How do AI and ML work?

Artificial Intelligence (AI) and Machine Learning (ML) are reshaping how decisions are made, how products are built, and how services are delivered. But for many people, they still feel like a black box—something magical or mysterious. They’re not. AI and ML systems are tools. And like any tool, it helps to know how they work if you want to use them well—or even just understand how they affect your life and work.

From Rules to Learning

In the past, computers did what we told them, step by step. These were rule-based systems. Think of them like a recipe: if the input is X, do Y. Everything had to be explicitly programmed. But not everything fits into neat rules. For more complex problems—like recognizing speech, identifying objects, or predicting customer behavior—we need a different approach. That’s where learning-based systems come in. These systems don’t follow hard-coded rules. They learn from data.

The Core Idea: Learning from Data

Here’s the key difference: Instead of telling the system what to do, we give it examples—and it figures out the patterns. Think of how a child learns what a car is. You don’t give them a long list of definitions. You just point out cars as you go about your day. Over time, they develop a sense of what a car looks like. Machine learning works the same way. It learns from examples, not from rules.

A Look Inside a Machine Learning System

Let’s take a real example: training a machine learning system to identify cars in photos.

1. We collect data: thousands of labeled photos—some with cars, some without.
2. We give the data to an algorithm. It tries to spot patterns that separate car images from non-car images.
3. It makes predictions: “This one has a car. That one doesn’t.”
4. It checks how far off it was and adjusts its internal math to do better next time.

That adjustment process repeats thousands of times. Each time, the system gets a little better by reducing what’s called a loss function—a measure of how wrong it was. Eventually, it gets good enough to recognize cars in new images it’s never seen before. It’s not memorizing. It’s learning patterns.

How Machine Learning Learns

Supervised Learning

This is the most common type of machine learning—and probably the easiest to understand. In supervised learning, you train the system using labeled examples. That means every piece of data you give it comes with the correct answer. Imagine you’re teaching a system to recognize cats in pictures. You give it thousands of images, and for each one, you tell it: “This one has a cat,” or “This one doesn’t.” The system studies the differences, learns patterns, and tries to predict the label for new images. Over time, with enough examples, it gets better at making predictions. Common examples of supervised learning include:

• Spam filters (trained on “spam” vs. “not spam” emails)
• Credit scoring systems (trained on past loan outcomes)
• Product recommendations (trained on what users bought or rated highly)

If you have labeled data and a clear goal, supervised learning is often the best approach.

Unsupervised Learning

Unsupervised learning works without labeled data. You give the system a bunch of raw information, and it tries to find structure or patterns on its own. Think of it like dumping a pile of puzzle pieces on the floor and asking the system to find which pieces belong together—even if you never told it what the finished puzzle looks like. One of the most common uses is clustering, where the system groups similar data points together. Practical examples include:

• Grouping customers by behavior (e.g., frequent shoppers, one-time buyers)
• Detecting unusual patterns that could point to fraud
• Organizing large document libraries by themes or topics

Unsupervised learning is useful when you don’t know what you’re looking for, or when it’s not practical to label thousands of examples.

Reinforcement Learning

This type of learning is inspired by how we train animals—or ourselves. The system learns by trial and error, making decisions, getting feedback, and adjusting its actions over time. Here’s how it works:

• The system takes an action.
• If the result is good, it gets a reward.
• If the result is bad, it gets a penalty.
• It tries again, using what it learned to do better next time.

Reinforcement learning is used when a system needs to make a series of decisions in a changing environment. Real-world applications include:

• Robotics (learning how to walk, pick up objects, or navigate space)
• Game-playing AI (like AlphaGo, which beat the world champion at Go)
• Self-driving cars (learning how to stay in lanes, avoid obstacles, and make real-time decisions)

It’s a powerful method, but often slower and more complex to train than the others.

Where You’ve Seen It in Action

Machine learning powers more things than you might expect. Some common examples:

• Your inbox: Spam filters that get better with every email you mark
• Streaming services: Recommendations based on what you’ve watched
• Smart assistants: Speech recognition and voice commands
• E-commerce: Product suggestions and dynamic pricing
• Healthcare: Diagnosing conditions from images or symptoms
• Banking: Fraud detection based on transaction patterns

You don’t always see it, but you use ML every day.

What AI and ML Can—and Can’t—Do

They’re good at spotting patterns in huge amounts of data. They’re fast, tireless, and scalable.

But they’re not human. They don’t understand context or meaning. They don’t have common sense. And they only learn what they’re trained on. Bad data? Bad results.

AI isn’t thinking. It’s calculating.

Final Thought

If you understand how AI and machine learning work, even at a basic level, you’ll make better decisions. You’ll ask better questions. And you’ll be more prepared for the future—because this isn’t going away.

You don’t need to code. But you do need to know what’s behind the curtain.

Learn More

Next Topic: What Are Neural Networkds?

Watch the video from IBM (What is AI? What is ML?) to learn more about AI, ML and their differences.