AI Hallucinates—and Does It Convincingly. Why That's Normal

Introduction

Imagine you ask a neural network a question and it responds quickly, elegantly, and confidently—so confidently, in fact, that you don't even want to doubt it. There's just one problem: in some cases, it's wrong. But the strangest part is that the neural network appears equally "confident" whether its answer is right or wrong.

How neural networks work and why errors occur

We all use various neural networks and it seems to us that they "know everything," but in reality they "know" nothing. A neural network—or AI, as it's commonly called now—doesn't verify facts, doesn't "remember," and doesn't doubt. The AI that most people understand as so-called LLM models simply predicts the next word, or more precisely, the next token (part of a word). And so, step by step, the response text is formed. From a mathematical standpoint, this behavior of LLM models can be described as selecting the most probable continuation of the response text. Anyone even slightly familiar with probability theory should grasp the following idea: the more often the model has "seen" similar texts, the easier it is for it to reproduce their structure and context. This creates an effect where a typical answer is presented as the correct one. But the most dangerous aspect is how the neural network makes mistakes. Notice that it doesn't say "I don't know"—it generates an answer that turns out to be logical, coherent, and sounds like expert opinion. This is because such models use an attention mechanism that essentially minimizes "absurdity" but doesn't guarantee "truth."

Real-world manifestations

This is clearly visible in real examples. A student might ask a neural network to provide academic articles on a particular topic and end up receiving a perfect bibliography, even formatted according to GOST standards. The list will look impeccable: author names, journal titles, publication years, even page numbers—but the problem is that not all the articles in the list actually exist. The neural network didn't "make a mistake"—it simply reproduced the most probable form of a bibliography, assembling a plausible but partially fictional result.