PPoSS: Planning: Eliminating the Bottlenecks to ML Usability and Scalability

机器学习(ML)有可能极大地造福于日常生活的许多方面。应用范围从消费和商业技术(虚拟助理)到交通(驾驶员辅助和自动驾驶汽车)到医疗保健(医院观察和医生辅助)和科学。不幸的是，使用ML技术来解决特定的、真实的问题仍然很困难。构建ML应用程序通常需要一个多步骤的迭代过程，该过程涉及原型、评估结果和修复故障的重复循环。该项目旨在极大地降低迭代过程的难度，使单个人更容易快速构建ML应用程序来解决实际问题。该项目的技术目标是大幅提高整个端到端ML模型开发工作流程的生产率，以便单个主题专家能够在几小时到几天(而不是几周或几个月)内为新任务构建准确的模型，并拥有大型数据集和访问数据中心规模的加速计算能力。为此，该项目将创建自动化工具，直接授权主题专家在快速迭代循环中执行模型开发任务。这些智能工具必须以低延迟执行，并可扩展到庞大的数据集和潜在的大型复杂模型。为了满足这些要求，这个项目将结合新的方法，从弱来源自动生成监督，并为培训和验证管理关键数据，新的ML编程抽象，以及可重新配置的ML硬件加速器，以支持一组不同的未来用例和算法技术。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.