Quick Takes

This post provides a valuable reframing of a common question in futurology: "here's an effect I'm interested in -- what sorts of things could cause it?" That style of reasoning ends by postulating causes.  But causes have a life of their own: they don't just cause the one effect you're interested in, through the one causal pathway you were thinking about.  They do all kinds of things. In the case of AI and compute, it's common to ask * Here's a hypothetical AI technology.  How much compute would it require? But once we have an answer to this question, we can always ask * Here's how much compute you have.  What kind of AI could you build with it? If you've asked the first question, you ought to ask the second one, too. The first question includes a hidden assumption: that the imagined technology is a reasonable use of the resources it would take to build.  This isn't always true: given those resources, there may be easier ways to accomplish the same thing, or better versions of that thing that are equally feasible.  These facts are much easier to see when you fix a given resource level, and ask yourself what kinds of things you could do with it. This high-level point seems like an important contribution to the AI forecasting conversation.  The impetus to ask "what does future compute enable?" rather than "how much compute might TAI require?" influenced my own view of Bio Anchors, an influence that's visible in the contrarian summary at the start of this post. ---------------------------------------- I find the specific examples much less convincing than the higher-level point. For the most part, the examples don't demonstrate that you could accomplish any particular outcome applying more compute.  Instead, they simply restate the idea that more compute is being used. They describe inputs, not outcomes.  The reader is expected to supply the missing inference: "wow, I guess if we put those big numbers in, we'd probably get magical results out."  But this

This post provides a valuable reframing of a common question in futurology: "here's an effect I'm interested in -- what sorts of things could cause it?" That style of reasoning ends by postulating causes.  But causes have a life of their own: they don't just cause the one effect you're interested in, through the one causal pathway you were thinking about.  They do all kinds of things. In the case of AI and compute, it's common to ask * Here's a hypothetical AI technology.  How much compute would it require? But once we have an answer to this question, we can always ask * Here's how much compute you have.  What kind of AI could you build with it? If you've asked the first question, you ought to ask the second one, too. The first question includes a hidden assumption: that the imagined technology is a reasonable use of the resources it would take to build.  This isn't always true: given those resources, there may be easier ways to accomplish the same thing, or better versions of that thing that are equally feasible.  These facts are much easier to see when you fix a given resource level, and ask yourself what kinds of things you could do with it. This high-level point seems like an important contribution to the AI forecasting conversation.  The impetus to ask "what does future compute enable?" rather than "how much compute might TAI require?" influenced my own view of Bio Anchors, an influence that's visible in the contrarian summary at the start of this post. ---------------------------------------- I find the specific examples much less convincing than the higher-level point. For the most part, the examples don't demonstrate that you could accomplish any particular outcome applying more compute.  Instead, they simply restate the idea that more compute is being used. They describe inputs, not outcomes.  The reader is expected to supply the missing inference: "wow, I guess if we put those big numbers in, we'd probably get magical results out."  But this