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Why deep-learning AIs are so easy to fool

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  • Douglas Heaven

Abstract

Artificial-intelligence researchers are trying to fix the flaws of neural networks.

Suggested Citation

  • Douglas Heaven, 2019. "Why deep-learning AIs are so easy to fool," Nature, Nature, vol. 574(7777), pages 163-166, October.
    • Handle: RePEc:nat:nature:v:574:y:2019:i:7777:d:10.1038_d41586-019-03013-5
    DOI: 10.1038/d41586-019-03013-5
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    Cited by:

    1. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    2. Dominik Bork & Syed Juned Ali & Georgi Milenov Dinev, 2023. "AI-Enhanced Hybrid Decision Management," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(2), pages 179-199, April.
    3. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
    4. Mingli Chen & Andreas Joseph & Michael Kumhof & Xinlei Pan & Xuan Zhou, 2021. "Deep Reinforcement Learning in a Monetary Model," Papers 2104.09368, arXiv.org, revised Jan 2023.
    5. Jin Xiao & Yuhang Tian & Yanlin Jia & Xiaoyi Jiang & Lean Yu & Shouyang Wang, 2023. "Black-Box Attack-Based Security Evaluation Framework for Credit Card Fraud Detection Models," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 986-1001, September.

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    Keywords

    Computer science; Information technology;

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