Even if you manage to truly forget about the disease, there must exist a mind "somewhere in the universe" that is exactly the same as yours except without knowledge of the disease. This seems quite unlikely to me, because you having the disease has interacted causally with the rest of your mind a lot by when you decide to erase its memory. What you'd really need to do is to undo all the consequences of these interactions, which seems a lot harder to do. You'd really need to transform your mind into another one that you somehow know is present "somewhere in the multiverse" which seems also really hard to know.
I deliberately left out a key qualification in that (slightly edited) statement, because I couldn't explain it until today.
I might be missing something crucial because I don't understand why this addition is necessary. Why do we have to specify "simple" boundaries on top of saying that we have to draw them around concentrations of unusually high probability density? Like, aren't probability densities in Thingspace already naturally shaped in such a way that if you draw a boundary around them, it's automatically simple? I don't see how you run the risk of drawing weird, noncontiguous boundaries if you just follow the probability densities.
One way in which "spending a whole lot of time working with a system / idea / domain, and getting to know it and understand it and manipulate it better and better over the course of time" could be solved automatically is just by having a truly huge context window. Example of an experiment: teach a particular branch of math to an LLM that has never seen that branch of math.
Maybe humans have just the equivalent of a sort of huge content window spanning selected stuff from their entire lifetimes, and so this kind of learning is possible for them.
You mention eight cities here. Do they count for the bet?
Waluigi effect also seems bad for s-risk. "Optimize for pleasure, ..." -> "Optimize for suffering, ...".
An optimistic way to frame inner alignment is that gradient descent already hits a very narrow target in goal-space, and we just need one last push.
A pessimistic way to frame inner misalignment is that gradient descent already hits a very narrow target in goal-space, and therefore S-risk could be large.
We should implement Paul Christiano's debate game with alignment researchers instead of ML systems
This community has developed a bunch of good tools for helping resolve disagreements, such as double cruxing. It's a waste that they haven't been systematically deployed for the MIRI conversations. Those conversations could have ended up being more productive and we could've walked away with a succint and precise understanding about where the disagreements are and why.
From a purely utilitarian standpoint, I'm inclined to think that the cost of delaying is dwarfed by the number of future lives saved by getting a better outcome, assuming that delaying does increase the chance of a better future.
That said, after we know there's "no chance" of extinction risk, I don't think delaying would likely yield better future outcomes. On the contrary, I suspect getting the coordination necessary to delay means it's likely that we're giving up freedoms in a way that may reduce the value of the median future and increase the chance of stuff like totalitarian lock-in, which decreases the value of the average future overall.
I think you're correct that there's also to balance the "other existential risks exist" consideration in the calculation, although I don't expect it to be clear-cut.