DR. ANDREW JACKSON
AI IN SUSTAINABILITY
DR. ANDREW JACKSON
TITLE: GROUP CHIEF AI OFFICER
COMPANY: G42
INDUSTRY: ARTIFICIAL INTELLIGENCE
With nearly three decades of AI and data science experience, Andrew is a founding G42 member who led JAIS Arabic LLM development. Former Palantir MENA expansion leader and Etihad Airways digital transformation architect.
Inception, a G42 company working with Space42, has customised CorrDiff from Nvidia’ s Earth-2 platform for detailed urban forecasts. Core42, G42’ s digital infrastructure company, hosts the platform using Nvidia hardware. The system demonstrated capabilities through an end-to-end fog simulation over the UAE, addressing localised weather phenomena affecting multiple industries.
Andrew Jackson, Chief AI Officer at G42, says:“ For AI to be truly transformative, it should be an accessible tool for governments and industries worldwide. Through our collaboration with Nvidia, we are bringing cutting-edge forecasting capabilities within reach of those who need them most. Because CorrDiff is designed to adapt to local weather behaviours, this technology is not only improving forecasting for the UAE but can also be tailored for regions worldwide facing climate volatility.”
AI forecasting systems adapt to local weather patterns across geographical regions. Traditional models use global atmospheric equations that may not capture regional variations. AI systems learn from local historical data to improve predictions for specific areas over time.
Energy companies benefit from hyper-local weather forecasting for renewable energy management. The International Energy Agency reports that improved weather forecasting could increase wind energy efficiency by 15 % through better wind pattern prediction. Current renewable energy forecasting operates at kilometre resolutions, making it difficult to predict conditions at individual installations. AI-powered systems provide forecasts for specific turbines or solar arrays.
How Nvidia physics AI models process climate data Physics AI models process climate data by learning atmospheric patterns through data analysis rather than relying solely on mathematical equations. This approach enables faster forecast generation whilst reducing computational infrastructure requirements compared to traditional systems. aimagazine. com 251