The CLAIRE COVID-19 initiative: approach, experiences and recommendations

被引:0
|
作者
Gianluca Bontempi
Ricardo Chavarriaga
Hans eD Canck
Emanuela Girardi
Holger Hoos
Iarla Kilbane-Dawe
Tonio Ball
Ann Nowé
Jose Sousa
Davide Bacciu
Marco Aldinucci
Manlio eD Domenico
Alessandro Saffiotti
Marco Maratea
机构
[1] Université Libre de Bruxelles,Computer Science Department, Machine Learning Group
[2] Zurich University of Applied Sciences (ZHAW),CLAIRE Office Switzerland, Geneva Center for Security Policy (GCSP), IEEE Brain Initiative
[3] CLAIRE Office Belgium,AI Lab
[4] Vrije Universiteit Brussel,Faculty of Medicine, Health and Life Sciences
[5] AI Experience Center / AI for the Common Good Initiative,Computational Intelligence and Machine Learning Group
[6] Leiden Institute of Advanced Computer Science (LIACS),Computer Science Department
[7] Universiteit Leiden,Complex Multilayer Networks Lab
[8] The Netherlands and University of British Columbia,AASS Cognitive Robotic Systems Lab, School of Science and Technology
[9] Parliament Hill Research Ltd,Dipartimento Di Informatica, Bioingegneria
[10] Neuromedical AI Lab Freiburg,undefined
[11] Vrije Universiteit Brussel,undefined
[12] Queen’s University Belfast,undefined
[13] Universita’di Pisa,undefined
[14] University of Torino,undefined
[15] FBK - Fondazione Bruno Kessler,undefined
[16] Orebro University,undefined
[17] Roboticae Ingegneria Dei Sistemi. University Genova,undefined
来源
关键词
Artificial intelligence; COVID-19; Emergency response;
D O I
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中图分类号
学科分类号
摘要
A volunteer effort by Artificial Intelligence (AI) researchers has shown it can deliver significant research outcomes rapidly to help tackle COVID-19. Within two months, CLAIRE’s self-organising volunteers delivered the World’s first comprehensive curated repository of COVID-19-related datasets useful for drug-repurposing, drafted review papers on the role CT/X-ray scan analysis and robotics could play, and progressed research in other areas. Given the pace required and nature of voluntary efforts, the teams faced a number of challenges. These offer insights in how better to prepare for future volunteer scientific efforts and large scale, data-dependent AI collaborations in general. We offer seven recommendations on how to best leverage such efforts and collaborations in the context of managing future crises.
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收藏
页码:127 / 133
页数:6
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