Machine learning models can be tricked into making mistakes using 'adversarial examples', which are intentionally designed inputs that exploit the model's weaknesses. These examples are similar to optical illusions for humans, where a person is shown an image that appears one way but is actually something else. Researchers have found that adversarial examples work across different mediums, including images and text. Securing systems against these attacks can be challenging due to their ability to evade detection. --- Why it matters: Understanding how to create and defend against adversarial examples is crucial for developing robust machine learning models in applications such as image recognition, natural language processing, and autonomous vehicles. Source: https://openai.com/index/attacking-machine-learning-with-adversarial-examples