“(although itiots might still fall for the “I’m an idiot like you” persona such as Donald Trump, Tucker Carlson, and particularly Alex Jones).”
This line is too current-culture-war for LessWrong. I began to argue with it in this comment, before deleting what I wrote, and limiting myself to this.
It changed to be much more swipe-focused. It’s been 5 years since I used it, but even in 2018, I remember being surprised at how much it had changed. Apparently now even open messaging is gone, and you need to have someone Like you before you can message them, though I haven’t actually checked this.
Yes, agree—I’ve looked into non-identical distributions in previous posts, and found that identicality isn’t important, but I haven’t looked at non-independence at all. I agree dependent chains, like the books example, is an open question!
Love this! Definitely belongs on LessWrong. High-quality sci-fi, that relates to social dynamics? Very relevant! I’ve been away from the site for a while, tiring of the content, but am glad I scrolled and saw this today.
Enjoyed this! Very well written. The two arrow graphs, where the second has everything squished down to the bottom, are especially charming
I don’t think the problem is this big if you’re trying to control one specific model. Given an RLHF’d model, equipped with a specific system prompt (e.g. helpless harmless assistant), you have either one or a small number of luigis, and therefore around the same amount of waluigis—right?
Good question!
Hmm! I’m not sure about this. The patient in the linked paper received hemodialysis (which, I think, manually takes the methanol out) before his body could get around to metabolizing it into formaldehyde and formic acid. For someone who doesn’t receive hemodialysis, I think the methanol would still have to be metabolized at some point, even if when that happens is much delayed? In which case the same toxic effects of formaldehyde and formic acid would hit, just much later.
Thanks!
What form/brand/dose do you take the ketone esters in?
I think the poker example is OK, and paragraphs like
“The second decision point was when the flop was dealt and you faced a bet. This time you decided to fold. Maybe that wasn’t the best play though. Maybe you should have called. Maybe you should have raised. Again, the goal of hand review is to figure this out.”
made sense to me. But the terminology in the dialogue was very tough: button, Rainbow, LAGgy, bdfs, AX, nut flush, nitty—I understood none of these. (I’ve played poker now and then, but never studied it). So keeping the example but translating it a bit further to more widely-used language (if possible) might be good.
Very interesting! I work in health insurance and we try to encourage vaginal delivery and discourage C-sections; the other side you present here is a surprise. Good stuff.
Very very good. The full power of science fiction—taking the concept of the redaction machines and finding this many interesting consequences of them, and fitting them into the story—really good
You don’t need to call tails to explore whether tails is possible, though—the information gain of a coin flip is the same whether you call heads or tails
I’m not the asker, but I think I get where they’re coming from. For a long time, linear and logistic regression were the king & queen of modeling. Then the introduction of non-linear functions like random forest and gradient boosters made us far more able to fit difficult data. So the original question has me wondering if there’s a similar possible gain in going from linearity to non-linearity in interpretability algorithms.
I agree with the encouragement to look harder for a sooner TMJ appointment. ADHD testing has similar waits now—looking in May, I was told everyone was booked up till September. But I lucked out, and the first testing doctor I talked to had just had some people cancel appointments, and nobody on his waitlist was responding, so I ended up seeing him a week later, in June, instead of in September. So there are opportunities for luck like this around. And this is without me looking out of state.
I like the trigger point idea. OP should note too that there are injection treatments for trigger points: https://www.webmd.com/pain-management/guide/trigger-point-injection
I just quit caffeine a month ago after years of daily dependence on it, and I feel better than I did on it. I now limit myself to 100mg a week. The dependence had a consistent moderate negative affect on my life, so I’d recommend people be very careful to avoid dependence.
Sure—there are plenty of cases where a pair of interactions isn’t interesting. In the image net context, probably you’ll care more about screening-off behavior at more abstract levels.
For example, maybe you find that, in your trained network, a hidden representation that seems to correspond to “trunk” isn’t very predictive of the class “tree”. And that one that looks like “leaves” is predictive of “tree”. It’d be useful to know if the reason “trunk” isn’t predictive is that “leaves” screens it off. (This could happen if all the tree trunks in your training images come with leaves in the frame).
Of course, the causality parts of the above analysis don’t address the “how should you assign labels in the first place” problem that the post is most focused on! I’m just saying both the ML parts and the causality parts work well in concert, and are not opposing methods.
Thank you!