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A Path to Generative Artificial Selves

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Progress in Artificial Intelligence (EPIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14116))

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Abstract

Artificial intelligence output are undeniably creative, but it has been argued that creativity should be assessed in terms of, not external products, but internal self-transformation through immersion in a creative task. Self-transformation requires a self, which we define as a bounded, self-organizing, self-preserving agent that is distinct from, and interacts with, its environment. The paper explores how self-hood, as well as self-transformation as a result of creative tasks, could be achieved in a machine using autocatalytic networks. The autocatalytic framework is ideal for modeling systems that exhibit emergent network formation and growth. The approach readily scales up, and it can analyze and detect phase transitions in vastly complex networks that have proven intractable with other approaches. Autocatalytic networks have been applied to both (1) the origin of life and the onset of biological evolution, and (2) the origin of minds sufficiently complex and integrated to participate in cultural evolution. The first entails the emergence of self-hood at the level of the soma, or body, while the second entails the emergence of self-hood at the level of a mental models of the world, or worldview; we suggest that humans possess both. We discuss the feasibility of an AI with creative agency and self-hood at the second (cognitive) level, but not the first (somatic) level.

Supported by the Natural Sciences and Engineering Research Council of Canada, grant number GR01855.

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Notes

  1. 1.

    The AI’s environment need not be the same one as our environment; it could, for example, be an artificial environment that exists in a computer.

  2. 2.

    See also [38] on the related concept of autopoiesis.

  3. 3.

    If there is an agent external to the machine that will repair it when it breaks, it may not have to exhibit RAF structure at all levels.

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Author information

Authors and Affiliations

  1. University of British Columbia, Kelowna, V1V 1V7, Canada

    Liane Gabora

  2. Thistledown Foundation, Ottawa, K1P 1H4, Canada

    Joscha Bach

  3. ABC Institute, Rupert-Karls-University Heidelberg, Heidelberg, Germany

    Joscha Bach

Authors
  1. Liane Gabora
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  2. Joscha Bach
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Correspondence to Liane Gabora .

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Editors and Affiliations

  1. Lucy Family Institute for Data and Society, Notre Dame, IN, USA

    Nuno Moniz

  2. GECAD, Polytechnic of Porto, Porto, Portugal

    Zita Vale

  3. GRIA—LIACC, University of Azores, Ponta Delgada, Portugal

    José Cascalho

  4. CISUC, University of Coimbra, Coimbra, Portugal

    Catarina Silva

  5. IEETA, University of Aveiro, Aveiro, Portugal

    Raquel Sebastião

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Gabora, L., Bach, J. (2023). A Path to Generative Artificial Selves. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14116. Springer, Cham. https://doi.org/10.1007/978-3-031-49011-8_2

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  • DOI: https://doi.org/10.1007/978-3-031-49011-8_2

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