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What is AI?
Artificial Intelligence
Position StatementPrinciplesWhat is AI?

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is a term you’ve probably heard often, but what does it really mean? At its core, AI refers to systems that can perform tasks which typically require human intelligence, such as recognising patterns, making decisions, or generating content (Russell and Norvig, 2021). From the apps on your phone to cutting-edge research in healthcare and climate science, AI is shaping the world around us.

The concept of AI isn’t new. It dates back to the 1950s, with roots in mathematics and computer science (Russell and Norvig, 2021). Today, AI is a broad and evolving field, and understanding its main categories can help demystify it.

Defining AI

We adopt the OECD’s widely recognised definition:

An AI system is a machine-based system that, for a given set of explicit or implicit objectives, infers, from the input it receives, how to generate output such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.

— (OEDC, 2024, p.4)

In simpler terms, AI systems learn from data and use that knowledge to make decisions or create outputs that affect the world.

Key Categories of AI

1. Machine Learning

Machine Learning (ML) is a type of AI that enables systems to learn from data without being explicitly programmed. Instead of following fixed instructions, ML algorithms identify patterns and improve over time (Scottish AI Alliance, 2025).
Common approaches include:

  • Supervised learning: Learning from labelled examples.
  • Unsupervised learning: Finding hidden patterns in unlabelled data.
  • Reinforcement learning: Learning through trial and error.

ML powers everyday tools like spam filters, recommendation engines, and predictive analytics.

2. Generative AI

Generative AI (often referred to as GenAI) describes the user-facing systems that generate content such as text, images, video, audio, and computer code. The underlying technology that enables these systems is a class of models known as Large Language Models (LLMs), which are trained on vast datasets (Stryker and Scapicchio, 2025) containing text, images, audio, and video. The training on large human-generated data sets so can mean that LLMs inherit human biases and stereotypes.

LLMs are deep learning neural networks that use transformer architectures and GPU-based hardware to process large volumes of data in parallel. They provide the computational engine behind generative AI systems, producing text and other forms of content in response to user “prompts” by drawing on the extensive patterns and relationships learned during training.

Examples include:

  • Chatbots that write text content or answer questions.
  • Tools that create realistic images, video or music.

Generative AI has transformed creative industries and research, enabling rapid content generation and simulation. It has the potential to improve efficiency and quality in certain areas of application.

3. Agentic AI

Agentic AI represents the next step: autonomous agents that act on behalf of users or systems. Unlike traditional models, these agents can plan, make decisions, and use external tools without human intervention (Gutowska and Stryker, 2025).

Imagine an AI system that not only suggests the best time for your trip but also books flights and accommodation automatically. Agentic AI systems coordinate multiple agents to achieve complex goals, demonstrating adaptability and independence. Agentic systems are high accessible and can be deployed to custom tasks to meet specific business needs.

Why Does AI Matter?

AI is everywhere, from healthcare diagnostics to climate modelling, from personalised learning to entertainment. Understanding its foundations helps us engage critically with its opportunities and challenges. AI also presents an opportunity for reducing the burden of menial repeatable tasks, freeing up time for more high value activities.

References

Russell, S. and Norvig, P. (2021) Artificial Intelligence: A Modern Approach. 4th edn. Pearson.

OECD (2024). Explanatory memorandum on the updated OECD definition of an AI system. [online] OECG.org. Available at: https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/03/explanatory-memorandum-on-the-updated-oecd-definition-of-an-ai-system_3c815e51/623da898-en.pdf. [Accessed 12 December 2025].

Scottish AI Alliance (2025). The different types of artificial intelligence [online] Scottish AI Alliance. Available at: https://www.scottishai.com/news/the-different-types-of-artificial-intelligence?rq=definitions [Accessed 12 December 2025].

Stryker, C. and Scapicchio, M. (2025). What is generative AI? [online] IBM.com. Available at: https://www.ibm.com/think/topics/generative-ai [Accessed 12 December 2025].

Gutowska, A. and Stryker, C. (2025). What Are AI Agents? [online] IBM.com. Available at: https://www.ibm.com/think/topics/ai-agents. [Accessed 12 December 2025].