Never Ending Reasoning and Learning: Opportunities and Challenges

Sriraam Natarajan, Kristian Kersting

Keywords: Causality, Continual Learning, Statistical Relational AI, Human allied AI

TL;DR: We motivate a never ending reasoning and learning system, present the challenges in this system, and highlight the potential opportunities that provide for interdisciplinary collaborations

Abstract:

Inspired by the motivation behind the Never-Ending Language Learner (NELL), a continual learning system that reads the web, we propose the never-ending reasoning and learning paradigm and one instance: the Never-Ending Reasoner and Learner (NERL), which continuously learns and reasons with causal models by actively interacting with domain experts.
NERL necessitates tight synergistic interaction between different communities---continual learning, causal modeling, statistical relational AI, and human-allied AI communities. We motivate NERL using the real, high-impact problem of global mitigation of adverse pregnancy outcomes, present the challenges in this system, and highlight the potential opportunities that provide for interdisciplinary collaborations.