Embeddings – Understanding Vectors in AI

Author(s): Shobhit Chauhan

Originally published on Towards AI.

Embeddings – Understanding Vectors in AI

We live in a world obsessed with the latest AI spectacles: the stunning photorealistic images, the philosophical conversations with a chatbot, the code that practically writes itself. We’re constantly hearing about billions of parameters and revolutionary new architectures. But before a Large Language Model can write a single, perfect sentence about existential dread or plan your next vacation, it has to perform an act of digital alchemy so fundamental, it’s almost magical. It has to take the glorious, chaotic, beautiful mess of human language — all the slang, all the metaphor, all the nuance — and boil it down to the only language a computer truly respects: pure mathematics.

The article delves into the significance of embeddings in AI, detailing how they help bridge the lexical gaps in understanding by encoding words and their meanings as vectors. It explores the process of transforming human language into numerical representations, demonstrating the importance of context through examples of semantic search and recommendation systems. By explaining various methods of generating embeddings, from traditional one-hot encoding to more sophisticated techniques like Word2Vec and transformers, it illustrates the ongoing evolution in natural language processing and its implications for artificial intelligence.

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Published via Towards AI