Episode 25: How AI Understands Language — The Transformer That Uncovers the Meaning Behind Words

How Does AI “Understand” Text?

When you hear that “AI understands text,” what kind of image comes to mind? You might wonder if a machine can really do something as complex as grasping the flow of a sentence or understanding how words relate to each other—just like a human does.

The answer lies in a technology called “Transformer.” The name might remind you of robots from movies, but this Transformer is actually one of the core technologies behind today’s AI. It’s essentially the “brain” that powers many modern AI systems.

How Transformer Detects Connections Between Words

Transformer is a relatively new type of AI model introduced in 2017. It was built on a completely different approach compared to earlier methods. At the heart of this model is something called the “self-attention mechanism.”

This mechanism allows each word in a sentence to look at all the other words and determine its own meaning and role based on those relationships. For example, take the sentence: “He ate the cake. It was delicious.” To understand what “it” refers to, you need to connect it back to “cake.”

The ability to detect these kinds of connections within text is what makes Transformer so powerful. Because it can grasp not just nearby words but also long-range relationships and overall flow, it can handle long passages without losing track of meaning.

From Translation to Conversation—Expanding Applications in Natural Language Processing

Thanks to this capability, Transformer has driven major advances in natural language processing—the field where computers work with human language. Tasks like translation, summarization, and answering questions have all seen dramatic improvements.

One especially notable development is AI models that can learn from massive amounts of text and hold natural conversations with people. Examples include GPT and BERT (we’ll explore these more deeply in other articles). All of them are built on top of Transformer technology, making it an essential part of today’s AI landscape.

That said, there are still challenges. These models require enormous computing resources and energy. And because they learn from large datasets, any bias present in that data can lead to biased responses from the AI itself.

A Future Built on Understanding Language

Let’s look at it from another angle. Imagine you’re writing a long novel—and you have to remember details from the first chapter while writing hundreds of pages later. That would be tough for most people. But Transformer handles this kind of task with ease.

The term “self-attention mechanism” may sound technical or intimidating at first. But at its core, it simply means looking beyond individual sentences—taking into account how everything fits together across an entire passage. In that sense, it’s like having an extremely wide field of vision—one that allows for calm and precise observation.

While this may seem like a highly specialized piece of technology, its essence lies in recognizing how words relate to one another. And it’s this ability that brings about human-like understanding and expression. When you think about it that way, doesn’t it start to feel more familiar?

Looking ahead, this technology will no doubt continue evolving. But at its foundation will always be the careful attention paid to how words connect with each other—a mindset that might even reflect how we communicate as people.