“Voyages in HD” – download the white paper

Ask yourself one question: do you understand your vessels’ performance accurately enough to really optimise anything?

Shipping is still a world of ‘low definition’. According to our survey:

  • Most shipping companies understand their vessels’ behaviour only “quite well”
  • Most shipping companies have only “somewhat accurate” fuel consumption predictions
  • Most shipping companies do not/cannot reassess voyage plans regularly, despite the constantly changing reality

There is still a level of systematic, accepted, inaccuracy at the heart of voyage planning- based on a low definition understanding of vessels. This is a purely technical challenge.

We need a high-definition revolution. The direct result will be thousands of tonnes of fuel saved by each company.

The HD revolution is an incredible opportunity

We need a high-definition revolution. The direct result will be thousands of tonnes of fuel saved by each company.

Take the first step: read our white paper on “Voyages in HD” below

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’.

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.

Fill in your details below and we’ll send you the white paper instantly:

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.

Read the full paper now

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