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)
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.
- 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