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arXiv · 1811.03571

Intrinsic Geometric Vulnerability of High-Dimensional Artificial Intelligence

Luca Bortolussi·Guido Sanguinetti
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Abstract

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.

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Luca Bortolussi, Guido Sanguinetti. 2018-11-08. Intrinsic Geometric Vulnerability of High-Dimensional Artificial Intelligence. https://arxiv.org/abs/1811.03571

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