Breaking AIs to make them better
Today’s artificial intelligence systems for image recognition are incredibly powerful, with enormous commercial potential. Nonetheless, current artificial neural networks (the deep learning algorithms that power image recognition) have a major flaw: they are easily broken by images that have been slightly modified. This lack of ‘robustness’ is a significant barrier for researchers attempting to develop better AIs. However, the exact cause of this phenomenon, as well as the underlying mechanisms, are largely unknown. Researchers at Kyushu University‘s Faculty of Information Science and Electrical Engineering published in PLOS ONE a method…