Different Conceptions of Learning: Function Approximation vs. Self-Organization

  • Pei Wang
  • Xiang Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9782)


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.



The authors thank the anonymous reviewers for their helpful comments and suggestions.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer and Information SciencesTemple UniversityPhiladelphiaUSA

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