Affiliation:
1. Division of Frontier Research, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China.
2. Institute of Atmospheric Sciences and CMA-FDU Joint Laboratory of Marine Meteorology, Department of Atmospheric and Oceanic Sciences,
Fudan University, Shanghai, China.
3. IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health,
Fudan University, Shanghai, China.
4. Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China.
Abstract
It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence algorithms, however, is challenged with its vagueness, nonquantitativeness, computational inefficiency, etc. During the past 18 years, these challenges have been essentially resolved, with the establishment of a rigorous formalism of causality analysis initially motivated from atmospheric predictability. This not only opens a new field in the atmosphere-ocean science, namely, information flow, but also has led to scientific discoveries in other disciplines, such as quantum mechanics, neuroscience, financial economics, etc., through various applications. This note provides a brief review of the decade-long effort, including a list of major theoretical results, a sketch of the causal deep learning framework, and some representative real-world applications pertaining to this journal, such as those on the anthropogenic cause of global warming, the decadal prediction of El Niño Modoki, the forecasting of an extreme drought in China, among others.
Publisher
American Association for the Advancement of Science (AAAS)
Reference34 articles.
1. Investigating Causal Relations by Econometric Models and Cross-spectral Methods
2. Pearl J. Causality: Models reasoning and inference . New York (NY): Cambridge Univ Press; 2019. ed. 2.
3. An Algorithm for Fast Recovery of Sparse Causal Graphs
4. Hu D. China and Albert Einstein: The reception of the physicist and his theory in China 1917-1979 . Cambridge (MA): Harvard Univ Press; 2005.
5. Bengio Y. An AI pioneer wants his algorithms to understand the ‘Why’. WIRED. 8 October 2019. https://www.wired.com/story/ai-pioneer-algorithms-understand-why.