2 months ago
- World models, unlike large language models, learn dynamics from observation and simulate outcomes to predict behavior in the physical world.
- Key figures like Yann LeCun, Demis Hassabis, Sam Altman, Fei-Fei Li, and Jensen Huang are advancing world models through different approaches, though definitions vary.
- Current AI applications in Earth science (e.g., fire detection, weather forecasting, flood prediction) have improved but struggle with complex systems like hurricanes and the carbon cycle.
- The Earth system is modeled in pieces due to unresolved physics and sparse observations, leaving critical gaps in understanding coupled dynamics.
- World models can address scientific bottlenecks by learning dynamics from joint Earth-system data while enforcing physical constraints, improving predictions for chaotic systems.
- Examples like AlphaFold and GraphCast work where physics is partly understood, but open systems (e.g., sea level, carbon cycle) remain challenging.
- World models offer tighter, more honest uncertainty ranges for decision-making, but cannot predict unprecedented regime shifts due to lack of data.
- Enterprise investment drives development, but public-good applications (e.g., climate predictions) require non-commercial support and institutional data sharing.
- The future of world models depends on what data and problems are prioritized, with potential to address humanity's hardest scientific challenges.