Is this just the thing where evidence is theory-laden? Like, for example, how the evidentiary value of the WHO report on the question of COVID origins depends on how likely one thinks it is that people would effectively cover up a lab leak?
DanielFilan
Karma: 7,735
AXRP Episode 29 - Science of Deep Learning with Vikrant Varma
To be clear, this is an equivalent way of looking at normal prior-ful inference, and doesn’t actually solve any practical problem you might have. I mostly see it as a demonstration of how you can shove everything into stuff that gets expressed as likelihood functions.
Why wouldn’t this construction work over a continuous space?
Bayesian inference without priors
AXRP Episode 28 - Suing Labs for AI Risk with Gabriel Weil
Thanks for finding this! Will link it in the transcript.
oops, thanks for the reminder
Sorry, it will be a bit before the video uploads. I’ll hide the link until then.
Proposal: merge with the separate tag “AI Control”
AXRP Episode 27 - AI Control with Buck Shlegeris and Ryan Greenblatt
Daniel Kahneman has died
How would you rate the Book of Mormon as a book? What’s your favourite part?
I recently heard of the book How to leave the Mormon church by Alyssa Grenfell, which might be good. Based on an interview with the author, it seemed like it was focussed on nuts-and-bolts stuff (e.g. “practically how do you explore alcohol in a way that isn’t dangerous”) and explicitly avoiding a permanent state of having an “ex-mormon” identity, which strikes me as healthy (altho I think some doubt is warranted on how good the advice is, given that the author’s social media presence is primarily focussed on being ex-mormon). The book is associated with a website.
NB: I have a casual interest in high-demand religions, but have never been a part of one (with the arguable exception of the rationality/EA community).
My guess is this won’t work in all cases, because norm enforcement is usually yes/no, and needs to be judged by people with little information. They can’t handle “you can do any 2 of these 5 things, but no more” or “you can do this but only if you implement it really skillfully”. So either everyone is allowed to impose 80 hour weeks, or no one can work 80 hour weeks, and I don’t like either of those options.
I think this might be wrong—for example, my understanding is that there are some kinds of jobs where it’s considered normal for people to work 80-hour weeks, and other kinds where it isn’t. Maybe the issue is that the “kind of job” norms can easily operate on lets you pick out things like “finance” but not “jobs that have already made one costly vulnerability bid”?
Superforecasting the Origins of the Covid-19 Pandemic
Katja responds on substack:
I’m calling people who know where they are (i.e. are not confused) not in simulations, for the sake of argument. But this shouldn’t matter, except for understanding each other.
It sounds like you are saying that ~100x more people live in confused simulations than base reality, but I’m questioning that. The resources to run a brain are about the same whether it’s a ‘simulation’ or a mind in touch with the real world. Why would future civilization spend radically more resources on simulations than on minds in the world? (Or if the non-confused simulations are also relevantly minds in the world, then there are a lot more of them than the confused simulations, so we are back to quite low probability of being mistaken.)
(I plan on continuing the conversation there, not here)
I don’t think this argument quite works? Like, suppose each base civilization simulates 100,000 civilizations. 100 are confused and think they’re base civilizations, and the rest are non-confused and know they’re simulations being run by a base civilization. In this world, most civilizations are right about their status, but most civilizations who think they’re base civilizations are wrong.
In my post, I didn’t require the distribution over meanings of words to be uniform. It could be any distribution you wanted—it just resulted in the prior ratio of “which utterance is true” being 1:1.