Episode 4: What Is a Neural Network? Understanding the AI System That Learns Like the Human Brain

AI Inspired by the Human Brain

Our brains are made up of countless nerve cells connected together, exchanging information and enabling us to think. Taking inspiration from this mechanism, a type of AI technology called a “neural network” was developed. Although the name might sound a bit technical, neural networks have already become deeply embedded in our everyday lives.

For example, facial recognition on smartphones, natural conversations with voice assistants, or product recommendations on online shopping sites—all of these rely quietly on neural networks behind the scenes. Without even realizing it, we benefit from this technology in many parts of our daily routines.

How Nodes Process Information

A neural network (also known as a Neural Network in English) is a type of model used to process information and make decisions or predictions. Here, “model” refers to a system that takes input data, interprets its meaning, and produces an appropriate output—an idea that helps make things easier to understand.

This model is made up of many small computing units called “nodes.” These nodes are often compared to neurons in the human brain. Each node processes a small piece of information. The nodes are connected by values known as “weights,” which determine how strongly one node influences another—essentially indicating how important certain pieces of information are.

In other words, input data passes through these nodes one after another, getting processed along the way. Through this flow, the system gradually builds toward making a decision or prediction.

Strengths and Weaknesses Seen Through Image Recognition

Let’s take image recognition as an example—say we want to determine whether a picture shows a cat or a dog. First, the image data enters the initial layer (called the input layer). From there, it moves through several middle layers (called hidden layers), where each node works to find features within the image. These could include clues like “pointy ears,” “fluffy fur,” or “round eyes.” By stacking these small hints together, the system forms its understanding. Finally, in the output layer, it delivers a result such as “likely a cat” or “likely a dog.”

This is how neural networks can detect subtle differences and complex patterns that may be hard for humans to notice. That’s why they’re said to be especially good at extracting meaningful insights from large amounts of data.

However, this technology also has its challenges. First of all, it requires vast amounts of data and long training times. Even after learning something once, there’s no guarantee it will always give correct answers—it can still make mistakes. Another issue is that it’s often difficult for people to understand why the system reached a particular conclusion; this lack of transparency makes explaining results challenging.

Why Neural Networks Are Still Widely Used

Despite these challenges, neural networks continue to attract significant attention. One key reason is their ability to “learn from experience,” much like humans do. Once they’ve learned something or gone through certain experiences, they can apply that knowledge flexibly in new situations—a unique strength of this technology.

Today’s neural networks aren’t limited to just images or sounds—they’re increasingly capable of handling text and video as well. Combined with more advanced techniques like “deep learning,” their capabilities are growing even further (we’ll explore deep learning in more detail in another article).

The term “neural network” might sound technical or intimidating at first glance—but behind it lies careful observation and understanding of how our own brains work. It’s part of an effort to teach computers how to think—a journey rooted in curiosity about human intelligence itself.

As you continue learning about AI going forward, we hope you’ll remember that this brain-inspired structure was one of its starting points. In our next article, we’ll take a closer look at how neural networks actually “learn”—and what kind of teaching methods are used.