Imperial College London
Portrait Picture

DrAntoineCully

Faculty of Engineering, Department of Computing

Reader in Machine Learning and Robotics
 
 
 
//

Contact

 

+44 (0)20 7594 8204a.cully Website

 
 
//

Location

 

354ACE ExtensionSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Kusters:2020:10.3389/fdata.2020.577974,
author = {Kusters, R and Misevic, D and Berry, H and Cully, A and Le, Cunff Y and Dandoy, L and Díaz-Rodríguez, N and Ficher, M and Grizou, J and Othmani, A and Palpanas, T and Komorowski, M and Loiseau, P and Moulin, Frier C and Nanini, S and Quercia, D and Sebag, M and Soulié, Fogelman F and Taleb, S and Tupikina, L and Sahu, V and Vie, J-J and Wehbi, F},
doi = {10.3389/fdata.2020.577974},
journal = {Frontiers in Big Data},
pages = {1--7},
title = {Interdisciplinary research in artificial intelligence: challenges and opportunities},
url = {http://dx.doi.org/10.3389/fdata.2020.577974},
volume = {3},
year = {2020}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The use of artificial intelligence (AI) in a variety of research fields is speeding up multiple digital revolutions, from shifting paradigms in healthcare, precision medicine and wearable sensing, to public services and education offered to the masses around the world, to future cities made optimally efficient by autonomous driving. When a revolution happens, the consequences are not obvious straight away, and to date, there is no uniformly adapted framework to guide AI research to ensure a sustainable societal transition. To answer this need, here we analyze three key challenges to interdisciplinary AI research, and deliver three broad conclusions: 1) future development of AI should not only impact other scientific domains but should also take inspiration and benefit from other fields of science, 2) AI research must be accompanied by decision explainability, dataset bias transparency as well as development of evaluation methodologies and creation of regulatory agencies to ensure responsibility, and 3) AI education should receive more attention, efforts and innovation from the educational and scientific communities. Our analysis is of interest not only to AI practitioners but also to other researchers and the general public as it offers ways to guide the emerging collaborations and interactions toward the most fruitful outcomes.
AU  - Kusters,R
AU  - Misevic,D
AU  - Berry,H
AU  - Cully,A
AU  - Le,Cunff Y
AU  - Dandoy,L
AU  - Díaz-Rodríguez,N
AU  - Ficher,M
AU  - Grizou,J
AU  - Othmani,A
AU  - Palpanas,T
AU  - Komorowski,M
AU  - Loiseau,P
AU  - Moulin,Frier C
AU  - Nanini,S
AU  - Quercia,D
AU  - Sebag,M
AU  - Soulié,Fogelman F
AU  - Taleb,S
AU  - Tupikina,L
AU  - Sahu,V
AU  - Vie,J-J
AU  - Wehbi,F
DO  - 10.3389/fdata.2020.577974
EP  - 7
PY  - 2020///
SN  - 2624-909X
SP  - 1
TI  - Interdisciplinary research in artificial intelligence: challenges and opportunities
T2  - Frontiers in Big Data
UR  - http://dx.doi.org/10.3389/fdata.2020.577974
UR  - https://www.frontiersin.org/articles/10.3389/fdata.2020.577974/full
UR  - http://hdl.handle.net/10044/1/85780
VL  - 3
ER  -
Download