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  • ITNOW-BCS-Environmental-consequence-dl-borowiec

    Rights statement: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in ITNow following peer review. The definitive publisher-authenticated versionDamian Borowiec, Richard R Harper, Peter Garraghan, The environmental consequence of deep learning, ITNOW, Volume 63, Issue 4, Winter 2021, Pages 10–11, https://doi.org/10.1093/itnow/bwab099 is available online at: https://academic.oup.com/itnow/article-abstract/63/4/10/6503628

    Accepted author manuscript, 214 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Environmental Consequence of Deep Learning

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Environmental Consequence of Deep Learning. / Borowiec, Damian; Harper, R.H.R.; Garraghan, Peter.
In: ITNOW, Vol. 63, No. 4, 11.01.2022, p. 10-11.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Borowiec, D, Harper, RHR & Garraghan, P 2022, 'Environmental Consequence of Deep Learning', ITNOW, vol. 63, no. 4, pp. 10-11. https://doi.org/10.1093/itnow/bwab099

APA

Borowiec, D., Harper, R. H. R., & Garraghan, P. (2022). Environmental Consequence of Deep Learning. ITNOW, 63(4), 10-11. https://doi.org/10.1093/itnow/bwab099

Vancouver

Borowiec D, Harper RHR, Garraghan P. Environmental Consequence of Deep Learning. ITNOW. 2022 Jan 11;63(4):10-11. doi: 10.1093/itnow/bwab099

Author

Borowiec, Damian ; Harper, R.H.R. ; Garraghan, Peter. / Environmental Consequence of Deep Learning. In: ITNOW. 2022 ; Vol. 63, No. 4. pp. 10-11.

Bibtex

@article{8c17187839184c2383fa639bc7ee65d3,
title = "Environmental Consequence of Deep Learning",
abstract = "Deep learning and artificial intelligence are often viewed as panacea technologies — ones which can decarbonise many industries. But what is the carbon cost of these systems? Damian Borowiec, Richard R. Harper and Peter Garraghan discuss.",
keywords = "deep learning, energy, machine learning, sustainability, green computing",
author = "Damian Borowiec and R.H.R. Harper and Peter Garraghan",
note = "This is a pre-copy-editing, author-produced PDF of an article accepted for publication in ITNow following peer review. The definitive publisher-authenticated versionDamian Borowiec, Richard R Harper, Peter Garraghan, The environmental consequence of deep learning, ITNOW, Volume 63, Issue 4, Winter 2021, Pages 10–11, https://doi.org/10.1093/itnow/bwab099 is available online at: https://academic.oup.com/itnow/article-abstract/63/4/10/6503628",
year = "2022",
month = jan,
day = "11",
doi = "10.1093/itnow/bwab099",
language = "English",
volume = "63",
pages = "10--11",
journal = "ITNOW",
issn = "1746-5702",
publisher = "Oxford University Press",
number = "4",

}

RIS

TY - JOUR

T1 - Environmental Consequence of Deep Learning

AU - Borowiec, Damian

AU - Harper, R.H.R.

AU - Garraghan, Peter

N1 - This is a pre-copy-editing, author-produced PDF of an article accepted for publication in ITNow following peer review. The definitive publisher-authenticated versionDamian Borowiec, Richard R Harper, Peter Garraghan, The environmental consequence of deep learning, ITNOW, Volume 63, Issue 4, Winter 2021, Pages 10–11, https://doi.org/10.1093/itnow/bwab099 is available online at: https://academic.oup.com/itnow/article-abstract/63/4/10/6503628

PY - 2022/1/11

Y1 - 2022/1/11

N2 - Deep learning and artificial intelligence are often viewed as panacea technologies — ones which can decarbonise many industries. But what is the carbon cost of these systems? Damian Borowiec, Richard R. Harper and Peter Garraghan discuss.

AB - Deep learning and artificial intelligence are often viewed as panacea technologies — ones which can decarbonise many industries. But what is the carbon cost of these systems? Damian Borowiec, Richard R. Harper and Peter Garraghan discuss.

KW - deep learning

KW - energy

KW - machine learning

KW - sustainability

KW - green computing

U2 - 10.1093/itnow/bwab099

DO - 10.1093/itnow/bwab099

M3 - Journal article

VL - 63

SP - 10

EP - 11

JO - ITNOW

JF - ITNOW

SN - 1746-5702

IS - 4

ER -