机器学习中的算力鸿沟：对学术贡献与审查的威胁？
The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?
AI总结：
本研究探讨了工业界与学术界在AI研究中的算力鸿沟，指出该鸿沟导致学术界在基础模型等计算密集型研究中代表性下降，并建议通过国家计算基础设施、开放科学倡议及第三方审计来提升学术界的算力获取与外部审查能力。
摘要：
工业界和学术界AI实验室在计算资源的使用程度上存在显著差异。我们提供了一项数据驱动的研究调查，探讨算力鸿沟在塑造机器学习研究中所扮演的角色。我们表明，算力鸿沟与计算密集型研究主题（尤其是基础模型）中纯学术研究团队代表性的下降同时发生。我们认为，学术界在推进相关技术、提供批判性评估和审查，以及此类模型的普及方面，可能会发挥较小的作用。与这种研究重点的转变同时发生的是，学术研究明显转向拥抱工业界开发的开源预训练模型。为了应对这一趋势带来的挑战，特别是对有影响力模型的审查减少，我们推荐了一些旨在深思熟虑地扩展学术见解的方法。国家赞助的计算基础设施结合开放科学倡议，可以明智地提升学术界的算力获取机会，优先考虑可解释性、安全性和保障性的研究。结构化访问计划和第三方审计也可能允许对工业界系统进行有分寸的外部评估。
英文摘要：
There are pronounced differences in the extent to which industrial and academic AI labs use computing resources. We provide a data-driven survey of the role of the compute divide in shaping machine learning research. We show that a compute divide has coincided with a reduced representation of academic-only research teams in compute intensive research topics, especially foundation models. We argue that, academia will likely play a smaller role in advancing the associated techniques, providing critical evaluation and scrutiny, and in the diffusion of such models. Concurrent with this change in research focus, there is a noticeable shift in academic research towards embracing open source, pre-trained models developed within the industry. To address the challenges arising from this trend, especially reduced scrutiny of influential models, we recommend approaches aimed at thoughtfully expanding academic insights. Nationally-sponsored computing infrastructure coupled with open science initiatives could judiciously boost academic compute access, prioritizing research on interpretability, safety and security. Structured access programs and third-party auditing may also allow measured external evaluation of industry systems.
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