Explore the intricacies of machine learning model behavior in this thought-provoking lecture by MIT Professor Aleksander Madry. Delve into the alignment between benchmark-driven ML paradigms and real-world applications, examining biases in datasets like ImageNet and how state-of-the-art models exploit them. Discover how these biases stem from data collection and curation processes, and learn to quantify them using standard tools. Gain insights into the challenges of deploying reliable and responsible AI in real-world scenarios, covering topics such as background bias, adversarial examples, and the consequences of benchmark-task misalignment. Understand the implications for model interpretability, training modifications, and the overall machine learning research pipeline.