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Robustness in AI systems helps guard against adversarial manipulations from "summary" of The Alignment Problem by Brian Christian
Adversarial attacks exploit vulnerabilities in AI systems, where minor perturbations in input data can lead to significantly erroneous outputs. Such manipulations challenge the reliability of these systems, especially in critical applications like autonomous driving or medical diagnosis. A robust AI system must withstand these perturbations without faltering, ensuring consistent performance across a wide range of scenarios. To achieve this resilience, various techniques are employed. For instance, adversarial training involves exposing models to adversarial examples during the training process. By doing so, the AI learns to recognize and properly respond to such manipulations, thereby strengthening its defense mechanisms. This proactive approach allows systems to build an internal understanding ...
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