Michele Morales

Artificial intelligence workloads are transforming data centers into extremely dense computing environments. Training large language models, running real-time inference, and supporting accelerated analytics rely heavily on GPUs, TPUs, and custom AI accelerators that consume far more power per rack than traditional servers. While a conventional enterprise rack once averaged 5 to 10 kilowatts, modern AI racks can exceed 40 kilowatts, with some hyperscale deployments targeting 80 to 120 kilowatts per rack.This surge in power density directly translates into heat. Traditional air cooling systems, which depend on large volumes of chilled air, struggle to remove heat efficiently at these levels. As…

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.Superior and Meticulously 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:…