The Unifying Themes of Machine Learning

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I think it's some combination of machine learning has figured out like the deep learning practitioners have figured out a bit more about the fundamental theory of what they're doing. And also, I think I personally have just learned more about this. What do you think some of those unifying themes are that maybe people didn't know, you know, five years ago? Yeah. Some things would feel like key themes of machine learning that didn't feel as obvious back in the day. One example is this is like variance reduction or techniques where you trade bias for variance.

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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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