Episode 15: Is AI Learning Like Ours? Understanding the Right Study Pace Through “Batch Size”

Even AI Needs the Right Amount of Study

When AI learns something new, there are many strategies involved. Among them, one key point is how much information it takes in at a time. This is something we can relate to as humans—trying to cram too much at once can be overwhelming, while taking in too little might feel inefficient.

Interestingly, AI also has its own “just right” pace for learning. One important concept that helps set this pace is called “batch size.”

What Is Batch Size? Understanding AI’s Study Units

Put simply, batch size refers to the chunk of data that AI learns from at one time.

AI becomes smarter by finding patterns and rules within large amounts of data. But instead of using all that data at once—which would be too heavy—it breaks it down into smaller parts and processes them step by step. Each of these chunks is called a “batch,” and the number of data points in one batch is what we call the “batch size.”

A Bit Like Human Studying: How Batch Size Works

Let’s say you want to train an AI to tell cats and dogs apart using 10,000 images. It wouldn’t be practical to have it study all 10,000 images at once—it would take too long and put a lot of strain on the computer’s memory.

Instead, you might break it down into groups of 100 images. That “100” becomes your batch size. This way, the AI can gradually build up its knowledge over time.

This approach actually resembles how we study as well. Trying to read an entire textbook in one sitting before a test isn’t very effective. It’s easier to understand when we break things down by chapter or topic.

That said, different batch sizes come with different characteristics. A small batch size allows for more flexibility—it can respond better to subtle changes in data. But because it requires more rounds of calculation, it also takes more time overall. On the other hand, a large batch size is more efficient in terms of processing speed but may miss out on small details or variations. As a result, it tends to produce more “average” learning outcomes.

To help picture this better, think about employee training sessions. A small-group workshop (like a small batch) allows for personalized attention based on each person’s understanding and reactions—but it takes more time and effort. A large lecture-style session (like a large batch) can deliver information quickly to many people at once but makes individual follow-up harder.

Just like that, AI needs to consider which batch size fits best depending on the situation. The ideal method changes based on what you’re trying to achieve.

Even AI Grows at Its Own Pace

In practice, this idea of batch size connects closely with other concepts like “learning rate” and “loss function.” We’ll explore those topics in future articles—but for now, just remember this: AI learns steadily at its own pace, much like we do.

Technical terms can sound intimidating at first glance, but they often reflect ideas that are surprisingly familiar from our everyday lives. Batch size is one such example. When you realize that even advanced technologies like AI rely on rhythms and strategies similar to our own ways of learning, they start to feel a bit more approachable—maybe even relatable.

In our next article, we’ll look into some of those other elements that support AI’s learning process. There’s no need to rush—take your time and absorb things step by step. Just like starting with a small “batch,” we’ll move forward together at a comfortable pace.