Author's): Shobhit Chauhan
Originally published in Towards Artificial Intelligence.
Embedding – Understanding vectors in AI
We live in a world obsessed with the latest AI glasses: stunning photorealistic images, philosophical conversations with a chatbot, code that practically writes itself. We constantly hear about billions of parameters and new, revolutionary architectures. But before a multilingual model can write one perfect sentence about existential dread or plan his next vacation, he must perform an act of digital alchemy so fundamental as to be magical. It has to take the glorious, chaotic, beautiful mess of human language – all the slang, all the metaphor, all the nuances – and reduce it to the only language that the computer truly respects: pure mathematics.
The article explores the fundamental role of embeddings in artificial intelligence, explaining how they fill the lexical gap by teaching machines the meaning and relationships between words. He discusses the embedding process as a form of digital alchemy, transforming human language into mathematical vectors that capture meaning, enabling AI systems to better understand context and semantics than traditional keyword-based methods. The intricacies of how embeddings are created, their applications in semantic search, recommendations and more, and the evolution towards contextual embeddings are thoroughly explored, demonstrating the transformative power of embeddings in modern AI technology.
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Published via Towards AI
