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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2408.10291 |
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Table des matières:
- It is argued that the pursuit of an ever increasing number of weights in large-scale machine learning applications, besides being energetically unsustainable, is also conducive to manipulative strategies whereby Science is easily served as a strawman for economic and financial power. If machine learning is meant to serve science ahead of vested business interests, a paradigm shift is needed: from more weights and little insight to more insight and less weights.