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Hybrid Intelligence

Journal
Business & Information Systems Engineering
ISSN
2363-7005
ISSN-Digital
1867-0202
Type
journal article
Date Issued
2019
Author(s)
Dellermann, Dominik
Ebel, Philipp  
Söllner, Matthias  
Leimeister, Jan Marco  
DOI
https://doi.org/10.1007/s12599-019-00595-2
Research Team
IWI6
Abstract
Research has a long history of discussing what is superior in predicting certain outcomes: statistical methods or the human brain. This debate has repeatedly been sparked off by the remarkable technological advances in the field of artificial intelligence (AI), such as solving tasks like object and speech recognition, achieving significant improvements in accuracy through deep-learning algorithms (Goodfellow et al. 2016), or combining various methods of computational intelligence, such as fuzzy logic, genetic algorithms, and case-based reasoning (Medsker 2012). One of the implicit promises that underlie these advancements is that machines will 1 day be capable of performing complex tasks or may even supersede humans in performing these tasks. This triggers new heated debates of when machines will ultimately replace humans (McAfee and Brynjolfsson 2017). While previous research has proved that AI performs well in some clearly defined tasks such as playing chess, playing Go...
Language
English
Keywords
Artificial intelligence
Future of work
Human-computer collaboration
Hybrid intelligence
Machine learning
Machines as teammates
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Springer Fachmedien
Publisher place
Wiesbaden
Volume
61
Number
5
Start page
637
End page
643
Pages
7
Official URL
https://link.springer.com/article/10.1007/s12599-019-00595-2?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst&utm_source=ArticleAuthorOnlineFirst&utm_medium=email&utm_content=AA_en_06082018&ArticleAuthorOnlineFirst_20190331
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/99163
Subject(s)

other research area

economics

information managemen...

Division(s)

IWI - Institute of In...

Eprints ID
256914
File(s)
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Thumbnail Image

open.access

Name

JML_744.pdf

Size

303.65 KB

Format

Adobe PDF

Checksum (MD5)

aea441a01e5bd69b1f28312c793a5d1b

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