Attacking Machine Learning with Adversarial Examples

Interesting article by OpenAI that shines some light on the problem of adversarial examples in AI safety. Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they're like optical illusions for machines. The post explores how adversarial examples work across different mediums and explains why securing systems against them can be difficult.


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