Neo4j also trumpeted the value of graphs as vector databases used in generative artificial intelligence. AI training requires ...

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

Context, not content, now drives AI visibility, making structured data the strategic data layer every enterprise must ...

Alongside text-based large language models (LLMs), including ChatGPT in industrial fields, GNN (Graph Neural Network)-based graph AI models that analyze unstructured data such as financial ...

In the current context where the demand for data security and privacy protection is increasingly urgent, automatic ...

From Relational to Graph Databases: The 'Association' Revolution in the Era of Big Data In this era of information explosion, ...

A study by ANL has shown how physics-based models and AI can improve trust in nuclear diagnostics by explaining faults and ...

TwinMind, available on Android and iOS, passively captures background audio to gain context and deliver on-the-go summaries.

Tairon connects AI agents with blockchain data through the first on-chain MCP Supergraph, enabling secure, real-time ...

A high-performance AI framework enhances anomaly detection in industrial systems using optimized Graph Deviation Networks and graph attention ...

The natural-language Mist AI and its Marvis virtual network assistant (VNA) platform work by gathering telemetry and user ...