text: 341,209,1357350848792301575,460489687,Juan Mateos Garcia,"""The privatisation of AI researchers"" In our latest working paper we study AI researcher career transitions between academic & industry. <LINK> Opening q: How do we preserve a public interest AI research sphere in the face of strong industry demand for talent? <LINK> 1. We study career transitions since 2020 using data from Microsoft Academic Graph.  We find a growing flow of researchers from academia to industry [1], particularly from elite universities [2], and particularly to tech companies [3] <LINK> 2. Our survival analysis shows that industry tends to recruit highly cited researchers with a specialism in deep learning. We also find evidence that female researchers are less likely to transition into industry after we account for other variables. <LINK> 3. We also compare the ""productivity""* of researchers who transition into industry with peers who stay in academia. ""Switchers"" see a bump in productivity which is offset over time. Are they burning out, over-specialising, being captured...? 🤷 ____ * Proxied w/ citation rank <LINK> Conclusion: Our results raise concerns about a potential hollowing out AI public research & knowledge as talented researchers transition into industry and become less productive over time. This provides a rationale to invest in public AI research & preserve its independence. <LINK> PS. Lots of potential avenues to explore in further work eg: It has bee fun to work with @kstathou, @Daniel_S_Hain & @RJurowetzki on this one :-) <LINK> * typo in # 2: since 2000",https://arxiv.org/abs/2102.01648,"The private sector is playing an increasingly important role in basic Artificial Intelligence (AI) R&D. This phenomenon, which is reflected in the perception of a brain drain of researchers from academia to industry, is raising concerns about a privatisation of AI research which could constrain its societal benefits. We contribute to the evidence base by quantifying transition flows between industry and academia and studying its drivers and potential consequences. We find a growing net flow of researchers from academia to industry, particularly from elite institutions into technology companies such as Google, Microsoft and Facebook. Our survival regression analysis reveals that researchers working in the field of deep learning as well as those with higher average impact are more likely to transition into industry. A difference-in-differences analysis of the effect of switching into industry on a researcher's influence proxied by citations indicates that an initial increase in impact declines as researchers spend more time in industry. This points at a privatisation of AI knowledge compared to a counterfactual where those high-impact researchers had remained in academia. Our findings highlight the importance of strengthening the public AI research sphere in order to ensure that the future of this powerful technology is not dominated by private interests. ","The Privatization of AI Research(-ers): Causes and Potential