Multi-Species Cohesion: Humans, machinery, AI and beyond
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912157219684352 |
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| author | Huo, Frank Yingjie Manrique, Pedro D. Johnson, Neil F. |
| author_facet | Huo, Frank Yingjie Manrique, Pedro D. Johnson, Neil F. |
| contents | The global chaos caused by the 19 July 2024 technology meltdown highlights the need for a theory of what large-scale cohesive behaviors -- dangerous or desirable -- could suddenly emerge from future systems of interacting humans, machinery and software including AI; when will they emerge; and how will they evolve and be controlled. Here we offer answers by introducing an aggregation model that accounts for the interacting entities' inter- and intra-species diversities. It yields a novel multi-dimensional generalization of existing aggregation physics. We derive exact analytic solutions for the time-to-cohesion and growth-of-cohesion for two species, and some generalizations for an arbitrary number of species. These solutions reproduce -- and offer a microscopic explanation for -- an anomalous nonlinear growth feature observed in various current real-world systems. Our theory suggests good and bad 'surprises' will appear sooner and more strongly as humans-machinery-AI etc. interact more -- but it also offers a rigorous approach for understanding and controlling this. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17410 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Species Cohesion: Humans, machinery, AI and beyond Huo, Frank Yingjie Manrique, Pedro D. Johnson, Neil F. Physics and Society Statistical Mechanics Pattern Formation and Solitons The global chaos caused by the 19 July 2024 technology meltdown highlights the need for a theory of what large-scale cohesive behaviors -- dangerous or desirable -- could suddenly emerge from future systems of interacting humans, machinery and software including AI; when will they emerge; and how will they evolve and be controlled. Here we offer answers by introducing an aggregation model that accounts for the interacting entities' inter- and intra-species diversities. It yields a novel multi-dimensional generalization of existing aggregation physics. We derive exact analytic solutions for the time-to-cohesion and growth-of-cohesion for two species, and some generalizations for an arbitrary number of species. These solutions reproduce -- and offer a microscopic explanation for -- an anomalous nonlinear growth feature observed in various current real-world systems. Our theory suggests good and bad 'surprises' will appear sooner and more strongly as humans-machinery-AI etc. interact more -- but it also offers a rigorous approach for understanding and controlling this. |
| title | Multi-Species Cohesion: Humans, machinery, AI and beyond |
| topic | Physics and Society Statistical Mechanics Pattern Formation and Solitons |
| url | https://arxiv.org/abs/2401.17410 |