Bayesian graphical models provide a principled framework for representing complex dependency structures among multivariate variables by combining graph theory with probabilistic inference. In these ...

Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.

We know that correlation does not imply causation, but careful analyses of correlations are often our only way to quantify cause and effect in domains ranging from healthcare to education. This ...

When you ask an artificial intelligence (AI) system to help you write a snappy social media post, you probably don’t mind if it takes a few seconds. If you want the AI to render an image or do some ...

Purpose-built network fabric designed to accelerate delivery of real-time and agentic AI applications with improved throughput and power efficiency while reducing token retrieval time, latency, and ...

Google researchers have warned that large language model (LLM) inference is hitting a wall amid fundamental problems with memory and networking problems, not compute. In a paper authored by ...

It is almost certainly not a coincidence that a networking expert at Google has risen to the top to be put in charge of the infrastructure development at the search engine, advertising, and now AI ...

The company says its new architecture marks a shift from training-focused infrastructure to systems optimized for continuous, low-latency enterprise AI workloads. 2026 is predicted to be the year that ...

The AI industry stands at an inflection point. While the previous era pursued larger models—GPT-3's 175 billion parameters to PaLM's 540 billion—focus has shifted toward efficiency and economic ...