Measuring the AI content of government-funded R&D projects : a proof of concept for the OECD Fundstat initiative / Izumi Yamashita ... [et al.]
.-- París : OECD Publishing, 2021
.-- 106 p. : gráf. ; 1 documento PDF
.-- (OECD Science, Technology and Industry Working Papers ;2021/09).
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.
The use of this work, whether digital or print, is governed by the Terms and Conditions to be found at http://www.oecd.org/termsandconditions.
10.1787/7b43b038-en doi
Inteligencia Artificial
AI
i+D
investigación
desarrollo
financiación gubernamental
Yamashita, Izumi
Organización de Cooperación y Desarrollo Económico
OECD Science, Technology and Industry Working Papers
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.
The use of this work, whether digital or print, is governed by the Terms and Conditions to be found at http://www.oecd.org/termsandconditions.
10.1787/7b43b038-en doi
Inteligencia Artificial
AI
i+D
investigación
desarrollo
financiación gubernamental
Yamashita, Izumi
Organización de Cooperación y Desarrollo Económico
OECD Science, Technology and Industry Working Papers