Autonomous ship makes transatlantic voyage

Following two years of design, construction and AI model training, the Mayflower Autonomous Ship (MAS) was officially launched in September 2020. Fast forward to today, MAS completed a historic transatlantic voyage from Plymouth, UK to its North American arrival in Halifax, Nova Scotia on June 5.

With no human captain or onboard crew, MAS is the first self-directed autonomous ship with technology that is scalable and extendible to traverse the Atlantic Ocean.

MAS was designed and built by marine research non-profit ProMare with IBM acting as lead technology and science partner, with IBM automation, AI and edge computing technologies powering the ship’s AI Captain to guide the vessel and make real-time decisions while at sea.

On board the ship, there are six AI-powered cameras, more than 30 sensors and 15 Edge devices, all of which input into actionable recommendations for the AI Captain to interpret and analyze. This makes it possible for the AI Captain to adhere to maritime law while making crucial split-second decisions, like rerouting itself around hazards or marine animals, all without human interaction or intervention.

The AI Captain has learned from data, postulates alternative choices, assesses and optimizes decisions, manages risk, and refines its knowledge through feedback, all while maintaining the highest ethical standards – which is similar to how machine learning is applied across industries like transportation, financial services, and healthcare. 

And furthermore, there is a transparent record of the AI Captain’s decision-making process that can help humans understand why the captain made certain decisions, transparency that is all too important in these heavily regulated industries.

IBM researchers Rosie Lickorish and James Sutton at work on MAS, April 2021.

Why MAS matters: Harnessing the power of data

The AI Captain is also the crux of why IBM believes that MAS’s experimental voyage will be a catalyst for the advancement of AI and AI-powered automation at the edge in various applications across industry. 

For example, leveraging AI to make sense of supply chain and logistics data helps manufacturers and distributors avoid supply chain disruption – taking advantage of localized compute at the edge to improve decision making, lower operating costs, protect personal and private information, and maintain the resilience of the business. 

This same technology is widely used across production environments to optimize processes, improve quality, protect workers, and lower the cost of maintaining production equipment.

So, while part of MAS’ mission was oriented around ocean research and discovery to help tackle some of the ocean’s biggest challenges, IBM is also focused on accelerating the application of AI and automation other businesses.

All photos credit: IBM

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