IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v12y2020i12p213-d452186.html
   My bibliography  Save this article

Complex Cognitive Systems and Their Unconscious. Related Inspired Conjectures for Artificial Intelligence

Author

Listed:
  • Gianfranco Minati

    (Italian Systems Society, 20161 Milan, Italy)

Abstract

The aim of the article is to propose a conceptual framework, constructs, and conjectures to act as a guide for future, related research finalized to design and implement versions of Artificial Intelligence encompassing an artificially simulated unconscious suitable for human-like artificial cognitive processing. This article considers the concept of the unconscious in psychoanalysis. The interdisciplinary understanding of this concept is considered to be the unavoidable property of sufficiently complex, cognitive processing. We elaborate on the possibility of an artificial unconscious, able to both self-acquired properties through usage, and self-profile through a supposed implicit, parasitic usage of explicit cognitive processing. Memory activities are considered to be integrated into cognitive processing, with memory no longer only being storage and reminding no longer only being finding. We elaborate on the artificial unconscious as an implicit, usage-dependent, self-profiling, and emergent process. Conceptual characteristics of the research project are the implementation of weighted networked, fuzzified memorizations; self-generated networks of links of inter-relationships as nodes, self-variation of the intensity of the links according to the use, and activation of internal self-processes such as the introduction of fictitious links intended as variations and combinations of the current ones. Application examples suitable for experimental implementation are also discussed with reference to chatbot technology that has been extended with features of an artificial unconscious. Thus, we introduce the concept of the AU-chatbot. The main purpose is to allow the artificial cognitive processing to acquire suitable human-like attitudes in representing, interfacing, and learning, potentially important in supporting and complementing human-centered activities. Examples of expected features are the ability to combine current and unconscious links to perform cognitive processing such as representing, deciding, memorizing, and solving equivalencies, and also learning meta-profiles, such as in supporting doctor–patient interactions and educational activities. We also discuss possible technologies suitable for implementing experiments for the artificial unconscious.

Suggested Citation

  • Gianfranco Minati, 2020. "Complex Cognitive Systems and Their Unconscious. Related Inspired Conjectures for Artificial Intelligence," Future Internet, MDPI, vol. 12(12), pages 1-24, November.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:12:p:213-:d:452186
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/12/12/213/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/12/12/213/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ma, Yang & Cheng, Guangquan & Liu, Zhong & Xie, Fuli, 2017. "Fuzzy nodes recognition based on spectral clustering in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 792-797.
    2. Gianfranco Minati & Giuseppe Vitiello, 2006. "Mistake Making Machines," Springer Books, in: Gianfranco Minati & Eliano Pessa & Mario Abram (ed.), Systemics of Emergence: Research and Development, pages 67-78, Springer.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Karimi-Majd, Amir-Mohsen & Fathian, Mohammad & Makrehchi, Masoud, 2018. "Consensus-based methodology for detection communities in multilayered networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 547-558.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:12:y:2020:i:12:p:213-:d:452186. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.