Intrinsic Geometric Vulnerability of High-Dimensional Artificial Intelligence

The success of modern Artificial Intelligence (AI) technologies depends critically on the ability to learn nonlinear 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. Artificial Intelligence (AI) is colonising all areas of human endeavour, and its impact is widely predicted to grow exponentially in the next decades.