Partnership between Climate AI Nordics and Global Wetland Center
We are excited to share our partnership with Global Wetland Center 🌍✨!
Read more on our partner page!
You May Also Enjoy
New Strategic Partnership: Climate AI Nordics and Climate Change AI
Published:
Climate AI Nordics (CAIN) is proud to announce a formal partnership with Climate Change AI (CCAI). This collaboration aims to bridge the gap between AI expertise and climate action by facilitating the exchange of knowledge, resources, and joint initiatives between the Nordic region and the global community. By aligning our missions, we seek to accelerate responsible AI-driven solutions for climate mitigation and adaptation.
More is different: emergent social conventions and tipping points in AI populations
Event date:
Webinar with Ariel Flint Ashery, City St George’s, University of London. Social conventions are the foundation of social coordination, shaping how individuals come together to form a society. In this talk, I will present theoretical and experimental findings that demonstrate the spontaneous emergence of social norms in LLM populations, as well as the existence of tipping points in social convention. I will show that agentic AI populations can establish social conventions and highlight how collective biases can emerge even when individual agents appear unbiased. I will conclude by stressing how the ability of AI agents to develop norms without explicit programming has significant implications for designing AI systems that align with human values and societal goals.
Climate AI Nordics Newsletter, April 2026
Published:
The April 2026 Climate AI Nordics newsletter highlights the SafeCityLearn benchmark and researcher Miguel Nobre da Costa, while sharing a wealth of upcoming events, webinars, and job opportunities at the intersection of AI and climate action across the Nordic region.
PhD Fellow in Machine Learning for Generative Emulators of the Arctic Ocean
Published:
The Nansen Environmental and Remote Sensing Center (NERSC) is offering a 3-year PhD fellowship focused on developing generative machine learning emulators to improve Arctic Ocean forecasting and investigate the reliability of data-driven climate models.