What is Machine Learning and what is AI?

Artificial intelligence is a broad field of computer science focused on creating systems that can perform tasks that typically require human intelligence. AI isn’t a single technology, it is a field of study that encompasses many different techniques, methods, and approaches. The goal of AI is to create machines that can “think,” or at least perform tasks, in ways that mimic human capabilities. Within this there are subfields, such as robotics, natural language processing, and computer vision. Machine learning is one of these subfields, and it’s one of the most popular and effective methods for building intelligent systems.

Machine learning is a specific approach to creating AI systems that learn from data. Instead of being explicitly programmed with rules for every possible scenario, ML models use data to improve their performance on tasks over time. In other words, they “learn” by identifying patterns in data and using those patterns to make predictions or decisions.

Machine learning

To understand this better, think of machine learning as a way of teaching computers by example rather than instruction. In traditional programming, if the system makes a mistake, you would need to manually adjust the rules. In machine learning, the model continuously learns and improves by adjusting itself based on errors and feedback, becoming more accurate over time. Machine learning models are essentially algorithms (or mathematical formulas) that find patterns in data.

In traditional programming, you would create a set of explicit rules for identifying spam, like flagging certain keywords (“prize,” “win,” “free”). The program would then follow these rules to classify emails. In ML you might show the system thousands of labeled examples of spam and non-spam emails. Over time, it would learn the patterns on its own, noticing certain phrases, words, or formats that are common in spam emails without needing explicitly programmed rules.

Types of ML

There are several types of machine learning, each suited to different types of tasks and goals. Each type of machine learning has unique strengths and is chosen based on the task at hand. Supervised learning, for example, is great for prediction tasks, while unsupervised learning is used for exploring and finding hidden patterns in data. Machine learning has become central to AI because it allows systems to adapt, generalise, and improve over time.

Machine learning in action

Machine learning powers a lot of the AI systems we interact with daily, even if we don’t always realise it. Think of AI as the broad goal, creating machines that can perform intelligent tasks. Machine learning is one approach to achieving this goal, enabling machines to improve over time by learning from data.

Without machine learning, AI would be limited to simpler tasks that don’t require adaptation or improvement over time. With machine learning, AI can tackle complex, data-driven tasks and continuously refine its performance, bringing us closer to building systems that truly mimic human intelligence.