Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?
The research project promises more efficient long-term recall by organizing knowledge around abstractions and cue-based ...
Graph optimisation problems encompass a diverse range of challenges aimed at finding optimal or near‐optimal solutions in networks or graphs. These problems are pivotal in areas such as communication ...
Hosted on MSN
New framework reduces memory usage and boosts energy efficiency for large-scale AI graph analysis
BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at the ...
Memory problems affect virtually everyone at some point in their daily lives, yet people often feel embarrassed or worried when they experience these perfectly normal cognitive hiccups. The human ...
AI systems are the ultimate amnesiacs. Despite an impressive ability to generate text, code, music, and more, they’re limited by the prompt immediately in front of them. Ask ChatGPT about a recipe it ...
A new technical paper titled “Memory-Centric Computing: Solving Computing’s Memory Problem” was published by researchers at ETH Zurich. “Computing has a huge memory problem. The memory system, ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results