The hallucination problem in AI refers to the generation of outputs
that may sound plausible but are either factually incorrect or
unrelated to the given context. These outputs often emerge from
the AI model’s inherent biases, lack of real-world understanding,
or training data limitations. For example, machine learning
systems used in self-driving cars can be tricked into seeing objects
that don’t exist. Researchers are exploring several approaches to
mitigate this risk, including developing defenses against adversarial attacks. However, it is still a challenge to protect deep neural networks from sabotage by hallucination.
Definitions – The hallucination problem in AI
