Spencer: I think that when you're learning subjects something that you should be really keeping your eye out for is the simplest question in the field. And a lot of the time people try to learn physics or something and their approach is to try and as quickly as possible be able to answer hard questions about complicated subjects, he says. "It feels to me now that it's a lot more productive to really seek out questions that are as simple sounding as possible while still being really hard to answer"
Read the full transcript here.
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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