As humans, it is our responsibility to take care of the planet. It is our home, and the way we treat the environment has everlasting impacts on future generations. Over the centuries as we have moulded and manipulated natural resources, we have created some life-changing technologies, but have also created environments that have negative ramifications to our planet. We have the opportunity to act now for a positive change and transform the land, seas and air by acting responsibly with social and environmental growth at the core. This World Environment Day, we’re taking a look at how some global AI experts are leveraging technology for positive social impact to benefit the environment and the world.
The predictions of the future of our planet are bleak unless we make drastic changes. Both the UN and other bodies are aware that the protection and improvement of the environment is an issue deserving of much attention, so the celebration of today encourages all generations to do their bit for the planet and support initiatives going on across the globe. This June 20 - 21 in San Francisco, we will be hosting the AI for Good Summit where researchers, government bodies and business-people will come together to share their work in the space. Here are some of the experts who will be sharing their work at the summit:
Carlos Felipe Gaitan Ospina, Chief Climate Scientist & Head of AI, Climate AI
Presentation: Climate Zones Classification for Agriculture Applications using Machine Learning
Modern climate classification techniques typically employ intra-annual variability of climate data--e.g. temperature and precipitation--in order to segment geographical regions into meaningful zones with similar physical characteristics. For instance, common classification schemes like the Koppen-Geiger rely on human-defined clusters. Carlos is working on a generalizable method for creating meaningful climatically-relevant zones tailored to each user’s needs. To accomplish this, he first explored several clustering methods by comparing performances and weighing potential advantages and/or disadvantages to each technique. Carlos and his team found out that their streamlined clustering procedure can benefit the real state and agricultural sectors especially, by taking into consideration climate change conditions.
Carlos did his doctoral studies at the University of British Columbia (Vancouver, Canada) working with William Hsieh in machine learning applications in the environmental sciences. He also holds a Bachelor degree in Civil Engineering and a Master degree in Hydrosystems from the Pontificia Universidad Javeriana (Bogota, Colombia). He is a member of the American Meteorological Society’s (AMS) Artificial Intelligence Committee. He previously worked as a VP of Weather Forecasting at Arable Labs and as Research Scientist for the South Central Climate Science Center at the Geophysical Fluid Dynamics Laboratory (GFDL) in Princeton, New Jersey.