Andrew discusses the intriguing process of creating adversarial examples by perturbing images in a standard classification dataset. By relabeling these images incorrectly and training a new model, he explores the potential outcomes when this model is tested on clean data. The conversation raises questions about the impact of useless features and the surprising results that could emerge from such a training approach.
