IBM, Harvard develop tool to tackle black box problem in AI translation

In recent years, machine translation has improved immensely thanks to advances in deep learning and neural networks. However, the advantages of neural networks come at the cost of not knowing for sure what goes on inside them, which means it's hard to troubleshoot their mistakes, such as when they translate "good morning" in Arabic to "attack them" in Hebrew. Researchers at IBM and Harvard University have developed a new debugging tool to address this issue. Presented at the IEEE Conference on Visual Analytics Science and Technology in Berlin last week, the tool lets creators of deep learning applications visualize the decision-making an AI makes when translating a sequence of words from one language to another. Called Seq2Seq-Vis, the tool is one of the several efforts that aim to interpret decisions made by deep neural networks.