Measuring the AI content of government-funded R&D projects

Izumi Yamashita, Akiyoshi Murakami, Stephanie Cairns, F. Galindo-Rueda
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引用次数: 3

Abstract

This report presents the results of a proof of concept for a new analytical infrastructure (“Fundstat”) for analysing government funding of R&D at the project level, exploiting the wealth of text-based information about funded projects. Reflecting the growth in popularity of artificial intelligence (AI) and the OECD Council Recommendation on AI’s emphasis on R&D investment, the report focuses on analysing government investments into AI-related R&D. Using text mining tools, it documents the creation of a list of key terms used to identify AI-related R&D projects contained in 13 funding databases from eight OECD countries and the EU, provides estimates for the total number and volume of government R&D funding, and characterises their AI funding portfolio. The methods and findings developed in this study also serve as a prototype for a new distributed mechanism capable of measuring and analysing government R&D support across key OECD priority areas and topics.
衡量政府资助研发项目的人工智能含量
本报告介绍了一种新的分析基础设施(“Fundstat”)的概念验证结果,该基础设施用于分析政府在项目层面的研发资助,利用有关资助项目的大量基于文本的信息。该报告反映了人工智能(AI)的普及程度和经合组织理事会关于AI的建议强调研发投资,重点分析了政府对人工智能相关研发的投资。使用文本挖掘工具,它记录了用于识别来自八个经合组织国家和欧盟的13个资助数据库中包含的人工智能相关研发项目的关键术语列表的创建,提供了政府研发资金的总数和数量的估计,并描述了其人工智能资助组合的特征。本研究中开发的方法和发现也可以作为一种新的分布式机制的原型,该机制能够衡量和分析经合组织关键优先领域和主题的政府研发支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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