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Artificial Intelligence (AI) Applications and the Shipping Industry

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Języki publikacji
EN
Abstrakty
EN
Artificial Intelligence (AI) can be simply approached as the (effective) simulation of human intelligence processes by computer systems. The issue of Maritime Autonomous Surface Ships (MASS), based on support by numerous AI applications, is providing a quite disruptive picture of how the shipping industry may be transformed in the future. After the necessary clarification of terms, a summary of certain important legal developments in relation to the on-going introduction of MASS type vessels into full service is provided. The role of trustworthy AI applications that can reliably serve the associated decision-making tasks is also discussed. In the near future, the vast majority of maritime transport needs will continue to be served by those vessels termed as “conventional” (regularly manned ships); the shipping industry is well known for its risk adverse behaviour and a slow pace of adaptation towards this new operating paradigm is the most probable path of adoption.
Twórcy
  • World Maritime University, Malmö, Sweden
Bibliografia
  • [1] Dalaklis, D. (2024). Future Trends in Autonomous Shipping: Discussing the Impact of Artificial Intelligence (AI) Applications, International Conference on Autonomous Shipping in Asia and the Pacific (United Nations Economic and Social Commission for Asia and the Pacific), Bangkok-Thailand, 28 February 2024. https://doi.org/ 10.13140/RG.2.2.35201.43369
  • [2] Chen, X., Ma, D., & Liu, R. W. (2024). Application of artificial intelligence in maritime transportation. Journal of Marine Science and Engineering, 12(3), 439. https://doi.org/10.3390/jmse12030439
  • [3] Ichimura, Y., Dalaklis, D., Kitada, M. & Christodoulou, A., (2022). Shipping in the era of digitalization: Mapping the future strategic plans of major maritime commercial actors, Digital Business, 2(1), 100022. https://doi.org/10.1016/j.digbus.2022.100022
  • [4] MARITIME EXECUTIVE. (2021). Autonomous Vessels are Becoming a Commercial Reality. https://maritime-executive.com/editorials/autonomous-vessels-are-becoming-a-commercial-reality, accessed 17 June 2024.
  • [5] Encyclopedia Britannica: Technology, https://www.britannica.com/technology/technology, retrieved 09/10/2024
  • [6] Dalaklis, D., Fonseca, T. & Schröder-Hinrichs, J.U. (2019). How will automation and digitalization impact the future of work in cargo transport and handling? The ITF/WMU Transport 2040 Report, The International Cargo Handling Coordination Association (ICHCA) International 2019 Conference: NEW RULES, NEW TECH, NEW WORLD, Valetta-Malta, 12 November 2019. https://doi.org/10.13140/RG.2.2.18537.44644
  • [7] Sanchez-Gonzalez, P.L., Díaz-Gutiérrez, D., Leo, T.J. & Núñez-Rivas, L.R. Toward Digitalization of Maritime Transport? Sensors. 2019, 19(4), 926. https://doi.org/10.3390/s19040926
  • [8] WORLD MARITIME UNIVERSITY (WMU). (2023). Transport 2040: Impact of Technology on Seafarers - The Future of Work. https://doi.org/http://dx.doi.org/10.21677/230613
  • [9] Yara Birkeland (Press kit, 2024), https://www.yara.com/news-and-media/media-library/press- kits/yara-birkeland-press-kit/, retrieved 09/03/2025.
  • [10] Report of the MSC-LEG-FAL Joint Working Group on MARITIME AUTONOMOUS SURFACE SHIPS (MASS) on its second session, MSC 107/5/1 C.F.R. (2023).
  • [11] DEVELOPMENT OF A GOAL-BASED INSTRUMENT FOR MARITIME AUTONOMOUS SURFACE SHIPS (MASS), MSC 108/WP.7 C.F.R. (2024).
  • [12] FORTUNE BUSINESS INSIGHTS. (2024). Autonomous Ships Market Size, Share, Industry Analysis, https://www.fortunebusinessinsights.com/industry-reports/autonomous-ship-market-101797, accessed 02 July 2024.
  • [13] O’Regan, G., 2012. “Artificial Intelligence. In: A Brief History of Computing” Springer, London. https://doi.org/10.1007/978-1-4471-2359
  • [14] Ilkou E. and Koutraki M., (2020), “Symbolic Vs Sub-symbolic AI Methods: Friends or Enemies?”. Paper presented at the CSSA'20: Workshop on Combining Symbolic and Sub-Symbolic Methods and their Applications co-located with CIKM2020, https://ceur-ws.org/Vol-2699/paper06.pdf, accessed 02 August 2024.
  • [15] Kovalishin, P., Nikitakos, N., Svilicic, B., Zhang, J., Nikishin, A., Dalaklis, D., Kharitonov, M. & A.A. Stefanakou, (2023), Using Artificial Intelligence (AI) methods for effectively responding to climate change at marine ports, Journal of International Maritime Safety, Environmental Affairs Shipping, 7, 1, 2186589. DOI: 10.1080/25725084.2023.2186589
  • [16] Tsaganos, G., Nikitakos, N., Dalaklis, D., Ölcer, A.I. & Papachristos, D. (2020). Machine Learning Algorithms in Shipping: Improving Engine Fault Detection and Diagnosis via Ensemble Methods, Journal of Maritime Affairs, 19(1), 51-72. https://link.springer.com/article/10.1007/s13437-019- 00192-w
  • [17] UNITED NATIONS CONFERENCE ON TRADE AND DEVELOPMENT (UNCTAD. (2023). Review of Maritime Transport 2023. https://unctad.org/system/files/official-document/rmt2021_en_0.pdf, accessed 03 April 2025.
  • [18] Munim, Z. H., Dushenko, M., Jimenez, V. J., Shakil, M. H., & Imset, M. (2020). Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions. Maritime Policy & Management, 47(5), 577-597. DOI: 10.1080/03088839.2020.1788731
  • [19] Xiao, G., Yang, D., Xu, L., Li, J., & Jiang, Z. (2024). The application of artificial intelligence technology in shipping: A bibliometric review. Journal of Marine Science and Engineering, 12(4), 624. https://doi.org/10.3390/jmse12040624
Uwagi
1. Pełne imię podano na stronie internetowej czasopisma w "Authors in other databases."
2. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9959c2a5-4970-4873-b4c3-06cf9de22b37
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