Neuro-Symbolic Bi-Directional Translation -- Deep Learning
  Explainability for Climate Tipping Point Research
Chace Ashcraft Jennifer Sleeman Caroline Tang Jay Brett Anand Gnanadesikan
In recent years, there has been an increase in using deep learning for
climate and weather modeling. Though results have been impressive,
explainability and interpretability of deep learning models are still a
challenge. A third wave of Artificial Intelligence (AI), which includes logic
and reasoning, has been described as a way to address these issues.
Neuro-symbolic AI is a key component of this integration of logic and reasoning
with deep learning. In this work we propose a neuro-symbolic approach called
Neuro-Symbolic Question-Answer Program Translator, or NS-QAPT, to address
explainability and interpretability for deep learning climate simulation,
applied to climate tipping point discovery. The NS-QAPT method includes a
bidirectional encoder-decoder architecture that translates between
domain-specific questions and executable programs used to direct the climate
simulation, acting as a bridge between climate scientists and deep learning
models. We show early compelling results of this translation method and
introduce a domain-specific language and associated executable programs for a
commonly known tipping point, the collapse of the Atlantic Meridional
Overturning Circulation (AMOC).