The Importance of Using Futurism in AI Alignment Research

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I think there is no real way around the fact that you have to engage in futurism in order to decide which alignment research is going to be useful. Guessing how the future of technology is going to develop is how we decided we should work on AI alignment at all. There's something pretty satisfying about the knowledge that we're just trying to develop some technology that is almost surely developable rather than trying to solve a problem that might be impossible.

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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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