AI News #4. The growing relevance of semantic search
Greetings, AI enthusiasts! In this edition of our newsletter, we'll take a break from our regular reporting style to focus on a single, captivating topic: semantic search engines, which are gaining growing momentum in the AI universe. We'll explore how traditional keyword searches are becoming obsolete and how, instead of needing to repeatedly rephrase queries till we get to desired results, semantic search gives us a search experience that truly understands our intentions from the start.
Decoding semantic search
Most online information is in text format, and to grasp your intent, semantic search engines leverage Natural Language Processing (NLP) and Machine Learning (ML). However, since machines can't directly process text, they use "embeddings," a technique that transforms text into a numerical (or vector) representation. Think of it as a compressed code, where words and phrases with similar meanings share similar numerical patterns. These embeddings can involve thousands of values, allowing machines to capture even the tiniest nuances of words and thus detect relevant information with extreme accuracy.
Beyond the hype
Creating embeddings is a complex task, but it's only a part of implementing semantic search. The next crucial step involves storing and navigating vast amounts of data, for which semantic search engines rely on specialized data structures called "indices." The choice of index is crucial and depends on various factors, such as data complexity and the necessary search speed and accuracy. Fortunately, many options are available for storing these indices as well, from vector databases to established search engines with built-in semantic search capabilities.
Putting the pieces together
When you initiate a search, your query is immediately transformed into an embedding vector, just like the documents it will be compared against. This sets the stage for the K-Nearest Neighbors (KNN) algorithm, a powerful tool for identifying the best matches. KNN calculates the distance between your query vector and each document vector. The closer the vectors, the more relevant the document is to your intent. Various distance formulas are used, but they all follow the same principle: identifying the closest matches based on meaning, not just exact keywords.
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Getting the answer
Finally, the search engine retrieves the top-k closest documents (most similar to your query) and presents them to you. These results focus on the context and semantic meaning of your search, even if they don't contain the exact phrasing you used.
Understanding the trade-offs
While semantic search boasts impressive capabilities, it's important to recognize its differences from traditional keyword search. Semantic search excels at understanding concepts and intent but isn't perfect for exact-match queries, where keyword search remains superior. Additionally, semantic search is computationally expensive, while keyword search is simpler and faster, especially if you know exactly what you're looking for. Keyword search focuses on the literal presence and order of words without delving into meaning or context.
We're eagerly awaiting to see where these advances take us. Stay tuned for our next edition, and we'll keep you updated on the latest and hottest news from the world of AI!
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