EAGER: Computer Progress and Economic Prosperity

信息技术（IT）的进步除了对美国生产力的贡献外，也是美国公司竞争优势的重要来源。本研究项目探索信息技术改进的替代来源，这些改进可以推动美国未来的经济繁荣，并确定产生改进所需的研究和政策干预。这项工作结合了两个领域的技术和内容——计算机科学和实证经济学。它应用了新的专业知识，并结合了新的学科和跨学科的观点。许多这样的分析也将是这些类型中的第一个被完成的，从而实现了EAGER的探索性目标。随着摩尔定律的贡献结束，该项目确定了计算机生产力改进的机会，重点放在三个特定领域：硬件、算法和软件。-最近由国际设备和系统路线图（IRDS）和异构集成路线图（HIR）制定的路线图建议将领域专业化作为硬件改进的替代驱动力。然而，专业化也破坏了半导体市场，破坏了芯片生产的经济效益。这个项目的硬件部分将检查多少专业化将是经济的。-总统科学技术顾问委员会在案例研究的基础上声称，在计算机改进方面，算法比硬件更重要。PI最近首次对算法进展进行了普查，并表明这种快速改进只发生在有限的几个领域。这个项目的算法组件将映射算法是如何被使用的，以了解算法的进步对用户有多大的好处，谁受益，以及随着时间的推移，这种情况发生了多大的变化。-机器学习（ML）已经产生了巨大的好处。然而，这种改善大部分来自深度学习，这是一个经济学可能影响未来收益的领域。持续的进步要么需要更好的软件性能工程，要么需要新的、更高效的机器学习技术。这个项目的软件/机器学习组件将探索这些方法的潜力。总的来说，这个EAGER项目将填补在理解各种计算和IT生产力改进方法的技术、经济和政策含义方面的重要空白，并提供对科学进步、产业政策和经济进步之间联系的更好理解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Besides contributing to U.S. productivity, information technology (IT) improvements have been an important source of competitive advantage for U.S. firms. This research project explores alternative sources of information technology improvements that could drive U.S. economic prosperity in the future, and identifies research and policy interventions that would be needed to generate the improvements. The work combines techniques and content from two fields – computer science and empirical economics. It applies new expertise, and incorporates novel disciplinary and interdisciplinary perspectives. Many of these analyses will also be the first of these types to be done, fulfilling the EAGER goal of being exploratory. The project identifies opportunities for computer productivity improvement as the contributions from Moore’s Law ends, focusing in three specific areas: hardware, algorithms, and software. - Recent roadmaps developed by the International Roadmap for Devices and Systems (IRDS) and the Heterogeneous Integration Roadmap (HIR) propose domain specialization as the alternative driving force for hardware improvement. However, specialization also fractures the semiconductor market, undermining the economics of chip production. The hardware component of this project will examine how much specialization would be economical. - The President’s Council of Advisors on Science and Technology has claimed, based on case studies, that algorithms are more important than hardware for computer improvements. The PI has recently created a first-of-its-kind census of algorithm progress, and shows that such rapid improvements only happened in a limited number of areas. The algorithm component of this project would map how algorithms are being used, to understand how much algorithm progress is benefiting users, who is benefiting, and how much this is changing over time. - Machine learning (ML) has been producing substantial benefits. However, much of this improvement has come from deep learning, an area where economics could impact future gains. Continued progress will require either better software performance engineering or new, more-efficient machine learning techniques. The software/ML component of this project will explore the potential for these approaches.Overall, this EAGER project will fill an important gap in understanding of the technical, economic, and policy implications of various approaches to computing and IT productivity improvement, and provide a better understanding of the connections between scientific advances, industrial policies, and economic progress.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.