Explore the challenges of robust machine learning in this 46-minute conference talk by Nikos Tsilivis from New York University. Delve into the performance discrepancies between neural networks in standard classification settings and worst-case scenarios. Examine the concept of implicit bias in optimization and its increased importance in robust settings. Investigate the reasons behind large generalization gaps and overfitting in robust Empirical Risk Minimization (ERM). Gain insights into the critical role of model specification, including optimization algorithms and architecture, in robust machine learning. Consider current and future challenges in the field as presented by Tsilivis at IPAM's Theory and Practice of Deep Learning Workshop.