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A Machine Consciousness Architecture Based on Deep Learning and Gaussian Processes

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Hybrid Artificial Intelligent Systems (HAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12344))

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Abstract

Recent developments in machine learning have pushed the tasks that machines can do outside the boundaries of what was thought to be possible years ago. Methodologies such as deep learning or generative models have achieved complex tasks such as generating art pictures or literature automatically. Machine Consciousness is a field that has been deeply studied and several theories based in the functionalism philosophical theory like the global workspace theory have been proposed. In this work, we propose an architecture that may arise consciousness in a machine based in the global workspace theory and in the assumption that consciousness appear in machines that have cognitive processes and exhibit conscious behaviour. This architecture is based in processes that use the recent Deep Learning and generative process models. For every module of this architecture, we provide detailed explanations of the models involved and how they communicate with each other to create the cognitive architecture. We illustrate how we can optimize the architecture to generate social interactions between robots and genuine pieces of art, both features correlated with machine consciousness. As far as we know, this is the first machine consciousness architecture that use generative models and deep learning to exhibit conscious social behaviour and to retrieve pictures and other subjective content made by robots.

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Acknowledgments

The authors acknowledge the use of the facilities of Centro de Computación Científica (CCC) at UAM and acknowledge financial support from Spanish Plan Nacional I+D+i, grants TIN2016–76406-P and TEC2016–81900-REDT and Spanish Ministry of Science and Innovation - State Research Agency, project PID2019–106827GB-I00.

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Authors and Affiliations

  1. Universidad Autónoma de Madrid, Madrid, Spain

    Eduardo C. Garrido Merchán

  2. Universidad Politécnica de Madrid, Madrid, Spain

    Martin Molina

Authors
  1. Eduardo C. Garrido Merchán
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  2. Martin Molina
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Correspondence to Eduardo C. Garrido Merchán .

Editor information

Editors and Affiliations

  1. University of Oviedo, Oviedo, Spain

    Enrique Antonio de la Cal

  2. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  3. University of A Coruña, Ferrol, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Merchán, E.C.G., Molina, M. (2020). A Machine Consciousness Architecture Based on Deep Learning and Gaussian Processes. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_29

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  • DOI: https://doi.org/10.1007/978-3-030-61705-9_29

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