  The success of modern Artificial Intelligence (AI) technologies depends
critically on the ability to learn non-linear functional dependencies from
large, high dimensional data sets. Despite recent high-profile successes,
empirical evidence indicates that the high predictive performance is often
paired with low robustness, making AI systems potentially vulnerable to
adversarial attacks. In this report, we provide a simple intuitive argument
suggesting that high performance and vulnerability are intrinsically coupled,
and largely dependent on the geometry of typical, high-dimensional data sets.
Our work highlights a major potential pitfall of modern AI systems, and
suggests practical research directions to ameliorate the problem.
