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Ontological Surprises: A Relational Perspective on Machine Learning

Author:

Lucian LeahuAuthors Info & Claims

DIS '16: Proceedings of the 2016 ACM Conference on Designing Interactive Systems

June 2016

Pages 182 - 186

https://doi.org/10.1145/2901790.2901840

Published: 04 June 2016 Publication History

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Abstract

This paper investigates how we might rethink design as the technological crafting of human-machine relations in the context of a machine learning technique called neural networks. It analyzes Google's Inceptionism project, which uses neural networks for image recognition. The surprising output of one the experiments reveals that such networks might be used to trace relations between entities. This paper contributes by fleshing out the necessary changes in the ways HCI builds, tests, and engages neural networks in the design of interactive systems from a relational perspective; it proposes a hybrid approach where machine learning algorithms are used to identify objects as well as connections between them; finally, it argues for remaining open to ontological surprises in machine learning as they may enable the crafting of different relations with and through technologies.

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Cited By

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Sivertsen CLøvlie A(2024)Exploring Aesthetic Qualities of Deep Generative Models through Technological (Art) MediationProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661498(2738-2752)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661498
Sivertsen CSalimbeni GLøvlie ABenford SZhu J(2024)Machine Learning Processes As Sources of Ambiguity: Insights from AI ArtProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642855(1-14)Online publication date: 11-May-2024
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Meyer LAaen JTranberg AKun PFreiberger MRisi SLøvlie A(2024)Algorithmic Ways of Seeing: Using Object Detection to Facilitate Art ExplorationProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642157(1-18)Online publication date: 11-May-2024
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Index Terms

Ontological Surprises: A Relational Perspective on Machine Learning
1. Human-centered computing

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Published In

DIS '16: Proceedings of the 2016 ACM Conference on Designing Interactive Systems

June 2016

1374 pages

ISBN:9781450340311

DOI:10.1145/2901790

General Chair:
Marcus Foth
Queensland University of Technology
,
Program Chairs:
Wendy Ju
Stanford University
,
Ronald Schroeter
Queensland University of Technology
,
Stephen Viller
University of Queensland

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 June 2016

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DIS '16

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SIGCHI

DIS '16: Designing Interactive Systems Conference 2016

June 4 - 8, 2016

QLD, Brisbane, Australia

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DIS '16 Paper Acceptance Rate 107 of 418 submissions, 26%;

Overall Acceptance Rate 1,158 of 4,684 submissions, 25%

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Cited By

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Sivertsen CLøvlie A(2024)Exploring Aesthetic Qualities of Deep Generative Models through Technological (Art) MediationProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661498(2738-2752)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661498
Sivertsen CSalimbeni GLøvlie ABenford SZhu J(2024)Machine Learning Processes As Sources of Ambiguity: Insights from AI ArtProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642855(1-14)Online publication date: 11-May-2024
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Meyer LAaen JTranberg AKun PFreiberger MRisi SLøvlie A(2024)Algorithmic Ways of Seeing: Using Object Detection to Facilitate Art ExplorationProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642157(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642157
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