The Morphospace of Consciousness: Three Kinds of Complexity for Minds and Machines
In this perspective article, we show that a morphospace, based on information-theoretic measures, can be a useful construct for comparing biological agents with artificial intelligence (AI) systems. The axes of this space label three kinds of complexity: (i) autonomic, (ii) computational and (iii) s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | NeuroSci |
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| Online Access: | https://www.mdpi.com/2673-4087/4/2/9 |
| _version_ | 1797593171530612736 |
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| author | Xerxes D. Arsiwalla Ricard Solé Clément Moulin-Frier Ivan Herreros Martí Sánchez-Fibla Paul Verschure |
| author_facet | Xerxes D. Arsiwalla Ricard Solé Clément Moulin-Frier Ivan Herreros Martí Sánchez-Fibla Paul Verschure |
| author_sort | Xerxes D. Arsiwalla |
| collection | DOAJ |
| description | In this perspective article, we show that a morphospace, based on information-theoretic measures, can be a useful construct for comparing biological agents with artificial intelligence (AI) systems. The axes of this space label three kinds of complexity: (i) autonomic, (ii) computational and (iii) social complexity. On this space, we map biological agents such as bacteria, bees, C. elegans, primates and humans; as well as AI technologies such as deep neural networks, multi-agent bots, social robots, Siri and Watson. A complexity-based conceptualization provides a useful framework for identifying defining features and classes of conscious and intelligent systems. Starting with cognitive and clinical metrics of consciousness that assess awareness and wakefulness, we ask how AI and synthetically engineered life-forms would measure on homologous metrics. We argue that awareness and wakefulness stem from computational and autonomic complexity. Furthermore, tapping insights from cognitive robotics, we examine the functional role of consciousness in the context of evolutionary games. This points to a third kind of complexity for describing consciousness, namely, social complexity. Based on these metrics, our morphospace suggests the possibility of additional types of consciousness other than biological; namely, synthetic, group-based and simulated. This space provides a common conceptual framework for comparing traits and highlighting design principles of minds and machines. |
| first_indexed | 2024-03-11T02:05:08Z |
| format | Article |
| id | doaj.art-4675f9faffa8483eb221a7d2a4b4ed59 |
| institution | Directory Open Access Journal |
| issn | 2673-4087 |
| language | English |
| last_indexed | 2024-03-11T02:05:08Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | NeuroSci |
| spelling | doaj.art-4675f9faffa8483eb221a7d2a4b4ed592023-11-18T11:54:48ZengMDPI AGNeuroSci2673-40872023-03-01427910210.3390/neurosci4020009The Morphospace of Consciousness: Three Kinds of Complexity for Minds and MachinesXerxes D. Arsiwalla0Ricard Solé1Clément Moulin-Frier2Ivan Herreros3Martí Sánchez-Fibla4Paul Verschure5Departament of Information and Communication Technologies, Universitat Pompeu Fabra (UPF), 08018 Barcelona, SpainComplex Systems Lab, Universitat Pompeu Fabra, 08003 Barcelona, SpainFlowers Research Group, Inria Bordeaux, 33405 Talence, FranceDepartament of Information and Communication Technologies, Universitat Pompeu Fabra (UPF), 08018 Barcelona, SpainDepartament of Information and Communication Technologies, Universitat Pompeu Fabra (UPF), 08018 Barcelona, SpainDonders Institute for Brain, Cognition and Behavior, Radboud University, 6525 GD Nijmegen, The NetherlandsIn this perspective article, we show that a morphospace, based on information-theoretic measures, can be a useful construct for comparing biological agents with artificial intelligence (AI) systems. The axes of this space label three kinds of complexity: (i) autonomic, (ii) computational and (iii) social complexity. On this space, we map biological agents such as bacteria, bees, C. elegans, primates and humans; as well as AI technologies such as deep neural networks, multi-agent bots, social robots, Siri and Watson. A complexity-based conceptualization provides a useful framework for identifying defining features and classes of conscious and intelligent systems. Starting with cognitive and clinical metrics of consciousness that assess awareness and wakefulness, we ask how AI and synthetically engineered life-forms would measure on homologous metrics. We argue that awareness and wakefulness stem from computational and autonomic complexity. Furthermore, tapping insights from cognitive robotics, we examine the functional role of consciousness in the context of evolutionary games. This points to a third kind of complexity for describing consciousness, namely, social complexity. Based on these metrics, our morphospace suggests the possibility of additional types of consciousness other than biological; namely, synthetic, group-based and simulated. This space provides a common conceptual framework for comparing traits and highlighting design principles of minds and machines.https://www.mdpi.com/2673-4087/4/2/9consciousnessbrain networksartificial intelligencesynthetic biologycognitive roboticscomplex systems |
| spellingShingle | Xerxes D. Arsiwalla Ricard Solé Clément Moulin-Frier Ivan Herreros Martí Sánchez-Fibla Paul Verschure The Morphospace of Consciousness: Three Kinds of Complexity for Minds and Machines NeuroSci consciousness brain networks artificial intelligence synthetic biology cognitive robotics complex systems |
| title | The Morphospace of Consciousness: Three Kinds of Complexity for Minds and Machines |
| title_full | The Morphospace of Consciousness: Three Kinds of Complexity for Minds and Machines |
| title_fullStr | The Morphospace of Consciousness: Three Kinds of Complexity for Minds and Machines |
| title_full_unstemmed | The Morphospace of Consciousness: Three Kinds of Complexity for Minds and Machines |
| title_short | The Morphospace of Consciousness: Three Kinds of Complexity for Minds and Machines |
| title_sort | morphospace of consciousness three kinds of complexity for minds and machines |
| topic | consciousness brain networks artificial intelligence synthetic biology cognitive robotics complex systems |
| url | https://www.mdpi.com/2673-4087/4/2/9 |
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