Related to: Making Beliefs Pay Rent, The Feeling of Idea Scarcity, Micro-Feedback Loops and Learning, The Three Stages of Rigor, Research as a Stochastic Decision Process, Chapter 22 of HPMOR.^[1]

TL;DR: I think new machine learning researchers often make one of two kinds of mistakes: not making enough contact with reality, and being too reluctant to form gears-level models of ML phenomena. Stereotypically, LW/AF researchers tend to make the former mistake, while academic and industry researchers tend to make the latter kind. In this post, I discuss what I mean by “touching reality” and why it’s important, speculate a bit on why people don’t do this, and then give concrete suggestions.

Epistemic status: I'm pretty frustrated with how slow I write, so this is an experiment in writing fast as opposed to carefully. That being said, this is ~the prevailing wisdom amongst many ML practitioners and academics, and similar ideas have been previously discussed in the LessWrong/Alignment Forum communities, so I'm pretty confident that it's directionally correct. I also believe (less confidently) that this is good advice for most kinds of research or maybe even for life in general.

Acknowledgments: Thanks to Adrià Garriga-Alonso for feedback on a draft of this post and Justis Mills for copyediting help.

Introduction:

Broadly speaking, I think new researchers in machine learning tend to make two kinds of mistakes:

Not making contact with reality. This is the failure mode where a new researcher reads a few papers that their friends are excited about, forms an ambitious hypothesis about how to solve a big problem in machine learning, and then spends months drafting a detailed plan. Unfortunately, after months of effort, our new researcher realizes that the components they were planning to use do not work nearly as well as expected, and as a result they’ve wasted months of effort on a project that wasn’t going to succeed.
Not being willing to make gears-level models. This is the failure mode where a new researcher decides to become agnostic to why anything happens, and believes empirical results and only empirical results even when said results don’t “make sense” on reflection. The issue here is that they tend to be stuck implementing an inefficient variant of grad student descent, only able to make small amo

...

Research Taste