The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research

TL;DR

This paper examines how the rise of deep learning has led to increased participation of large firms and elite universities in AI research, driven by unequal access to computing power, which may hinder the democratization of AI.

Contribution

It provides empirical evidence of the compute divide's role in shifting AI research dominance towards elite institutions and firms since 2012.

Findings

Large firms and elite universities increased their AI research participation since 2012.

Firms mainly collaborate with elite universities, crowding out mid- and lower-tier universities.

Access to computing power explains the divergence between large firms and non-elite universities.

Abstract

Increasingly, modern Artificial Intelligence (AI) research has become more computationally intensive. However, a growing concern is that due to unequal access to computing power, only certain firms and elite universities have advantages in modern AI research. Using a novel dataset of 171394 papers from 57 prestigious computer science conferences, we document that firms, in particular, large technology firms and elite universities have increased participation in major AI conferences since deep learning's unanticipated rise in 2012. The effect is concentrated among elite universities, which are ranked 1-50 in the QS World University Rankings. Further, we find two strategies through which firms increased their presence in AI research: first, they have increased firm-only publications; and second, firms are collaborating primarily with elite universities. Consequently, this increased presence of firms and elite universities in AI research has crowded out mid-tier (QS ranked 201-300) and lower-tier (QS ranked 301-500) universities. To provide causal evidence that deep learning's unanticipated rise resulted in this divergence, we leverage the generalized synthetic control method, a data-driven counterfactual estimator. Using machine learning based text analysis methods, we provide additional evidence that the divergence between these two groups - large firms and non-elite universities - is driven by access to computing power or compute, which we term as the "compute divide". This compute divide between large firms and non-elite universities increases concerns around bias and fairness within AI technology, and presents an obstacle towards "democratizing" AI. These results suggest that a lack of access to specialized equipment such as compute can de-democratize knowledge production.