[论文解读] A 20-Year Community Roadmap for Artificial Intelligence Research in the US

Yolanda Gil, Bart Selman|arXiv (Cornell University)|Aug 7, 2019

Explainable Artificial Intelligence (XAI)被引用 63

一句话总结

本文概述了一个由社区推动的20年路线图，通过持续投资和协调的跨学科努力，改造美国的AI研究，解决公平性、可解释性、可信度和安全性。

ABSTRACT

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.

研究动机与目标

• 推动转变AI研究体系的必要性，以实现AI的全部潜力。
• 突显AI部署中的社会、伦理和安全挑战。
• 提出在美国推动AI研发的协调、长期投资与治理框架。
• 总结CCC/AAAI协同工作坊在两十年内构建国家路线图的成果。

提出的方法

• 描述数十年的AI进展与部署带来的推动力。
• 呈现一个由社区驱动的过程（CCC研讨会）以制定长期路线图。
• 阐明未来二十年的主要建议和战略方向。
• 强调在研究、教育和基础设施方面需要显著且持续的投入。
• 突出关于公平、可解释性、可信度、安全性以及伦理考量的目标。

实验结果

研究问题

• RQ1 在未来二十年内，需对美国AI研究体系进行哪些系统性变革？
• RQ2 实现公平、可解释、可信和安全的AI需要哪些投资、治理和基础设施？
• RQ3 AI研究界应如何跨学科和跨部门协作以应对社会影响？
• RQ4 来自CCC/AAAI社区努力的优先建议有哪些，以指导政策和资金投入？

主要发现

• 本文呈现了来自社区努力以制定二十年AI路线图的主要建议。
• 它主张对AI研究体系进行根本性转变。
• 它呼吁进行显著且持续的投资以推动长期进展。
• 它强调AI系统中公平性、可解释性、可信度和安全性的重要性。
• 它通过CCC与AAAI的协作展示了一种协调的跨学科方法。