The definition of AI has evolved significantly since the concept was first brought to light in the 1950s when mathematician and computer scientist Alan Turing questioned whether machines could be intelligent.
Since then, there has been widespread discussion and debate on the meaning of AI. In late 2023, the OECD updated its definition to help governments align on the term and ensure they have a foundation for legislating and regulating the use of AI, which in turn allows for interoperability across jurisdictions. According to OECD, AI refers to machine-based systems that are designed to make predictions, recommendations, or decisions for specific human-defined objectives, integrating both human and machine inputs to influence real or virtual environments.
Narrow AI Vs General AI
AI can be categorised into two types: Narrow AI and General AI. Narrow AI (also known as Weak AI) is designed and trained to complete a specific task without human interference. This means that knowledge gained from performing that task will not automatically be applied to other tasks. Apple’s Siri and Amazon’s Alexa are two of the most well-known Narrow AI systems.
General AI (also known as Strong AI) refers to AI that is designed to understand, learn, and apply knowledge and intelligence to a range of tasks in a similar way that a human being would do. General AI is a theoretical concept. It would require machines to possess the cognitive abilities that rival those of a human being.
Generative AI
Generative AI refers to deep-learning models that are capable of learning and generating statistically probable outputs from raw data. According to IBM, generative AI models have been used for many years in statistics but in the last few years advances in deep learning have made it possible for generative AI to analyse images, speech, and other data. Today, generative AI learns from existing data and creates new content based on what it has learned.
One example is Copilot by Microsoft. The generative AI chatbot is hailed as ‘your everyday AI companion’ by Microsoft and helps users to analyse data, automate repetitive tasks, summarise meeting discussions, generate content and emails or other documents, and has many other features. Shipping company Wallenius Wilhelmsen recently implemented Copilot to help streamline processes, empower decision making, and cultivate a culture of innovation and inclusion by promoting learning and monitoring progress within the organisation.
Large language models
One subset of AI that has received widespread attention within the last few years is the large language model (LLM). LLMs describe specific applications within the broader field of AI. They make use of deep learning techniques to process and generate language and represent advancements in Natural Language Processing (NLP), a crucial branch of AI dedicated to the interaction between computers and human language.
LLMs can be trained to understand the fine details of huge data sets. In shipping, this means that they can be deployed to navigate complex documents and extract critical details to optimise deliveries, reducing manual labour and the risk of human error. One of the most well-known LLMs is ChatGPT. The platform first launched in November 2022 but has advanced.
For further insight into the areas highlighted in this article, take a look at our thought leadership report, produced in collaboration with Lloyd’s Register titled, “Beyond the Horizon, Opportunities and Obscales in the Maritime AI Boom” This report offers an in-depth analysis of the current state of operational AI in the shipping industry, shedding light on the latest trends, groundbreaking developments, and successful implementations. You can download a copy of the report here.
