An Opinionated Guide to ML Research

This post is fantastic. It talks about a too-infrequently-discussed topic: problem taste.

Your ability to choose the right problems to work on is even more important than your raw technical skill. This taste in problems is something you’ll develop over time by watching which ideas prosper and which ones are forgotten.

and more:

Sometimes, people who are both exceptionally smart and hard-working fail to do great research. In my view, the main reason for this failure is that they work on unimportant problems. When you embark on a research project, you should ask yourself: how large is the potential upside? Will this be a 10% improvement or a 10X improvement? I often see researchers take on projects that seem sensible but could only possibly yield a small improvement to some metric.



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