Crowd science for hybrid AI applications; Service-Oriented System Engineering (SOSE)

Bibliographische Detailangaben
Parent link:Service-Oriented System Engineering (SOSE).— 2021.— [P. 172-175]
1. Verfasser: Taran Е. А. Ekaterina Aleksandrovna
Körperschaft: Национальный исследовательский Томский политехнический университет Школа базовой инженерной подготовки Отделение социально-гуманитарных наук
Weitere Verfasser: Malanina V. A. Veronika Anatolievna, Casati F. Fabio
Zusammenfassung:Title screen
Most AI applications are hybrid, that is, employ machines to make inferences but can fall back on humans when the algorithm is not confident enough. This is true for a wide class of applications ranging from self-driving cars to decision making and process automation in enterprise AI. In this WIP paper we present our vision and progress towards an AI and crowd service that trains, assess and refines ML systems intended to be used in hybrid context. We specifically focus on crowdsourcing as a mean to assist ML algorithm development, and on the different ways in which crowd and machine can interact before, during and after the training process in a synergic way that goes well beyond the 'traditional' application of crowd workers to provide data labels for ML training.
Режим доступа: по договору с организацией-держателем ресурса
Sprache:Englisch
Veröffentlicht: 2021
Schlagworte:
Online-Zugang:https://doi.org/10.1109/SOSE52839.2021.00027
Format: Elektronisch Buchkapitel
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=666777

MARC

LEADER 00000naa0a2200000 4500
001 666777
005 20250818165337.0
035 |a (RuTPU)RU\TPU\network\37981 
035 |a RU\TPU\network\37876 
090 |a 666777 
100 |a 20220126d2021 k y0engy50 ba 
101 0 |a eng 
135 |a drcn ---uucaa 
181 0 |a i  
182 0 |a b 
200 1 |a Crowd science for hybrid AI applications  |f Е. А. Taran, V. A. Malanina, F. Casati 
203 |a Text  |c electronic 
300 |a Title screen 
320 |a [References: 10 tit.] 
330 |a Most AI applications are hybrid, that is, employ machines to make inferences but can fall back on humans when the algorithm is not confident enough. This is true for a wide class of applications ranging from self-driving cars to decision making and process automation in enterprise AI. In this WIP paper we present our vision and progress towards an AI and crowd service that trains, assess and refines ML systems intended to be used in hybrid context. We specifically focus on crowdsourcing as a mean to assist ML algorithm development, and on the different ways in which crowd and machine can interact before, during and after the training process in a synergic way that goes well beyond the 'traditional' application of crowd workers to provide data labels for ML training. 
333 |a Режим доступа: по договору с организацией-держателем ресурса 
463 |t Service-Oriented System Engineering (SOSE)  |o Proceedings 15th IEEE International conference, 23-26 August 2021, Virtual, Oxford  |v [P. 172-175]  |d 2021 
610 1 |a электронный ресурс 
610 1 |a труды учёных ТПУ 
610 1 |a crodwsourcing 
610 1 |a hybrid learning 
610 1 |a machine learning 
610 1 |a краудсорсинг 
610 1 |a гибридное обучение 
610 1 |a машинное обучение 
700 1 |a Taran  |b Е. А.  |c Economist  |c Senior Lecturer of Tomsk Polytechnic University  |f 1981-  |g Ekaterina Aleksandrovna  |3 (RuTPU)RU\TPU\pers\35026  |9 18317 
701 1 |a Malanina  |b V. A.  |c economist  |c Associate Professor of Tomsk Polytechnic University, Candidate of economic sciences  |f 1977-  |g Veronika Anatolievna  |3 (RuTPU)RU\TPU\pers\34896  |9 18214 
701 1 |a Casati  |b F.  |c Italian economist and Professor at the University of Trento (Italy)  |c Professor of Tomsk Polytechnic University, candidate of technical Sciences  |f 1971-  |g Fabio  |3 (RuTPU)RU\TPU\pers\39820 
712 0 2 |a Национальный исследовательский Томский политехнический университет  |b Школа базовой инженерной подготовки  |b Отделение социально-гуманитарных наук  |3 (RuTPU)RU\TPU\col\23512 
801 2 |a RU  |b 63413507  |c 20220126  |g RCR 
856 4 |u https://doi.org/10.1109/SOSE52839.2021.00027 
942 |c CF