Sage Bergerson is seeking funding to support research analyzing the implications of compute access, working with the research nonprofit Epoch 

• The research paper Sage Bergerson is collaborating on aims to analyze the implications of compute access, focusing on how research agendas in academic and industry machine learning labs are evolving due to this access. It also seeks to address the risks associated with decreased scrutiny of machine learning models in the industry, providing evidence informative to those working in AI policy. 

• This project aims to influence strategic policy decisions to integrate machine learning into society more smoothly and safely, reducing uncertainties about the future of AI.

• She has completed an NLP research fellowship project at NYU and wrote a paper the summer before as a fellow at the Stanford Existential Risk Initiative.

Outcomes: This resulted in a (as-yet unpublished) paper:

• Tamay Besiroglu et al., “The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny? - The paper provides a data-driven analysis of the compute divide between industrial and academic AI labs, showing a decline in academic involvement in compute-intensive research, particularly in foundation models, and suggests policy recommendations to address the resulting challenges, including loss of scrutiny.