Crowd science for hybrid AI applications

Crowd science for hybrid AI applications

Xehetasun bibliografikoak
Parent link:	Service-Oriented System Engineering (SOSE): Proceedings 15th IEEE International conference, 23-26 August 2021, Virtual, Oxford. [P. 172-175].— , 2021
Egile nagusia:	Taran Е. А. Ekaterina Aleksandrovna
Erakunde egilea:	Национальный исследовательский Томский политехнический университет Школа базовой инженерной подготовки Отделение социально-гуманитарных наук
Beste egile batzuk:	Malanina V. A. Veronika Anatolievna, Casati F. Fabio
Gaia:	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
Hizkuntza:	ingelesa
Argitaratua:	2021
Gaiak:	электронный ресурс труды учёных ТПУ crodwsourcing hybrid learning machine learning краудсорсинг гибридное обучение машинное обучение
Sarrera elektronikoa:	https://doi.org/10.1109/SOSE52839.2021.00027
Formatua:	Baliabide elektronikoa Liburu kapitulua
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=666777

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