Deep Learning: Systems and Responsibility

Abstract

Deep learning enables numerous applications across diverse areas. Data systems researchers are also increasingly experimenting with deep learning to enhance data systems performance. We present a tutorial on deep learning, highlighting the data systems nature of neural networks as well as research opportunities for advancements through data management techniques. We focus on three critical aspects: (1) classic design tradeoffs in neural networks which we can enrich through a systems and data management perspective, e.g., thinking critically about storage, data movement, and computation; (2) classic design problems in data systems which we can reconsider with neural networks as a viable design option, e.g., to replace or help system components that make complex decisions such as database optimizers; and (3) essential considerations for responsible application of neural networks in critical human-facing problems in society and how these also link to data management and performance considerations. While these are seemingly a diverse set of rich topics, they are strongly interconnected through data management, and their combination offers rich opportunities for future research.