Protecting Artificial Intelligence from Itself

Applications using artificial intelligence can be fooled by adversarial examples, creating confusion in the model decisions. Input sanitization can help by filtering out improbable inputs before they are given to the model, argued Katharine Jarmul at Goto Berlin 2018. We need to start thinking of the models and the training data we put into them as potential security breaches, she said. Katharine Jarmul, data scientist, O'Reilly author and co-founder of KIProtect, spoke about protecting artificial intelligence from itself at Goto Berlin 2018. InfoQ is covering this conference with Q&A, summaries, and articles.