Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910501986893824 |
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| author | Brewer, Wesley Kashi, Aditya Dash, Sajal Tsaris, Aristeidis Yin, Junqi Shankar, Mallikarjun Wang, Feiyi |
| author_facet | Brewer, Wesley Kashi, Aditya Dash, Sajal Tsaris, Aristeidis Yin, Junqi Shankar, Mallikarjun Wang, Feiyi |
| contents | In a post-ChatGPT world, this paper explores the potential of leveraging scalable artificial intelligence for scientific discovery. We propose that scaling up artificial intelligence on high-performance computing platforms is essential to address such complex problems. This perspective focuses on scientific use cases like cognitive simulations, large language models for scientific inquiry, medical image analysis, and physics-informed approaches. The study outlines the methodologies needed to address such challenges at scale on supercomputers or the cloud and provides exemplars of such approaches applied to solve a variety of scientific problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17812 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars Brewer, Wesley Kashi, Aditya Dash, Sajal Tsaris, Aristeidis Yin, Junqi Shankar, Mallikarjun Wang, Feiyi Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing In a post-ChatGPT world, this paper explores the potential of leveraging scalable artificial intelligence for scientific discovery. We propose that scaling up artificial intelligence on high-performance computing platforms is essential to address such complex problems. This perspective focuses on scientific use cases like cognitive simulations, large language models for scientific inquiry, medical image analysis, and physics-informed approaches. The study outlines the methodologies needed to address such challenges at scale on supercomputers or the cloud and provides exemplars of such approaches applied to solve a variety of scientific problems. |
| title | Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars |
| topic | Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2406.17812 |