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Alien Versus Natural-Like Artificial General Intelligences

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Artificial General Intelligence (AGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13921))

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Abstract

A natural-like artificial general intelligence (AGI) is defined to be an AGI that includes mammalian-like mechanisms such as core usage of navigation maps, spatial and temporal binding, predictive coding, lifelong learning, and innate knowledge procedures. If it includes core mechanisms which allow full causal and analogical processing, then it is also considered to be a human-like AGI. An AGI which is not a natural-like AGI is termed an alien AGI. We consider (for sake of example) as a natural-like AGI a largely conceptual cognitive architecture (the Causal Cognitive Architecture 5) inspired by the mammalian brain. We consider (for sake of example) as an alien AGI the large language model ChatGPT. We show for a non-numeric simple example, that the natural-like AGI is able to solve the problem by automatic core mechanisms, but an alien AGI has difficulty arriving at a solution. It may be, that alien AGIs’ understanding of the world is so different from a human understanding that to allow alien AGIs to do tasks done originally by humans, is to eventually invite strange failures in the tasks.

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Authors and Affiliations

  1. Sheppard Clinic North, Vaughan, ON, Canada

    Howard Schneider

  2. University of Illinois, Springfield, IL, USA

    Piotr Bołtuć

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  1. Howard Schneider
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  2. Piotr Bołtuć
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Correspondence to Howard Schneider .

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Editors and Affiliations

  1. Department of Psychology, Stockholm University, Stockholm, Sweden

    Patrick Hammer

  2. Örebro University, Örebro, Sweden

    Marjan Alirezaie

  3. University of Gothenburg, Gothenburg, Sweden

    Claes Strannegård

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Schneider, H., Bołtuć, P. (2023). Alien Versus Natural-Like Artificial General Intelligences. In: Hammer, P., Alirezaie, M., Strannegård, C. (eds) Artificial General Intelligence. AGI 2023. Lecture Notes in Computer Science(), vol 13921. Springer, Cham. https://doi.org/10.1007/978-3-031-33469-6_24

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  • DOI: https://doi.org/10.1007/978-3-031-33469-6_24

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  • Online ISBN: 978-3-031-33469-6

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