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The Author measure. Furthermore, even if adequate training data are attainable, if efforts to

Marlantes obtain the data address the original prob-

Kyle E. Marlantes is a naval architect, software developer, and PhD candidate at the University of Michigan, where lem directly, then a resulting AI is ren- he develops methods to leverage data in engineering ap- dered unnecessary. When considering AI plications.

for an application, it is very important to consider the data requirements for a suc- cessful deployment, the feasibility and cost of acquiring the data, and if acquir- ing the data will provide the necessary insight without AI.

The availability of data remains the greatest barrier to utilizing AI in more ap- plications within the maritime industry.

Designing methods which reduce train- ing data requirements, leverage small amounts of data to make predictions, and derive deeper insight from less informa- tion are front and center in the current academic and industrial research world.

One signi? cant example of this is using

AI in engineering applications, speci? cal- ly in ship design and analysis. When de- signing a ship, data is often not plentiful, but if a small initial data set could be lev- eraged, then signi? cant insight could be gained earlier in the design process, fast- er, and cheaper. Researchers are working on this very problem, with some recent work showing promise, but the methods take considerable domain knowledge to be successful. However, as these efforts continue to gain traction, it is possible that AI will considerably bene? t ship de- signers and engineers within this decade.

As the conversation around AI contin- ues to unfold, it is more important than ever to keep a pragmatic mindset. When evaluating the technology for a proposed application, we must keep the limitations of data—in terms of quality, quantity, and availability—front and center. AI can yield insight from data in ways that were previously impossible, but strategic deployment is critical to deriving real value. For the maritime industry, with a long history of unique challenges and solutions, AI should be yet another tool in the toolbox, to be adopted when the question—and the data—are well-suited to the technology.

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