The new collaboration, claimed to be the first in the marine sector, allows Rolls-Royce to use Google’s Cloud Machine Learning Engine to further train the company’s artificial intelligence (AI) based on object classification system for detecting, identifying and tracking the objects a vessel can encounter at sea.
Karno Tenovuo, Rolls-Royce, senior vice president 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.”
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.”
Rolls-Royce will be able to tap on Google Cloud’s software to create bespoke machine learning models which can interpret large and diverse marine data sets created by Rolls-Royce.
By accessing this software through the Cloud, the models can be developed from anywhere in the world and are accessible globally allowing thousands of users. Models can therefore be trained on large quantities (terabytes) of data. This will be essential as autonomous ships become commonplace.
In the longer term, Rolls-Royce and Google plan to undertake joint research on unsupervised and multimodal learning. The two companies will also test whether speech recognition and synthesis are viable solutions for human-machine interfaces in marine applications. They will also work on optimizing the performance of local neural network computing on board ships using open source machine intelligence software libraries such as Google’s TensorFlow.
Rolls-Royce believes that intelligent awareness systems will make vessels safer, easier and more efficient to operate by providing crew with an enhanced understanding of their vessel’s surroundings. This will be achieved by fusing data from a range of sensors with information from existing ship systems, such as Automatic Identification System (AIS) and radar. Data from other sources, including global databases, will also have a role.
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