Artificial Intelligence

Many industries are currently in the process of digital innovation where several digital initiatives are being driven simultaneously by several groupings within the industry, such as Blockchain, IoT, Big Data, artificial intelligence etc.

Nevertheless, the largest threat to green energy might also be the greatest opportunity, namely the integration of artificial intelligence in the green energy industry by digitalization and machine learning. Digitalization is incorporation of digital technology into all aspects of everyday shipping & transport operation. Machine learning is a way for artificial intelligence to progressively improve ship performance on specific tasks based on the big data collected in the digitalized maritime environment.

Regulations intended to influence technological design or business operation has largely taken the form of performance or design standards. Imagine a digitalized marine environment using cloud computing where everyone follows a set standards and stakeholders know what to expect. Stakeholders have to consider the cost associated with to falling behind. Here the objective would be to create a framework for standardizing data, message formats and interface specifications. Investors and stockholders face changing landscapes, where risks and new opportunities are characterized by an increasingly complex decision-making space in need of innovative financing and investment models in order to adequately assess the uncertainty and risks in new technology and innovation within the green/clean energy and transport marked.

Data Science

Data science involves many disciplines to produce a thorough look into raw data. Data science includes concepts like data mining, data inference, predictive modelling, and machine learning algorithm development, to extract patterns from complex datasets and transform them into actionable business strategies. Data Science is a combination of multiple disciplines, including mathematics, statistics, computer & information science and machine learning.

Business Intelligence vs. Data Science:

  • Business Intelligence basically analyses the previous data to find hindsight and insight to describe business trends. It enables to take data from external and internal sources, prepare it, run queries on it and answer questions. Business intelligence can evaluate the impact of certain events in the near future.
  • Data Science is a more forward-looking approach, an exploratory way with the focus on analysing the past or current data and predicting the future outcomes with the aim of making informed decisions. It answers the open-ended questions as to what and how events occur.
Data science enables machine learning (ML) models to learn from the vast amounts of data, rather than mainly relying upon business analysts to see what they can discover from the data.

Making Predictions - Forecast

To build a model to determine the future trend, machine learning algorithms can be considered as an optional tool to solve the problem.

Artificial Intelligence => Data Science & Machine Learning => Deep Learning

To better understand data science, here is a simple breakdown:

  • Artificial Intelligence (AI) means getting a computer to mimic human behaviour in some way.
  • Data science is a subset of AI, and it refers more to the overlapping areas of statistics, scientific methods, and data analysis, that are used to extract meaning and insights from data.
  • Machine learning is also a subset of AI, consisting of the techniques that enable computers to learn from data and deliver AI applications.
  • Deep learning is a subset of machine learning that enables computers to solve more complex problems.
  • Whereas data analytics is mainly concerned with statistics, mathematics, and statistical analysis.

The objective is to update on the latest technologies, and offer coverage of sub-categories of AI, such as data science, machine learning, deep learning, transfer learning, causality, probabilistic programming, data analytics, and safe AI.

Machine Learning

Advanced Machine Learning algorithms will be capable of improving voyage optimization, such as fuel efficiency, minimizing crew performance, improving voyage costs estimates, calculating the optimal route in a minute, give recommendations on speed, course and etc.

Although machine learning is already used in many areas of the digital world, its adaptation to the maritime industry remains limited so far. Since sea transport requires smart tools, the application of machine learning offers maximum benefits for sustainable transport. In terms of comprehensive analysis, marine professionals and researchers should pay particular attention to appropriate algorithms to address specific shipping problems in voyage optimization, stability of transportation, forecasting maintenance and repair, control of freight rates, digitalization on the bridge and control engine, energy efficiency management and enhancement maritime security. 

Big data is used to control sensors on a ship and to perform predictive analysis. Enhanced decision making through big data analytics is being actively implemented to avoid and predict additional costs and can be used throughout the life of a ship. 

Artificial intelligence (AI) and machine learning (ML) has many potential applications in the maritime industry, and shipping companies see AI and machine learning as keys to achieving a competitive advantage. These technologies are currently able to give an economic effect from the use, optimize and increase the efficiency of a shipping company. 


Digitalization and new developments in the field of artificial intelligence, blockchain, IoT and automation are becoming increasingly relevant for sea transport. They help streamline existing processes, create new business opportunities, and transform supply chains and trade geography. 

Despite the potential, opportunities and benefits offered by these technologies, they also entail risks and potential costs. This demand for the role of interoperability and global standards, and the need to ensure that digitalization works towards the sustainable technology development within the maritime industry. 

Digital tools for optimizing ship energy resources at sea make it possible to plan routes taking into account the condition of the vessel and external factors, such as weather, wind, waves, etc., and also including route planning in terms of fuel consumption. 

In the future, smart ships will include digital information, computer coding, and new technological infrastructure, which is also the driving force behind the modern world. 

Internet of Things (IoT) and Cyber Security

Cybersecurity is concerned with the protection of IT, IoT, information and data from unauthorized access, manipulation, and disruption. Cyber risk management should be an inherent part of safety and security culture conducive to the safe and efficient operation of the ship and be implemented at various levels of the company, including senior management ashore and onboard personnel.

IMO Resolution MSC.428(98) identifies an urgent need to raise awareness on cyber risk threats and vulnerabilities to support safe and secure shipping, which is operationally resilient to cyber risks. Thus, all maritime stakeholders should work towards safeguarding shipping from current and emerging cyber threats and vulnerabilities. The resolution furthermore affirms that the SMS should consider cyber risk management in accordance with the objectives and functional requirements of the ISM Code. 

Connecting onboard sensors to shore for data analytics will improve ship and fleet operations in multiple ways, such as optimized maintenance, cargo handling, and route planning, savings in fuel and lubes consumption, and reduced service costs. Ships of the future will have an entire network of sensors measuring all aspects of operations.

Security concerns are a barrier to the adoption of IoT, but leaders in maritime digitalization are taking the necessary steps to safeguard their fleet from current and emerging threats and vulnerabilities.



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