities. By recognizing that AI communicates through mathematical relationships, practitioners can innovate more efficient algorithms and training methods. For instance, the success of transformer models is largely attributed to their ability to leverage vector embeddings, allowing them to capture nuanced relationships between words and phrases. Understanding these underlying mechanics not only shapes the development of more advanced natural language processing applications but also drives improvements in machine learning architectures across various domains.