AI in Shipping: Challenges and Opportunities

A Q&A with DeepSea’s Director of AI Research, Antonis Nikitakis

Q: What is your most exciting piece of AI research at the moment ?

Explainability is critical for building trust in AI systems. Therefore, our most exciting AI research currently involves researching methods that improve models’ explainability – to enable ever-greater trust to be built amongst stakeholders. AI can be seen as a “black box” whose input and operations may not be transparent and understandable to outsiders, particularly when they are based on complex algorithms or deep learning models. AI can be vulnerable to bias or discrimination, especially if it is trained on biased datasets or if it is designed without sufficient consideration of ethical concerns. This can lead to concerns about the accuracy or reliability of the system, which can compromise trust and confidence in its use. 

One of the most promising ways to enable trust is to promote models and methods that allow humans to understand the logic behind the system’s decisions. At DeepSea, we conduct research on models that integrate concepts and principles from Physics and Naval Engineering. This enables us to explain how the models operate and that physical principles are always respected. Additionally, we enhance explainability by introducing new methods and metrics for evaluating the performance of AI models in extrapolation regimes (i.e., outside the given datasets). Having a reliable approach to evaluating performance regardless of the available data quantity and quality is a significant step forward in promoting explainability.

Q: How is AI changing the competitive landscape of the shipping industry? What advantages can AI offer to early adopters?

AI is transforming the shipping industry in various ways, from optimising operations and increasing efficiency to improving decision-making capabilities with vessel-specific weather routing. The advantages that AI offers to early adopters are significant. We are already seeing the major impact that AI can have in our work with Wallenius Wilhelmsen. Utilising DeepSea’s Performance Routing technology, the vehicle carrier operator became the first global shipping company to adopt a fully AI-based approach to voyage optimisation. As an early adopter, Wallenius has already started to reap the quantifiable benefits of fuel savings and a reduced environmental impact.

AI can also provide early adopters with better decision-making capabilities reducing the risk of costly mistakes and improving their maintenance and operational efficiency. For example, with DeepSea’s Cassandra today we can very precisely estimate the fouling state of a vessel and its trend to help the operations team to optimally schedule a cleaning.

Q: What types of data are being collected and analysed by AI in the shipping industry?

Today there is a very large volume of data that is being collected. We have various sensor data from onboard sensors that is being collected regarding all key machinery and subsystems of the vessel such as hull, main engine, electronic chart display and information system (ECDIS), and generator engines, among others. This information is important for optimising vessel routes, predicting maintenance needs, and ensuring that each vessel is operating at maximum efficiency. Then, there is weather data, which is critical for ensuring the safety of vessels and crew members. There is live weather data from sensors, and forecast weather and historical data collected from third-party providers. Furthermore, there is manually-reported operational data (e.g. noon reports) and also live and historical data from the automatic identification system (AIS) which is responsible for transmitting a ship’s position so that other ships are aware of its position. By analysing all these types of data and turning them into practical insights, we can help shipping organisations to optimise their fleet performance, improve their operational efficiency, save on fuel costs, and decrease their carbon footprint.

Q: What are some of the challenges associated with implementing AI?

Most of the challenges in implementing AI today involve data-related challenges, challenges related to the model evaluation and its robustness, and stakeholder trust which I mentioned earlier. One major challenge is that of distributional shift. Put simply, distributional shift has to do with the mismatch that could exist in an AI model between the training environment and the actual deployment. This “shifted” data can be challenging for machine learning models. In shipping, for example, we have a prominent ‘latent phenomenon’ which is hull fouling. The fouling state of a vessel is a dynamic phenomenon that prevents a generic model from fitting the data. Data drift due to sensor miscalibration can also lead to a distributional shift. Special considerations and methods should be taken into account to ensure a consistent fit across the dataset of a vessel. DeepSea has developed sophisticated data-driven methods for unsupervised anomaly detection which can detect when such issues may appear.

