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No. Reaching the top of the knowledge pyramid in your subfield over time has remained constant (typically in your early to mid 20s, as you complete your PhD).
by GuiA 7y ago
No. Reaching the top of the knowledge pyramid in your subfield over time has remained constant (typically in your early to mid 20s, as you complete your PhD).
We have more sub fields, but that’s not particularly a problem - once you’re an effective researcher, jumping branches - and even trunks - is far from impossible. A few months surrounded with talented people and dedication is typically enough - eg my grad school advisor, a CS HCI professor, got into genomics when meeting his wife and went on to publish, as first author, genomics papers in nature.
See also the recent rise of machine learning - many of the top contributors to that field were not doing machine learning research 10 years ago.
Similarly, motivated high schoolers now learn things that you needed a PhD to tackle 100 years ago.
Knowledge is weird and non linear, and as humans we have many super powers: we stand on the shoulders of our predecessors, we build better ways to preserve knowledge, to share it, better ways to teach, better ways to test our theories, etc.
It took thousands and thousands of years of man hours of work to get us to basic calculus, something that a motivated high schooler can get very comfortable with in a few weeks at most. The most educated Roman citizen would take a bit of time to compute something as straightforward as (8238*2949), which is now something you expect elementary school students to compute in minutes at most.
If we could jump in time 100 years in the future and see what 18 year olds know and can do then, we’d probably be blown away.
- anthk 7y ago>See also the recent rise of machine learning - many of the top contributors to that field were not doing machine learning research 10 years ago. Machine learning is older than you think.
- pure-awesome 7y agoThe previous commenter was not saying that machine learning didn't exist 10 years ago. They were saying that "many of the TOP CONTRIBUTORS TO THAT FIELD were not doing machine learning research 10 years ago." There are also people who were doing machine learning 30 years ago and are still doing so. But there are people who were doing, say, fluid dynamics simulations 10 years ago who self-taught machine learning, and are now making significant contributions to machine learning, by transferring their knowledge of programming, optimization, calculus, etc.