You end up with people who are working in a field and people who study the field of inquiry academically. Sometimes and especially within the machine learning space you end up with people who are actively working as a practitioner. Those very same practitioners of the craft of machine learning are publishing academic articles at a rate never before seen within any field of study at large within the academy. The best way to describe that effort would be to call them the pracademics of the machine learning space. Intellectually it’s probably good to have people write about things who really understand how they are occurring. You certainly get solid firsthand accounts of what people are creating.
Research is the process of digging into things, investigating, studying, or maybe just seeking to understand the things better. Original research is often brought out to describe novel inquiry. Some of these pracademics are pushing things into a new frontier for machine learning. The DALL-E-2 AI system was introduced and changed the way people think about how an AI system would create realistic images and art based on a prompt [1].
During the course of working on this Substack post, you can imagine I was surprised to find a webpage called “AI Brain Drain” [2]. That site opens up with a clear, “Welcome to the AI Brain Drain Index.” They pretty much are tracking the number of AI faculty that have left academic areas to go work within industry. You can see charts and other figures that sort of run from 2004 to apparently 2018. The researchers must have stopped making charts in the last few years, but the ones they made are pretty nice. All of that research seems to have yielded a paper you can read [3]. According to the SSRN website where I downloaded the paper, 928 downloads have occurred on this one.
Gofman, M., & Zhao, J. (2022, July 31). Artificial intelligence, education, and entrepreneurship. Journal of Finance [Forthcoming]. https://doi.org/10.2139/ssrn.3449440 or at SSRN: https://ssrn.com/abstract=3449440
An article from Inside Higher Education called “AI Academy Under Siege” from author Oren Etzioni took a look at AI experts leaving institutions of higher education and what might be some potential solutions to that situation [4]. You could find more from the work of Professor Michael Gofman [5]. You could check out an editorial published in Springer by Lars Kunze called, “Can We Stop the Academic AI Brain Drain?” [6]. Outside of those sources, one of the articles that I really liked was from Ben Dickson titled, “What Is the AI Brain Drain?” [7]. You could pivot to an article that I enjoyed less called, “Brain Drain of AI Researchers: Academia vs Industry” [8].
Let’s close this one out with an interesting look at, “AI Brain Drain to Google and Pals Threatens Public Sector’s Ability to Moderate Machine-Learning Bias” [9]. That article links back out to a paper that was interesting.
Jurowetzki, R., Hain, D. S., Mateos-Garcia, J., & Stathoulopoulos, K. (2021). The privatization of AI research(-ers): Causes and potential consequences. https://regmedia.co.uk/2021/02/04/drain.pdf
Top 5 Tweets of the week:
Footnotes:
[1] https://openai.com/dall-e-2/
[2] http://www.aibraindrain.org/
[3] https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3449440
[5] https://simon.rochester.edu/blog/deans-corner/brain-drain
[6] https://link.springer.com/article/10.1007/s13218-019-00577-2
[7] https://bdtechtalks.com/2019/09/26/artificial-intelligence-brain-drain/
[8] https://medium.com/codex/brain-drain-of-ai-researchers-academia-vs-industry-8e385e8fd517
[9] https://www.theregister.com/2021/02/04/ai_brain_drain/
What’s next for The Lindahl Letter?
Week 103: Rethinking the future of ML
Week 104: That 2nd year of posting recap
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