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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.17381 |
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| _version_ | 1866914222244364288 |
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| author | Rolnick, David Aspuru-Guzik, Alan Beery, Sara Dilkina, Bistra Donti, Priya L. Ghassemi, Marzyeh Kerner, Hannah Monteleoni, Claire Rolf, Esther Tambe, Milind White, Adam |
| author_facet | Rolnick, David Aspuru-Guzik, Alan Beery, Sara Dilkina, Bistra Donti, Priya L. Ghassemi, Marzyeh Kerner, Hannah Monteleoni, Claire Rolf, Esther Tambe, Milind White, Adam |
| contents | In this position paper, we argue that application-driven research has been systemically under-valued in the machine learning community. As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important. Such work offers the potential for significant impact not merely in domains of application but also in machine learning itself. In this paper, we describe the paradigm of application-driven research in machine learning, contrasting it with the more standard paradigm of methods-driven research. We illustrate the benefits of application-driven machine learning and how this approach can productively synergize with methods-driven work. Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation. We outline how these processes may be improved. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17381 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Application-Driven Innovation in Machine Learning Rolnick, David Aspuru-Guzik, Alan Beery, Sara Dilkina, Bistra Donti, Priya L. Ghassemi, Marzyeh Kerner, Hannah Monteleoni, Claire Rolf, Esther Tambe, Milind White, Adam Machine Learning Artificial Intelligence In this position paper, we argue that application-driven research has been systemically under-valued in the machine learning community. As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important. Such work offers the potential for significant impact not merely in domains of application but also in machine learning itself. In this paper, we describe the paradigm of application-driven research in machine learning, contrasting it with the more standard paradigm of methods-driven research. We illustrate the benefits of application-driven machine learning and how this approach can productively synergize with methods-driven work. Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation. We outline how these processes may be improved. |
| title | Application-Driven Innovation in Machine Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2403.17381 |