Lessons from My First Two Years of AI Research
web.mit.edu
web.mit.edu
I’ve been pushing for more ensembles and multi-label classifiers because I want to orchestrate the pieces with logic to fill gaps until stats methods outperform (given enough new data in nice annotation-friendly structures).
The “math” types seem to feel like there’s a neural net solution to every problem or that we can expand the multi class model to cover more domains despite being mostly saturated with high accuracy. Sounds awesome but I’m impatient!
The “biology” folks seem to be most attracted to neatness or parsimony. We had some great bikeshedding sessions around adjacency list vs materialized path (ltree) in postgres for label hierarchies. Abstractions can be useful too!
Any tips on being a better experimentalist and pushing academic colleagues towards better solutions in the field?
I give the same advice to new PhDs. I wrote mini-overviews of stuff I was researching, doing and thinking for the first two years. Write it with LaTeX too. By the time you have to write your actual thesis you can merge papers, overviews, ideas, and will be done in a few months (instead of years for the people who didn't write along the way).
But you're right, there's an idea I can go write up right now.
"What are the important problems of your field?"
And after a week or so, "What important problems are you working on?"
And after some more time I came in one day and said, "If what you are doing is not important, and if you don't think it is going to lead to something important, why are you at Bell Labs working on it?"
Full transcript @ http://www.cs.virginia.edu/~robins/YouAndYourResearch.html
I feel like most people don't know what are the great problems in their field.
As a student, you might know that a problem is important, but you might not yet have the tools to solve it; you might not even be aware of which tools can solve it.
So true