The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?

• URL: http://arxiv.org/abs/2401.02452v2
• Date: Mon, 8 Jan 2024 12:37:58 GMT
• Title: The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?
• Authors: Tamay Besiroglu, Sage Andrus Bergerson, Amelia Michael, Lennart Heim, Xueyun Luo, Neil Thompson
• Abstract summary: We show that a compute divide has coincided with a reduced representation of academic-only research teams in compute intensive research topics. To address the challenges arising from this trend, we recommend approaches aimed at thoughtfully expanding academic insights.
• Score: 1.0985060632689174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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