Episode 6: How AI Learns in a World Without Clear Answers — A New Approach Called Unsupervised Learning

AI’s New Way of Learning Without Being Taught

When we think about how AI learns, many people imagine someone teaching it directly. In fact, most AI systems do learn based on answers provided by humans. For example, by showing it many labeled images like “this is a cat” or “this is a dog,” the AI learns what makes something look like a cat or a dog. This approach is called “supervised learning.”

But in reality, there are many types of data where we don’t know the correct answer. So how can AI learn in those situations?

Finding Patterns Without Answers — How Unsupervised Learning Works

This is where the concept of “unsupervised learning” comes in. As the name suggests, this method doesn’t rely on a “teacher” or any predefined answers. Instead, the AI explores the data on its own to discover patterns and rules hidden within.

To compare it to human experience, imagine walking around an unfamiliar city without anyone guiding you. As you explore, you might notice that one area has lots of cafes or that another street feels quiet and residential. You’re picking up on features and vibes just by observing.

In unsupervised learning, there are no labels attached to the data. So the AI compares similarities and differences among pieces of information to decide things like “these seem to belong together” or “this one looks different.” This process is known as “clustering.”

Another commonly used technique is called “dimensionality reduction,” which helps simplify complex data by pulling out only the most important parts. This makes it easier for both humans and machines to understand what’s going on.

In short, unsupervised learning gives AI the ability to find structure and order in a world without clear answers.

The Strengths and Challenges of Unsupervised Learning Through Everyday Experiences

Let’s bring this idea closer to home with an example. Imagine traveling abroad for the first time. You don’t speak the language or know much about local customs. But as you watch people around you, you might start thinking things like “those people look like tourists” or “these must be local students.” You’re grouping people based on clothing, behavior, or other clues—without anyone telling you who they are.

AI does something similar: it picks up on trends and patterns from raw information and tries to make sense of them in its own way.

This method has some major advantages. First, since humans don’t need to label every piece of data manually, huge amounts of information can be processed quickly. Also, AI might uncover hidden patterns or relationships that even humans wouldn’t notice.

However, there are also challenges. It can be hard to understand why certain groupings were made or whether those groupings actually make sense in a meaningful way. That’s why interpreting results from unsupervised learning requires thoughtful analysis.

Recently, this approach has evolved further and is now being applied in areas like large language models (LLMs). We’ll cover that more in another article—but even when dealing with human language and emotions, this ability to notice and understand things independently plays an important role.

Finding Value in Ambiguity — The Expanding Potential Ahead

In our daily lives too, we often have moments when we just “get” something without being told—like sensing someone’s mood or understanding how a place feels just by being there. These insights come from experience and observation rather than clear-cut instructions.

Unsupervised learning aims for a similar kind of intuitive understanding.

There are many aspects of life that can’t be explained through simple right-or-wrong answers. And now AI is beginning to step into these complex and ambiguous areas as well—discovering new insights and value along the way.

In our next article, we’ll explore another form of learning that’s different from both supervised and unsupervised methods: reinforcement learning. This approach lets AI grow through trial and error—a process that shares some surprising similarities with how humans learn too. Stay tuned!