Have you heard of distributional shift? Are you involved in shipping? If the answers are (no) and (yes), you need to read this!
Unless you happen to be one of the small (but increasing) number of data scientists in the shipping domain, chances are you haven’t heard of this phenomenon. But, in fact, it’s one of the greatest obstacles to using data really effectively – not only in maritime, but across every industry.
As our co-founder Konstantinos says,
Robustness to distributional shift is the difference between AI that works in theory, and AI that works for customers.
Distributional shift is a pretty big term for a pretty simple thing. And in shipping, it’s critical.
Let’s think about hull fouling. We all know what it is – but consider what this marine growth does to the data that is collected from your ship. The level of fouling affects some vitally-important things, including (1) the resistance of the hull; (2) the power required by the engines; (3) the impact of currents; (4) the fuel consumption; (5) the level of emissions.
To make matters worse, all these things – alongside many other data points – are interrelated in a very complex way. We can think of this as the “DNA” of the ship. With me so far?
To make matters EVEN WORSE – this entire complex web of data is changing over time, as fouling develops, in a largely non-predictable way. We can all agree – this makes understanding the data difficult.
The same data phenomenon is seen in data sets from diverse sectors throughout the world.
That’s distributional shift! … so what?
Understanding the data shows us how to significantly increase vessel efficiency – through voyage and vessel optimisation – and the best tool in the world to make sense of this data is artificial intelligence.
However, how a solution provider handles distributional shift can makes the difference between an approach that provides a measurable improvement to your vessels’ fuel consumption, and one that could even have a negative impact..
At DeepSea our pioneering AI research in distributional shift over the past five years powers our Pythia and Cassandra platforms – enabling, today, roughly an 8% average efficiency improvement per vessel.
Every day we have the great pleasure of working with shipping companies that are passionate about adopting new technologies to make their organisations greener, leaner and better connected. However, we also see that there is still a lack of information available to help aspiring companies make decisions about what to adopt, why, and what to expect.
Our technology manifesto
That’s why DeepSea follows a three-step ‘technology manifesto’ with you in mind:
- Phase 1: Pioneering research into AI approaches in shipping
- Phase 2: Proving the real-world utility of these approaches
- Phase 3: Advocating for common and transparent standards throughout the industry
So now you know why distributional shift is so important! If you want to dig deeper – have a look at our exciting project alongside world-class researchers from the Universities of Basel, Cambridge, HES-SO Valais and Lausanne – The Shifts Project.
For more information on proving the real-world utility of AI approaches, check out our work on model validation.