Science and Technology

Green hydrogen once symbolized a sweeping solution to decarbonize nearly every sector of the economy. Governments announced grand strategies, investors poured capital into electrolyzer startups, and projections promised rapid cost declines. Today, the narrative is more measured. Rather than disappearing, green hydrogen is finding its footing in specific, high-value applications where it solves problems that electricity alone cannot. This shift from hype to targeted use cases reflects lessons learned about economics, infrastructure, and real-world constraints.Understanding Green Hydrogen and the Early HypeGreen hydrogen is produced by splitting water through electrolysis powered by renewable electricity, resulting in hydrogen with near-zero direct emissions.…

Modern AI systems are no longer constrained primarily by raw compute. Training and inference for deep learning models involve moving massive volumes of data between processors and memory. As model sizes scale from millions to hundreds of billions of parameters, the memory wall—the gap between processor speed and memory throughput—becomes the dominant performance bottleneck.Graphics processing units and AI accelerators are capable of performing trillions of operations per second, yet their performance can falter when data fails to arrive quickly enough. At this point, memory breakthroughs like High Bandwidth Memory (HBM) become essential.Why HBM Stands Apart at Its CoreHBM is a…

Artificial intelligence systems, especially large language models, can generate outputs that sound confident but are factually incorrect or unsupported. These errors are commonly called hallucinations. They arise from probabilistic text generation, incomplete training data, ambiguous prompts, and the absence of real-world grounding. Improving AI reliability focuses on reducing these hallucinations while preserving creativity, fluency, and usefulness.Higher-Quality and Better-Curated Training DataImproving the training data for AI systems stands as one of the most influential methods, since models absorb patterns from extensive datasets, and any errors, inconsistencies, or obsolete details can immediately undermine the quality of their output.Data filtering and deduplication: By…

Artificial intelligence workloads are reshaping data centers into exceptionally high‑density computing ecosystems, where training large language models, executing real‑time inference, and enabling accelerated analytics depend on GPUs, TPUs, and specialized AI accelerators that draw significantly more power per rack than legacy servers; whereas standard enterprise racks previously operated around 5 to 10 kilowatts, today’s AI‑focused racks often surpass 40 kilowatts, and certain hyperscale configurations aim for 80 to 120 kilowatts per rack.This rise in power density inevitably produces substantial heat. Traditional air cooling systems, which rely on circulating significant amounts of chilled air, often fail to dissipate heat effectively at…