https://msucerl.org/cmse101/tags/environment/Recent content in Environment on AI and Society CourseHugoen-usThu, 21 May 2026 00:00:00 +0000https://msucerl.org/cmse101/use-cases/5-1-climate-modeling-weather-prediction/Thu, 21 May 2026 00:00:00 +0000https://msucerl.org/cmse101/use-cases/5-1-climate-modeling-weather-prediction/<h1 id="climate-modeling--weather-prediction">Climate Modeling &amp; Weather Prediction</h1> <h2 id="context--systems-architecture">Context &amp; Systems Architecture</h2> <p>AI-driven atmospheric modeling has emerged as a disruptive paradigm shift threatening to overturn traditional numerical weather prediction (NWP). Historically, weather forecasting relied on massive supercomputers executing complex systems of physical fluid dynamics and thermodynamic differential equations. In 2023, Google DeepMind released <strong>GraphCast</strong>, a deep learning model capable of generating highly accurate 10-day global weather forecasts in under 60 seconds on a single GPU—matching or exceeding the predictive skill of the European Centre for Medium-Range Weather Forecasts (ECMWF), the historic global gold standard.</p>https://msucerl.org/cmse101/use-cases/5-2-carbon-cost-of-ai/Thu, 21 May 2026 00:00:00 +0000https://msucerl.org/cmse101/use-cases/5-2-carbon-cost-of-ai/<h1 id="the-carbon-cost-of-ai">The Carbon Cost of AI</h1> <h2 id="context--systems-architecture">Context &amp; Systems Architecture</h2> <p>The rapid integration of artificial intelligence into daily digital infrastructure is frequently presented as a clean, virtual shift that saves human labor and carbon footprint. This framing hides a massive, highly material physical reality: AI runs on an incredibly resource-intensive global network of factories, power grids, and cooling infrastructure. The expansion of generative AI and LLM clusters has triggered an unprecedented surge in electricity demand, forcing tech conglomerates to expand fossil-fuel dependencies and directly undermining global carbon reduction mandates.</p>