South Korean startup Seadronix wants to reduce the issue of marine accidents, 75% of which are caused by human error, according to a 2019 Allianz safety and shipping report. The company just secured a $5.8 million Series A extension to scale its AI-based ship berthing monitoring and navigation systems to help cargo ships navigate safely and assist port operators anchoring their vehicles at harbor. The fresh funds, led by SoftBank Ventures Asia, bring Seadronix's the total round up to $8.3 million. Seadronix will use the capital to grow its team beyond the current headcount of 30 employees and enter global markets, including Singapore and Europe, where its "smart ports" are located, Byeolteo Park, CEO and co-founder, said in an interview with TechCrunch. A smart port uses technologies including AI, big data, Internet of Things and 5G to provide more security and save energy by digitalizing the way huge ships enter docks and handle logistics at the ports.
Russian tankers carrying oil chemicals and oil products are increasingly concealing their movements, a phenomenon that some maritime experts warn could signal attempts to evade unprecedented sanctions prompted by the invasion of Ukraine. In the week ending March 25, there were at least 33 occurrences of so-called "dark activity" -- operating while onboard systems to transmit their locations are turned off -- by Russian tankers, said Windward Ltd., an Israeli consultancy that specializes in maritime risk using artificial intelligence and satellite imagery. That's more than double the weekly average of 14 in the past year. The dark operations occurred mainly in or around Russia's exclusive economic zone, according to Windward, which conducted the research at Bloomberg's request. The ships engaging in dark activity include vessels connected to big corporations and multinational shipping firms, as well as small businesses, according to Windward.
FrontM is on a mission to be the EDGE-intelligent app marketplace where the world goes to connect, inform and care for remote teams and customers. The innovation focuses on overcoming digital poverty in remote and isolated environments, such as the Blue Economy. The World Bank defines the blue economy as the "sustainable use of ocean resources for economic growth, improved livelihoods and jobs while preserving the health of the ocean ecosystem." FrontM's initial use cases include the maritime commercial shipping market, particularly transforming shore-ship team collaboration, automation of workflows, crew safety and welfare. FrontM is proud to be recognised by Innovate UK and receiving a grant to study the feasibility of integration of Edge AI enablement technology from Hammer Of The Gods (HOT-G).
The maritime industry is experiencing a technological revolution that affects shipbuilding, operation of both seagoing and inland vessels, cargo management, and working practices in harbors. This ongoing transformation is driven by the ambition to make the ecosystem more sustainable and cost-efficient. Digitalization and automation help achieve these goals by transforming shipping and cruising into a much more cost- and energy-efficient, and decarbonized industry segment. The key enablers in these processes are always-available connectivity and content delivery services, which can not only aid shipping companies in improving their operational efficiency and reducing carbon emissions but also contribute to enhanced crew welfare and passenger experience. Due to recent advancements in integrating high-capacity and ultra-reliable terrestrial and non-terrestrial networking technologies, ubiquitous maritime connectivity is becoming a reality. To cope with the increased complexity of managing these integrated systems, this article advocates the use of artificial intelligence and machine learning-based approaches to meet the service requirements and energy efficiency targets in various maritime communications scenarios.
Modern neural language models widely used in tasks across NLP risk memorizing sensitive information from their training data. As models continue to scale up in parameters, training data, and compute, understanding memorization in language models is both important from a learning-theoretical point of view, and is practically crucial in real world applications. An open question in previous studies of memorization in language models is how to filter out "common" memorization. In fact, most memorization criteria strongly correlate with the number of occurrences in the training set, capturing "common" memorization such as familiar phrases, public knowledge or templated texts. In this paper, we provide a principled perspective inspired by a taxonomy of human memory in Psychology. From this perspective, we formulate a notion of counterfactual memorization, which characterizes how a model's predictions change if a particular document is omitted during training. We identify and study counterfactually-memorized training examples in standard text datasets. We further estimate the influence of each training example on the validation set and on generated texts, and show that this can provide direct evidence of the source of memorization at test time.
As the technology improves all around us, the dependence of humans on machines has increased over time admirably. The term'Artificial Intelligence' or AI was adopted first in the year 1956 by John McCarthy, an American Computer Scientist at the Darthmouth Conference. Since then, Artificial Intelligence has evolved over the years such that today, there are infinite uses of Artificial Intelligence and Machine Learning from manufacturing sectors to academics, healthcare, telecommunication and Academics. Before Learning about Autonomous Shipping, let us learn about the basics or the fundamental pillars that autonomous shipping is based on. Artificial Intelligence (AI) is the simulation of human intelligence by machines.
