Scientific research communities are embracing AI-based solutions to target tractable scientific tasks and improve research workflows. However, the development and evaluation of such solutions are scattered across multiple disciplines. We formalize the problem of scientific AI benchmarking, and propose a system called SAIBench in the hope of unifying the efforts and enabling low-friction on-boarding of new disciplines. The system approaches this goal with SAIL, a domain-specific language to decouple research problems, AI models, ranking criteria, and software/hardware configuration into reusable modules. We show that this approach is flexible and can adapt to problems, AI models, and evaluation methods defined in different perspectives. The project homepage is https://www.computercouncil.org/SAIBench
@article{arxiv.2206.05418,
title = {SAIBench: Benchmarking AI for Science},
author = {Yatao Li and Jianfeng Zhan},
journal= {arXiv preprint arXiv:2206.05418},
year = {2022}
}
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Published in BenchCouncil Transactions on Benchmarks, Standards and Evaluations (TBench)