Simulating everything, sort of: The promise and limits of world models

I work in a project with a similar goal, I think, but that was not related to AI: climate digital twins. I worked on two, EDITO (European Digital Twin of the Ocean, edito.eu), and currently on DestinE ClimateDT (Destination Earth Climate Adaptation Digital Twin, https://destination-earth.eu/).

These digital twins aim to simulate climate, oceans, atmosphere, wind speed, temperature, etc., regionally or globally. They apply physics, mathematics, software engineering, and high-performance computing to simulate the world. Both digital twins I mentioned use EuroHPC resources like CSC LUMI, BSC MareNostrum5, CINECA Leonardo, and soon others (and we also use other smaller HPCs).

Now there are some ongoing work about integrating AI/ML, LLM, etc.. I find it a better approach than building for LLM/AI-first, as it integrates existing components, and allows for AI to be used too. I think this could possibly be integrated as a component in a solution like this described in the article, as a component.

(There are other digital twins, for heart, human body, cities, urban climate, rivers, biodiversity, etc.)

This is good to know. I’ve been building a world. I’m starting to find more world builders