What if your AI could not only retrieve information but also uncover the hidden relationships that make your data truly meaningful? Traditional vector-based retrieval methods, while effective for ...

SurrealDB 3.0 launches with $23M in new funding and a pitch to replace multi-database RAG stacks with a single engine that handles vectors, graphs, and agent memory transactionally.

Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

An executive guide to artificial intelligence, from machine learning and general AI to neural networks. Read now This is a summary of the thesis taken by scientist, best-selling author, and ...

Graph database vendor Neo4j Inc. is teaming up with Snowflake Inc. to make a library of Neo4j’s graph analytics functions available in the Snowflake cloud. The deal announced today allows users to ...

There’s been a debate of sorts in AI circles about which database is more important in finding truthful information in generative AI applications: graph or vector databases. AWS decided to leave the ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More What do you get when you combine two of the most up-and-coming paradigms ...

The Graph, the decentralized indexing system that works much like Google for blockchains, has introduced a data standard for Web3. Called GRC-20, the standard would define how information is ...

Roughly 80% of enterprise data sits in emails, contracts, call transcripts, and PDFs where traditional databases can't touch it. Much of this "unstructured" data isn't ignored because it lacks value, ...