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One Dimensional Turing-Like Handshake Test for Motor Intelligence
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Performance vs. competence in human-machine comparisons.

Chaz Firestone1

  • 1Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218 chaz@jhu.edu.

Proceedings of the National Academy of Sciences of the United States of America
|October 14, 2020
PubMed
Summary
This summary is machine-generated.

Comparing human and machine intelligence requires distinguishing between performance and competence. This approach ensures fair comparisons, revealing true differences in cognitive abilities rather than superficial limitations.

Keywords:
artificial intelligencecognitiondeep learningdevelopmentperception

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Biologically inspired machine learning models achieve high accuracy and can predict brain activity.
  • Machines exhibit "unhumanlike" failures, questioning their validity as models of the human mind.
  • Existing research often overlooks the performance-competence distinction in human-machine comparisons.

Purpose of the Study:

  • To introduce the performance-competence distinction for species-fair human-machine comparisons.
  • To argue for the essential role of this distinction in evaluating artificial intelligence.
  • To identify factors promoting species-fairness in comparative studies.

Main Methods:

  • Drawing on foundational insights from cognitive science.
  • Applying the performance-competence distinction to human-machine behavioral analysis.
  • Focusing on image classification tasks to illustrate comparison factors.

Main Results:

  • The performance-competence distinction is crucial for accurate human-machine comparisons.
  • Neglecting this distinction can lead to misinterpretations of machine "failures".
  • Three factors for species-fair comparisons in image classification were identified.

Conclusions:

  • Species-fair comparisons are vital for accurately assessing artificial intelligence.
  • This framework helps differentiate superficial limitations from deep cognitive disparities.
  • Future research should adopt a species-fair approach to advance understanding of natural and artificial intelligence.