Progress will come from systems that can combine language understanding with explicit spatial and structural reasoning.

Text mining and knowledge graphs connect cell-culture parameters to glycosylation for faster bioprocess optimization.

Enterprise AI can’t scale without a semantic core. The future of AI infrastructure will be built on semantics, not syntax.

As AI agents move into production, teams are rethinking memory. Mastra’s open-source observational memory shows how stable context can outperform RAG while cutting token costs.

Like many of us, [Tim]’s seen online videos of circuit sculptures containing illuminated LED filaments. Unlike most of us, however, he went a step further by using graph theory to design glowing ...

The Kennedy College of Science, Richard A. Miner School of Computer & Information Sciences, invites you to attend a doctoral dissertation proposal defense by Nidhi Vakil, titled: "Foundations for ...

Here are strategic revision techniques, important topics, diagram practice, and exam-day strategies to help CBSE Class 12 ...

Condensed-matter physics and materials science have a silo problem. Although researchers in these fields have access to vast amounts of data – from experimental records of crystal structures and ...

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

Read more about Artificial intelligence could change future of antimicrobial drug discovery: Here's why on Devdiscourse ...