Difference between revisions of "Machine Learning"

From Earth Science Information Partners (ESIP)
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* '''Email List''', [https://lists.esipfed.org/mailman/listinfo/esip-machinelearning https://lists.esipfed.org/mailman/listinfo/esip-machinelearning]
 
* '''Email List''', [https://lists.esipfed.org/mailman/listinfo/esip-machinelearning https://lists.esipfed.org/mailman/listinfo/esip-machinelearning]
 
* '''Upcoming meetings'''
 
* '''Upcoming meetings'''
** Telecon: Friday, 12/21, 9:00PT/10:00MT/11:00CT/12:00ET, Access Code: 933-291-469
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** Telecon: 3rd Friday of the month, 9:00PT/10:00MT/11:00CT/12:00ET, Access Code: 422-305-101
*** [https://global.gotomeeting.com/join/933291469    GoToMeeting]
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*** [https://global.gotomeeting.com/join/422305101    GoToMeeting]
*** Dial In: United States: +1 (646) 749-3122  
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*** Dial In: United States: +1 (571) 317-3122  
** 2019 Winter Meeting Session: Wed, 1/16/19, 11:00ET, Telecon access information to be provided
 
  
* '''Contact Chairs'''  
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* '''Contacts'''  
 
**Anne Wilson, Laboratory for Atmospheric and Space Physics (LASP)
 
**Anne Wilson, Laboratory for Atmospheric and Space Physics (LASP)
 +
**Yuhan Rao,  Fellow, University of Maryland
 
**Beth Huffer, Lingua Logica
 
**Beth Huffer, Lingua Logica
 +
**Shawn Polson, Laboratory for Atmospheric and Space Physics (LASP)
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** Bill Teng, Goddard
 
**Arif Albayrak, Goddard
 
**Arif Albayrak, Goddard
 
**Hook Hua, JPL
 
**Hook Hua, JPL
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===Resources===
 
===Resources===
 
==== Current news ====
 
==== Current news ====
 +
September 26, 2019, [https://www.nytimes.com/2019/09/26/technology/ai-computer-expense.html At Tech's Leading Edge, Worry About a Concentration of Power]
 +
:"The huge computing resources these companies have pose a threat — the universities cannot compete...”  "Academics are also raising concerns about the power consumed by advanced A.I. software. Training a large, deep-learning model can generate the same carbon footprint as the lifetime of five American cars, including gas, ..."
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* July 2019, [https://arxiv.org/pdf/1907.10597.pdf Green AI]
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*16 February 2019, [https://www.bbc.com/news/science-environment-47267081  AAAS: Machine learning 'causing science crisis']
  
 
* November 7, 2018, [https://www.washingtonpost.com/opinions/chinas-application-of-ai-should-be-a-sputnik-moment-for-the-us-but-will-it-be/2018/11/06/69132de4-e204-11e8-b759-3d88a5ce9e19_story.html China's application of AI should be a Sputnik moment for the US.  But will it be?]
 
* November 7, 2018, [https://www.washingtonpost.com/opinions/chinas-application-of-ai-should-be-a-sputnik-moment-for-the-us-but-will-it-be/2018/11/06/69132de4-e204-11e8-b759-3d88a5ce9e19_story.html China's application of AI should be a Sputnik moment for the US.  But will it be?]
  
 
* August 21, 2018, [https://www.executivegov.com/2018/08/fy-2019-ndaa-to-authorize-10m-for-ai-national-security-commission/ FY 2019 NDAA to Authorize $10M for an AI National Security Commission]
 
* August 21, 2018, [https://www.executivegov.com/2018/08/fy-2019-ndaa-to-authorize-10m-for-ai-national-security-commission/ FY 2019 NDAA to Authorize $10M for an AI National Security Commission]
 
* July 20, 2018, [https://www.fedscoop.com/dod-joint-ai-center-established/ Pentagon's Joint AI Center is 'established', but there's much more to figure out]
 
  
 
====Papers====
 
====Papers====
  
 +
*''Tackling Climate Change with Machine Learning'', [[https://arxiv.org/pdf/1906.05433.pdf Tackling Climate Change with Machine Learning]].
 
*''Hidden Technical Debt in Machine Learning Systems'', [[File:NIPS-5656-hidden-technical-debt-in-machine-learning-systems.pdf‎]].
 
*''Hidden Technical Debt in Machine Learning Systems'', [[File:NIPS-5656-hidden-technical-debt-in-machine-learning-systems.pdf‎]].
  

Revision as of 11:59, October 23, 2019

Machine learning, everybody’s doing it. Scientists are applying machine learning to their scientific algorithms and using the results to justify various conclusions. Is machine learning the silver bullet that will help us answer our scientific questions?

The purpose of this cluster is to educate ourselves and the ESIP community about machine learning through asking and answering questions and sharing experiences and resources. The scope of the cluster includes topics like the following:

  • What is machine learning? What can machine learning do? How is machine learning different from data science? From data analytics?
  • What types of machine learning algorithms are there and how do they compare to each other? Under what circumstances is each best applied and/or not applicable?
  • What are symbolic and subsymbolic approaches to machine learning and their subtypes?
  • What are machine learning SWOTs: strengths, weaknesses, opportunities, threats? Some considerations include:
    • models, model choices and biases
    • p-hacking, data dredging
  • What machine learning tools are available?
  • In particular, regarding deep learning/neural networks
    • How do supervised, semi-supervised, and unsupervised networks differ?
    • How to integrate truth labeled data?
    • Under what circumstances is it okay to not understand what the network learned? When is it not okay?
    • How to deal with a lack of training data?
    • How to (try to) understand what was learned?

Possible cluster outputs could include:

  • A machine learning tool survey and SWOT analysis
  • Training material, on line [and maybe a guide to available platforms and resources? e.g., using AWS ML platform]
  • Training and/or other sessions at ESIP
  • Recommendations regarding the application of ML in earth and space sciences


News


Archive

Activities

Get Involved

  • Contacts
    • Anne Wilson, Laboratory for Atmospheric and Space Physics (LASP)
    • Yuhan Rao, Fellow, University of Maryland
    • Beth Huffer, Lingua Logica
    • Shawn Polson, Laboratory for Atmospheric and Space Physics (LASP)
    • Bill Teng, Goddard
    • Arif Albayrak, Goddard
    • Hook Hua, JPL

Resources

Current news

September 26, 2019, At Tech's Leading Edge, Worry About a Concentration of Power

"The huge computing resources these companies have pose a threat — the universities cannot compete...” "Academics are also raising concerns about the power consumed by advanced A.I. software. Training a large, deep-learning model can generate the same carbon footprint as the lifetime of five American cars, including gas, ..."

Papers