On the details of surviving the environment of academia and adjacent institutions.
Post COVID-19 seminars
Yes, research seminars are now even more global. See researchseminars.org.
Alexandre Afonso advises “How academia resembles a drug gang”:
If you are mobile, strategic and concerned with employment conditions, you might want to exploit these differences and avoid the outsider boxes at different stages of your career. This would mean avoiding the UK for your PhD and avoiding Germany after your PhD.
Maxwell Tabarrok, Overfitting In Academia
But our selection criteria do not match with what we want academics to do. The out of sample prediction ability of academics is worse than random and laymen. They know lots about research they’ve already been tested on, but are rarely better than random at guessing the results of experiments in their field that they haven’t seen before. Citations and academic rank do not increase the ability of economists to predict the outcome of economic experiments or political events.
A current meta-question. One starting point: John Schulman’s Opinionated Guide to ML Research, which discusses stuff like this:
Idea-Driven vs Goal-Driven Research
Roughly speaking, there are two different ways that you might go about deciding what to work on next.
- Idea-driven. Follow some sector of the literature. As you read a paper showing how to do X, you have an idea of how to do X even better. Then you embark on a project to test your idea.
- Goal-driven. Develop a vision of some new AI capabilities you’d like to achieve, and solve problems that bring you closer to that goal. (Below, I give a couple case studies from my own research, including the goal of using reinforcement learning for 3D humanoid locomotion.) In your experimentation, you test a variety of existing methods from the literature, and then you develop your own methods that improve on them.
John links to some other articles on this theme. Richard Hamming, You and your research based off his work at Bell labs, voices some useful ideas on social engineering and genius and effort.
On this matter of drive Edison says, “Genius is 99% perspiration and 1% inspiration.” He may have been exaggerating, but the idea is that solid work, steadily applied, gets you surprisingly far. The steady application of effort with a little bit more work, intelligently applied is what does it. That’s the trouble; drive, misapplied, doesn’t get you anywhere. I’ve often wondered why so many of my good friends at Bell Labs who worked as hard or harder than I did, didn’t have so much to show for it.
The notion of stereotype threat has gotten some press lately; I want to also bring people’s attention to the slightly less-discussed notion of solo-status.
Michael Nielson, Principles of Effective Research:
People who concentrate mostly on self-development usually make early exits from their research careers. They may be brilliant and knowledgeable, but they fail to realize their responsibility to make a contribution to the wider community. The academic system usually ensures that this failure is recognized, and they consequently have great difficulty getting jobs. Although this is an important problem, in this essay I will focus mostly on the converse problem, the problem of focusing too much on creative research, to the exclusion of self-development.
See research funding.
See strategic ignorance.