Artificial intelligence (AI) processing rests on the use of vectorised data. In other words, AI turns real-world information into data that can be used to gain insight, searched for and manipulated.

Companies across every industry increasingly understand that making data-driven decisions is a necessity to compete now, in the next five years, in the next 20 and beyond. Data growth — unstructured ...

Learn how to use vector databases for AI SEO and enhance your content strategy. Find the closest semantic similarity for your target query with efficient vector embeddings. A vector database is a ...

Vector databases unlock the insights buried in complex data including documents, videos, images, audio files, workflows, and system-generated alerts. Here’s how. The world of data is rapidly changing ...

Vector databases are all the rage, judging by the number of startups entering the space and the investors ponying up for a piece of the pie. The proliferation of large language models (LLMs) and the ...

Even though traditional databases now support vector types, vector-native databases have the edge for AI development. Here’s how to choose. AI is turning the idea of a database on its head.

Timescale is looking to further advance its namesake open-source database platform with new AI capabilities announced today. Timescale was founded in 2017 as a time series database (TSDB) technology ...

At the beginning of last year, who knew that generative artificial intelligence and ChatGPT would seize the moment? A year ago, we forecast that data, analytics and AI providers would finally get ...

Oracle announced a suite of agentic AI capabilities integrated directly into Oracle AI Database, enabling AI agents to securely access enterprise data where it already exists, rather than requiring ...