Tokens, Embeddings, and the Future of AI: Decoding the Linguistic Backbone of Modern Search
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Tokens, Embeddings, and the Future of AI: Decoding the Linguistic Backbone of Modern Search
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This episode explains the difference between tokens and embeddings, fundamental concepts in Artificial Intelligence (AI) and Large-Scale Language Models (LSMs) that impact SEO. Tokens are basic textual units, such as words, used in traditional keyword searching through sparse embeddings that consider only frequency. In contrast, dense embeddings are numerical representations that capture the semantic meaning and context of words, making them crucial for natural language understanding in modern AI systems. The article traces the evolution of Google search, highlighting how technologies like RankBrain, BERT, and MUM utilize embeddings to enhance the relevance of Search results. Finally, it presents hybrid search as a solution that combines the efficiency of semantic search with the accuracy of lexical (token-based) search, ensuring that AI systems can handle information both inside and outside their training domain.