Machine learning for science and society
The special issue on “Machine Learning for Science and Society” showcases machine learning work with influence on our current and future society. These papers address several key problems such as how we perform repairs on critical infrastructure, how we predict severe weather and aviation turbulence...
| Principais autores: | , |
|---|---|
| Outros Autores: | |
| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
Springer US
2016
|
| Acesso em linha: | http://hdl.handle.net/1721.1/103130 |
| _version_ | 1826213660483125248 |
|---|---|
| author | Rudin, Cynthia Wagstaff, Kiri L. |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Rudin, Cynthia Wagstaff, Kiri L. |
| author_sort | Rudin, Cynthia |
| collection | MIT |
| description | The special issue on “Machine Learning for Science and Society” showcases machine learning work with influence on our current and future society. These papers address several key problems such as how we perform repairs on critical infrastructure, how we predict severe weather and aviation turbulence, how we conduct tax audits, whether we can detect privacy breaches in access to healthcare data, and how we link individuals across census data sets for new insights into population changes. In this introduction, we discuss the need for such a special issue within the context of our field and its relationship to the broader world. In the era of “big data,” there is a need for machine learning to address important large-scale applied problems, yet it is difficult to find top venues in machine learning where such work is encouraged. We discuss the ramifications of this contradictory situation and encourage further discussion on the best strategy that we as a field may adopt. We also summarize key lessons learned from individual papers in the special issue so that the community as a whole can benefit. |
| first_indexed | 2024-09-23T15:52:48Z |
| format | Article |
| id | mit-1721.1/103130 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:52:48Z |
| publishDate | 2016 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1031302022-09-29T16:46:13Z Machine learning for science and society Rudin, Cynthia Wagstaff, Kiri L. Sloan School of Management Rudin, Cynthia The special issue on “Machine Learning for Science and Society” showcases machine learning work with influence on our current and future society. These papers address several key problems such as how we perform repairs on critical infrastructure, how we predict severe weather and aviation turbulence, how we conduct tax audits, whether we can detect privacy breaches in access to healthcare data, and how we link individuals across census data sets for new insights into population changes. In this introduction, we discuss the need for such a special issue within the context of our field and its relationship to the broader world. In the era of “big data,” there is a need for machine learning to address important large-scale applied problems, yet it is difficult to find top venues in machine learning where such work is encouraged. We discuss the ramifications of this contradictory situation and encourage further discussion on the best strategy that we as a field may adopt. We also summarize key lessons learned from individual papers in the special issue so that the community as a whole can benefit. National Science Foundation (U.S.) (grant IIS-1053407) United States. National Aeronautics and Space Administration 2016-06-16T20:44:35Z 2016-06-16T20:44:35Z 2013-11 2013-10 2016-05-23T12:15:01Z Article http://purl.org/eprint/type/JournalArticle 0885-6125 1573-0565 http://hdl.handle.net/1721.1/103130 Rudin, Cynthia, and Kiri L. Wagstaff. “Machine Learning for Science and Society.” Machine Learning 95.1 (2014): 1–9. en http://dx.doi.org/10.1007/s10994-013-5425-9 Machine Learning Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ The Author(s) application/pdf Springer US Springer US |
| spellingShingle | Rudin, Cynthia Wagstaff, Kiri L. Machine learning for science and society |
| title | Machine learning for science and society |
| title_full | Machine learning for science and society |
| title_fullStr | Machine learning for science and society |
| title_full_unstemmed | Machine learning for science and society |
| title_short | Machine learning for science and society |
| title_sort | machine learning for science and society |
| url | http://hdl.handle.net/1721.1/103130 |
| work_keys_str_mv | AT rudincynthia machinelearningforscienceandsociety AT wagstaffkiril machinelearningforscienceandsociety |