Book Review

Petersen, Eike; Compute, DTU

doi:10.13169/prometheus.38.2.0246

ScienceOpen: research and publishing network

For Publishers

For Researchers

Blog
About

Search
Advanced search

271

views

recommends

If you have found this article useful and you think it is important that researchers across the world have access, please consider donating, to ensure that this valuable collection remains Open Access.

Prometheus is published by Pluto Journals, an Open Access publisher. This means that everyone has free and unlimited access to the full-text of all articles from our international collection of social science journals. Furthermore Pluto Journals authors don’t pay article processing charges (APCs).

scite_

Prometheus

Volume 38, Issue 2

Record: found
Abstract: found
Article: found

Is Open Access

Book Review

Published

book-reviews

Author(s): Eike Petersen DTU Compute

Publication date Pub: 30 August 2022

Journal: Prometheus

Publisher: Pluto Journals

Bookmark

Content

Author and article information

Journal

Journal ID (doi): 10.13169/prometheus.38.2.0246

Title: Prometheus

Abbreviated Title: PROM

Publisher: Pluto Journals

ISSN (Electronic): 1470-1030

Publication date Pub: 30 August 2022

Publication date Collection: 2022

Volume: 38

Issue: 2

Pages: 246-250

Author notes

ewipe@ 123456dtu.dk

Article

DOI: 10.13169/prometheus.38.2.0246

SO-VID: eadbe846-7477-4e4b-b2f7-d12aee5e4c0a

License:

All content is freely available without charge to users or their institutions. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission of the publisher or the author. Articles published in the journal are distributed under a http://creativecommons.org/licenses/by/4.0/.

History

Page count

Pages: 5

Product

Is AI Good for the Planet? Benedetta Brevini (2021) 160pp., $US13 paperback, Polity Press, Cambridge, ISBN: 978-1509547951

References

L. AnthonyB. KandingR. Selvan (2020) ‘Carbontracker: tracking and predicting the carbon footprint of training deep learning models’, Proceedings of the ICML Workshop on Challenges in Deploying and Monitoring Machine Learning Systems, available at https://doi.org/10.48550/arxiv.2007.03051. (accessed May 2022).
J. BughinJ. SeongJ. ManyikaM. ChuiR. Joshi (2018) Notes from the AI Frontier: Modeling the Impact of AI on the World Economy, McKinsey Global Institute report, available at https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-applications-and-value-of-deep-learning (accessed May 2022).
M. Chui et al. (2021) The State of AI in 2021, McKinsey Global Institute report, available at https://www.mckinsey.com/business-functions/quantumblack/our-insights/global-survey-the-state-of-ai-in-2021 (accessed May 2022).
K. Crawford (2021) Atlas of AI, Yale University Press, New Haven.
U. GuptaY. KimS. LeeJ. TseH-H. LeeG.-Y. WeiD. BrooksC.-J. Wu (2021) ‘Chasing carbon: the elusive environmental footprint of computing’, paper presented at IEEE International Symposium on High-Performance Computer Architecture (HPCA), available at https://doi.org/10.1109/HPCA51647.2021.00076. (accessed May 2022)
P. HendersonJ. HuJ. RomoffE. BrunskillD. JurafskyPineau. J. (2020) ‘Towards the systematic reporting of the energy and carbon footprints of machine learning’, Journal of Machine Learning Research, 21, 248, pp.1−43.
J. HickelG. Kallis (2020) ‘Is green growth possible?’, New Political Economy, 25, 4, pp.469–86.
IEA (2021) Data Centres and Data Transmission Networks, International Energy Agency, Paris, available at https://www.iea.org/reports/data-centres-and-data-transmission-networks (accessed May 2022).
IPCC (2022) Climate Change 2022: Mitigation of Climate Change, Intergovernmental Panel on Climate Change, Sixth Assessment Report, Working Group III, available at https://www.ipcc.ch/report/sixth-assessment-report-working-group-3/ (accessed May 2022).
G. KallisC. KerschnerJ. Martinez-Alier (2012) ‘The economics of degrowth’, Ecological Economics, 84, pp.172–80.
E. Massé (2021) Three Years under the EU GDPR: An Implementation Progress Report, Access Now report, available at https://www.accessnow.org/gdpr-three-years/ (accessed May 2022).
W. McDonoughM. Braungart (2002) Cradle to Cradle: Remaking the Way we Make Things, North Point Press, New York.
M. Mitchell (2019) Artificial Intelligence: A Guide for Thinking Humans, Pelican Books, Harmondsworth UK.
D. PattersonJ. GonzalezU. HölzleQ. HungC. LiangL.-M. MunguiaD. RothchildD. SoM. TexierJ. Dean (forthcoming) ‘The carbon footprint of machine learning training will plateau, then shrink’, IEEE Computer, available at https://doi.org/10.36227/techrxiv.19139645.v4 (accessed May 2022).
K. Pretz (2021) ‘Stop calling everything AI, machine-learning pioneer says’, IEEE Spectrum, 31 March, available at https://spectrum.ieee.org/stop-calling-everything-ai-machinelearning-pioneer-says (accessed May 2022).
K. Raworth (2017) Doughnut Economics: Seven Ways to Think like a 21st-Century Economist, Chelsea Green Publishing, White River Junction VT.
D. RolnickP. DontiL. KaackK. … KochanskiY. Bengio (forthcoming) ‘Tackling climate change with machine learning’, ACM Computing Surveys, 55, 2, paper 42, available at https://doi.org/10.1145/3485128 (accessed May 2022).
K. SchwabP. Vanham (2021) Stakeholder Capitalism: A Global Economy that Works for Progress, People and Planet, John Wiley, Hoboken NJ.
E. StrubellA. GaneshA. McCallum (2020) ‘Energy and policy considerations for modern deep learning research’, Proceedings of the AAAI Conference on Artificial Intelligence, 34, 9, paper 9, available at https://doi.org/10.1609/aaai.v34i09.7123 (accessed May 2022).
D. TuiaD. TuiaB. KellenbergerS. BeeryB. … CostelloeT. Berger-Wolfe (2022) ‘Perspectives in machine learning for wildlife conservation’, Nature Communications, 13, paper 792, available at https://doi.org/10.1038/s41467-022-27980-y (accessed May 2022).
M. Whittaker (2021) ‘The steep cost of capture’, ACM Interactions, 28, 6, available at https://doi.org/10.1145/3488666 (accessed May 2022).
S. Zuboff (2019) The Age of Surveillance Capitalism, Profile Books, London.

