I work at the Alignment Research Center (ARC). I write a blog on stuff I'm interested in (such as math, philosophy, puzzles, statistics, and elections): https://ericneyman.wordpress.com/
Note that "toss-up" races are races where the general election (i.e. between the Democratic and Republican candidates) is a toss-up. By guess is that in such races, an extra $2,500 spent on TV ads is necessary to net a candidate one extra vote. This is because the pool of persuadable voters is much smaller: most voters will vote for the Democrat no matter what or vote for the Republican no matter what. By contrast, spending goes a lot further in primary elections.
There’s a cottage industry that thrives off of sneering, gawking, and maligning the AI safety community. This isn't new, but it's probably going to become more intense and pointed now that there are two giant super PACs that (allegedly) see safety as a barrier to [innovation/profit, depending on your level of cynicism]. Brace for some nasty, uncharitable articles.
One such article came out yesterday; I think it's a fairly representative example of the genre.
My guess for Bores was:
I think that similarly for Wiener, I don't think it makes a huge difference (maybe 15% or so?) whether you donate today vs. late December. Today vs. tomorrow doesn't make much difference; think of it as a gradual decay over these couple months. But I think it's much better (1.3x?) to donate in late December than early January, because having an impressive Q4 2025 fundraising number will be helpful for consolidating support. (Because Wiener is more of a known quantity to voters and party elites than Bores is, this is a less important factor for Wiener than it is for Bores.)
[Link to donate; or consider a bank transfer option to avoid fees, see below.]
Nancy Pelosi has just announced that she is retiring. Previously I wrote up a case for donating to Scott Wiener, an AI safety champion in the California legislature who is running for her seat, in which I estimated a 60% chance that Pelosi would retire. While I recommended donating on the day that he announced his campaign launch, I noted that donations would look much better ex post in worlds where Pelosi retires, and that my recommendation to donate on launch day was sensitive to my assessment of the probability that she would retire.
I know some people who read my post and decided (quite reasonably) to wait to see whether Pelosi retired. If that was you, consider donating today!
You can donate through ActBlue here (please use this link rather than going directly to his website, because the URL lets his team know that these are donations from people who care about AI safety).
Note that ActBlue charges a 4% fee. I think that's not a huge deal; however, if you want to make a large contribution and are already comfortable making bank transfers, shoot be a DM and I'll give you instructions for making the bank transfer!
Oh yup, thanks, this does a good job of illustrating my point. I hadn't seen this graphic!
This would require a longer post, but roughly speaking, I'd want the people making the most important decisions about how advanced AI is used once it's built to be smart, sane, and selfless. (Huh, that was some convenient alliteration.)
And so I'm pretty keen on interventions that make it more likely that smart, sane, and selfless people are in a position to make the most important decisions. This includes things like:
This deserves a full post, but for now a quick take: in my opinion, P(no AI takeover) = 75%, P(future goes extremely well | no AI takeover) = 20%, and most of the value of the future is in worlds where it goes extremely well (and comparatively little value comes from locking in a world that's good-but-not-great).
Under this view, an intervention is good insofar as it affects P(no AI takeover) * P(things go really well | no AI takeover). Suppose that a given intervention can change P(no AI takeover) and/or P(future goes extremely well | no AI takeover). Then the overall effect of the intervention is proportional to ΔP(no AI takeover) * P(things go really well | no AI takeover) + P(no AI takeover) * ΔP(things go really well | no AI takeover).
Plugging in my numbers, this gives us 0.2 * ΔP(no AI takeover) + 0.75 * ΔP(things go really well | no AI takeover).
And yet, I think that very little AI safety work focuses on affecting P(things go really well | no AI takeover). Probably Forethought is doing the best work in this space.
(And I don't think it's a tractability issue: I think affecting P(things go really well | no AI takeover) is pretty tractable!)
(Of course, if you think P(AI takeover) is 90%, that would probably be a crux.)
If you donate through the link on this post, he will know! The /sw_ai at the end is ours -- that's what lets him know.
(The post is now edited to say this, but I should have said it earlier, sorry!)
Just so people are aware, I added the following note to the cost-effectiveness analysis. I intend to return to it later:
[Edit: the current cost-effectiveness analysis fails to account for the opportunity cost of Scott Wiener remaining in the State Senate for another two years -- 2027-2028 -- until he needs to leave due to term limits. I think this is an important consideration. My current all-things-considered belief is that this consideration is almost canceled out by the other neglected effect of strengthening ties between AI alignment advocates and Wiener in worlds where he loses and remains in the State Senate for those two years. However, this analysis is subject to change.]
Thanks for the suggestion!
For what it's worth, we believe that a mechanistic estimator can beat all sampling-based methods, no matter how sophisticated they are. The philosophical reason for this is that sophisticated sampling-based methods outperform simple Monte Carlo by exploiting structure in the function whose average value they're estimating -- but a mechanistic estimator can exploit that same structure, too.
In fact, I think it almost follows from the MSP that we can beat any sampling-based method. To see this, suppose you have some sophisticated estimator Est(Mθ,r), which is given a neural net Mθ and some random coin flips r as input, and produces a sophisticated, unbiased, low-variance estimate of E[Mθ] using r. Now, define the architecture M′ as: M′θ(x)=Est(Mθ,x). The MSP says that we need to be able to estimate the average output of M′θ (which is the same as the average output of Mθ) with squared error less than or equal to the variance of M′θ, in the time that it takes to run M′θ. (We're taking ε=1 here.) In other words, given any sophisticated sampling algorithm for estimating the average output of Mθ, there needs to be a corresponding mechanistic estimator that gets lower (or equal) error in the same amount of time.
(I think this argument isn't perfectly tight, because it'll probably run into the same uniformity issues that I discussed in the "getting rid of ε" appendix, which is why I said "almost follows" rather than "follows".)