Explore the concept of adversarial examples and human-machine learning alignment in this lecture by Aleksander Madry from MIT. Delve into the comparison between deep networks and human vision, examining the natural perspective on adversarial examples. Investigate why adversarial perturbations are problematic from both human and machine learning viewpoints. Analyze the robust features model and its implications for interpretability, training modifications, and robustness tradeoffs. Discover how robustness relates to perception alignment and improved representations. Address the challenge of unusual correlations in data and learn about counterfactual analysis using robust models. Gain insights into the origin of adversarial examples stemming from non-robust features in datasets.