The research paradigm of the Observation–Hypothesis–Prediction–Experimentation loop has been practiced by researchers for years towards scientific discovery. However, with the data explosion in both mega-scale and milli-scale scientific research, it has been sometimes very difficult to manually analyze the data and propose new hypothesis to drive the cycle for scientific discovery.
In this paper, we introduce an Explainable AI-assisted paradigm for science discovery. The key is to use Explainable AI (XAI) to help derive data or model interpretations and science discoveries. We show how computational and data-intensive methodology — together with experimental and theoretical methodology — can be seamlessly integrated for scientific research. To demonstrate the AI-assisted science discovery process, and to pay our respect to some of the greatest minds in human history, we show how Kepler’s laws of planetary motion and Newton’s law of universal gravitation can be rediscovered by (explainable) AI based on Tycho Brahe’s astronomical observation data, whose works were leading the scientific revolution in the 16-17th century. This work also highlights the importance of Explainable AI (as compared to black-box AI) in science discovery to help humans prevent or better prepare for the possible technological singularity which may happen in the future.
In this paper, we introduce an Explainable AI-assisted paradigm for science discovery. The key is to use Explainable AI (XAI) to help derive data or model interpretations and science discoveries. We show how computational and data-intensive methodology — together with experimental and theoretical methodology — can be seamlessly integrated for scientific research. To demonstrate the AI-assisted science discovery process, and to pay our respect to some of the greatest minds in human history, we show how Kepler’s laws of planetary motion and Newton’s law of universal gravitation can be rediscovered by (explainable) AI based on Tycho Brahe’s astronomical observation data, whose works were leading the scientific revolution in the 16-17th century. This work also highlights the importance of Explainable AI (as compared to black-box AI) in science discovery to help humans prevent or better prepare for the possible technological singularity which may happen in the future.
Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
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