STONewsArchive: Unsupervised Machine Learning in the Military Domain
Title:
Unsupervised Machine Learning in the Military Domain
Start_Publishing:
21/06/2021
Panel_Page:
IST
Page_ID:
3823
Main_Body_Multi:
IST-ET-113 Exploratory Team (ET) on Unsupervised Machine Learning in the Military Domain, 27 May 2021, virtually
Several military applications that require the use of Artificial Intelligence (AI) suffer from having a limited amount of labelled data (i.e. real data acquired from the battlefield), which is necessary for training existing deep learning supervised models. The availability of new approaches that can reach appropriate intelligent solutions without the need of labelled data would be of great use in several military domains (situation awareness, autonomous vehicle driving, analysis of big military data, etc.). IST-ET-113 aims to extend the supervised existing solutions in such a way that they can work in an unsupervised setting too, by starting from existing neural models (e.g., neural trees, CNN, etc.) and trying to adapt them to work in an unsupervised way. This Exploratory Team intends to establish innovative solutions to
Develop new deep learning architectures able to work in an unsupervised way;
Develop new deep learning algorithms able to work with limited amount of annotated data;
Develop intelligent transfer learning methods for using available (also civil) data sets for military applications;
Reduce the costs for the provision of assets (e.g. data set annotation, QS, …); and
Acquire new techniques for incremental learning in military applications.
This activity will contribute to NATO’s vision on Unsupervised Machine Learning in the field of military applications. IST-ET-113 held its latest meeting, virtually, on 27 May 2021. For further information,
please
contact the IST Panel Office
.
Page_Intro:
Several military applications that require the use of Artificial Intelligence (AI) suffer from having a limited amount of labelled data (i.e. real data acquired from the battlefield), which is necessary for training existing deep learning supervised models. The availability of new approaches that can reach appropriate intelligent solutions without the need of labelled data would be of great use in several military domains (situation awareness, autonomous vehicle driving, analysis of big military data, etc.). IST-ET-113 aims to extend the supervised existing solutions in such a way that they can work in an unsupervised setting too, by starting from existing neural models (e.g., neural trees, CNN, etc.) and trying to adapt them to work in an unsupervised way.
HomePageImage:
AI-Machine-Learning-credit-flickr.jpg
HomePageBodyText:
IST-ET-113 Exploratory Team (ET) on Unsupervised Machine Learning in the Military Domain, 27 May 2021, virtually
Credit: Flickr
Several military applications that require the use of Artificial Intelligence (AI) suffer from having a limited amount of labelled data (i.e. real data acquired from the battlefield), which is necessary for training existing deep learning supervised models. The availability of new approaches that can reach appropriate intelligent solutions without the need of labelled data would be of great use in several military domains (situation awareness, autonomous vehicle driving, analysis of big military data, etc.). IST-ET-113 aims to extend the supervised existing solutions in such a way that they can work in an unsupervised setting too, by starting from existing neural models (e.g., neural trees, CNN, etc.) and trying to adapt them to work in an unsupervised way. This Exploratory Team intends to establish innovative solutions to
Develop new deep learning architectures able to work in an unsupervised way;
Develop new deep learning algorithms able to work with limited amount of annotated data;
Develop intelligent transfer learning methods for using available (also civil) data sets for military applications;
Reduce the costs for the provision of assets (e.g. data set annotation, QS, …); and
Acquire new techniques for incremental learning in military applications.
This activity will contribute to NATO’s vision on Unsupervised Machine Learning in the field of military applications. IST-ET-113 held its latest meeting, virtually, on 27 May 2021. For further information,
please contact the IST Panel Office
.
Created at 21/06/2021 15:17 by ad.rodes
Last modified at 21/06/2021 15:21 by ad.rodes
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