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Knowledge in Animals and Machines.

L A Paul1,2,3,4

  • 1Department of Philosophy, Yale University, New Haven, Connecticut 06511 la.paul@yale.edu.

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Summary
This summary is machine-generated.

This study introduces a framework for understanding knowledge in animals and machines. It proposes a taxonomy of knowledge, bridging biological and artificial intelligence systems like large language models (LLMs).

Keywords:
LLMartificial intelligenceclassificationconceptepistemologyinstrumentalknowledgemachinephilosophyrepresentationtaxonomyworld model

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Area of Science:

  • Philosophy of Mind
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Existing frameworks for knowledge often fail to encompass both biological and artificial systems.
  • Understanding knowledge in diverse entities like animals and machines requires a unified conceptual approach.

Purpose of the Study:

  • To develop a conceptual framework for knowledge applicable to both animals and machines.
  • To engineer a novel concept of knowledge specifically for large language models (LLMs).
  • To propose a comprehensive taxonomy of knowledge spanning biological and artificial categories.

Main Methods:

  • Drawing upon philosophical theories of knowledge.
  • Developing a novel conceptual framework.
  • Engineering a new concept of large language model (LLM) knowledge.
  • Proposing a taxonomy of knowledge.

Main Results:

  • A conceptual framework for knowledge in animals and machines has been established.
  • A new concept of large language model (LLM) knowledge has been engineered.
  • A taxonomy categorizing knowledge across biological and artificial entities, from C. elegans to LLMs, has been proposed.

Conclusions:

  • The developed framework provides a foundation for rigorously accounting for knowledge realization across diverse systems.
  • The approach respects the varied biological and physical origins of knowledge.
  • This work bridges the understanding of cognition in natural and artificial intelligence.