A Greener, Safer, and More Understandable AI for Natural Science and Technology

Abstract

More rational, open-minded use of quantitative Big Data in Science and Technology is required for better real-world problem solving as well as for the stabilization of shared belief structures in society. Modern instrumentation gives informative but overwhelming data streams. A thermal video camera with suitable spatiotemporal subspace modeling allows us to detect surface temperature changes of, for example, engines, that can reveal something going on inside. An RGB video camera responds to both motions and color changes in nature, often with spatiotemporal change patterns that we can discover and describe mathematically, validate statistically, interpret graphically, and then use for sensible things. A hyperspectral Vis./NIR satellite camera with hundreds of wavelengths reveals changes in clouds and at each earth location, again and again. Today we know how to decode such overwhelming streams of high-dimensional data into physical and chemical causalities by minimalistic hybrid multivariate subspace models. We thereby combine prior knowledge with the ability to discover new, reliable variation patterns. Minimalistic subspace models handle such data. These “open-ended” multivariate linear hybrid models are computationally fast, statistically safe, and graphically understandable. The minimalistic subspace models are therefore suitable for both data modeling (based on multivariate measurements) and metamodeling (based on input–output simulation results for nonlinear mechanistic models' behavioral repertoire). That makes it easier to combine high-dimensional streams of real-world measurements and complicated, slow mechanistic models. Implemented as minimalistic foundation models with hierarchies of extended subspace models, this can form a basis for faster discovery and problem solving in Natural Science & Technology.