To some, METR’s “time horizon plot” indicates that AI utopia—or apocalypse—is close at hand. The truth is more complicated.

Mathematicians are still trying to understand fundamental properties of the Fourier transform, one of their most ubiquitous ...

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

One of the most striking outcomes of the surface-minimization framework is its ability to explain structural features that ...

It’s increasingly recognised that moving from prompts to context is critical for achieving scalable, adaptive high-impact enterprise AI ...

Knowledge representation is a fundamental aspect of AI, which allows machines to understand, think, and even make choices similarly to humans. By organizing inf ...

Abstract: Graph neural networks (GNNs) have gained increasing popularity in understanding graph-structured data due to their ability to derive meaningful representations by aggregating complicated ...

For years, SEOs optimized pages around keywords. But Google now understands meaning through entities and how they relate to one another: people, products, concepts, and their topical connections ...

Consider this problem I assign every year in my algebra-based physics class: A rocket is launched toward space with a steady speed of 6m/s. After 10 seconds, the engine providing that speed cuts off ...

Abstract: Graph-level anomaly detection (GLAD) aims to distinguish anomalous graphs that exhibit significant deviations from others. The graph-graph relationship, revealing the deviation and ...

Understand the merits of large language models vs. small language models, and why knowledge graphs are the missing piece in the equation. Enterprise AI tends to default to large language models (LLMs) ...