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Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars

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arxiv 2406.17812 v1 pith:FQI5NYVT submitted 2024-06-24 cs.LG cs.AIcs.DC

Wesley Brewer , Aditya Kashi , Sajal Dash , Aristeidis Tsaris , Junqi Yin , Mallikarjun Shankar , Feiyi Wang This is my paper
classification cs.LGcs.AIcs.DC
keywords scientificartificialintelligenceaddressapproachesexemplarsproblemsscalable
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge

    cs.LG 2025-08 conditional novelty 4.0 of 10

    Sparse MoE adapters with per-client expert selection and a thresholded load-balancing loss improve federated fine-tuning accuracy under non-IID data compared with LoRA baselines.

  2. Data Readiness for Scientific AI at Scale

    cs.AI 2025-07 conditional novelty 4.0 of 10

    Scientific data can be graded on a five-level readiness scale crossed with five processing stages, yielding a maturity matrix for AI training at supercomputer scale.

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