Despite the challenges, outstanding challenges and questions still remain. In an age where model size and data set size translates directly to performance, what will be the role of individual research labs? The situation today is that few academic groups have the resources to reproduce state-of-the-art results from industry in many areas of machine learning, making the issue of access and reproducibility particularly concerning at the current moment. Furthermore, challenges related to data set size and complexity, extrapolation, interpretation, and relevance still require innovative solutions. Nevertheless, researchers continue to make progress in all areas, marking the current time as a particularly exciting era for machine learning in materials research.