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== '''With Artificial Intelligence & Machine Learning - What Lies Ahead for Earth Surface Modeling ?''' == | == '''With Artificial Intelligence & Machine Learning - What Lies Ahead for Earth Surface Modeling ?''' == | ||
A Forum, 1030-1230pm, 24th May 2018, SEEC Room | A Forum, 1030-1230pm, Thursday 24th May 2018, SEEC Room C120 (main auditorium) | ||
Convened by Chris JENKINS (INSTAAR, Boulder CO) and Jeff OBELCZ (NRL, Stennis, MS) | Convened by Chris JENKINS (INSTAAR, Boulder CO) and Jeff OBELCZ (NRL, Stennis, MS) | ||
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=== '''Questions for the Forum''' === | === '''Questions for the Forum''' === | ||
* What can AI and ML currently do that might benefit Earth Surface Dynamics Modeling ? | * What can AI and ML currently do that might benefit Earth Surface Dynamics Modeling ? | ||
* What is the relationship between Process Modelling and AI | * What is the relationship between Process Modelling and AI&ML ? | ||
* How should CSDMS Community Respond to the Appearance of AI | * How should CSDMS Community Respond to the Appearance of AI&ML | ||
* What Earth Surface Dynamics Modeling-related tasks are not suited for AI | * What Earth Surface Dynamics Modeling-related tasks are not suited for AI&ML? Why? | ||
=== '''Agenda and Survey Docs''' === | |||
Please see the Agenda at [https://drive.google.com/open?id=1zqRAEm7G3twkjXWOVTETMbNDvlQAD4oB HERE] | |||
Please complete and return the simple [https://docs.google.com/forms/d/1xl7v5Z8MFd5M4pmcA2eqQb9zlc-O838Ipvy0G8MjCx0/edit Pre-Clinic Survey] (3 questions, ~1 minute) | |||
=== '''Other groupings''' === | |||
NSF May 2018. [https://www.nsf.gov/news/news_summ.jsp?cntn_id=245418&WT.mc_id=USNSF_51&WT.mc_ev=click Statement on Artificial Intelligence for American Industry.] NSF director outlines vision for AI that benefits the economy and U.S. workers. | |||
IS-GEO, May 2018. [https://is-geo.org/ The IS-GEO Research Coordination Network aims to support an emerging community of researchers in intelligent systems (IS) and geosciences (GEO).] A NSF Research Coordination Network. | |||
=== '''Background Reading''' === | === '''Background Reading''' === | ||
* Participants can share URL's here to papers that discuss the workings of AI | * Participants can share URL's here to papers that discuss the workings of AI&ML and applications in fields such as ours. Some papers are also relevant to questions for the forum (above). | ||
A number of copies will be made available at the forum. | |||
Jones, N. 2018. [https://www.nature.com/news/how-machine-learning-could-help-to-improve-climate-forecasts-1.22503 How machine learning could help to improve climate forecasts]. Nature 548, 379–380 (24 August 2017) doi:10.1038/548379a . | Jones, N. 2018. [https://www.nature.com/news/how-machine-learning-could-help-to-improve-climate-forecasts-1.22503 How machine learning could help to improve climate forecasts]. Nature 548, 379–380 (24 August 2017) doi:10.1038/548379a . | ||
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Abbot, J. & Marohasy,J. 2013. [https://www.witpress.com/elibrary/wit-transactions-on-ecology-and-the-environment/172/24639 The Application Of Artificial Intelligence For Monthly Rainfall Forecasting In The Brisbane Catchment, Queensland, Australia.] WIT Transactions on Ecology and the Environment, 172, 125 - 135. DOI:10.2495/RBM130111 | Abbot, J. & Marohasy,J. 2013. [https://www.witpress.com/elibrary/wit-transactions-on-ecology-and-the-environment/172/24639 The Application Of Artificial Intelligence For Monthly Rainfall Forecasting In The Brisbane Catchment, Queensland, Australia.] WIT Transactions on Ecology and the Environment, 172, 125 - 135. DOI:10.2495/RBM130111 | ||
Karpatne, A., et al., 2017. [https://arxiv.org/pdf/1711.04708.pdf Machine Learning for the Geosciences: Challenges and Opportunities.] Workshop on Mining Big Data in Climate and Environment (MBDCE 2017), 17th SIAM International Conference on Data Mining (SDM 2017). | |||
Burghard, C. 2017. [http://www.nvidia.com/object/idc-deep-learning-in-healthcare.html From Bench to Bedside: Deep Learning’s Journey in Healthcare. ] (Registration required) | |||
Datascience 2018. [https://www.datascience.com/resources Resources.] Culver City CA (Commercially oriented source of up-to-date briefings, useful even down to technical levels.) | |||
Joppa, L.N. 2017. [https://www.nature.com/articles/d41586-017-08675-7 The case for technology investments in the environment. Create an artificial-intelligence platform for the planet, urges Lucas N. Joppa.] Nature 552, 325-328 (2017), doi:10.1038/d41586-017-08675-7. | |||
