Human-artificial intelligence systems: How human survival first principles influence machine learning world models

Research output: Contribution to journalArticleScientificpeer-review

Abstract

World models is a construct that is used to represent internal models of the world. It is an important construct for human-artificial intelligence systems, because both natural and artificial agents can have world models. The term, natural agents, encompasses individual people and human organizations. Many human organizations apply artificial agents that include machine learning. In this paper, it is explained how human survival first principles of interactions between energy and entropy influence organization’s world models, and hence their implementations of machine learning. First, the world models construct is related to human organizations. This is done in terms of the construct’s origins in psychology theory-building during the 1930s through its applications in systems science during the 1970s to its recent applications in computational neuroscience. Second, it is explained how human survival first principles of interactions between energy and entropy influence organizational world models. Third, a practical example is provided of how survival first principles lead to opposing organizational world models. Fourth, it is explained how opposing organizational world models can constrain applications of machine learning. Overall, the paper highlights the influence of interactions between energy and entropy on organizations’ applications of machine learning. In doing so, profound challenges are revealed for human-artificial intelligence systems.
Original languageEnglish
Article number260
Number of pages15
JournalSystems
Volume10
Issue number6
DOIs
Publication statusPublished - 17 Dec 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • active inference
  • explainability
  • human–artificial intelligence systems
  • machine learning
  • non-reinforced learning
  • preferences
  • reinforcement learning
  • triple-loop learning
  • world models

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