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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2601.17510 |
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| _version_ | 1866914277681528832 |
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| author | Donoho, David L. Kang, Jian Lin, Xihong Mukherjee, Bhramar Nettleton, Dan Nugent, Rebecca Rodriguez, Abel Xing, Eric P. Zheng, Tian Zhu, Hongtu |
| author_facet | Donoho, David L. Kang, Jian Lin, Xihong Mukherjee, Bhramar Nettleton, Dan Nugent, Rebecca Rodriguez, Abel Xing, Eric P. Zheng, Tian Zhu, Hongtu |
| contents | This article presents the full, original record of the 2024 Joint Statistical Meetings (JSM) town hall, "Statistics in the Age of AI," which convened leading statisticians to discuss how the field is evolving in response to advances in artificial intelligence, foundation models, large-scale empirical modeling, and data-intensive infrastructures. The town hall was structured around open panel discussion and extensive audience Q&A, with the aim of eliciting candid, experience-driven perspectives rather than formal presentations or prepared statements. This document preserves the extended exchanges among panelists and audience members, with minimal editorial intervention, and organizes the conversation around five recurring questions concerning disciplinary culture and practices, data curation and "data work," engagement with modern empirical modeling, training for large-scale AI applications, and partnerships with key AI stakeholders. By providing an archival record of this discussion, the preprint aims to support transparency, community reflection, and ongoing dialogue about the evolving role of statistics in the data- and AI-centric future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17510 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | "Rebuilding" Statistics in the Age of AI: A Town Hall Discussion on Culture, Infrastructure, and Training Donoho, David L. Kang, Jian Lin, Xihong Mukherjee, Bhramar Nettleton, Dan Nugent, Rebecca Rodriguez, Abel Xing, Eric P. Zheng, Tian Zhu, Hongtu Machine Learning Artificial Intelligence This article presents the full, original record of the 2024 Joint Statistical Meetings (JSM) town hall, "Statistics in the Age of AI," which convened leading statisticians to discuss how the field is evolving in response to advances in artificial intelligence, foundation models, large-scale empirical modeling, and data-intensive infrastructures. The town hall was structured around open panel discussion and extensive audience Q&A, with the aim of eliciting candid, experience-driven perspectives rather than formal presentations or prepared statements. This document preserves the extended exchanges among panelists and audience members, with minimal editorial intervention, and organizes the conversation around five recurring questions concerning disciplinary culture and practices, data curation and "data work," engagement with modern empirical modeling, training for large-scale AI applications, and partnerships with key AI stakeholders. By providing an archival record of this discussion, the preprint aims to support transparency, community reflection, and ongoing dialogue about the evolving role of statistics in the data- and AI-centric future. |
| title | "Rebuilding" Statistics in the Age of AI: A Town Hall Discussion on Culture, Infrastructure, and Training |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.17510 |