minusdash
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minusdash has not written any posts yet.
I don't really understand what you mean about math academia. Those references would be appreciated.
Those are indeed impressive things you did. I agree very much with your post from 2010. But the fact that many people have this initial impression shows that something is wrong. What makes it look like a "twilight zone"? Why don't I feel the same symptoms for example on Scott Alexander's Slate Star Codex blog?
Another thing I could pinpoint is that I don't want to identify as a "rationalist", I don't want to be any -ist. It seems like a tactic to make people identify with a group and swallow "the whole package". (I also don't think people should identify as atheist either.)
I prefer public discussions. First, I'm a computer science student who took courses in machine learning, AI, wrote theses in these areas (nothing exceptional), I enjoy books like Thinking Fast and Slow, Black Swan, Pinker, Dawkins, Dennett, Ramachandran etc. So the topics discussed here are also interesting to me. But the atmosphere seems quite closed and turning inwards.
I feel similarities to reddit's Red Pill community. Previously "ignorant" people feel the community has opened a new world to them, they lived in darkness before, but now they found the "Way" ("Bayescraft") and all this stuff is becoming an identity for them.
Sorry if it's offensive, but I feel as if many people had no... (read 700 more words →)
PCA doesn't tell much about causality though. It just gives you a "natural" coordinate system where the variables are not linearly correlated.
What do you mean by getting surprised by PCAs? Say you have some data, you compute the principal components (eigenvectors of the covariance matrix) and the corresponding eigenvalues. Were you surprised that a few principal components were enough to explain a large percentage of the variance of the data? Or were you surprised about what those vectors were?
I think this is not really PCA or even dimensionality reduction specific. It's simply the idea of latent variables. You could gain the same intuition from studying probabilistic graphical models, for example generative models.
You asked about emotional stuff so here is my perspective. I have extremely weird feelings about this whole forum that may affect my writing style. My view is constantly popping back and forth between different views, like in the rabbit-duck gestalt image. On one hand I often see interesting and very good arguments, but on the other hand I see tons of red flags popping up. I feel that I need to maintain extreme mental efforts to stay "sane" here. Maybe I should refrain from commenting. It's a pity because I'm generally very interested in the topics discussed here, but the tone and the underlying ideology is pushing me away. On the other hand I feel an urge to check out the posts despite this effect. I'm not sure what aspect of certain forums have this psychological effect on my thinking, but I've felt it on various reddit communities as well.
Qualitative day-to-day dimensionality reduction sounds like woo to me. Not a bit more convincing than quantum woo (Deepak Chopra et al.). Whatever you're doing, it's surely not like doing SVD on a data matrix or eigen-decomposition on the covariance matrix of your observations.
Of course, you can often identify motivations behind people's actions. A lot of psychology is basically trying to uncover these motivations. Basically an intentional interpretation and a theory of mind are examples of dimensionality reduction in some sense. Instead of explaining behavior by reasoning about receptors and neurons, you imagine a conscious agent with beliefs, desires and intentions. You could also link it to data compression (dimensionality reduction is a... (read more)
"impression that more advanced statistics is technical elaboration that doesn't offer major additional insights"
Why did you have this impression?
Sorry for the off-topic, but I see this a lot in LessWrong (as a casual reader). People seem to focus on textual, deep-sounding, wow-inducing expositions, but often dislike the technicalities, getting hands dirty with actually understanding calculations, equations, formulas, details of algorithms etc (calculations that don't tickle those wow-receptors that we all have). As if these were merely some minor additions over the really important big picture view. As I see it this movement seems to try to build up a new backbone of knowledge from scratch. But doing this they repeat the mistakes... (read more)
It can still be evidence-based, just on a larger budget. I mean, you can get higher quality examinations, like MRI and CT even if the public insurance couldn't afford it. Just because they wouldn't do it by default and only do it for your money doesn't mean it's not evidence based. Evidence-based medicine doesn't say that this person needs/doesn't need this treatment/examination, it gives a risk/benefit/cost analysis. The final decision also depends on the budget.
I'm not talking about back and forth between true and false, but between two explanations. You can have a multimodal probability distribution and two distant modes are about equally probable, and when you update, sometimes one is larger and sometimes the other. Of course one doesn't need to choose a point estimate (maximum a posteriori), the distribution itself should ideally be believed in its entirety. But just as you can't see the rabbit-duck as simultaneously 50% rabbit and 50% duck, one sometimes switches between different explanations, similarly to an MCMC sampling procedure.
I don't want to argue this too much because it's largely a preference of style and culture. I think the discussions are very repetitive and it's an illusion that there is much to be learned by spending so much time thinking meta.
Anyway, I evaporate from the site for now.