Thanks!
Maxwell Peterson
Karma: 864
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.
This post does a sort of head-to-head comparison of causal models and deep nets. But I view the relationship between them differently—they’re better together! The causal framework gives us the notion of “screening off”, which is missing from the ML/deep learning framework. Screening-off turns out to be useful in analyzing feature importance.
A workflow that 1) uses a complex modern gradient booster or deep net to fit the data, then 2) uses causal math to interpret the features—which are most important, which screen off which—is really nice. [This workflow requires fitting multiple models, on different sets of variables, so it’s not just fit a single model in step 1), analyze it in step 2), done].
Causal math lacks the ability to auto-fit complex functions, and ML-without-causality lacks the ability to measure things like “which variables screen off which”. Causality tools, paired with modern feature-importance measures like SHAP values, help us interpret black-box models.
From my personal experience, I would have them take up one or two competitive arts.
Timing yourself to improve your personal best at, say, running, does not count. Running on a track against, or in a longer race against, a small handful of people that you can potentially beat does count, although I would lean toward recommending something where you have to deal with the counter-moves of your opponent. Boxing or kickboxing training that includes sparring against others counts; doing boxing training at a gym that does not do sparring doesn’t count. Playing chess or Go counts. Basketball, hockey, soccer, etc, all count. Playing online competitive video games technically counts under this definition, but I’m excluding it; those mostly make me feel bad.
Having an opponent that will challenge you with counter-moves, and do their best to get one over on you, but who you can beat if you train and try hard enough, has no substitute. Winning against someone who has put everything into the fight gives confidence that you can apply all over the place. Plus it feels great.
My experience: I’ve spent the past two years running and lifting. These mean I look great physically, and am healthy, and get the exercise endorphins and stuff. But they didn’t meet the competitive need! I’ve recently gotten back into boxing at a sparring gym. The competitive aspect of being in the ring, trying to best the other guy, is something I’ve really been missing. It also directs my training at a real concrete purpose, instead of the colder “increase the weight / increase running speed” metric-tracking approach to those forms of exercise.
I also play Go, and it used to serve this purpose well in my life.
(This competitiveness stuff might be more important for men than it is for women—I’m not sure. I’d definitely give this advice to a man, and I’d give it as a ‘maybe’ to a woman. Of course, women can get a lot of value from competition; I’m just not sure if the lack of it would gnaw at them the way it was gnawing at me.)
I have pretty bad energy-level problems and have been looking for more things to try to fix them. I’d always thought quitting caffeine would make it so I could attain my current energy levels without caffeine; it never occurred to me that energy levels after quitting could be higher. So this is very interesting. Thanks for sharing.
That’s great! Happy to hear it—thanks for reporting back, especially in such detail.
Whoops. That’s a big mistake on my part. Appreciate the correction.
Thanks! I’ll give it a read
I am having trouble concording “a low signal:noise ratio biases the effects, often towards zero” with the result in the final section, where you say
“the genetic correlation has ended up much bigger than the environmental correlation. This happened due to the measurement error; if it was not for the measurement error, they would be of similar magnitudes.”
In the second statement, the noise (measurement error) was high, so there’s a low signal:noise ratio—is that right? If so, doesn’t the first statement suggest the genetic correlation should be biased towards zero, instead of being inflated?
Thanks!
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.