Q: Are there any ethical considerations or potential risks associated with using AI in the shipping industry?  How can they be addressed?

For every decision support system based on AI there is always concern whether the recommended actions are biased due to the underlying models. In the era of deep learning, the biases can be driven directly from the data without it being obvious which parts of the data led to such biases – or how to correct them. At DeepSea, we try to alleviate such issues using a multi-faceted approach to improve a model’s interpretability, and to measure and guarantee models unbiasedness across the board. To learn how we boost model robustness, have a look at our paper on ‘How to judge the real-world effectiveness of AI in shipping’.

Decarbonisation in Shipping: An interview with Eurobulk’s Marilena Apostolidou

Marilena Apostolidou, Eurobulk’s Environmental & Performance Compliance Manager, shares her views on sustainability and decarbonisation in shipping.

Q: What does it mean to be a Performance & Environmental Compliance Manager? 

Environmental compliance means adopting and implementing environmental requirements – national and international environmental rules, laws, and regulations. Environmental concerns have raised compliance standards across the globe. Especially in the Shipping industry, more Regulations regarding Sustainability and Environmental protection are being enforced, whilst Regulators and other Stakeholders in the maritime industry are intensifying their efforts to reduce greenhouse gas (GHG) emissions from shipping.

In this fast-evolving period for shipping regulation, the protection of the environment and the increasingly stringent measureds designed to achieve the goals of the IMO have made this role of utmost importance. 

An Environmental Compliance Manager is responsible for managing an organisation’s environmental performance, and ensuring that the company complies with environmental laws & regulations. Part of the role is to assess the organisation’s current business practices and to develop strategies that improve and meet environmental targets. Some common daily tasks are to perform environmental audits and assessments, develop environmental strategies and policies. The role lies with Technical and Operations departments, validates the information, corrects as necessary and transmits it in a format compliant to local and international regulations. 

Q: What are the biggest challenges that you face in your role?

Recently, a number of important decisions have been adopted by the IMO. In view of these decisions, various questions have been raised – how will we manage and implement these new requirements within the specified time limits. What will be the impact on shipping’s decarbonisation targets? Will deadlines have to be delayed? How is port state control going to deal with the enforcement of these regulations under current restrictions? And how will regulators move forward with the ‘new normal’? 

Sometimes it feels like you’re stuck in a never-ending cycle of falling behind… however, you know that it’s also imperative to get things done. 

In many ways, managing environmental compliance is like trying to hit a moving target – and doing so while working under pressure and major time constraints. Regulations are constantly changing, but the deadlines are unforgiving. The main challenge for shipping companies today, and the industry as a whole, relates to the implementation of these new regulations.

Q: What is your sustainability strategy at Eurobulk? What are you aiming to accomplish?

We are committed to protecting the environment – and this commitment is reflected in our Environmental Protection, Safety and Quality Policy. We seek to minimise the impact of our operations on both air quality and the marine environment. 

To support our policy, we have an environmental management system in place, incorporated in our HSEQ manual, defining our objectives, action plans, strategic ambition, and the corresponding deadlines for our work – all in an effort to reduce potential negative impacts on the marine environment. 

Moreover, we recently created a Compliance department which handles all the issues concerning environmental challenges and the necessity of meeting relevant regulations. This department is concurrently keeping abreast of IMO announcements, the new regulations, market developments, and technology improvements. We are also continually educating our company’s employees on the new and forthcoming regulations, the environmental challenges, how the performance and design of ships might be affected, and what needs to be done in order to ensure compliance whilst minimising service disruption. 

Q: What steps is Eurobulk taking to ensure that it meets these goals?

Eurobulk is a member of the Hellenic Marine Environmental Protection Association (HELMEPA) since 2012, in order to support its environmental efforts, and to be part of a community which provides training and other benefits to our crew and shore-based employees. 

