Tim and Florian delve into the nuances of adversarial examples in machine learning, questioning when overfitting ends and adversarial examples begin. They highlight the importance of addressing distribution shifts and low tails of distribution in critical applications like self-driving cars, contrasting them with less critical applications such as ad blocking.
