Paper short abstract:

A.I. models have unique predictive powers yet do not acknowledge their limitations. I will discuss consequences of this condition in NVIDIA’s digital twin of Earth. Based on research in A.I. enabled remote sensing, I will suggest how model diversity can produce counter-weights to large A.I. models.

Paper long abstract:

The debate surrounding the influence of A.I. foundation models has precedent in the earth sciences, where model limitations have long been understood as inherent to the modeling process itself. Climate scientist Reto Knutti, for example, long ago argued for the use of model diversity to bound model uncertainty.

Countering model hegemony can occur in different ways. Small A.I. models that readily share their own limitations can be effective in resource constrained environments. I will discuss some tradeoffs between computing intense, data hungry neural networks versus data sparse algorithms in the mapping contested land use conditions, and suggest how model diversity might be able to combine benefits of small models with the power of the largest systems.

Earth system models are limited by uncertainty-hiding large neural networks in unique ways. Early earth system models produced a set of estimates that require interpretation by experts. However, the latest physics informed neural networks inherent the opacity of neural networks, and add seductive visualization. Climate scientists working with systems such as NVIDIA’s digital twin of Earth, EARTH-2 can share the vast volumes of data, collapsed to a few simple metrics in earlier specialists’ models, with the public in visually compelling ways. The resulting cinematic climate crisis communication shifts the emphasis from plausible scientific inference to a visual experience of climate futures that makes scrutiny more challenging. While EARTH-2’s powerful visualizations will make climate science visually more accessible, they suggest unrealistic, on demand, countermeasures to climate change.