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 success in the "real world" matters and invented a fiction where they are the heroes by having joined some great organization much higher above the general public, who are just irrational automata still living in the dark.

I dislike the heavy use of insider terminology that make communication with "outsiders" about these ideas quite hard because you get used to referring to these things by the in-group terms, so you get kind of isolated from your real-life friends as you feel "they won't understand, they'd have to read so much". When actually many of the concepts are not all that new and could be phrased in a way that the "uninitiated" can also get it.

There are too many cross references in posts and it keeps you busy with the site longer than necessary. It seems that people try to prove they know some concept by using the jargon and including links to them. Instead, I'd prefer authors who actively try to minimize the need for links and jargon.

I also find the posts quite redundant. They seem to be reiterations of the same patterns in very long prose with people's stories intertwined with the ideas, instead of striving for clarity and conciseness. Much of it feels a lot like self-help for people with derailed lives who try to engineer their life (back) to success. I may be wrong but I get a depressed vibe from reading the site too long. It may also be because there is no lighthearted humor or in-jokes or "fun" or self-irony at all. Maybe because the members are just like that in general (perhaps due to mental differences, like being on the autism spectrum, I'm not a psychiatrist).

I can see that people here are really smart and the comments are often very reasonable. And it makes me wonder why they'd regard a single person such as Yudkowsky in such high esteem as compared to established book authors or academics or industry people in these areas. I know there has been much discussion about cultishness, and I think it goes a lot deeper than surface issues. LessWrong seems to be quite isolated and distrusting towards the mainstream. Many people seem to have read stuff first from Yudkowsky, who often does not reference earlier works that basically state the same stuff, so people get the impression that all or most of the ideas in "The Sequences" come from him. I was quite disappointed several times when I found the same ideas in mainstream books. The Sequences often depict the whole outside world as dumber than it is (straw man tactics, etc).

Another thing is that discussion is often too meta (or meta-meta). There is discussion on Bayes theorem and math principles but no actual detailed, worked out stuff. Very little actual programming for example. I'd expect people to create github projects, IPython notebooks to show some examples of what they are talking about. Much of the meta-meta-discussion is very opinion-based because there is no immediate feedback about whether someone is wrong or right. It's hard to test such hypotheses. For example, in this post I would have expected an example dataset and showing how PCA can uncover something surprising. Otherwise it's just floating out there although it matches nicely with the pattern that "some math concept gave me insight that refined my rationality". I'm not sure, maybe these "rationality improvements" are sometimes illusions.

I also don't get why the rationality stuff is intermixed with friendly AI and cryonics and transhumanism. I just don't see why these belong that much together. I find them too speculative and detached from the "real world" to be the central ideas. I realize they are important, but their prevalence could also be explained as "escapism" and it promotes the discussion of untestable meta things that I mentioned above, never having to face reality. There is much talk about what evidence is but not much talk that actually presents evidence.

I needed to develop a sort of immunity against topics like acausal trade that I can't fully specify how they are wrong, but they feel wrong and are hard to translate to practical testable statements, and it just messes with my head in the wrong way.

And of course there is also that secrecy around and hiding of "certain things".

That's it. This place may just not be for me, which is fine. People can have their communities in the way they want. You just asked for elaboration.

There's also the whole Lesswrong-is-dying thing that might be contribute to the vibe you're getting. I've been reading the forum for years and it hasn't felt very healthy for a while now. A lot of the impressive people from earlier have moved on, we don't seem to be getting that many new impressive people coming in and hanging out a lot on the forum turns out not to make you that much more impressive. What's left is turning increasingly into a weird sort of cargo cult of a forum for impressive people.