Autonomous systems are becoming ubiquitous and gaining momentum within the marine sector. Since the electrification of transport is happening simultaneously, autonomous marine vessels can reduce environmental impact, lower costs, and increase efficiency. Although close monitoring is still required to ensure safety, the ultimate goal is full autonomy. One major milestone is to develop a control system that is versatile enough to handle any weather and encounter that is also robust and reliable. Additionally, the control system must adhere to the International Regulations for Preventing Collisions at Sea (COLREGs) for successful interaction with human sailors. Since the COLREGs were written for the human mind to interpret, they are written in ambiguous prose and therefore not machine-readable or verifiable. Due to these challenges and the wide variety of situations to be tackled, classical model-based approaches prove complicated to implement and computationally heavy. Within machine learning (ML), deep reinforcement learning (DRL) has shown great potential for a wide range of applications. The model-free and self-learning properties of DRL make it a promising candidate for autonomous vessels. In this work, a subset of the COLREGs is incorporated into a DRL-based path following and obstacle avoidance system using collision risk theory. The resulting autonomous agent dynamically interpolates between path following and COLREG-compliant collision avoidance in the training scenario, isolated encounter situations, and AIS-based simulations of real-world scenarios.
Collision avoidance is a vital capability of any marine vessel navigating in public waterways; this is particularly true for autonomous surface vehicles (ASVs), which cannot benefit by the real-time guidance of a human operator. Safe maritime navigation remains a challenge due to the fact that it requires the seamless coordination of multiple complex subsystems. First, vessels must be able to perceive their surroundings under a wide range of environmental conditions. This is typically accomplished using one or more line-of-sight sensors, which emit electromagnetic or acoustic signals, and detect the reflections produced by nearby obstacles (Robinette et al., 2019). However, in the marine environment, vessels can also utilize the Automatic Information System (AIS) protocol to track nearby vessels. The merits and drawbacks of these sensing modalities will be discussed in Section 1.1. Once an obstacle is detected, the ASV must react quickly and intelligently to avoid it, in accordance with the "rules of the road" set forth by the 1972 International Regulations for Prevention of Collisions at Sea (COLREGs) (International Maritime Organization, 2003). Many ASVs remain unable to perform one or more of these crucial tasks, limiting their adoption beyond the oceanographic research community. B. Cole is with the Laboratory for Autonomous Marine Sensing Systems, Department of Mechanical Engineering.
We propose a novel framework to classify large-scale time series data with long duration. Long time seriesclassification (L-TSC) is a challenging problem because the dataoften contains a large amount of irrelevant information to theclassification target. The irrelevant period degrades the classifica-tion performance while the relevance is unknown to the system.This paper proposes an uncertainty-aware multiple instancelearning (MIL) framework to identify the most relevant periodautomatically. The predictive uncertainty enables designing anattention mechanism that forces the MIL model to learn from thepossibly discriminant period. Moreover, the predicted uncertaintyyields a principled estimator to identify whether a prediction istrustworthy or not. We further incorporate another modality toaccommodate unreliable predictions by training a separate modelbased on its availability and conduct uncertainty aware fusion toproduce the final prediction. Systematic evaluation is conductedon the Automatic Identification System (AIS) data, which is col-lected to identify and track real-world vessels. Empirical resultsdemonstrate that the proposed method can effectively detect thetypes of vessels based on the trajectory and the uncertainty-awarefusion with other available data modality (Synthetic-ApertureRadar or SAR imagery is used in our experiments) can furtherimprove the detection accuracy.
Stakeholders in the Nigerian maritime industry have identified deeper application of technology as a way to achieving an efficient port system in Nigeria. At a recent one-day Town Hall Meeting on Hitch Free Port Operations in Nigeria organised by JournalNG in Lagos, they urged the federal government to consider applying the Webb Port system being used in neighbouring Benin Republic. While making a presentation at the event, Managing Director of Webb Fontaine Nigeria Limited, Ope Babalola disclosed that his company has assisted Benin Republic in achieving ICT port system that harmonised the country's interests through a single transaction. According to him, the system has helped in saving time, producing more accurate results, protecting government revenue and facilitating trade. Tankian Coulibaly an official from Webb Fontaine in Benin Republic said his company helped in Beninois government to set up a port community integration system called Webb Port.