Different conceptions of learning: Function approximation vs. self-organization

Abstract

This paper compares two understandings of “learning” in the context of AGI research: algorithmic learning that approximates an input/output function according to given instances, and inferential learning that organizes various aspects of the system according to experience. The former is how “learning” is often interpreted in the machine learning community, while the latter is exemplified by the AGI system NARS. This paper describes the learning mechanism of NARS, and contrasts it with canonical machine learning algorithms. It is concluded that inferential learning is arguably more fundamental for AGI systems.