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Brainstorming the Impact of Artificial Intelligence (AI) Applications upon the Shipping Industry

The presentation from the Thalesians Marine talk by Dr. Dimitrios Dalaklis (AFNI) is now available on ResearchGate: Brainstorming about the Impact of Artificial Intelligence (AI) Applications upon the Shipping Industry.

AI can be defined as the simulation of human intelligence processes by “machines”, especially computer systems. AI has already helped numerous organizations to boost their revenues by streamlining the related business procedures, automating repetitive jobs, and improving customer service. AI has the potential to lead to a massive productivity boom –but one which won’t be shared equally across economies around the world. When the discussion is shifted to the wider shipping industry, the so-called “Digitalization” phenomenon, which also includes the topic of “Maritime Autonomous Surface Ships (MASS), provides a quite disruptive picture of how this industry may be transformed in the near future. The rather simplistic and most times confusing term “Autonomous Vessels” is often used to describe systems that -to some extend- are able to make decisions by themselves, requiring no human input. Indicative examples of AI applications of immediate interest for shipping include expert systems, natural language processing, speech recognition and machine vision (among others). Furthermore, it is necessary to consider that the hardware element of sensors on board contemporary ships has already kind of “exhausted” the room of further improvement; the use of advanced software applications and utilisation of AI tools to improve more the capabilities of the various systems used to support the conduct of navigation seems can be viewed as the best alternative way forward. This briefing will firstly provide the necessary definitions/clarifications in relation to MASS and AI applications. Then, it will explain the reasons why only advanced AI tools can pave the way towards autonomous systems and eventually to fully unmanned (or, uncrewed) ships. Finally, it will briefly explore what tools are available today to help “humans” and “machines” effectively collaborate together in the same working environment (i.e., image and/or speech recognition). A conclusion standing out is that building, improving and running AI applications requires immense computing power; a Cloud-based architecture can offer that in a flexible and easy “scalable” environment (at relatively low-cost and without huge initial investments). In addition, effective management of “Big Data” and deploying the right analytical tools should be approached as a prerequisite for AI and in turn, AI applications can provide the solution to process unstructured data and derive useful insights from it.

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