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Is AI Robustness the Cost of Accuracy?

Anyone poised to choose an AI model solely based on its accuracy might want to think again. A key issue, according to IBM Research, is how resistant the AI model is to adversarial attacks.

TOKYO, Jan. 31, 2019 – 

Anyone poised to choose an AI model solely based on its accuracy might want to think again. A key issue, according to IBM Research, is how resistant the AI model is to adversarial attacks.

IBM researchers, collaborating with other research institutes, are presenting two new papers on the vulnerability of AI. One study focuses on how to certify the robustness of AI against adversarial attacks. The other examines an efficient way to test resilience of AI models already deployed.

Of course, accuracy is the Holy Grail of AI. If computers can't beat humans, why bother with AI? Indeed, AI's ability to recognize images and classify them has vastly improved over the last several years. As demonstrated in the results of ImageNet competitions between 2010 and 2017, computer vision can already outperform human abilities. AI's accuracy in classifying objects in a dataset jumped from 71.8% to 97.3% in just seven years.

Companies big and small have used ImageNet as a benchmark for their image classification algorithms against the dataset. Winning an ImageNet competition has bestowed bragging rights for AI algorithm superiority.

Robustness gap

However, the scientific community has begun paying attention to recent studies highlighting a robustness gap in well-trained deep neural networks versus adversarial examples.

Last summer, a team of researchers including IBM Research, the University of California at Davis, MIT, and JD AI Research published a paper entitled "Is Robustness the Cost of Accuracy? – A Comprehensive Study on the Robustness of 18 Deep Image Classification Models."

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