How to Reduce Variance in Machine Learning

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A lot of stuff in machine learning is basically just using this trick over and over again. I think this becomes clearer at the extreme. Like imagine for some weird reason everyone in New Mexico had exactly the same income as everyone else in New Mexico. Then all you would need to do is sample one person and you'd completely know everything about the state, right? So that kind of shows why the more variance there is, the more people you want to draw from that state.

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How hard is it to arrive at true beliefs about the world? How can you find enjoyment in being wrong? When presenting claims that will be scrutinized by others, is it better to hedge and pad the claims in lots of caveats and uncertainty, or to strive for a tone that matches (or perhaps even exaggerates) the intensity with which you hold your beliefs? Why should you maybe focus on drilling small skills when learning a new skill set? What counts as a "simple" question? How can you tell when you actually understand something and when you don't? What is "cargo culting"? Which features of AI are likely in the future to become existential threats? What are the hardest parts of AI research? What skills will we probably really wish we had on the eve of deploying superintelligent AIs?

Buck Shlegeris is the CTO of Redwood Research, an independent AI alignment research organization. He currently leads their interpretability research. He previously worked on research and outreach at the Machine Intelligence Research Institute. His website is shlegeris.com.

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