Leveraging AI for Climate Resilience in Africa: Challenges, Opportunities, and the Need for Collaboration
Event date: 2025-01-30
Event location:
Welcome to this week’s Learning Machines seminar.
This seminar is a collaboration between RISE and Climate Change AI Nordics – ccainordics.com.
Title: Leveraging AI for Climate Resilience in Africa: Challenges, Opportunities, and the Need for Collaboration
Speaker: Amal Nammouchi, Karlstad University and AfriClimate AI
Abstract: As climate change issues become more pressing, their impact in Africa calls for urgent, innovative solutions tailored to the continent’s unique challenges. While Artificial Intelligence (AI) emerges as a critical and valuable tool for climate change adaptation and mitigation, its effectiveness and potential are contingent upon overcoming significant challenges such as data scarcity, infrastructure gaps, and limited local AI development. This talk explores the role of AI in climate change adaptation and mitigation in Africa. It advocates for a collaborative approach to build capacity, develop open-source data repositories, and create context-aware, robust AI-driven climate solutions that are culturally and contextually relevant.
About the speaker: Amal Nammouchi is a PhD student at Karlstad University’s SOLVE Research Centre, focusing on optimizing renewable energy communities under uncertainty and advancing sustainable energy systems. She co-founded AfriClimate AI, a non-profit tackling Africa’s climate challenges with AI solutions, while contributing to responsible AI development through research on Large Language Models. With over a decade of leadership experience in community building and international development, Amal is also an award-winning entrepreneur dedicated to transforming research into impactful real-world solutions.
Location: This is an online seminar. Connect using Zoom.
Date: 2025-01-30 15:00
Upcoming seminars:
- 2025-02-20: Abdulhakim Abdi, Lund University
- All seminars are 15:00 CET.
More information and coming seminars: https://ri.se/lm-sem
– The Learning Machines Team