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Shipping forecast: Cloudy with a chance of machine learning

Rolls-Royce and Google on the same boat with navigating a path towards an AI driven autonomous future.

By James Nunns

Google is shipping out its Cloud Machine Learning Engine to Rolls-Royce to help make autonomous ships a reality.

The hope is that the Google Cloud Machine Learning Engine will be able to train Rolls-Royce’s intelligent awareness systems, an AI-based classification system for detecting, identifying, and tracking the objects that a vessel could come across at sea.

Signed at the Google Cloud Summit in Sweden, the agreement will help the company to create bespoke machine learning models that’ll be able to uncover valuable insights from data sets that are created by Rolls-Royce. Google Cloud Machine Learning

Karno Tenovuo, Rolls-Royce, SVP Ship Intelligence said: “While intelligent awareness systems will help to facilitate an autonomous future, they can benefit maritime businesses right now making vessels and their crews safer and more efficient. By working with Google Cloud we can make these systems better faster, saving lives.”

The size of the data sets shouldn’t be a problem, in fact they should help the technology to create more accurate predictions. Rolls-Royce’s expertise in the area will be used towards preparing the data to train the models, this should ensure that the data is relevant and that there’s enough of it to create accurate models.

The companies said that as part of the machine learning process, the models are evaluated in “practical marine applications,” so that further refinements can be applied.

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Eva Fors, Head of Google Cloud Sales Nordics said: “By exploring the possibilities presented by machine learning, Rolls-Royce can combine the latest technology advancements with its deep knowledge of the maritime industry, ultimately bringing significant improvements to the sector.”

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The future will bring further research between the two companies in order to test unsupervised and multimodal learning, in addition to testing whether speech recognition and synthesis are viable solutions for “human-machine interfaces” in marine applications.

Work will also be undertaken on optimising the performance of local neural network computing on board ships using tech such as TensorFlow and other open source software libraries.

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