Machine learning (ML) has the potential to dramatically benefit many aspects of daily life. Applications range from consumer and business technologies (virtual assistants) to transportation (driver assistance and self-driving cars) to health care (hospital observation and physician assistance) and science. Unfortunately, using ML techniques to solve specific, real-world problems remains difficult. Building ML applications often requires a multi-step, iterative process that involves repeated cycles of prototyping, evaluating results, and fixing failures. This project aims to dramatically reduce the difficulty of this iterative process, making it easier for a single person to quickly build ML applications to solve real-world problems.
The technical goal of this project is to dramatically scale the productivity of the entire end-to-end ML model-development workflow so that a single subject-matter expert, armed with a large dataset and access to datacenter-scale accelerated compute capability, can build accurate models for new tasks in hours to days instead of weeks or months. To this end, the project will create automated tools that directly empower subject-matter experts to perform model-development tasks, in rapid iterative loops. These intelligent tools must execute with low latency and scale to huge datasets and potentially large, complex models. To meet these requirements, this project will combine new methods for automatically generated supervision from weak sources and for curating critical data for training and validation, new ML programming abstractions, and reconfigurable ML hardware accelerators to support a heterogeneous set of future use cases and algorithmic techniques.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
