Summary of Scalable Artificial Intelligence For Science: Perspectives, Methods and Exemplars, by Wesley Brewer et al.
Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars
by Wesley Brewer, Aditya Kashi, Sajal Dash, Aristeidis Tsaris, Junqi Yin, Mallikarjun Shankar, Feiyi Wang
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper explores the potential of using scalable artificial intelligence for scientific discovery in a post-ChatGPT world. The authors suggest that scaling up AI on high-performance computing platforms is crucial to tackle complex problems. The perspective highlights various scientific use cases, including cognitive simulations, large language models for scientific inquiry, medical image analysis, and physics-informed approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes using artificial intelligence (AI) for scientific discovery, focusing on complex problems that require scalable solutions. By applying AI on high-performance computing platforms or the cloud, scientists can tackle challenges like cognitive simulations, large language model development, medical image analysis, and physics-informed approaches. |
Keywords
* Artificial intelligence * Large language model
