Episode 24: Can AI Read Between the Lines? How BERT Understands Language and Powers Search and Text Prediction

An AI That Reads Between the Lines—The Key Is BERT

Have you ever wondered, “How does AI understand the meaning of a sentence?” For example, when you type a question into a search engine, sometimes it seems to return an answer that really gets what you meant. Or when you’re typing on your smartphone, it suggests words as if it knows exactly what you’re trying to say.

Behind these natural interactions is an AI technology quietly doing its job—something called “BERT.”

What Is BERT? A Mechanism That Understands Meaning from Context

BERT is a language model introduced by Google in 2018. Its full name is “Bidirectional Encoder Representations from Transformers,” which means “a Transformer-based system that reads sentences from both directions.”

That might sound a bit technical, but the key ideas are “bidirectional” and “context.”

Previously, many AI systems could only read text in one direction—either left to right or right to left. Because of this limitation, they weren’t very good at fully grasping the overall meaning or nuance of a sentence.

But BERT works differently. It looks at both the words before and after a certain point in a sentence at the same time. It learns by trying to guess hidden (or “masked”) words based on their surrounding context. For instance, in the sentence “I drank coffee at 〇〇,” BERT would try to predict what fits best in place of 〇〇—maybe “a café” or “the station”—based on the rest of the sentence.

This kind of masked learning allows BERT not only to understand how words are connected but also to grasp their underlying meaning and flow.

Why Search and Predictive Text Feel More Natural Now

The way BERT works is somewhat similar to how we read novels. Sometimes, you can’t fully understand a character’s feelings just from one line—you need to read what came before and after to get the whole picture.

Take this sentence: “She hung up the phone with tears in her eyes.” On its own, we don’t know why she’s crying. But if earlier it said, “Her boss had scolded her unfairly,” then her reaction makes sense.

BERT operates in much the same way. Instead of looking at each word in isolation, it tries to understand how everything connects—the relationships between words, their flow, even their emotional tone. Thanks to this ability, services like search engines and chatbots have become smarter and more natural-feeling.

That said, BERT isn’t perfect at everything. For example, it’s not particularly good at writing new sentences from scratch—it’s more like a specialist in reading comprehension. Also, using BERT requires large amounts of data and powerful computers, so applying it effectively takes some ingenuity. Because of these challenges, newer models have been developed based on BERT that improve upon its limitations (we’ll cover those in another article).

A New Way for AI to Understand Language

Even so, BERT brought about a major shift in how we think about language understanding in AI. Before its arrival, most systems relied on surface-level information like word order or distance between terms. But now there’s more focus on deeper elements like meaning and relationships.

This shift has brought AI closer to us in everyday situations—whether through conversations or searching for information.

Language is incredibly subtle and complex. The same word can mean different things depending on how it’s used or who says it. And yet, technologies like BERT are taking on that complexity head-on.

Just knowing a little about how AI interacts with language might change how you see the tools you use every day. In our next article, we’ll take a closer look at the foundation behind BERT—a mechanism called “Transformer.” Stay tuned!