Industry: Difference between revisions
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⚫ | ''This page is about the intersection of industrial systems and machine learning in the context of climate change mitigation. For an overview of industry as a whole, please see the [https://en.wikipedia.org/wiki/Industry Wikipedia page] on this topic.''[[File:Industry.png|thumb|500x500px|Selected opportunities to use machine learning to reduce greenhouse gas emissions in industry.|alt=]]Industrial production, logistics, and building materials are leading causes of difficult-to-eliminate GHG emissions<ref>{{Cite journal|last=Davis|first=Steven J.|last2=Lewis|first2=Nathan S.|last3=Shaner|first3=Matthew|last4=Aggarwal|first4=Sonia|last5=Arent|first5=Doug|last6=Azevedo|first6=Inês L.|last7=Benson|first7=Sally M.|last8=Bradley|first8=Thomas|last9=Brouwer|first9=Jack|last10=Chiang|first10=Yet-Ming|last11=Clack|first11=Christopher T. M.|date=2018-06-29|title=Net-zero emissions energy systems|url=https://www.sciencemag.org/lookup/doi/10.1126/science.aas9793|journal=Science|language=en|volume=360|issue=6396|pages=eaas9793|doi=10.1126/science.aas9793|issn=0036-8075}}</ref>. Fortunately for ML researchers, the global industrial sector spends billions of dollars annually gathering data on factories and supply chains<ref>Mike Gualtieri, Noel Yuhanna, Holger Kisker, Rowan Curran, Brandon Purcell, Sophia Christakis, Shreyas Warrier, and Matthew Izzi. The Forrester Wave: Big data streaming analytics, Q1 2016. Forrester.com, January 2016.</ref> – aided by improvements in the cost and accessibility of cloud-based storage and computing, as well as sensors and data-gathering mechanisms such as QR codes and image recognition (vaguely referred to as "Industry 4.0" and "Smart/Connected Factories"). |
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''This page is about the intersection of industrial systems and machine learning in the context of climate change mitigation. For an overview of industry as a whole, please see the [https://en.wikipedia.org/wiki/Industry Wikipedia page] on this topic.'' |
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ML can potentially reduce industrial emissions by helping to streamline supply chains, invent cleaner materials and chemicals, design for fewer materials<ref>{{Cite journal|last=Kazi|first=Rubaiat Habib|last2=Grossman|first2=Tovi|last3=Cheong|first3=Hyunmin|last4=Hashemi|first4=Ali|last5=Fitzmaurice|first5=George|date=2017-10-20|title=DreamSketch: Early stage 3D design explorations with sketching and generative design.|url=http://dx.doi.org/10.1145/3126594.3126662|journal=Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology|location=New York, NY, USA|publisher=ACM|volume=|pages=|doi=10.1145/3126594.3126662|isbn=978-1-4503-4981-9|via=}}</ref>, improve production quality, predict machine breakdowns, optimize heating and cooling systems<ref>Richard Evans and Jim Gao. DeepMind AI reduces Google data centre cooling bill by 40%. DeepMind blog, 20, 2016.</ref>, and prioritize the use of clean electricity over fossil fuels<ref>{{Cite journal|last=Zhang|first=Xiao|last2=Hug|first2=Gabriela|last3=Kolter|first3=J. Zico|last4=Harjunkoski|first4=Iiro|date=2016-07|title=Model predictive control of industrial loads and energy storage for demand response|url=http://dx.doi.org/10.1109/pesgm.2016.7741228|journal=2016 IEEE Power and Energy Society General Meeting (PESGM)|publisher=IEEE|doi=10.1109/pesgm.2016.7741228|isbn=978-1-5090-4168-8}}</ref><ref>{{Cite journal|last=Berral|first=Josep Ll.|last2=Goiri|first2=Íñigo|last3=Nou|first3=Ramón|last4=Julià|first4=Ferran|last5=Guitart|first5=Jordi|last6=Gavaldà|first6=Ricard|last7=Torres|first7=Jordi|date=2010|title=Towards energy-aware scheduling in data centers using machine learning|url=http://dx.doi.org/10.1145/1791314.1791349|journal=Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking - e-Energy '10|location=New York, New York, USA|publisher=ACM Press|doi=10.1145/1791314.1791349|isbn=978-1-4503-0042-1}}</ref>. |
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ML can have a substantial positive climate impact on the industrial sector under the following circumstances: |
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* When firms have an incentive to share their proprietary data and/or algorithms with researchers and other firms. |
* When firms have an incentive to share their proprietary data and/or algorithms with researchers and other firms. |
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* When aspects of production or shipping can be readily fine-tuned or adjusted, and there are clear objective functions. |
* When aspects of production or shipping can be readily fine-tuned or adjusted, and there are clear objective functions. |
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* When firms’ incentives align with reducing emissions (for example, through efficiency gains, regulatory compliance, or high GHG prices). |
* When firms’ incentives align with reducing emissions (for example, through efficiency gains, regulatory compliance, or high GHG prices). |
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Yet greater efficiency may increase the production of goods and thus GHG emissions (via the Jevons paradox) unless industrial actors have sufficient incentives to reduce overall emissions<ref>{{Cite journal|last=Sorrell|first=Steve|date=2009-04|title=Jevons’ Paradox revisited: The evidence for backfire from improved energy efficiency|url=http://dx.doi.org/10.1016/j.enpol.2008.12.003|journal=Energy Policy|volume=37|issue=4|pages=1456–1469|doi=10.1016/j.enpol.2008.12.003|issn=0301-4215}}</ref>. |
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== Machine Learning Application Areas == |
== Machine Learning Application Areas == |