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

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Artificial General Intelligence (AGI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9782))

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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.

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Acknowledgments

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

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Authors and Affiliations

  1. Department of Computer and Information Sciences, Temple University, Philadelphia, USA

    Pei Wang & Xiang Li

Authors
  1. Pei Wang
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  2. Xiang Li
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Corresponding author

Correspondence to Pei Wang .

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Editors and Affiliations

  1. Galleria 1, IDSIA, Manno, Switzerland

    Bas Steunebrink

  2. Temple University, Phoenixville, Pennsylvania, USA

    Pei Wang

  3. Hong Kong Polytechnic University, Hong Kong, Hong Kong

    Ben Goertzel

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Wang, P., Li, X. (2016). Different Conceptions of Learning: Function Approximation vs. Self-Organization. In: Steunebrink, B., Wang, P., Goertzel, B. (eds) Artificial General Intelligence. AGI 2016. Lecture Notes in Computer Science(), vol 9782. Springer, Cham. https://doi.org/10.1007/978-3-319-41649-6_14

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  • DOI: https://doi.org/10.1007/978-3-319-41649-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41648-9

  • Online ISBN: 978-3-319-41649-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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