Our priority is to minimise CO2, NOx and PM emissions from our ships’ engines, and to reduce the export of aquatic organisms by fitting our ships with ballast water management plants. Amongst other plans, we have implemented a program to renew our fleet with modern, more efficient, tonnage- either by contracting new buildings or by replacing older ships with newer eco vessels. We are confident that we will achieve our goal to support the SDG 13 of the 2030 UN targets and the IMO’s Greenhouse Gas Strategy for the reduction of carbon intensity.

The bottom line is that any action to reduce emissions per mile-sailed improves a ship’s rating. Both technical and operational improvements should be considered when considering how to achieve this goal.

Q: How is technology assisting you in reaching your energy efficiency and vessel/fleet performance goals?

In an effort to accurately monitor our fleet’s performance and emissions, we have introduced a number of performance software modules – alongside a specialised team to evaluate their effectiveness. 

Most of our vessels are equipped with digital mass flowmeters, and we are gradually installing telemetric equipment for high-frequency data collection – allowing us to better monitor the fleet’s fuel consumption and improve overall digitalisation. DeepSea helps us in this direction – it specialises in vessel performance monitoring and optimisation utilising artificial intelligence, and provides us with the appropriate equipment to collect and analyse our vessel data. We use DeepSea to track the performance of our vessels, calculate our ships’ emissions, and compare the actual condition of each ship against design condition in real-time. Monitoring each ship’s performance and consumption trends, and performing timely hull cleaning, is one way to keep our CII in check. 

Moreover, we are committed to continuing our sustainability journey and playing our part in the decarbonisation of shipping. Energy efficiency technologies will continue to play a crucial role in this mission, and we believe that collaborating more closely with DeepSea will not only lead to fuel savings and emissions reductions across our fleet, but also that sustainable growth will create additional value for our stakeholders in the mid- and long-term.

Q: How do you measure ROI and success?

To monitor the impact of vessel improvement projects we use both qualitative and quantitative KPIs. Regarding the quantitative KPIs, a vessel’s performance is always considered over time – i.e. against the performance achieved during previous quarters or on similar legs. Improvement in fuel consumption, CO2 emissions and speed performance are key indications of current status. As far as the qualitative aspects are concerned, the ability to fully monitor a vessel remotely, without human intervention, allows the performance manager and operator/superintendent to quickly identify and solve any issues. For example, we can understand if our generator engines are properly loaded and equally shared over time. This allows us to better plan overhauling intervals.  Certainly, “performance” is not just about monitoring hull fouling. 

A sustainability strategy is not one-dimensional, but multidimensional. It takes into consideration both current and future challenges. 

Q: Should environmental & performance compliance only concern senior management, or does the entire workforce play a role in it? In what ways?

“Green shipping” is the concept of sustainable development applied to the shipping sector – incorporating both environmental and social responsibility. Environmental & performance compliance is, for sure, not only a concern for senior management – but it is certainly where it begins. The roadmap for environmental & performance compliance consists of six vision areas – Oceans, Communities, People, Transparency, Finance, and Energy – each with its own set of objectives, desired outcomes and interrelated milestones.

We understand that to achieve long-term sustainability, companies must continuously improve the environmental performance of their vessels to comply with the latest regulations. This requires cooperation between several departments – and of course between shore personnel and crew – in order to collect and maintain all the necessary information to achieve compliance in this new era. In my position, I do my best to ensure our fleet meets the environmental objectives whilst remaining responsible stewards of the environment. 

In order to achieve this, the Performance & Environmental Compliance department arranges a series of trainings throughout the year. We collect data over time, and validate all information collected from vessels on a quarterly basis. We arrange meetings with the involved departments prior to the submission of results. We keep Masters and Chief Engineers engaged throughout the whole process of environmental compliance, helping them to understand the importance of being accurate, aware and responsible regarding the new challenges and demands coming from the IMO.

In many ways, managing environmental compliance is like trying to hit a moving target – and doing so while working under pressure and major time constraints. Regulations are constantly changing, but the deadlines are unforgiving.

