Climate Change & Data Analysis - Global greenhouse emissions are plateauing and projected to fall, driven by wind, solar energy, carbon capture, and nature-based solutions [2] - Climate science uses data to build mathematical models of weather trends, tested against real-world outcomes, improving predictions [3][4] - Climate models have become more accurate due to increased data from sensors, radar, aircraft, balloons, and satellites, enhancing temporal, spatial, and quantitative accuracy [8][9] Food Security - Climate change is making current farming locations less suitable, but predictive models can help farmers adapt planting decisions [10][14] - Models can predict wheat harvest yields more than a year in advance, as demonstrated by a model predicting a low harvest in Kansas in 2023 and a rebound in 2024 [12][13] Water Resource Management - Climate change is altering water distribution patterns, necessitating renegotiation of water sharing agreements based on predictive models [16][17] - Predictive models can forecast river flows years in advance, enabling proactive water management and storage strategies [17][18] Environmental Justice - Climate science can now trace carbon pollution to its source and consequences, removing plausible deniability for those responsible for environmental damage [20][21][22] - Researchers can demonstrate the impact of carbon emissions on specific regions, such as the impact of agricultural fires in India on Nepal, and on vulnerable nations [21][22]

We say accelerated computing is sustainable computing because it is inherently fundamentally more energy efficient and Nvidia has really pioneered this acceleration of compute power. We've improved the energy efficiency of the accelerated computing platform by 100,000 times over the past decade. And that type of energy efficiency improvement is really critical to us unlocking the next level of performance that allows us to build bigger and bigger models and do more and more with AI.The potential positive im ...