Session II will take place on Wednesday, 29 January 2025 from 11:00 am to 12:30 pm, Room A-122.

Session I will take place on Wednesday, 29 January 2025, from 9:30 am to 10:30 am in Room A-122.

Session II will take place on Thursday, 30 January 2025, from 11:00 am to 12:30 pm in room A-122.

Chairs:

• Pascal Horton, Mobiliar Lab of Natural Risks, Oeschger Center for Climate Research, University of Bern, Switzerland
• Olivia Martius, Mobiliar Lab of Natural Risks, Oeschger Center for Cliamte Research, Universtiy of Bern
• Noelia Felipe, Frauenhofer HHI, Germany
• Vitus Benson, Max-Planck Insitute, Germany

Session I will take place on Thursday, 30 January 2025, from 9:30 am to 10:30 am in room A-122.

Program ML & Forecasting II

1. Presentations

2. Interactive Game: Forecasting rare events with generative human AI
It is 11:45 am in Bern, Switzerland, and the International Committee on Unforeseen Risks is about to convene. Their latest session intends to prepare humanity for what is yet to come. RIMMA, the generative AI system built to forecast catastrophic outcomes, is opening the meeting with its latest assessment report. "Preparedness is key to mitigating the impacts of rare but catastrophic events. Here is a list of the top 10 unforeseen catastropheeeees that atataatat….“. What happened? A power outage struck the building, and now the committee is left to resort to their human intelligence to prepare a list they can present to the head of state within 45 minutes.

We all become imagineers in this slot and play a collaborative game to forecast rare events! Join us for an interactive activity that will query your creativity and spark a discussion on using AI systems for forecasting extreme events. After a brief introduction, we will play the game (no prerequisites required), stimulating our discussion on machine learning in early warning systems.

3. Wrap-Up discussion: Opportunities & Risks of ML for Early Warning Systems

The session will conclude with a 15-minute discussion on the opportunities and risks of ML for early warning systems.

This session covers presentations on the topic of weather forecasts for health management