#2 Transforming Data Storage and Retrieval: The Rise of Vector Databases in AI and Beyond

The advent of vector databases has introduced a paradigm shift in data storage and retrieval, enabling fast and accurate similarity searches based on high-dimensional vectors representing features or attributes of diverse data types. In this conference talk, we will delve into the inner workings of vector databases, discussing their underlying principles and the transformative embedding functions that facilitate their use across various domains. We will explore the critical advantage of vector databases - the ability to perform semantic search and retrieval based on vector distance or similarity rather than relying on exact matches or predefined criteria. Through real-world examples, we will demonstrate how vector databases can be utilized to find similar images, documents, and products based on their inherent characteristics, revealing the immense potential of this technology in applications such as natural language processing, computer vision, and recommendation systems.