Leverage white-collar workers with AI
Loading...
Author (Corporation)
Publication date
2019
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editor (Corporation)
Supervisor
Parent work
Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)
Special issue
DOI of the original publication
Series
Series number
Volume
Issue / Number
Pages / Duration
Patent number
Publisher / Publishing institution
Place of publication / Event location
Palo Alto
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
Based on the example of automated meeting minutes taking, the paper highlights the potential of optimizing the allocation of tasks between humans and machines to take the particular strengths and weaknesses of both into account. In order to combine the functionality of supervised and unsupervised machine learning with rule-based AI or traditionally programmed software components, the capabilities of AI-based system actors need to be incorporated into the system design process as early as possible.
Keywords
Subject (DDC)
Event
AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)
Exhibition start date
Exhibition end date
Conference start date
25.03.2019
Conference end date
27.03.2019
Date of the last check
ISBN
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Diamond
License
Citation
Jüngling, S., & Hofer, A. (2019). Leverage white-collar workers with AI. In A. Martin, K. Hinkelmann, A. Gerber, D. Lenat, & P. Clark (Eds.), Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019). https://doi.org/10.26041/fhnw-6456