The two-day RE•WORK Deep Learning Summit Boston 2019 gathered more than 60 speakers from top AI labs such as MIT CSAIL, Uber AI Labs, Adobe Research and other experts from the AI healthcare industry who provided high-level deep learning technical discussions and industry application insights. There were two separate tracks this year: Deep Learning and Healthcare Machines are using deep neural networks to solve previously challenging problems such as Go and Atari games. Speaking in the Deep Learning track, Uber AI Labs Co-Founder and Research Scientist Jason Yosinki said that although researchers can train more complex networks than ever on today's large-scale datasets, the gap between what they can build and what they can understand is widening. Yosinki believes this understanding gap hinders progress toward overall AI system competency and bodes poorly for a future world increasingly reliant on algorithms with decreasing interpretability. Yosinki surveyed research aimed at closing this gap, including interactive model exploration, optimization, and visualization.