From Kepler to Newton: Explainable AI-based Paradigm for Science Discovery

• URL: http://arxiv.org/abs/2111.12210v2
• Date: Thu, 25 Nov 2021 22:38:02 GMT
• Title: From Kepler to Newton: Explainable AI-based Paradigm for Science Discovery
• Authors: Zelong Li and Jianchao Ji and Yongfeng Zhang
• Abstract summary: We introduce an Explainable AI-based paradigm for science discovery. To demonstrate the AI-based science discovery process, we show how Kepler's laws of planetary motion and the Newton's law of universal gravitation can be rediscovered by (Explainable) AI.
• Score: 16.392568986688595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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-based 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-based 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 the 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 important role of Explainable AI (as compared to Blackbox AI) in science discovery to help humans prevent or better prepare for the possible technological singularity which may happen in the future.

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