The productivity of science: is there a slowdown and if so why?
A number of scholars and technology commentators have argued that good ideas are becoming harder to find and the productivity of research may be stagnating. If correct, the long-term economic consequences of such a development would be many and significant. This session of the OECD workshop on artificial intelligence and the productivity of science held from 29 October to 5 November 2021 brings together some of the contributors to this debate, and aims to consider the evidence from diverse perspectives.
▬ Contents of this video ▬▬▬▬▬▬▬▬▬▬
00:00:06 Welcome and scene-setting
00:23:10 - Session 1.1: What can bibliometrics contribute to understanding research productivity? (Giovanni Abramo)
00:45:40 - Session 1.2: Economy-wide and cross-country studies
00:45:40 - Declining R&D efficiency – evidence from Japan (Tsutomu Miyagawa)
01:11:48 - Evidence from China and Germany (Paul Hünermund and Philipp Boeing)
01:32:01 - Are ‘Flows of Ideas’ and ‘Research Productivity’ in secular decline? (Didier Sornette)
02:10:05 - Session 1.3: Evidence from individual domains of technology and science
02:10:05 - Micro-electronics (Henry Kressel)
02:34:57 - Theoretical physics (Sabine Hossenfelder)
02:57:17 - ‘Eroom’s Law’ (Jack W.Scannell)
03:17:15 - Agriculture (Matt Clancy)
03:39:03 - Machine learning (Tamay Besiroglu)
04:07:59 - Session 1.4: Other forms and issues of measurement
04:07:59 - Quantifying the ‘cognitive extent’ of science and how it has changed over time (Staša Milojević)
04:37:29 - What does Total Factor Productivity indicate about research productivity? (Ben Southwood)
04:55:55 - A quantitative and qualitative approach to productivity in science (Hector Zenil and Ross King)
Also available :
Day 2 on the current limits of AI in science and on systemic conditions affecting the productivity of science ([ Ссылка ])
Day 3 on the current limits of AI in science and one systemic conditions affecting the productivity of science ([ Ссылка ])
Day 4 on AI, science and the developing world and on Policy priorities to increase the impact of AI on science ([ Ссылка ])
Day 5 on AI, science and the developing world (continued) and on the future: what could AI achieve in science in the next 10 years? ([ Ссылка ])
▬ Read more about the OECD project on “AI and the Productivity of Science” ▬▬▬▬
The OECD project on “AI and the Productivity of Science” addresses the critically important issue of the rate of scientific progress, whether this is stagnating, as recently argued by a number of scholars, and how AI could raise the pace of progress in science and discovery. This OECD and Fondation Ipsen workshop, a part of the project, brought together technical and policy experts to examine the evidence on a purported productivity decline in science as well as the ways that AI is currently used across different fields of science – from neuroscience to materials science - and across all stages in the scientific process. The workshop advanced the debate on what governments can do to maximise the positive impacts of AI on science, today and in the decades to come. Workshop presentations and expert discussions will become part of a comprehensive publication on the topic, to be released in early 2022.
[ Ссылка ]
[ Ссылка ]