Q: What advice would you give to your peers regarding setting a sustainability strategy?

A sustainability strategy is not one-dimensional, but multidimensional. It takes into consideration both current and future challenges. 

The need for rapid decarbonisation by 2050, the increased scrutiny and pressure from financial stakeholders, and the labour and human-rights risks faced by seafarers worldwide (as highlighted by the ongoing crew change crisis) all need to be taken into consideration.

There is no ‘silver bullet’ for environmental compliance. Often, shipowners and operators must tailor their approaches and solutions to specific vessel types, sizes or fleets. It is clear that, to comply with all these challenges, team-work, cooperation, stakeholder collaboration and energy are necessary. 

While new technological advancements have created more compliance options, shipowners and operators must carefully evaluate which solutions make the most sense for their business model. Whichever technology is chosen, it must ensure a measurable efficiency improvement across the fleet.

Also, shipowners, ship-builders and operators must stay on top of the numerous regional, domestic and international environmental requirements and understand the impact of these requirements on maritime transport. Proper reporting, evaluation and monitoring of fuel consumption is now of paramount importance. Optimisation and correct monitoring can improve vessel ratings at the same time as maximising the competitive advantage for commercial operators. The Carbon Intensity Indicator will also have a direct impact on competitive advantage. Vessels in the better-performing categories (A and B) may see commercial benefits. Vessels in the middle of category C represent the Required Annual Operational CII – effectively the acceptable baseline of the regulation and the market. Vessels in the D and E categories will have to demonstrate improvements promptly, moving progressively towards category C – if they are to stay compliant and market-viable.

What is Performance Routing? Read the white paper

Weather routing, evolved.

Adding vessel performance into the weather routing equation has an incredible effect: it turns traditional weather routing into a powerful decarbonisation tool that can make your vessels 8% more efficient on average – with almost no up-front investment and little disruption to existing processes.

Download the white paper AI: Harnessing Decarbonisation’s Secret Weapon to understand the principles behind this high-impact approach to voyage optimisation, and why now is the time for you to adopt it.

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Will your ship become sentient? CEO Q&A

A Q&A with Konstantinos Kyriakopoulos, CEO and Co-founder of DeepSea Technologies

There’s been a lot of news recently about “sentient” artificial intelligence. Will DeepSea’s AI be sentient any time soon?

Sentience is the ability to experience emotions and sensations. Machine learning algorithms are trained on data – pairs of inputs and outputs – and they learn how to reconstruct the outputs from inputs by themselves. A lot of AI nowadays is trained on inputs and outputs that come from humans – for example, language models – which are given large amounts of text and learn how to reproduce those conversations. You can even hide pieces of the conversation, and the AI will eventually learn to fill in the gaps. If you start talking to it – or asking it questions – the AI will then figure out what is statistically the most likely next phrase that a human would say or answer.

When you start talking about AI that is trained on emotion recognition and emotional language, emotional pictures and so on – that’s where it starts to get somewhat philosophical.

When a machine starts to process internal representations in a similar way to how a human “feels” something, at what point can you say that the algorithm itself is truly experiencing the sensation in question?

That is what the conversation about sentient AI is about. It is primarily a philosophical conversation, but it is also a great opportunity to discuss how these neural networks work from the inside.

DeepSea’s AI is not trained on humans. It is trained on ships, on ship engines and on the environment in which the ship moves, it is trained on the sea. It obtains the properties of these elements rather than the properties of humans. So, sentience is not relevant in our case.

What is the coolest thing about AI?

The coolest thing about the application of AI and deep learning is the sheer flexibility that it has.

You can train these systems to do essentially anything you want.

If you start off with any problem, you can always come up with a way to give the AI the right data and structure to learn how to solve it. This opens up an enormous number of opportunities in all sorts of different fields. 

One of the most exciting problems that we have worked on at DeepSea is how to figure out the level of fouling of a vessel, even though this is not measured. Something very real has happened to the vessel – barnacles are becoming stuck to it. It is impacting the performance of the vessel – but it cannot be measured directly. With intelligent AI, this becomes something we can understand quickly and accurately. 

