At the IEEE Conference on Visual Analytics Science and Technology in Berlin, IBM and Harvard University researchers presented Seq2Seq-Vis, a tool to debug machine translation tools. Translation tools rely on neural networks, which, because they are opaque, make it difficult to determine how mistakes were made. For that reason, it’s known as the “black box problem.” Seq2Seq-Vis allows deep-learning app creators to visualize AI’s decision-making process as it translates a sequence of words from one language to another. Continue reading IBM, Harvard University Develop New Tool for AI Translation