Abstract

Humans always play the role of a teacher to AI in the last decades. One day, AI will become wiser than humans in various fields. At that time, humans need to learn from AI to improve themselves. However, most of the current high-performance machine learning models are black boxes and challenging to understand. So a system with better explainability is needed to help humans to understand AI. Therefore, the authors proposed a double-track approach to use expert systems to supplement the current machine learning paradigm to solve this problem. Under lifelong machine learning, the double-track approach can be wiser and wiser and achieve high performance but keeps outstanding explainability.

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