Comments

Comment on this article

[1] L. AnthonyB. KandingR. Selvan (2020) ‘Carbontracker: tracking and predicting the carbon footprint of training deep learning models’, Proceedings of the ICML Workshop on Challenges in Deploying and Monitoring Machine Learning Systems, available at https://doi.org/10.48550/arxiv.2007.03051. (accessed May 2022).

[2] J. BughinJ. SeongJ. ManyikaM. ChuiR. Joshi (2018) Notes from the AI Frontier: Modeling the Impact of AI on the World Economy, McKinsey Global Institute report, available at https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-applications-and-value-of-deep-learning (accessed May 2022).

[3] M. Chui et al. (2021) The State of AI in 2021, McKinsey Global Institute report, available at https://www.mckinsey.com/business-functions/quantumblack/our-insights/global-survey-the-state-of-ai-in-2021 (accessed May 2022).

[4] K. Crawford (2021) Atlas of AI, Yale University Press, New Haven.

[5] U. GuptaY. KimS. LeeJ. TseH-H. LeeG.-Y. WeiD. BrooksC.-J. Wu (2021) ‘Chasing carbon: the elusive environmental footprint of computing’, paper presented at IEEE International Symposium on High-Performance Computer Architecture (HPCA), available at https://doi.org/10.1109/HPCA51647.2021.00076. (accessed May 2022)

[6] P. HendersonJ. HuJ. RomoffE. BrunskillD. JurafskyPineau. J. (2020) ‘Towards the systematic reporting of the energy and carbon footprints of machine learning’, Journal of Machine Learning Research, 21, 248, pp.1−43.

[7] J. HickelG. Kallis (2020) ‘Is green growth possible?’, New Political Economy, 25, 4, pp.469–86.

[8] IEA (2021) Data Centres and Data Transmission Networks, International Energy Agency, Paris, available at https://www.iea.org/reports/data-centres-and-data-transmission-networks (accessed May 2022).

[9] IPCC (2022) Climate Change 2022: Mitigation of Climate Change, Intergovernmental Panel on Climate Change, Sixth Assessment Report, Working Group III, available at https://www.ipcc.ch/report/sixth-assessment-report-working-group-3/ (accessed May 2022).

[10] G. KallisC. KerschnerJ. Martinez-Alier (2012) ‘The economics of degrowth’, Ecological Economics, 84, pp.172–80.

[11] E. Massé (2021) Three Years under the EU GDPR: An Implementation Progress Report, Access Now report, available at https://www.accessnow.org/gdpr-three-years/ (accessed May 2022).

[12] W. McDonoughM. Braungart (2002) Cradle to Cradle: Remaking the Way we Make Things, North Point Press, New York.

[13] M. Mitchell (2019) Artificial Intelligence: A Guide for Thinking Humans, Pelican Books, Harmondsworth UK.

[14] D. PattersonJ. GonzalezU. HölzleQ. HungC. LiangL.-M. MunguiaD. RothchildD. SoM. TexierJ. Dean (forthcoming) ‘The carbon footprint of machine learning training will plateau, then shrink’, IEEE Computer, available at https://doi.org/10.36227/techrxiv.19139645.v4 (accessed May 2022).

[15] K. Pretz (2021) ‘Stop calling everything AI, machine-learning pioneer says’, IEEE Spectrum, 31 March, available at https://spectrum.ieee.org/stop-calling-everything-ai-machinelearning-pioneer-says (accessed May 2022).

[16] K. Raworth (2017) Doughnut Economics: Seven Ways to Think like a 21st-Century Economist, Chelsea Green Publishing, White River Junction VT.

[17] D. RolnickP. DontiL. KaackK. … KochanskiY. Bengio (forthcoming) ‘Tackling climate change with machine learning’, ACM Computing Surveys, 55, 2, paper 42, available at https://doi.org/10.1145/3485128 (accessed May 2022).

[18] K. SchwabP. Vanham (2021) Stakeholder Capitalism: A Global Economy that Works for Progress, People and Planet, John Wiley, Hoboken NJ.

[19] E. StrubellA. GaneshA. McCallum (2020) ‘Energy and policy considerations for modern deep learning research’, Proceedings of the AAAI Conference on Artificial Intelligence, 34, 9, paper 9, available at https://doi.org/10.1609/aaai.v34i09.7123 (accessed May 2022).

[20] D. TuiaD. TuiaB. KellenbergerS. BeeryB. … CostelloeT. Berger-Wolfe (2022) ‘Perspectives in machine learning for wildlife conservation’, Nature Communications, 13, paper 792, available at https://doi.org/10.1038/s41467-022-27980-y (accessed May 2022).

[21] M. Whittaker (2021) ‘The steep cost of capture’, ACM Interactions, 28, 6, available at https://doi.org/10.1145/3488666 (accessed May 2022).

[22] S. Zuboff (2019) The Age of Surveillance Capitalism, Profile Books, London.

Prometheus

Book Review

Journal

Author notes

Article

History

Page count

Product

Categories

Comment on this article