ITU

Ontological Surprises: A Relational Perspective on Machine Learning

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

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Ontological Surprises : A Relational Perspective on Machine Learning. / Leahu, Lucian.

Proceedings of the 2016 ACM Conference on Designing Interactive Systems. Association for Computing Machinery, 2016. p. 182-186.

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Harvard

Leahu, L 2016, Ontological Surprises: A Relational Perspective on Machine Learning. in Proceedings of the 2016 ACM Conference on Designing Interactive Systems. Association for Computing Machinery, pp. 182-186. https://doi.org/10.1145/2901790.2901840

APA

Leahu, L. (2016). Ontological Surprises: A Relational Perspective on Machine Learning. In Proceedings of the 2016 ACM Conference on Designing Interactive Systems (pp. 182-186). Association for Computing Machinery. https://doi.org/10.1145/2901790.2901840

Vancouver

Leahu L. Ontological Surprises: A Relational Perspective on Machine Learning. In Proceedings of the 2016 ACM Conference on Designing Interactive Systems. Association for Computing Machinery. 2016. p. 182-186 https://doi.org/10.1145/2901790.2901840

Author

Leahu, Lucian. / Ontological Surprises : A Relational Perspective on Machine Learning. Proceedings of the 2016 ACM Conference on Designing Interactive Systems. Association for Computing Machinery, 2016. pp. 182-186

Bibtex

@inproceedings{1187297a50084f0cb3ccb389a1a9f94b,
title = "Ontological Surprises: A Relational Perspective on Machine Learning",
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{\textquoteright}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.",
author = "Lucian Leahu",
year = "2016",
doi = "10.1145/2901790.2901840",
language = "English",
isbn = "978-1-4503-4031-1",
pages = "182--186",
booktitle = "Proceedings of the 2016 ACM Conference on Designing Interactive Systems",
publisher = "Association for Computing Machinery",
address = "United States",

}

RIS

TY - GEN

T1 - Ontological Surprises

T2 - A Relational Perspective on Machine Learning

AU - Leahu, Lucian

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

U2 - 10.1145/2901790.2901840

DO - 10.1145/2901790.2901840

M3 - Article in proceedings

SN - 978-1-4503-4031-1

SP - 182

EP - 186

BT - Proceedings of the 2016 ACM Conference on Designing Interactive Systems

PB - Association for Computing Machinery

ER -

ID: 81203665