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

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

Bibliographic Details
Parent link:Service-Oriented System Engineering (SOSE).— 2021.— [P. 172-175]
Main Author: Taran Е. А. Ekaterina Aleksandrovna
Corporate Author: Национальный исследовательский Томский политехнический университет Школа базовой инженерной подготовки Отделение социально-гуманитарных наук
Other Authors: Malanina V. A. Veronika Anatolievna, Casati F. Fabio
Summary: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
Language:English
Published: 2021
Subjects:
электронный ресурс
труды учёных ТПУ
crodwsourcing
hybrid learning
machine learning
краудсорсинг
гибридное обучение
машинное обучение
Online Access:https://doi.org/10.1109/SOSE52839.2021.00027
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=666777

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