Article
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Collaborative Natural and Artificial Intelligence: a Multilayer Network Interpretation
Version 1
: Received: 9 July 2024 / Approved: 9 July 2024 / Online: 10 July 2024 (11:51:24 CEST)
How to cite: Moro Visconti, R. Collaborative Natural and Artificial Intelligence: a Multilayer Network Interpretation. Preprints 2024, 2024070777. https://doi.org/10.20944/preprints202407.0777.v1 Moro Visconti, R. Collaborative Natural and Artificial Intelligence: a Multilayer Network Interpretation. Preprints 2024, 2024070777. https://doi.org/10.20944/preprints202407.0777.v1
Abstract
The revolutionary idea of complementary intelligence involves a unique strategy in which artificial intelligence (AI) works with human cognitive abilities embodied by natural intelligence (NI), creating a harmonious partnership instead of engaging in a competitive relationship. This innovative concept can potentially revolutionize how we approach scientific research and discovery. This cutting-edge process capitalizes on pooling the vast information from various distinct and autonomous sources to generate forecasts that frequently outshine human perspectives by significant margins, leading to groundbreaking advancements in scientific development based on hybrid intelligence. Multilayer networks extend traditional network theory to adjacent layers linked through copula nodes. Artificial intelligence, with its retrieval capacity to collect and interpret big data, sharply increases the number of nodes and their linking interpretation. This process, amplified by multilayer networks, dramatically improves the scalability of the networked ecosystem. Multilayer networks can represent NI and AI in complementary hyperplanes, connecting them with copula nodes and edge properties. Thus, it is possible to measure the interaction of NI and AI, assessing their scalable value co-creation properties. This study examines the interaction of NI and AI with an innovative and multidisciplinary approach. A theoretical framework of these related concepts precedes practical insights for possible applications in future research.
Keywords
network theory; nodes; edges; scalability; hybrid intelligence; complementary intelligence; value co-creation; power laws
Subject
Computer Science and Mathematics, Computer Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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