Episode 50: Why Does AI Make Confident Mistakes? Understanding the Truth Behind “Hallucination”

Sounds Convincing but Is Actually Wrong? What “Hallucination” Means

Imagine asking an AI, “What’s the tallest mountain in the world?” It would likely answer without hesitation, “Mount Everest.” So far, so good.

But what if you asked, “What was the first railway built in Japan?” and it replied, “The line connecting Tokyo and Nagoya”? That might sound plausible, but it’s not correct. The actual answer is “from Shimbashi to Yokohama.” This kind of response—where an AI gives a confident but incorrect answer—is known as a “hallucination.”

Why Does AI Make Confident Mistakes?

A hallucination occurs when AI generates information that isn’t true but presents it as if it were. The term comes from the English word “hallucination,” meaning to see something that isn’t really there. It’s named this way because the AI behaves as though it sees something that doesn’t exist.

This phenomenon is common in large language models (LLMs) and text-generating AIs. GPT series models are a well-known example.

So why do these mistakes happen? It’s because AI doesn’t actually understand or know things like humans do. Instead, it learns how words tend to appear together by analyzing massive amounts of text data. Based on probabilities—like “this kind of question is often followed by these kinds of words”—it builds sentences. As a result, sometimes it relies only on what sounds right rather than what is factually correct.

A human might research or study Japanese railway history before answering such a question. But AI responds based purely on learned patterns without any real background knowledge. That’s a key difference.

Benefits and Risks in Everyday Situations

At first glance, these kinds of errors might seem like simple misunderstandings. But what if they occurred in fields like medicine or law—areas where mistakes could have serious consequences?

Imagine a misdiagnosis based on incorrect information or an error in drafting a legal contract. These could lead to major problems. That’s why addressing this issue is so important.

Many researchers and companies are working on solutions, including a technique called RAG (Retrieval-Augmented Generation). This method pulls accurate information from trusted external databases and uses it to generate responses. By doing so, it helps reduce hallucinations (we’ll explore this more in another article).

That said, even humans make mistakes due to faulty memory or assumptions. We’ve all had moments during conversations where we thought, “Wait—is that really true?” In that sense, AI shares some similarities with us—it also has limits when relying only on learned data. However, unlike people, AI lacks experience and emotional judgment. That’s why it’s more important than ever not to assume everything it says is correct—and instead take time to verify things ourselves.

What You Should Know to Use AI Safely

The word “hallucination” may sound unsettling at first. But behind it lies both the unique limitation of AI—trying to understand the world through language alone—and the ongoing technical efforts being made to overcome those limits.

At the same time, this challenge is one we can address through human responsibility and creativity. As our daily lives and work become increasingly intertwined with AI, knowing about this phenomenon becomes a small but meaningful form of preparation.

When an AI quietly speaks from across your screen—how much can you trust its voice? Perhaps having the ability to judge that for ourselves is one of humanity’s greatest strengths.