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Freud and the algorithm: neuropsychoanalysis as a framework to understand artificial general intelligence

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  • Luca M. Possati

    (University of Porto)

Abstract

The core hypothesis of this paper is that neuropsychoanalysis provides a new paradigm for artificial general intelligence (AGI). The AGI agenda could be greatly advanced if it were grounded in affective neuroscience and neuropsychoanalysis rather than cognitive science. Research in AGI has so far remained too cortical-centric; that is, it has privileged the activities of the cerebral cortex, the outermost part of our brain, and the main cognitive functions. Neuropsychoanalysis and affective neuroscience, on the other hand, affirm the centrality of emotions and affects—i.e., the subcortical area that represents the deepest and most ancient part of the brain in psychic life. The aim of this paper is to define some general design principles of an AGI system based on the brain/mind relationship model formulated in the works of Mark Solms and Jaak Panksepp. In particular, the paper analyzes Panksepp’s seven effective systems and how they can be embedded into an AGI system through Judea Pearl’s causal analysis. In the conclusions, the author explains why building a sub-cortical AGI is the best way to solve the problem of AI control. This paper is intended to be an original contribution to the discussion on AGI by elaborating positive arguments in favor of it.

Suggested Citation

  • Luca M. Possati, 2021. "Freud and the algorithm: neuropsychoanalysis as a framework to understand artificial general intelligence," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-19, December.
    • Handle: RePEc:pal:palcom:v:8:y:2021:i:1:d:10.1057_s41599-021-00812-y
    DOI: 10.1057/s41599-021-00812-y
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    References listed on IDEAS

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    1. Ragnar Fjelland, 2020. "Why general artificial intelligence will not be realized," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-9, December.
    2. Karl Friston, 2009. "Causal Modelling and Brain Connectivity in Functional Magnetic Resonance Imaging," PLOS Biology, Public Library of Science, vol. 7(2), pages 1-6, February.
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