This other Ryan Greenblatt is my old account[1]. Here is my LW account.
Account lost to the mists of time and expired university email addresses.
I think people wouldn't normally consider it Pascalian to enter a postive total returns lottery with a 1 / 20,000 (50 / million) chance of winning?
And people don't consider it to be Pascalian to vote, to fight in a war, or to advocate for difficult to pass policy that might reduce the chance of nuclear war?
Maybe you have a different-than-typical perspective on what it means for something to be Pascalian?
absence of evidence of good arguments against it is evidence of the absence of said arguments. (tl;dr - AI Safety people, engage with 1a3orn more!)
There are many (edit: 2) comments responding and offering to talk. 1a3orn doesn't appear to have replied to any of these comments. (To be clear, I'm not saying they're under any obligation here, just that there isn't a absence of attempted engagement and thus you shouldn't update in the direction you seem to be updating here.)
The limited duty exemption has been removed from the bill which probably makes compliance notably more expensive while not improving safety. (As far as I can tell.)
This seems unfortunate.
I think you should still be able to proceed in a somewhat reasonable way by making a safety case on the basis of insufficient capability, but there are still additional costs associated with not getting an exemption.
Further, you can't just claim an exemption prior to starting training if you are behind the frontier which will substantially increase the costs on some actors.
This makes me more uncertain about whether the bill is good, though I think it will probably still be net positive and basically reasonable on the object level. (Though we'll see about futher amendments, enforcement, and the response from society...)
(LW x-post)
I agree that these models assume something like "large discontinuous algorithmic breakthroughs aren't needed to reach AGI".
(But incremental advances which are ultimately quite large in aggregate and which broadly follow long running trends are consistent.)
However, I interpreted "current paradigm + scale" in the original post as "the current paradigm of scaling up LLMs and semi-supervised pretraining". (E.g., not accounting for totally new RL schemes or wildly different architectures trained with different learning algorithms which I think are accounted for in this model.)
Both AI doomers and accelerationists will come out looking silly, but will both argue that we are only an algorithmic improvement away from godlike AGI.
A common view is a median around 2035-2050 with substantial (e.g. 25%) mass in the next 6 years or so.
This view is consistent with both thinking:
(This is pretty similar to my view.)
I don't think many people think "we are only an algorithmic improvement away from godlike AGI". In fact, I can't think of anyone who thinks this. Some people think that 1 substantial algorithmic advance + continued scaling/general algorithmic improvement, but the continuation of other improvements is key.
I think there are a bunch of meta effects from working in an object level job: