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Do Machine-Learning Machines Learn?

  • Selmer BringsjordEmail author
  • Naveen Sundar Govindarajulu
  • Shreya Banerjee
  • John Hummel
Conference paper
Part of the Studies in Applied Philosophy, Epistemology and Rational Ethics book series (SAPERE, volume 44)

Abstract

We answer the present paper’s title in the negative. We begin by introducing and characterizing “real learning” (\(\mathcal {RL}\)) in the formal sciences, a phenomenon that has been firmly in place in homes and schools since at least Euclid. The defense of our negative answer pivots on an integration of reductio and proof by cases, and constitutes a general method for showing that any contemporary form of machine learning (ML) isn’t real learning. Along the way, we canvass the many different conceptions of “learning” in not only AI, but psychology and its allied disciplines; none of these conceptions (with one exception arising from the view of cognitive development espoused by Piaget), aligns with real learning. We explain in this context by four steps how to broadly characterize and arrive at a focus on \(\mathcal {RL}\).

Notes

Acknowledgement

We are deeply appreciative of feedback received at PT-AI 2017, the majority of which is addressed herein. The first author is also specifically indebted to John Hummel for catalyzing, in vibrant discussions at MAICS 2017, the search for formal arguments and/or theorems establishing the proposition Hummel and Bringsjord co-affirm: viz., statistical machine learning simply doesn’t enable machines to actually learn, period. Bringsjord is also thankful to Sergei Nirenburg for valuable conversations. Many readers of previous drafts have been seduced by it’s-not-really-learning forms of learning (including worse-off-than artificial neural network (ANN) based deep learning (DL) folks: Bayesians), and have offered spirited objections, all of which are refuted herein; yet we are grateful for the valiant tries. Bertram Malle stimulated and guided our sustained study of types of learning in play in psychology\(^+\), and we are thankful. Jim Hendler graciously read an early draft; his resistance has been helpful (though perhaps now he’s a convert). The authors are also grateful for five anonymous reviews, some portions of which reflected at least partial and passable understanding of our logico-mathematical perspective, from which informal notions of “learning” are inadmissible in such debates as the present one. Two perspicacious comments and observations from two particular PT-AI 2017 participants, subsequent to the conference, proved productive to deeply ponder. We acknowledge the invaluable support of “Advanced Logicist Machine Learning” from ONR, and of “Great Computational Intelligence” from AFOSR. Finally, without the wisdom, guidance, leadership, and raw energy of Vincent Müller, PT-AI 2017, and any ideas of ours that have any merit at all, and that were expressed there and/or herein, would not have formed.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Selmer Bringsjord
    • 1
    Email author
  • Naveen Sundar Govindarajulu
    • 1
  • Shreya Banerjee
    • 1
  • John Hummel
    • 2
  1. 1.Rensselaer Polytechnic InstituteTroyUSA
  2. 2.UrbanaUSA

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