Breaking AIs to make them better

Today's artificial intelligence systems used for image recognition are incredibly powerful with massive potential for commercial applications. Nonetheless, current artificial neural networks--the deep learning algorithms that power image recognition--suffer one massive shortcoming: they are easily broken by images that are even slightly modified. This lack of "robustness" is a significant hurdle for researchers hoping to build better AIs. However, exactly why this phenomenon occurs, and the underlying mechanisms behind it, remain largely unknown. Aiming to one day overcome these flaws, researchers at Kyushu University's Faculty of Information Science and Electrical Engineering have published in PLOS ONE a method called "Raw Zero-Shot" that assesses how neural networks handle elements unknown to them.