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Adversarial examples are inputs to a machine learning model that are deliberately designed to cause the model to make a mistake. They are crafted to fool the model into thinking that the input is something else. Adversarial examples can be used to create AI hallucinations.
Overfitting –
Overfitting is a common problem in machine learning, where a model performs well on training data but poorly on new data. Overfitting can lead to AI hallucinations, as the model may be too closely tied to the training data and unable to generalize to new data.
