Topics

Stanford’s new GNN-powered method cracks the NP-hard subgraph matching problem, enabling fast motif discovery in massive graphs from biology to social networks.

As data grows complex, pattern mining faces six key challenges—reshaping algorithms, efficiency, and insight discovery—unlocking smarter, faster, and more targeted decision-making in modern data-driven systems.

In an infinite data library, AI learns to spend its thinking wisely—prioritizing surprising patterns over common ones, transforming brute-force search into strategic, curiosity-driven discovery.

GeoPT learns physics from synthetic geometry, not labeled data — cutting training data by 60% and hitting peak performance 4x faster on industrial simulations.

What if AI organizes knowledge like the human brain—or even the universe? Researchers uncover stunning geometric patterns shaping how large language models understand the world.

A new method reduces prediction sets by up to 30 percent, helping clinicians diagnose diseases from medical images more efficiently.

Meet Stacey Engle, CEO of Twin Protocol, to find out how to take part in the next communication revolution by training your own Digital Twin.

Researchers have found a way to teach AI how to feel surfaces, by leveraging surface quantum interactions with laser light.