Prism Courage, a 134,000-tonne commercial tanker, recently sailed from the Gulf of Mexico to South Korea while controlled mostly by an artificial intelligence system called HiNAS 2.0. Avikus, a subsidiary of South Korean technology giant Hyundai, recently announced that Prism Courage, a tanker designed to transport natural gas, had become the first large ship to make an ocean passage of over 10,000 km (6,210 miles) autonomously. The key to this incredible achievement was HiNAS 2.0, an AI-powered system capable of analyzing different kinds of sensor readings in real-time and responding to them swiftly, efficiently, and, most importantly, in accordance with the rules of maritime laws. Just like airplanes, ships have very advanced auto-pilots capable of keeping them on a steady course, responding to GPS waypoints and currents, and even bringing them into harbor in case the human crew is no longer present on board or capable of doing it. However, sailing autonomously for tens of thousands of kilometers through the Atlantic is a lot more complex than putting a ship on autopilot. Apart from steering the tanker in real0-time, Avikus' HiNAS 2.0 system is capable of picking the optimal routes and best speeds to reach its destination, by analyzing data collected through advanced sensors.

In a voyage lasting 40 days and conquering approximately 3 500 unmanned miles at sea, the Mayflower Autonomous Ship arrived in North America in Halifax, Nova Scotia on June 5, 2022. Following two years of design, construction and AI model training, the Mayflower Autonomous Ship (MAS) was officially launched in September 2020. Fast forward to 5 June 2022, and the ship completed an historic transatlantic voyage from Plymouth, UK to its North American arrival in Halifax, Nova Scotia. 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 artificial intelligence (AI) captain to guide the vessel and make real-time decisions while at sea.

Physical motion models offer interpretable predictions for the motion of vehicles. However, some model parameters, such as those related to aero- and hydrodynamics, are expensive to measure and are often only roughly approximated reducing prediction accuracy. Recurrent neural networks achieve high prediction accuracy at low cost, as they can use cheap measurements collected during routine operation of the vehicle, but their results are hard to interpret. To precisely predict vehicle states without expensive measurements of physical parameters, we propose a hybrid approach combining deep learning and physical motion models including a novel two-phase training procedure. We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model, which limits the uncertainty introduced by the neural network to a known quantity. We have evaluated our approach for the use case of ship and quadcopter motion. The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.

Recent work suggests that convolutional neural networks of different architectures learn to classify images in the same order. To understand this phenomenon, we revisit the over-parametrized deep linear network model. Our analysis reveals that, when the hidden layers are wide enough, the convergence rate of this model's parameters is exponentially faster along the directions of the larger principal components of the data, at a rate governed by the corresponding singular values. We term this convergence pattern the Principal Components bias (PC-bias). Empirically, we show how the PC-bias streamlines the order of learning of both linear and non-linear networks, more prominently at earlier stages of learning. We then compare our results to the simplicity bias, showing that both biases can be seen independently, and affect the order of learning in different ways. Finally, we discuss how the PC-bias may explain some benefits of early stopping and its connection to PCA, and why deep networks converge more slowly with random labels.

A robotic recreation of the 17th century Mayflower ship has finally completed a 3,500 journey across the Atlantic Ocean, in 40 days. Mayflower Autonomous Ship (MAS) – a 50-foot-long autonomous research vessel piloted by artificial intelligence (AI) – arrived in Halifax, Canada on Sunday (June 5). MAS, which carried no humans on board and relied on artificial intelligence, had set sail from Turnchapel Wharf, Plymouth, England in the early hours of April 27. The ship was smooth sailing until the second week of May when a generator issue diverted it to Portugal's Azores islands so a team member could fly in to do repairs. During the latter stages of the journey the decision was made to head to Halifax – as opposed to Virginia as previously planned – due to more mechanical issues.

Avikus, a subsidiary of Hyundai, has successfully completed the first transoceanic voyage of a large merchant ship using autonomous navigation technologies, the company said in a press release. With the increase in computational capabilities, autonomous navigation solutions are being tested in various fields of transportation. Autonomous cars are expected to bring in a new era of human transportation, and the maritime industry is also not very far behind. Last year, we reported on a fertilizer company that deployed a fully electric and autonomous container ship in Norway to save 40,000 truck trips every year. While this deployment was over a short distance, maritime transportation involves crossing oceans and often in very congested port areas.

A groundbreaking AI-powered ship designed by IBM has successfully crossed the Atlantic, albeit not quite as planned. The Mayflower – named after the ship which carried Pilgrims from Plymouth, UK to Massachusetts, US in 1620 – is a 50-foot crewless vessel that relies on AI and edge computing to navigate the often harsh and unpredictable oceans. IBM's Mayflower has been attempting to autonomously complete the voyage that its predecessor did over 400 years ago but has been beset by various problems. The initial launch was planned for June 2021 but a number of technical glitches forced the vessel to return to Plymouth. Back in April 2022, the Mayflower set off again.

The Associated Press reports on "a crewless robotic boat that had tried to retrace the 1620 sea voyage of the Mayflower" from the U.K. to Massachusetts' Plymouth Rock. And after five weeks it finally did reach North America. "The technology that makes up the autonomous system worked perfectly, flawlessly," an IBM computing executive involved in the project told the Associated Press. But "Mechanically, we did run into problems." It's especially disappointing because they'd tried the same voyage last year.

Autonomous ships just took a small but important step forward. Hyundai's Avikus subsidiary says it has completed the world's first autonomous navigation of a large ship across the ocean. The Prism Courage (pictured) left Freeport in the Gulf of Mexico on May 1st, and used Avikus' AI-powered HiNAS 2.0 system to steer the vessel for half of its roughly 12,427-mile journey to the Boryeong LNG Terminal in South Korea's western Chungcheong Province. The Level 2 self-steering tech was good enough to account for other ships, the weather and differing wave heights. The autonomy spared the crew some work, of course, but it may also have helped the planet. Avikus claims HiNAS' optimal route planning improved the Prism Courage's fuel efficiency by about seven percent, and reduced emissions by five percent.

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.