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A Modified Mirror Test as a Visual Guide for the Self-awareness Trait in Wild Antarctica Penguins, Pygoscelis adeliae
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Self-Concern Across Scales: A Biologically Inspired Direction for Embodied Artificial Intelligence.

Matthew Sims1

  • 1Institute for Philosophy II, Ruhr-University Bochum, Bochum, Germany.

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Summary

Current AI intelligence metrics are human-centric. This study introduces "self-concern," a system's drive for self-maintenance, as a foundation for basic biological intelligence and a potential AI goal.

Keywords:
anticipatory controlartificial intelligenceartificial symbiosesbasal cognitioncommon fateembodied cognitiongoal directed behaviourhomeostasis

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Area of Science:

  • Artificial Intelligence
  • Systems Biology
  • Philosophy of Mind

Background:

  • Current AI intelligence is evaluated via designer-assigned tasks, not agent-intrinsic needs.
  • This evaluation reflects human intelligence more than AI capabilities.
  • A new framework is needed to assess AI intelligence from the agent's perspective.

Purpose of the Study:

  • Introduce the concept of "self-concern" as a fundamental property of complex systems.
  • Explore self-concern as a basis for biological intelligence and continued viability.
  • Investigate organizational conditions for self-concern in biological systems to inform AI development.

Main Methods:

  • Conceptual analysis of intelligence and self-maintenance in complex systems.
  • Review of biological systems to identify core organizational conditions for self-concern.
  • Proposal for emulating these conditions in embodied artificial intelligence.

Main Results:

  • Self-concern is defined as a system's tendency to maintain states compatible with its continued existence.
  • Self-concern is posited as the foundation of basic intelligence in biological systems.
  • Identified necessary organizational conditions for self-concern in biological systems.

Conclusions:

  • Shifting AI evaluation to agent-intrinsic properties like self-concern is crucial.
  • Emulating biological self-concern in AI could lead to more genuine, human-like intelligence.
  • This research provides a foundational step towards developing AI with intrinsic motivation and self-preservation capabilities.