Machine Learning (ML) has been a buzzword in the technological space for years; The idea of a machine improving itself over time, recognizing patterns, and aiding its human counterparts appeals to many industries.
With machine learning, businesses and organizations across a broad scope of industries will be able to analyze larger quantities of data and derive meaningful insights faster. Because of this, overall operational efficiency will improve, processes will be streamlined, and assets will be protected, ultimately enhancing every aspect of an organization’s workflow.
To truly embrace machine learning and maximize the effectiveness of such technologies, machine learning methodologies must be transparent.
As the maritime ecosystem evolves due to regulations, global events, industry growth, and market fluctuations, the incorporation of ML can provide businesses and organizations with a key advantage, enabling improved insights into real-time and evolving data.
What is machine learning (and why is it needed?)
Today, machine learning is not what it was in the 1940s when the first machine learning model was created; however, the driving force behind it has not changed. When initially developed, the inspiration for machine learning was human brain cells’ interaction and their ability to aid one another. In technological terms, that translated into machines helping themselves get better.
Today, machine learning is a digital process that uses statistical techniques to train machines to perform certain tasks.
For example, the real-estate market has shifted and properties are compared online rather than through agents. Machine learning algorithms can, in this instance, provide accurate insight into the value of a property, taking into consideration hundreds of data points including historical sale prices, room sizes, property age, location, facilities, and more. For the real-estate market, this is relatively easy since the data in question is static, easily aggregated, and mathematical models can easily identify patterns.
However, in the maritime ecosystem it is not as easy. The data points that must be considered are dynamic and behavioral-based, and the information is spread over multiple sources.
As the amount of data increases, needs evolve, and it is no longer possible to rely on easily identifiable patterns and trends, or human experts alone to find them. Trained models must be used to evaluate unpredictable behaviors, find hidden correlations, and provide predictions on their relevance.
When predictions support business decisions, prioritizes available resources, optimizes operations, and protects organizations from risk, they transform into meaningful insight.
To create a machine learning model that effectively recognizes such patterns from complex data, best-in-class domain expertise needs to be included. In the maritime domain this includes expert data sciences as well as maritime expertise. When combined, machine learning can be trained to provide effective insight that meets the industries need.
What, and why, must machines learn?
Like brain cells, there are different types of machine learning models with different roles; in the real-world, different ML models are applied to understand different things, namely, risk exposure. ,
There are two types of machine learning:
- Supervised machine learning – where a machine is trained using labeled data
- Unsupervised machine learning – where a machine does not need training or labeled data and works on its own to discover information and hidden patterns
Both supervised and unsupervised ML models need to be able to take into consideration real-time and dynamic data to be meaningful.
As an example, hundreds of illicit activities cases are used to train the ML models to highlight risk patterns and unusual behaviors. By tagging thousands of abnormal behaviors and extracting hundreds of features based on domain expertise, the machine can identify and predict potential risks. Examples for such behaviors and features may be: first time visit to an area, deviation from course, or a port call in a sanctioned area.
As data complexity increases the need to gain clarity from complexity, it becomes increasingly difficult to rely on domain experts. The incorporation of machine learning can help understand connections between behaviors and desired predictions, considering historical data and hidden trends.
When this happens, businesses have all the information they need to make decisions to optimize and protect their operations.
Machine learning in maritime enables instant clarity
The maritime ecosystem is at an inflection point today as the entire industry is undergoing rapid digitalization driven by regulation, global events, market fluctuations, and more.
As a result of these changes, more businesses need immediate access to best-in-class insights pertaining to their maritime activities. To truly see the state of the seas, it is necessary to consider static, historical, and behavioral data of each vessel. Human resources alone cannot do that; that is where machine learning comes into play.
Through technology, namely, machine learning, it is possible to gain access to accurate dynamic insights, understand real-time risks, and make insight backed decisions as needed.
Powerful machine learning models can learn connections and determine the risk of vessels. This will enable organizations to gain a clear understanding of each vessel’s risk and the overall state of their fleet and their counterparties.
Machine learning algorithms are dynamic enough to suit almost any range of industry and any predictive need. For example, for insurance purposes, a business might want to know the connection between a vessel’s static features, past voyages, and behavior to determine the vessel’s probability of being in an accident within a specific timeframe. To know that, it is crucial to factor in port calls as well as the density of the ports during those calls, the depth features, the number of sailing days, and more. On the other hand, governments might be more concerned with the relationships between first-time port visits, selected trade route, duration of ship-to-ship (STS), and how that impacts the probability of a vessel engaging in illicit activity.
Today, businesses and organizations cannot look at events in isolation if they want to ensure future business readiness; they must use machine learning to understand the context and connections between events and their impact.
Building the right machine learning model for your business needs
Machines cannot work alone; they need humans just as much as humans need them. That is why to be truly effective in providing clarity, decision-ready insight, and operational effectiveness, great machine learning needs to be backed by a great team.
Data scientists must train machines to learn what tags to identity and what existing case results were to find connections for future events; that data needs to be fused, cleaned, and processed to retain relevance, and data scientists must be able to take that clean data and build the models that identify the connections.
Obtaining data alone does not provide clarity and certainly does not provide insight; however, when the data is cleaned, irrelevant data filtered, and historical behavior is considered, information becomes valuable. Ultimately, however, domain experts need to lend their knowledge to machines to provide insight that makes data worthwhile.
In the maritime ecosystem, raw data is global and diversified. It comprises multiple data types that include static data, AIS data, historical information about entities and their activities, and over 500 proprietary features for each vessel, area, route, and more.
To make sense of this data and gain intelligence-based risk analysis, both supervised, and unsupervised learning models must be created using dynamic data. This, in turn, will create an accurate and dynamic risk-based profile.
The continuous evolution of the different machine learning models propel advanced ML algorithms further and create a truly best-in-class solution that offers unique output that goes beyond machine learning alone. With expertise backed machine learning models, the insight yielded will go far beyond the models themselves, creating a winning solution for organizations across all industries.