8Vaniver5yThanks for the detailed response! I'll respond to a handful of points: I certainly agree that there are people here who match that description, but it's also worth pointing out that there are actual experts too. One of the things I find most charming about LW, compared to places like RationalWiki, is how much emphasis there is on self-improvement and your mistakes, not mistakes made by other people because they're dumb. I'm not sure this is avoidable, and in full irony I'll link [http://wiki.lesswrong.com/wiki/Inferential_distance] to the wiki page that explains why. In general, there are lots of concepts that seem useful, but the only way we have to refer to concepts is either to refer to a label or to explain the concept. A number of people read through the sequences and say "but the conclusions are just common sense!", to which the response is, "yes, but how easy is it to communicate common sense?" It's one thing to be able to recognize that there's some vague problem, and another thing to be able to say "the problem here is inferential distance; knowledge takes many steps to explain, and attempts to explain it in fewer steps simply won't work, and the justification for this potentially surprising claim is in Appendix A." It is one thing to be able to recognize a concept as worthwhile; it is another thing to be able to recreate that concept when a need arises. Now, I agree with you that having different labels to refer to the same concept, or conceptual boundaries or definitions that are drawn slightly differently, is a giant pain. When possible, I try to bring the wider community's terminology to LW, but this requires being in both communities, which limits how much any individual person can do. Part of that is just seeding effects--if you start a rationality site with a bunch of people interested in transhumanism, the site will remain disproportionately linked to transhumanism because people who aren't transhumanists will be more likely to leave and peop
3[anonymous]5yThe applicable word is metaphysics. Acausal trade is dabbling in metaphysics to "solve" a question in decision theory, which is itself mere philosophizing, and thus one has to wonder: what does Nature care for philosophies? By the way, for the rest of your post I was going, "OH MY GOD I KNOW YOUR FEELS, MAN!" So it's not as though nobody ever thinks these things. Those of us who do just tend to, in perfect evaporative cooling fashion, go get on with our lives outside this website, being relatively ordinary science nerds.

Beyond Statistics 101

by JonahS 2 min read26th Jun 2015132 comments


Is statistics beyond introductory statistics important for general reasoning?

Ideas such as regression to the mean, that correlation does not imply causation and base rate fallacy are very important for reasoning about the world in general. One gets these from a deep understanding of statistics 101, and the basics of the Bayesian statistical paradigm. Up until one year ago, I was under the impression that more advanced statistics is technical elaboration that doesn't offer major additional insights  into thinking about the world in general.

Nothing could be further from the truth: ideas from advanced statistics are essential for reasoning about the world, even on a day-to-day level. In hindsight my prior belief seems very naive – as far as I can tell, my only reason for holding it is that I hadn't heard anyone say otherwise. But I hadn't actually looked advanced statistics to see whether or not my impression was justified :D.

Since then, I've learned some advanced statistics and machine learning, and the ideas that I've learned have radically altered my worldview. The "official" prerequisites for this material are calculus, differential multivariable calculus, and linear algebra. But one doesn't actually need to have detailed knowledge of these to understand ideas from advanced statistics well enough to benefit from them. The problem is pedagogical: I need to figure out how how to communicate them in an accessible way.

Advanced statistics enables one to reach nonobvious conclusions

To give a bird's eye view of the perspective that I've arrived at, in practice, the ideas from "basic" statistics are generally useful primarily for disproving hypotheses. This pushes in the direction of a state of radical agnosticism: the idea that one can't really know anything for sure about lots of important questions. More advanced statistics enables one to become justifiably confident in nonobvious conclusions, often even in the absence of formal evidence coming from the standard scientific practice.

IQ research and PCA as a case study

In the early 20th century, the psychologist and statistician Charles Spearman discovered the the g-factor, which is what IQ tests are designed to measure. The g-factor is one of the most powerful constructs that's come out of psychology research. There are many factors that played a role in enabling Bill Gates ability to save perhaps millions of lives, but one of the most salient factors is his IQ being in the top ~1% of his class at Harvard. IQ research helped the Gates Foundation to recognize iodine supplementation as a nutritional intervention that would improve socioeconomic prospects for children in the developing world.

The work of Spearman and his successors on IQ constitute one of the pinnacles of achievement in the social sciences. But while Spearman's discovery of IQ was a great discovery, it wasn't his greatest discovery. His greatest discovery was a discovery about how to do social science research. He pioneered the use of factor analysis, a close relative of principal component analysis (PCA).

The philosophy of dimensionality reduction

PCA is a dimensionality reduction method. Real world data often has the surprising property of "dimensionality reduction":  a small number of latent variables explain a large fraction of the variance in data.

This is related to the effectiveness of Occam's razor: it turns out to be possible to describe a surprisingly large amount of what we see around us in terms of a small number of variables. Only, the variables that explain a lot usually aren't the variables that are immediately visibleinstead they're hidden from us, and in order to model reality, we need to discover them, which is the function that PCA serves. The small number of variables that drive a large fraction of variance in data can be thought of as a sort of "backbone" of the data. That enables one to understand the data at a "macro /  big picture / structural" level.

This is a very long story that will take a long time to flesh out, and doing so is one of my main goals.