Author's): Hayanan
Originally published in Towards Artificial Intelligence.
How artificial intelligence learns meaning, not just keywords
Modern AI systems are no longer judged by how quickly they retrieve data, but by how we understand it. As users interact with applications in increasingly natural ways, typing vague descriptions, asking open-ended questions, and even loading traditional keyword-based marine images, they quickly reach their limits. Exact matches fail when intent is ambiguous, context is implicit, and meaning goes beyond words. This gap between human expression and machine understanding is where new data infrastructure has become essential.
In the evolution of AI applications, vector databases are playing a key role by enabling systems to understand meaning through numerical representations, facilitating semantic searches instead of traditional keyword matching. This understanding allows for the development of smarter systems capable of processing unstructured data, improving the performance of search engines and recommendation systems. Bridging the gap between raw data and actionable insights, vector databases support a variety of applications, including customer service, e-commerce, and conversational agents, while addressing the challenges of rapid data growth in today's digital landscape.
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Published via Towards AI
