AI Hallucinations, Simply Explained

Picture this: you ask a chatbot about a historical event, and it confidently gives you details that seem accurate—only to discover later that these details are completely made up. You have just encountered what we call an AI hallucination.

In artificial intelligence, a hallucination refers to a situation where a neural network produces outputs that seem coherent and reasonable but are factually incorrect or made up.

For example, a language model might claim a famous figure was born in the wrong city, or an image generation model might produce an image that doesn’t match the provided prompt.

Unlike human error, which can be random or accidental, AI hallucinations are generated based on patterns the model has learned. The model does not even recognize that its output is wrong; it simply generates what seems statistically likely from its past training data.

Hallucinations are particularly frequent in generative models, machine learning models that generate new content based on past training examples.

Some common generative models include:

• Variational Autoencoders (VAEs), autoencoder-based models that generate new similar data by learning a probabilistic latent space.

• Generative Adversarial Networks (GANs), a model that balances a generator against a discriminator to create realistic outputs.

• Transformers (such as GPT), models that generate sequences, like text, by predicting the next token/word based on context.

Hallucinations are happening everywhere and can occur for many reasons, from model design to pure chance. Since many models don’t have sophisticated reasoning techniques, they present their answers with high confidence.