Data centers are crucial for storing, processing, and distributing vast amounts of data in the modern era, as internet-based data-transfer services are essential in our daily work and personal lives.

A new study in Genome Biology benchmarks eight antibody language models across five design tasks, finding that AbLang, ...

AI workloads are fundamentally different from traditional enterprise applications. Training and inference at scale introduce sustained high-density compute, extreme east–west traffic, and ...

Designing for AI ultimately requires a shift in mindset. Cooling must be engineered as an integrated system that supports ...

A new technical paper titled “Scaling Intelligence: Designing Data Centers for Next-Gen Language Models” was published by Intel Corporation and Georgia Tech. An excerpt from the paper’s abstract: “Our ...

Integrating AI into chip workflows is pushing companies to overhaul their data management strategies, shifting from passive storage to active, structured, and machine-readable systems. As training and ...

The end goal of database design is to be able to transform a logical data model into an actual physical database. A logical data model is required before you can even begin to design a physical ...

Design thinking is critical for developing data-driven business tools that surpass end-user expectations. Here's how to apply the five stages of design thinking in your data science projects. What is ...

Many engineering teams still rely on architecture optimized for transactional apps, not for AI systems that mix structured and unstructured data and live event streams. This legacy architecture has ...