Marone, C. 2018. [https://www.nature.com/articles/s41561-018-0117-5?WT.ec_id=NGEO-201805&spMailingID=56516686&spUserID=MTg1NDc3MTE2MzY3S0&spJobID=1384567859&spReportId=MTM4NDU2Nzg1OQS2 Training machines in Earthly ways]. Nature Geoscience 11, 301-302 (2018), doi: 10.1038/s41561-018-0117-5 . | |||
Rahimi, A. 2018 [http://www.argmin.net/2017/12/05/kitchen-sinks/ Reflections on Random Kitchen Sinks] 'arg minblog' May 2018. | |||
and commentary: | |||
Hutson, M. 2018. [http://www.sciencemag.org/news/2018/05/ai-researchers-allege-machine-learning-alchemy AI researchers allege that machine learning is alchemy] Science May 2018. | |||
Thessen, A.E. 2016. [https://oneecosystem.pensoft.net/articles.php?id=8621 Adoption of Machine Learning Techniques in Ecology and Earth Science.] One Ecosystem 1: e8621. DOI10.3897/oneeco.1.e8621. | |||
Lehman et al. 2018. [https://arxiv.org/abs/1803.03453 The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities]. arXiv:1803.03453. (aka Complex models produce unexpected results) | |||
[http://www.sciencemag.org/news/2018/02/missing-data-hinder-replication-artificial-intelligence-studies Machine learning replicability crisis] | |||
=== '''Displays during the Forum''' === | === '''Displays during the Forum''' === | ||
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<small>''Dateline: CJ | <small>''Dateline: CJ 17May2018''</small><small>Small text</small> |
Latest revision as of 11:46, 26 March 2019
2018 CSDMS Annual Meeting:
With Artificial Intelligence & Machine Learning - What Lies Ahead for Earth Surface Modeling ?
A Forum, 1030-1230pm, Thursday 24th May 2018, SEEC Room C120 (main auditorium)
Convened by Chris JENKINS (INSTAAR, Boulder CO) and Jeff OBELCZ (NRL, Stennis, MS)
Questions for the Forum
- What can AI and ML currently do that might benefit Earth Surface Dynamics Modeling ?
- What is the relationship between Process Modelling and AI&ML ?
- How should CSDMS Community Respond to the Appearance of AI&ML
- What Earth Surface Dynamics Modeling-related tasks are not suited for AI&ML? Why?
Agenda and Survey Docs
Please see the Agenda at HERE
Please complete and return the simple Pre-Clinic Survey (3 questions, ~1 minute)
Other groupings
NSF May 2018. Statement on Artificial Intelligence for American Industry. NSF director outlines vision for AI that benefits the economy and U.S. workers.
IS-GEO, May 2018. The IS-GEO Research Coordination Network aims to support an emerging community of researchers in intelligent systems (IS) and geosciences (GEO). A NSF Research Coordination Network.
Background Reading
- Participants can share URL's here to papers that discuss the workings of AI&ML and applications in fields such as ours. Some papers are also relevant to questions for the forum (above).
A number of copies will be made available at the forum.
Jones, N. 2018. How machine learning could help to improve climate forecasts. Nature 548, 379–380 (24 August 2017) doi:10.1038/548379a .
Grover, A. et al. 2015. A Deep Hybrid Model for Weather Forecasting. 2015 ACM, DOI: http://dx.doi.org/10.1145/2783258.2783275.
Abbot, J. & Marohasy,J. 2013. The Application Of Artificial Intelligence For Monthly Rainfall Forecasting In The Brisbane Catchment, Queensland, Australia. WIT Transactions on Ecology and the Environment, 172, 125 - 135. DOI:10.2495/RBM130111
Karpatne, A., et al., 2017. Machine Learning for the Geosciences: Challenges and Opportunities. Workshop on Mining Big Data in Climate and Environment (MBDCE 2017), 17th SIAM International Conference on Data Mining (SDM 2017).
Burghard, C. 2017. From Bench to Bedside: Deep Learning’s Journey in Healthcare. (Registration required)
Datascience 2018. Resources. Culver City CA (Commercially oriented source of up-to-date briefings, useful even down to technical levels.)
Joppa, L.N. 2017. The case for technology investments in the environment. Create an artificial-intelligence platform for the planet, urges Lucas N. Joppa. Nature 552, 325-328 (2017), doi:10.1038/d41586-017-08675-7.
Marone, C. 2018. Training machines in Earthly ways. Nature Geoscience 11, 301-302 (2018), doi: 10.1038/s41561-018-0117-5 .
Rahimi, A. 2018 Reflections on Random Kitchen Sinks 'arg minblog' May 2018. and commentary: Hutson, M. 2018. AI researchers allege that machine learning is alchemy Science May 2018.
Thessen, A.E. 2016. Adoption of Machine Learning Techniques in Ecology and Earth Science. One Ecosystem 1: e8621. DOI10.3897/oneeco.1.e8621.
Lehman et al. 2018. The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities. arXiv:1803.03453. (aka Complex models produce unexpected results)
Machine learning replicability crisis
Displays during the Forum
- Posters, and printed materials for distribution will be available at the event
Online Resources for the Forum
- This Wiki will serve Abstracts, URL's, Posters, Images supplied by participants before and during the meeting
Dateline: CJ 17May2018Small text