I think I want to be a scientist. Actually, no. I want to be a scientist, and say that confidently. I owe it all to Professor Jieun Lee who instructed “Anthropology of Science and Technology” at Yonsei University. (Not to be confused with the Korean pop-star IU, who has the same given name.)
It must have been when we were reading Beamtimes and Lifetimes: The World of High Energy Physicists by Sharon Traweek. In class, we examined some of the cultural differences between how physicists in the United States and in Japan operate differently. Traweek also does a great job of introducing the topic of the process of becoming a scientist.
This class made me sit down and seriously think: what is the process for becoming a scientist in the modern age? In 2023? Of course, the same barriers exist as before: social and economic.
As of right now, the hot topic is machine learning and artificial intelligence. It seems to be the future for computer science to many in the public eye, especially with the scary success of humungous deep neural networks funded by some of the biggest corporate global entities:
- Alphabet with Google and their various products that all make use of machine intelligence.
- Microsoft and Bing and all those other experiments.
If you want to participate in these research efforts, you must navigate the structure of that particular corporate institution. That can mean a lot of things.
If you want to be a scientist, another alternative right now is maybe schooling. If academic institutions see that you are a “worthy” candidate (by whatever metrics they choose), then they might fund you.
But more and more nowadays I see cross contamination between these two institutions. There’s nothing wrong with that of course, at least I don’t think so? Just an observation.