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Understanding Is a Process.

Leslie M Blaha1, Mitchell Abrams2, Sarah A Bibyk1

  • 1711th Human Performance Wing, U.S. Air Force Research Laboratory, Wright-Patterson Air Force Base, OH, United States.

Frontiers in Systems Neuroscience
|April 18, 2022
PubMed
Summary
This summary is machine-generated.

Understanding is a dynamic process, not a fixed state. This study proposes natural language probes to assess the evolving degree of understanding in humans and intelligent systems by analyzing successes and failures.

Keywords:
behavioral measurementcommon groundexplainable AIhuman-machine teaminghuman-robot interactionmental modelsmutual understandingnatural language processing

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Linguistics

Background:

  • Current tests of understanding, like the Turing test, are insufficient for assessing true comprehension.
  • Intelligent systems often excel at specific tasks but lack generalized understanding.
  • Existing methods fail to capture the dynamic nature of understanding.

Purpose of the Study:

  • To reframe understanding as a temporal process rather than a static state.
  • To introduce a novel methodology for evaluating understanding through natural language.
  • To provide a framework for measuring the degree of understanding in both humans and AI.

Main Methods:

  • Proposing natural language-based probes to assess understanding.
  • Analyzing successes and failures in communication to map understanding.
  • Observing how understanding changes over time and experience.

Main Results:

  • Failures in understanding provide critical insights into process breakdowns.
  • Natural language interactions serve as the primary observable for the understanding process.
  • A method is presented to quantify the degree of understanding.

Conclusions:

  • Understanding is best conceptualized as an ongoing process, not a binary state.
  • Natural language probes offer a robust method for evaluating the nuances of understanding.
  • This approach allows for a more accurate assessment of both human and artificial intelligence.