When two science disciplines meet: Evaluating dynamics of conjunction. The encounter between astrophysics and artificial intelligence

被引:0
作者
Marcovich, Anne [1 ]
Shinn, Terry [1 ]
机构
[1] CNRS, GEMASS, Paris, France
来源
SOCIAL SCIENCE INFORMATION SUR LES SCIENCES SOCIALES | 2021年 / 60卷 / 03期
关键词
artificial intelligence; big data; evaluation; intelligibility; transverse tool;
D O I
10.1177/05390184211025848
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
This article points out some issues raised by the encounter between astrophysics (AP) and a newly emergent mathematical tool/discipline, namely artificial intelligence (AI). We suggest that this encounter has interesting consequences in terms of science evaluation. Our discussion favors an intra science perspective, both on the institutional and cognitive side. This encounter between machine learning (ML) and astrophysics points to three different consequences. (1) As a transverse tool, a same ML algorithm can be used for a diversity of very different disciplines and questions. This ambition and analytic intellectual architecture frequently identify similarities among apparently differentiated fields. (2) The perimeter of the disciplines involved in a research can lead to many and novel ways of collaboration between scientists and to new ways of evaluation of their work. And (3), the impossibility for the human mind to understand the processes involved in ML work raises the question of the reliability of results.
引用
收藏
页码:372 / 377
页数:6
相关论文
共 13 条
[1]   An upper limit on the stochastic gravitational-wave background of cosmological origin [J].
Abbott, B. P. ;
Abbott, R. ;
Acernese, F. ;
Adhikari, R. ;
Ajith, P. ;
Allen, B. ;
Allen, G. ;
Alshourbagy, M. ;
Amin, R. S. ;
Anderson, S. B. ;
Anderson, W. G. ;
Antonucci, F. ;
Aoudia, S. ;
Arain, M. A. ;
Araya, M. ;
Armandula, H. ;
Armor, P. ;
Arun, K. G. ;
Aso, Y. ;
Aston, S. ;
Astone, P. ;
Aufmuth, P. ;
Aulbert, C. ;
Babak, S. ;
Baker, P. ;
Ballardin, G. ;
Ballmer, S. ;
Barker, C. ;
Barker, D. ;
Barone, F. ;
Barr, B. ;
Barriga, P. ;
Barsotti, L. ;
Barsuglia, M. ;
Barton, M. A. ;
Bartos, I. ;
Bassiri, R. ;
Bastarrika, M. ;
Bauer, Th. S. ;
Behnke, B. ;
Beker, M. ;
Benacquista, M. ;
Betzwieser, J. ;
Beyersdorf, P. T. ;
Bigotta, S. ;
Bilenko, I. A. ;
Billingsley, G. ;
Birindelli, S. ;
Biswas, R. ;
Bizouard, M. A. .
NATURE, 2009, 460 (7258) :990-994
[2]  
de Chadarevian Soraya., 2004, Models: The Third Dimension of Science
[3]  
Djorgovski SG., 2005, CLARK LAKE LONG WAVE, V345
[4]   Deconvolution of confocal microscopy images using proximal iteration and sparse representations [J].
Dupe, E-X. ;
Fadili, M. J. ;
Starck, J. -L. .
2008 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1-4, 2008, :736-+
[5]   Surveying the reach and maturity of machine learning and artificial intelligence in astronomy [J].
Fluke, Christopher J. ;
Jacobs, Colin .
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY, 2020, 10 (02)
[6]  
Frieman J., 2020, DARK ENERGY SURVEY S
[7]  
García-Berro E, 1999, ASTR SOC P, V169, P30
[8]   Thinking science with thinking machines: The multiple realities of basic and applied knowledge in a research border zone [J].
Hoffman, Steve G. .
SOCIAL STUDIES OF SCIENCE, 2015, 45 (02) :242-269
[9]  
Huutoniemi K., 2016, OXFORD HDB INTERDISC, P498
[10]   Big Data, new epistemologies and paradigm shifts [J].
Kitchin, Rob .
BIG DATA & SOCIETY, 2014, 1 (01)