What Is Artificial Intelligence?
Artificial intelligence, or AI, refers to computer systems designed to perform tasks commonly associated with human intelligence, such as recognising patterns, understanding language, making predictions or generating content.
Unlike the fictional robots often associated with AI, most real-world systems are developed for specific purposes. A translation tool, a fraud detection system and a chatbot may all use artificial intelligence, even though their capabilities are very different.
Much of today’s AI relies on machine learning, a technique that allows computers to identify patterns in data rather than depending entirely on instructions written by programmers.
An important distinction is that AI does not necessarily understand information in the way humans do. A system may produce convincing answers or recognise complex patterns without possessing human reasoning, consciousness or genuine comprehension.
Traditional Software vs AI: What's the Difference?
Traditional software generally follows explicitly programmed instructions. A developer specifies rules, and the computer executes them.
Consider a simple email filter. Conventional software might classify every message containing a particular phrase as spam. The rule is fixed: if the specified condition is met, the programme takes the corresponding action.
A machine-learning-based spam filter works differently. Instead of relying exclusively on predefined rules, it learns statistical patterns from examples of legitimate and unwanted emails. It can then use those patterns to classify messages it has never encountered before.
This distinction is fundamental: traditional software primarily executes predefined logic, while machine-learning systems derive patterns from data and use them to generate outputs.
However, the boundary is not absolute. Traditional software can be highly complex, AI can incorporate explicit rules, and not every AI technique depends on machine learning.
How Does AI Learn?
Machine learning typically begins with training data. During training, an algorithm adjusts the parameters of a mathematical model to improve its performance on a particular task.
Once trained, the model can apply what it has learned to new inputs. This process is called inference.
Generative AI, including large language models, uses learned patterns to produce new content, such as text, images or computer code.
Importantly, learning does not mean that a system continuously improves whenever someone uses it. Many AI models remain unchanged until they undergo further training or updates.
Why Does the Distinction Matter for AI Regulation?
The difference between AI and conventional software is more than a technical question.
Under Article 3 (1) of the EU AI Act, an AI system is defined by characteristics including machine-based operation, varying levels of autonomy and the ability to infer how to generate outputs from inputs.
This distinction helps determine which technologies fall within the Regulation’s scope. Not every automated process qualifies as an AI system, and not every AI system is subject to the same regulatory requirements.
Final Thoughts
AI is not simply traditional software with a more sophisticated interface. Its ability to infer patterns and generate outputs introduces new possibilities, but also raises questions about reliability, transparency and human oversight.
Understanding how these systems differ is therefore essential, particularly when their outputs begin to influence decisions affecting people’s rights and opportunities.
The more important question may no longer be whether a machine can perform a task, but how much responsibility we are prepared to delegate to it.
