Did you know that ML models can be tricked by adversarial examples? These are inputs deliberately designed to cause the model to make a mistake. They look normal to humans but exploit weaknesses in the algorithms. For instance, a picture with imperceptible noise can be misclassified by a highly accurate image recognition system. This highlights the importance of robustness in ML and reminds us that these systems, while advanced, still have vulnerabilities to address. Share your thoughts or any surprising facts you've come across in the world of ML!