update please!
rai
Karma: 76
This is cool. I asked it a question in a field I’m not knowledgable about at all but my roommate is. I think having the structure there to make it maximally easy to elicit his expertise is super valuable.
Wow, somewhere along the way I got the impression that MIRI is a quiet org that occasionally speaks loudly… so much so that I never thought to check if they put out a newsletter.
It was gluten.
This is shockingly similar to what I’m going through. And the fries that fucked me up the other night are indeed fried in canola oil. I’m cautiously optimistic but I know how complicated these things can be -_-. Will report back!
Ok so modulo @GeneSmith’s comments about this gene potentially being red herring, if there were a genetic basis to this, we’d expect to see prevalance of this trait increase due to genetic drift and removal of the selection pressure right?
I don’t have a good sense of how quickly we’d expect to be able to detect those population level differences with the rate at which calories have become more available and the rate of growth of the variant.
Figure 7 in @guzey’s post shows that it’s directionally been increasing but a lot has changed about our environments and lifestyles so it doesn’t rule out that maybe people are in-fact less pre-disposed to sleep but something else is increasing it.
https://www.lesswrong.com/posts/HvcZmKS43SLCbJvRb/theses-on-sleep
Since the matter at hand is a genetic basis that could flip a switch to needing “a lot less sleep” we should still expect to see the variance in sleep duration increase in the population even if the mean is increasing. I have no idea what the data says about that though.
That matrix goes a long way in showing that there isn’t much correlation between diseases in the natural distribution. What is the reason to believe those correlations will remain low when you are making edits resulting in an extremely unlikely genome?
Kernel of something that might inspire someone else who knows more than I.
Assuming weights that have “grokked” a task are more interpretable, is there use in modifying loss functions to increase grokking likelihood? Perhaps by making it path dependent on the updates of the weights themselves?
I think it’d be good to add an endnote mentioning that while saving for your FIRE number is relatively straightforward, withdrawing it can be much more complicated. I know my intuitions didn’t serve me at all when thinking about that phase. https://earlyretirementnow.com/2018/06/27/ten-things-the-makers-of-the-4-rule-dont-want-you-to-know/
Would love that! You can send them to the email above, comment here, whatever works for you.