Search technology has evolved from matching words to understanding meaning. In this talk, we’ll unravel how this transformation happened, from the early days of sparse vector models like TF-IDF and BM25, which relied purely on keyword overlap, to today’s dense vector embeddings powered by neural networks and transformers. We’ll explore how semantic search captures context, relationships, and intent, allowing machines to “understand” language rather than just count words.
Using intuitive examples and visual demonstrations, we’ll demystify how embeddings represent meaning in high-dimensional space, how similarity is computed, and why this shift is revolutionizing information retrieval, recommendation, and question answering.
Attendees will leave with a clear conceptual map of how sparse and dense approaches differ, and how they can work together to build smarter, more intuitive search systems.
Using intuitive examples and visual demonstrations, we’ll demystify how embeddings represent meaning in high-dimensional space, how similarity is computed, and why this shift is revolutionizing information retrieval, recommendation, and question answering.
Attendees will leave with a clear conceptual map of how sparse and dense approaches differ, and how they can work together to build smarter, more intuitive search systems.