So what does DeepSea’s AI learn about my ship? 

One great thing about AI is that the same techniques and technologies can be applied to radically different fields. One such technique which we have applied very successfully at DeepSea is transfer learning – which essentially means having one vessel learn from another.

At DeepSea, we have been able to apply transfer learning so we can deploy our optimisation solutions on ships where we have little, or poor-quality, data – for example data that comes from daily reports rather than high-frequency sensors. Every vessel has some data available – AIS, vessel particulars, daily report data – but that is not enough to train AI from scratch. However, what we have found is that if you have a vessel-independent model that learns the key characteristics about motion, dynamics and physics that are common to all vessels, you can use the same technique of transfer learning to take that model and transfer it to a new vessel. 

How is DeepSea’s AI different from that of other companies?

AI is a relative newcomer to shipping, and there has been a lot of critical foundational work to do – DeepSea has assembled the leading AI team in the industry to do it. 

Our research focuses on a number of vital areas. Perhaps most important of all in driving real impact for our customers is understanding causality – i.e. what factor affects what. What this means is that at DeepSea we can predict how much fuel your vessel will consume in any arbitrary conditions (eg. the weather, the speed, the draft and so on) as each of them varies over the course of a voyage. This factor-specific prediction is vital for figuring out what speed and what route your vessel should take in order to minimise energy requirement and therefore fuel consumption. 

This is an area where most people who are trying to do modelling of any kind – especially ship modelling – fail. When trying to model a ship, there are all sorts of different spurious correlations we have to contend with. A less sophisticated machine learning model will not necessarily determine correctly which part of the effect is due to which factor. 

As far as I am aware, we are the only organisation that has effectively solved this critical problem. What does this actually mean for our customers? Well, typically, these sophisticated models drive our Performance Routing platform, and directly translate into an average 8% fuel saving.

These models are the difference between a standard weather routing approach – which often leads to even greater fuel consumption than sailing without it – and a true optimisation of a voyage.

What made you start DeepSea?

My background is in deep learning from a research perspective. My research at the University of Cambridge focused on language and speech. The idea for DeepSea came about in a conversation that I had with Roberto Coustas, the other founder of the company. I was looking for applications of Al that differ from the ‘standard’, well-worn paths. Roberto, coming from a shipping perspective and having studied AI himself, was aware of a key problem in the industry that seemed insoluble – truly being able to predict ship behaviour.

Shipping companies struggle to do things like fouling detection or true voyage optimisation because they do not have the ability to accurately predict, in different conditions (and especially in conditions of extreme weather), what the precise fuel consumption would be. As a result, all the decisions they take lack a very important piece of information – what is the predicted impact? If we can predict this accurately, a shipping organisation can coordinate far better to minimise its fuel expenditure – and therefore its emissions and environmental impact. Without this ability, there is huge waste. By applying deep learning, we felt we could actually solve this major problem. 

Is AI the answer to everything?

Sadly not! AI is a specific technology that solves specific types of problems – particularly problems that have to do with lack of predictability. 

AI has to work side-by-side with humans. There are things that only humans can do – for example, captaining the vessel, making day-to-day safety decisions, inspecting the engine, etc. There are many things the AI does not know – issues with traffic, complicated port constraints, geopolitical factors.

The task, for us and for modern shipping companies, is to take people that have been used to working in one way and support them to work in a different way – a way where they can exploit the powers of AI within the context of their own skills, goals and insights.

Should I trust this ‘black box’? 

Ah the old ‘black box’. Yes, it’s true – this is one of the major challenges of Al (actually not just for users – it’s equally frustrating for researchers and those working in the field!). 

The problem is that, if you want humans to engage with this technology interactively, the AI needs to be as transparent as possible about why it is making the suggestions that it is making. What we are working toward is moving from the ‘black box’ to the ‘translucent box’ – so you can start seeing what is happening inside. 

