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.
The increasing divide between industrial and academic AI labs in terms of computing resources is a significant factor that shapes machine learning research. This data-driven survey highlights the implications of this compute divide on the representation of academic-only research teams in compute intensive research topics, particularly foundation models.
It is important to note the multi-disciplinary nature of the concepts discussed in this content. The field of AI involves expertise from various domains such as computer science, data science, mathematics, and ethics. To fully understand and analyze the implications of the compute divide, one needs to consider these different perspectives.
The article suggests that the reduced representation of academic-only research teams in compute intensive research areas can potentially limit academia’s role in advancing associated techniques, providing critical evaluation and scrutiny, and in the diffusion of such models. This shift in research focus has led to an increased reliance on open source, pre-trained models developed within the industry.
To address the challenges arising from this trend, the article recommends approaches aimed at expanding academic insights thoughtfully. This includes nationally-sponsored computing infrastructure and open science initiatives that can increase academic compute access. By prioritizing research on interpretability, safety, and security, academia can continue to contribute valuable insights to the development and evaluation of AI models.
Moreover, structured access programs and third-party auditing are proposed as potential solutions to ensure measured external evaluation of industry systems. This would help alleviate concerns about reduced scrutiny of influential models developed by industry.
Overall, the content emphasizes the need for collaboration and coordination between academia and industry in order to maintain a balanced and comprehensive advancement of AI technology. The multi-disciplinary nature of AI research makes it crucial to consider diverse perspectives and expertise to overcome the challenges posed by the compute divide. By implementing the recommended approaches, academia can continue to play a significant role in advancing AI techniques and ensuring their responsible development.
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