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From the Perspective of Artificial Intelligence: A New Approach to the Nature of Consciousness
Resource type
Journal Article
Authors/contributors
- Manzotti, Riccardo (Author)
- Jeschke, Sabina (Author)
Title
From the Perspective of Artificial Intelligence: A New Approach to the Nature of Consciousness
Abstract
Consciousness is not only a philosophical but also a technological issue, since a conscious agent has evolutionary advantages. Thus, to replicate a biological level of intelligence in a machine, concepts of machine consciousness have to be considered. The widespread internalistic assumption that humans do not experience the world as it is, but through an internal ‘3D virtual reality model’, hinders this construction. To overcome this obstacle for machine consciousness a new theoretical approach to consciousness is sketched between internalism and externalism to address the gap between experience and physical world. The ‘internal interpreter concept’ is replaced by a ‘key-lock approach’. Here, consciousness is not an image of the external world but the world itself.
A possible technological design for a conscious machine is drafted taking advantage of an architecture exploiting selfdevelopment of new goals, intrinsic motivation, and situated cognition. The proposed cognitive architecture does not pretend to be conclusive or experimentally satisfying but rather forms the theoretical the first step to a full architecture model on which the authors currently work on, which will enable conscious agents e.g. for robotics or software applications.
Publication
International Journal of Advanced Research in Artificial Intelligence
Volume
3
Issue
12
Date
2014
Journal Abbr
ijarai
Language
en
ISSN
21654069, 21654050
Short Title
From the Perspective of Artificial Intelligence
Accessed
3/7/25, 7:16 AM
Library Catalog
DOI.org (Crossref)
Citation
Manzotti, R., & Jeschke, S. (2014). From the Perspective of Artificial Intelligence: A New Approach to the Nature of Consciousness. International Journal of Advanced Research in Artificial Intelligence, 3(12). https://doi.org/10.14569/IJARAI.2014.031201
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