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.

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]

Epistemic status: Written quickly in ~3 hours as opposed to carefully, but I'm pretty sure it's directionally correct. ^[2]

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

Introduction: two common mistakes in ML research

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 amounts of incremental progress via approximate blind search, and end up doing whatever is popular at the moment.

That’s not to say that these mistakes are mutually exclusive: embarrassingly, I think I’ve managed to fail in both ways in the past.

That being said, this post is about the first failure mode, which I think ...

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