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Beyond mimicry: a framework for evaluating genuine intelligence in artificial systems.

Sarfaraz K Niazi1

  • 1Pharmaceutical Sciences, University of Illinois, Chicago, IL, United States.

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Summary

The new Machine Perturbational Complexity & Agency Battery (mPCAB) offers a neurophysiological framework for evaluating artificial intelligence, moving beyond mimicry to assess genuine understanding and cognitive processes in AI systems.

Keywords:
agencyartificial intelligencecreativityevaluation frameworksmachine consciousnessneuromorphic computingorganoid intelligenceperturbational complexity

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Neuroscience

Background:

  • Current AI benchmarks often prioritize task mimicry over genuine intelligence and cognitive processes.
  • Existing evaluations lack a framework for comparing diverse AI substrates (digital, neuromorphic, biological).
  • There is a need for systematic assessment of advanced AI capabilities like long-term reasoning, norm internalization, and transformational creativity.

Purpose of the Study:

  • Introduce the Machine Perturbational Complexity & Agency Battery (mPCAB), a novel substrate-independent framework for AI evaluation.
  • Apply neurophysiological methods to assess consciousness and cognitive mechanisms in artificial systems.
  • Address research gaps in AI by evaluating long-term reasoning, norm internalization, and transformational creativity.

Main Methods:

  • Developed mPCAB with four key components: perturbational complexity, global workspace assessment, norm internalization, and agency.
  • Analyzed theories of consciousness (GNW, IIT, PP, HOT) to identify AI implementation targets.
  • Mapped human cognitive functions to computational counterparts and defined a creativity taxonomy from combinational to transformational.

Main Results:

  • Demonstrated mPCAB's feasibility and metric comparability across digital, neuromorphic, and biological substrates in pilot studies.
  • Established a framework linking AI mechanisms with functions, enabling systematic cross-substrate comparisons.
  • Integrated ethical considerations for AI development, including bias reduction and rights issues.

Conclusions:

  • mPCAB shifts AI evaluation from superficial benchmarks to mechanism-based assessment.
  • The framework supports the development of mind-like machines with genuine understanding.
  • Promotes responsible AI advancements by incorporating cognitive and ethical dimensions.