Episode 9: How Does AI Get Smarter? Understanding the Role and Mechanism of Training Data

How Does AI Get Smarter? The Clue Lies in How It Learns

These days, it’s not unusual to hear the term “AI (Artificial Intelligence)” in the news or everyday conversations. But have you ever wondered, “How does AI actually become smart?”
For example, think about an AI that can talk naturally with people or tell the difference between a cat and a dog in a photo. Behind these abilities lies something called “training data”—a large collection of information. In fact, for AI to “learn” anything, this training data is absolutely essential.

What Is Training Data? The Material That Helps AI Learn

Training data refers to the material used to teach AI knowledge and rules. For instance, if you’re building an AI that can distinguish between cats and dogs, you would start by collecting many photos of cats and dogs. Each image comes with a label like “this is a cat” or “this is a dog.” Using this labeled information, the AI gradually learns what features make something look like a cat or resemble a dog. This process is what we call “learning” in the context of AI.

This mechanism is quite similar to how humans learn things. Imagine a young child looking at an animal picture book while an adult says, “This one’s a lion,” or “That’s an elephant.” By seeing these images repeatedly, the child begins to remember the names and characteristics of different animals.
However, while humans might only need to see something a few times to remember it, AI often requires thousands—or even tens of thousands—of images to learn effectively. That’s because it relies heavily on identifying patterns from large amounts of information.

Used in Your Smartphone Too? The Convenience and Pitfalls of Training Data

Let’s consider a more familiar example. When you type on your smartphone, it often predicts what word you might want next. For instance, after typing “Thank you,” it might suggest “very much” as the next phrase.
This feature works thanks to training data made up of countless past messages and conversations people have typed. Based on patterns like “most people tend to follow this word with that one,” the system predicts what you’re likely to say next.

While convenient, this system also has its risks. If the training data includes mostly biased content, that bias can be passed on directly to the AI. For example, it might start favoring certain expressions or making decisions that lack fairness.

That’s why choosing what kind of training data to use is extremely important. It’s not just about having lots of data—the information must come from diverse perspectives and be high in quality. These days, not only do we use carefully selected human-curated data, but we also pull massive amounts of information automatically from across the internet. Technologies are being developed to extract only what’s needed from this vast pool and prepare it for use—and we’ll explore those in another article.

Experience Builds Strength—Why Training Data Matters

AI isn’t magic. Its intelligence depends entirely on what kind of experiences—or rather, what kind of training data—it has gone through. Just like people grow through various experiences and even mistakes over time, AI also learns by going through many examples and gradually builds its own way of thinking and understanding.

And at the foundation of all this lies training data. Simply knowing how this works can deepen your understanding of many different AI technologies you’ll encounter—and perhaps make them feel just a bit closer to your everyday life.

In our next article, we’ll look at how we check whether what an AI has learned from its training data is actually useful—that involves something called “validation data.” By learning about these systems step by step, your relationship with AI will become more familiar too.