On the optimisation side, we have a tool that makes available the impact of different factors in the equation. For example, it can explain exactly how much you are saving when you make a certain change – why a certain route is more optimal than another – and so forth. Across the entire AI world, this area of ‘explainability’ is really a work in progress and the subject of a lot of academic research.

But I think you can also trust something you don’t fully understand, as long as there is evidence that it works, and there is sufficient support for the approach. Right now, we are involved in an important initiative with a number of leading universities, called the Shifts Project, which is leading the way globally in finding new ways to tackle some of the greatest challenges to the real-world application of AI. I’d like to think that the backing of universities such as Cambridge and Johns Hopkins, alongside our ever-growing list of clients, should be enough to get hardened AI-sceptics to engage us in conversation.


The world’s most advanced shipping companies are starting to adopt AI at a rapid pace. To understand why – and what you could achieve with AI – download our white paper AI: Harnessing decarbonisation’s secret weapon now:

Moving The Goalposts with Wallenius Wilhelmsen – watch the webinar

A landmark success story in Voyage Optimisation

Wallenius Wilhelmsen is rolling DeepSea’s AI-powered Performance Routing solution out across its entire fleet of 120+ ships.

This decision follows a rigorous trial process. The results were impressive: a 6.9% improvement in vessel efficiency and more than 170,000 tonne predicted reduction in emissions across the fleet.

Join Sergey Ushakov from Wallenius Wilhelmsen, and DeepSea, to understand

  • Why Wallenius Wilhelmsen decided to explore this technology
  • The data requirements to harness it successfully
  • The full results of the process
  • How you can make a similar efficiency improvement across your fleet

Watch the full video here:

How to judge the real-world effectiveness of AI in shipping?

Artificial Intelligence. It can do many things: understand whalesong, compose film scripts on-demand, and decrease the fuel consumption of a ship at sea. However, “AI” has also quickly become the greatest buzzword of the 21st century – wooing customers, investors and governments alike. 

It’s vital that serious researchers working to popularise this exciting approach continue to pursue rigorous methods of proving the real value of what they’re creating. In fact, we believe every end-user looking to employ this sort of service, in shipping or any industry, should demand it.

No-bullshit AI

At DeepSea we have a no-bullshit approach to AI – and so should you.

We have pioneered a way of verifying the accuracy – and therefore utility – of a ship’s AI-generated model in real-world conditions. This is important – the more accurate the virtual model, the more efficient a ship can be made, and vice-versa.

The new approach was developed by seven of our thirteen-strong team of research scientists, and first published in May 2022. 

The few models that currently provide an estimation of their accuracy all do so based on testing with data obtained from the same distribution (i.e. representative of similar conditions and containing similar biases) as the data used to train the model. For example, if the model is trained on data from the vessel’s historical behaviour, in a narrow range of well-experienced wind speeds or drafts, it is also tested on data with these speeds and drafts. Thus, the tests performed can’t tell if the model is reproducing the biases in the training data – and whether it will work as well in different, never-seen-before conditions. As anyone familiar with maritime data will know, real ship-at-sea data is actually highly variable. Most model accuracy figures reported in publications and marketing materials thus bear no relation to the actual utility of those models in real use cases.

DeepSea has long researched approaches to solving the technical challenge of boosting models’ ability to understand unseen (“out-of-domain”) conditions. However, before this recent publication, there has been no benchmark for evaluating this sort of competence within a vessel model. With this announcement, we are signalling that this rigorous test is a key part of our AI methodology. Moreover, we are releasing the details of the approach for global researchers to utilise themselves, in the hope of catalysing greater transparency across the industry.

This research is an important step in helping our customers and the wider market to understand the true power, while alleviating the limitations, of an AI-based approach. Coupled with the daily real-world impact we’re seeing on fuel consumption and CII ratings, we believe this sort of information is key to popularising this incredible technology throughout the industry.

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