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.

This article is a collaboration with David Gossett, Principal with Infornautics, who builds first mover technologies that have no instruction set and need to be invented from scratch. He believes data has a story to tell if we apply the right machine models. His specialty is unstructured data. This article is intended to be provocative, to summon curiosity into the issues that plague us today when it comes to machine learning. Three years ago, I wrote this article, Artificial Intelligence Needs to Reset. The AI Hype that was supposed to transpire into all-things automated is still far off. Since that time, we've experienced speed bumps that have pointed to issues including lack of model accountability (black boxes), bias, lack of data representation in the training set etc. An AI Ethics movement emerged to demand more responsible tech, increased model transparency and verifiable models that do what they're supposed to do without impairment or harm to individuals or groups, in the process. Our future is Artificial Intelligence. It's been conjectured that this wonderful AI will be our savior.

If business wants to use AI at scale, adhering to the technical guidelines for responsible AI development isn't enough. It must obtain society's explicit approval to deploy the technology. Six years ago, in March 2016, Microsoft Corporation launched an experimental AI-based chatbot, TayTweets, whose Twitter handle was @TayandYou. Tay, an acronym for "thinking about you," mimicked a 19-year-old American girl online, so the digital giant could showcase the speed at which AI can learn when it interacts with human beings. Living up to its description as "AI with zero chill," Tay started off replying cheekily to Twitter users and turning photographs into memes. Some topics were off limits, though; Microsoft had trained Tay not to comment on societal issues such as Black Lives Matter. Soon enough, a group of Twitter users targeted Tay with a barrage of tweets about controversial issues such as the Holocaust and Gamergate. They goaded the chatbot into replying with racist and sexually charged responses, exploiting its repeat-after-me capability. Realizing that Tay was reacting like IBM's Watson, which started using profanity after perusing the online Urban Dictionary, Microsoft was quick to delete the first inflammatory tweets. Less than 16 hours and more than 100,000 tweets later, the digital giant shut down Tay.

Ocean wave climate has a significant impact on near-shore and off-shore human activities, and its characterisation can help in the design of ocean structures such as wave energy converters and sea dikes. Therefore, engineers need long time series of ocean wave parameters. Numerical models are a valuable source of ocean wave data; however, they are computationally expensive. Consequently, statistical and data-driven approaches have gained increasing interest in recent decades. This work investigates the spatio-temporal relationship between North Atlantic wind and significant wave height (Hs) at an off-shore location in the Bay of Biscay, using a two-stage deep learning model. The first step uses convolutional neural networks (CNNs) to extract the spatial features that contribute to Hs. Then, long short-term memory (LSTM) is used to learn the long-term temporal dependencies between wind and waves.

Every year, tropical hurricanes affect North and Central American wildlife and people. The ability to forecast hurricanes is essential in order to minimize the risks and vulnerabilities in North and Central America. Machine learning is a newly tool that has been applied to make predictions about different phenomena. We present an original framework utilizing Machine Learning with the purpose of developing models that give insights into the complex relationship between the land–atmosphere–ocean system and tropical hurricanes. We study the activity variations in each Atlantic hurricane category as tabulated and classified by NOAA from 1950 to 2021. By applying wavelet analysis, we find that category 2–4 hurricanes formed during the positive phase of the quasi-quinquennial oscillation. In addition, our wavelet analyses show that super Atlantic hurricanes of category 5 strength were formed only during the positive phase of the decadal oscillation. The patterns obtained for each Atlantic hurricane category, clustered historical hurricane records in high and null tropical hurricane activity seasons. Using the observational patterns obtained by wavelet analysis, we created a long-term probabilistic Bayesian Machine Learning forecast for each of the Atlantic hurricane categories. Our results imply that if all such natural activity patterns and the tendencies for Atlantic hurricanes continue and persist, the next groups of hurricanes over the Atlantic basin will begin between 2023 ± 1 and 2025 ± 1, 2023 ± 1 and 2025 ± 1, 2025 ± 1 and 2028 ± 1, 2026 ± 2 and 2031 ± 3, for hurricane strength categories 2 to 5, respectively. Our results further point out that in the case of the super hurricanes of the Atlantic of category 5, they develop in five geographic areas with hot deep waters that are rather very well defined: (I) the east coast of the United States, (II) the Northeast of Mexico, (III) the Caribbean Sea, (IV) the Central American coast, and (V) the north of the Greater Antilles.