English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Increasingly, modern Artificial Intelligence (AI) research has become morecomputationally intensive. However, a growing concern is that due to unequalaccess to computing power, only certain firms and elite universities haveadvantages in modern AI research. Using a novel dataset of 171394 papers from57 prestigious computer science conferences, we document that firms, inparticular, large technology firms and elite universities have increasedparticipation in major AI conferences since deep learning's unanticipated risein 2012. The effect is concentrated among elite universities, which are ranked1-50 in the QS World University Rankings. Further, we find two strategiesthrough which firms increased their presence in AI research: first, they haveincreased firm-only publications; and second, firms are collaborating primarilywith elite universities. Consequently, this increased presence of firms andelite universities in AI research has crowded out mid-tier (QS ranked 201-300)and lower-tier (QS ranked 301-500) universities. To provide causal evidencethat deep learning's unanticipated rise resulted in this divergence, weleverage the generalized synthetic control method, a data-driven counterfactualestimator. Using machine learning based text analysis methods, we provideadditional evidence that the divergence between these two groups - large firmsand non-elite universities - is driven by access to computing power or compute,which we term as the "compute divide". This compute divide between large firmsand non-elite universities increases concerns around bias and fairness withinAI technology, and presents an obstacle towards "democratizing" AI. Theseresults suggest that a lack of access to specialized equipment such as computecan de-democratize knowledge production.

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