All of these examples show that a great deal of effort is going into AI solutions in more or less every branch of the maritime industry. The optimisations promised by these applications are set to lead to considerable savings, giving the companies involved a serious competitive edge in the not-too-distant future.
So will companies be left behind if they do not go down the AI route? Many of the projects are still at the early stages of development, with plenty of R&D to be invested in them yet. And yet the transition from research project to real-life application can be surprisingly speedy.
Risks of AI developments within the maritime industry
Validation and trust
Testing autonomous systems at sea is much more complex than the equivalent process within the automotive industry. Cars are limited in size and their range of action is easy to predict, which means that setting up the relevant tests is straightforward. There is a lot more work involved in testing AI applications on a ship. For one thing, it is harder to access systems out at sea. Plus, the costs are higher and there is more chance of a serious incident occurring. Unexpected situations often arise at sea and in port settings. So how can an AI solution be expected to prove that it can be trusted to make the right decisions beforehand?
“It will be really interesting to coordinate the interaction between AI and man, as we find that the decisions made by artificial intelligence are sometimes pretty hard to follow. That’s one of the major challenges faced in many sectors – not just in the case of underwater vehicles,” explains Dr Jeronimo Dzaack, Head of Technology, Innovation and Sustainability at ATLAS ELEKTRONIK, who is working on the MUM submarine project (as outlined above).
AI needs data. And the principle “the more the better” often applies. In other words, companies looking to develop AI technology need to have enough data at their disposal or at the very least need to start collecting data. In the case of projects that span multiple supply chains and involve several service providers, all parties have to be willing to provide data and interfaces. Although these kinds of collaborative projects often equate to the greatest potential for optimisation for everyone involved, it can be tricky to convince third parties to contribute their data.
Time, manpower and investment
AI projects require time – at least somewhere between six and twelve months – and plenty of resources. Given that medium-sized companies are unlikely to have the relevant development capability in house, they need to bring in people in the know. AI specialists are hard to find and cost a pretty penny when you do find them.
One way around this is to work with research and funding institutions, such as the Mittelstand 4.0 centres of excellence for medium-sized companies (German) or the Maritime Cluster Northern Germany. The NautilusLog project (German) is a great example of a successful partnership, with a digital logbook having been created using a ship simulator that the start-up with the same name as the project was able to use while keeping costs neutral.
Compliance and regulations
Maritime regulations sometimes lag slightly behind advancements in technology. This is even more evident when it comes to AI and robotics applications.
With more reliance on networking and digitalisation comes a greater risk of cyber attacks. And so it is vital that cybersecurity is always given due care and attention as part of AI projects.
People’s attitudes to work
When we hear mention of ‘artificial intelligence’, images of workforces being replaced by machines come to mind – even more so if robots and autonomous vehicles are involved. This underlying fear can cause employees to feel anxious and concerned. In the worst case scenario, they may sabotage AI projects or be reluctant to even start working with this technology.
The ability to address these worries through open communication and training is key if AI projects are to prove successful. This technology is still in its early stages and has a long road of development ahead of it within the maritime industry. Right now, assistance systems are the order of the day. They are designed to make people’s jobs easier and allow us to gain better control over our networked world as its complexity continues to increase.
Plus, we mustn’t forget that we are faced with an ever-dwindling workforce thanks to our ageing population. If we don’t call on robots to help us, there will come a day when we are unable to do everything we need to do to keep our globalised economy running.
Opportunities presented by AI developments within the maritime industry
As outlined above, AI provides a way to optimise processes in virtually every branch of the industry, allowing for time and money to be saved. For this to be possible, data has to be available or there needs to at least be a way of collecting data.
We recommend that companies looking to get involved with AI follow these top tips:
- Start small: Don’t try to dive in at the deep end with full automation. Instead, it is much better to start small in an area where data is already available.
- Get help: There is plenty of help on hand for medium-sized companies, with services being offered by the likes of the Mittelstand 4.0 centres of excellence. In fact, Bremen has been identified as an ideal location for AI applications. The state is ready to help medium-sized companies with numerous initiatives and funding opportunities, underpinned by the AI strategy and AI centre of excellence currently under construction in Bremen(German).
- Be open-minded: It can be a brilliant idea to network and collaborate with competitors or suppliers in certain areas because it means that more data will be available to you and AI systems will be more precise.
- Stay informed: Even if you’re not exactly going to be taking the lead on AI programming yourself, you should at least stay up-to-date with the latest developments. And that applies whether you’re an engineer or a management executive. Most importantly, you need to have an understanding of what AI can do and where its limitations lie.