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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/2401.10268 |
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| _version_ | 1866912033904001024 |
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| author | Liang, Lizhen Zhuang, Han Zou, James Acuna, Daniel E. |
| author_facet | Liang, Lizhen Zhuang, Han Zou, James Acuna, Daniel E. |
| contents | Artificial intelligence (AI) has seen fast paced development in industry and academia. However, striking recent advances by industry have stunned the field, inviting a fresh perspective on the role of academic research on this progress. Here, we characterize the impact and type of AI produced by both environments over the last 25 years and establish several patterns. We find that articles published by teams consisting exclusively of industry researchers tend to get greater attention, with a higher chance of being highly cited and citation-disruptive, and several times more likely to produce state-of-the-art models. In contrast, we find that exclusively academic teams publish the bulk of AI research and tend to produce higher novelty work, with single papers having several times higher likelihood of being unconventional and atypical. The respective impact-novelty advantages of industry and academia are robust to controls for subfield, team size, seniority, and prestige. We find that academic-industry collaborations produce the most impactful work overall but do not have the novelty level of academic teams. Together, our findings identify the unique and nearly irreplaceable contributions that both academia and industry make toward the progress of AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10268 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The complementary contributions of academia and industry to AI research Liang, Lizhen Zhuang, Han Zou, James Acuna, Daniel E. Computers and Society Artificial Intelligence Social and Information Networks Artificial intelligence (AI) has seen fast paced development in industry and academia. However, striking recent advances by industry have stunned the field, inviting a fresh perspective on the role of academic research on this progress. Here, we characterize the impact and type of AI produced by both environments over the last 25 years and establish several patterns. We find that articles published by teams consisting exclusively of industry researchers tend to get greater attention, with a higher chance of being highly cited and citation-disruptive, and several times more likely to produce state-of-the-art models. In contrast, we find that exclusively academic teams publish the bulk of AI research and tend to produce higher novelty work, with single papers having several times higher likelihood of being unconventional and atypical. The respective impact-novelty advantages of industry and academia are robust to controls for subfield, team size, seniority, and prestige. We find that academic-industry collaborations produce the most impactful work overall but do not have the novelty level of academic teams. Together, our findings identify the unique and nearly irreplaceable contributions that both academia and industry make toward the progress of AI. |
| title | The complementary contributions of academia and industry to AI research |
| topic | Computers and Society Artificial Intelligence Social and Information Networks |
| url | https://arxiv.org/abs/2401.10268 |