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Nanashi comments on The most important meta-skill - Less Wrong
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That's interesting, I wasn't aware of that reputation. That's good to know and certainly justifies your skepticism.
All that said, I think one can still evaluate your point (and in my case, my Less Wrong post) based on its internal logic and how consistent it is with one's own observations, without needing research to back it up. It would be easy enough to dismiss your own post for the very reasons you cited. Consider the following:
"In general, people new to a community are notoriously bad at gauging the pulse of said community. To reformulate in Bayesian terms, based on the length of time you've been posting here, the prior probability of your statement being true is low, so shouldn't you provide some proofs or evidence -- or why should I (or anyone) believe you?"
But to me, your logic checks out, and is fairly consistent with my own observations (that most self-help publications tend to be garbage), so that shifts the probabilities significantly in your favor. I'm hoping that people will evaluate my own post by similar criteria, rather than immediately dismissing it.
I've started commenting here recently, but I'm a long time lurker (>1 year). Also, I was speaking about self-help articles in general, not conditional on whether they are posted on LW -- it makes sense, because pretty much anyone can post on LW.
Now I found a somewhat less extreme example of what I think is an OK post on self-help although it doesn't have scientific references, because a) the author told us what actual results he achieved and, more importantly, b) the author explained why he thinks that the advice works in the first place.
Personally, I don't find your post consistent with my observations, but it's not my main objection -- my main objection is that throwing an instruction without any justification is a bad practice, especially on such a controversial topic, especially in a rationalist community.
That's totally fine, like I said, your post made sense and was consistent with what I've seen.
I still don't really think that stating my qualifications would do much. In this context, it still just seems too much like bragging. "I helped build a multi-million dollar company, I compete in barbecue competitions and consistently place in the top 10% of the field and was sponsored by a major barbecue website, was ranked in the top 100 players in the world for a popular collectible card game, learned how to code with no formal education (and used that knowledge wrote a somewhat well-received calibration test, and also write a bunch of boring business platforms), wrote an article about a baseball statistic I co-developed and was published in a publication that's important for people who care about baseball stats, learned how to be a carpenter, at one point was a licensed pharmacy technician, blah blah blah"
Even though I'm sure there's a less crass way to phrase it, to me it still sounds exceedingly arrogant. I might be overreacting though. You tell me: if I prefaced my post with that, would you be more or less inclined to take me seriously?
I do like the idea of explaining why I think the advice works in the first place. I will start writing something up about that and append it to the original post.
If I may suggest spending some space on explaining why do you think your experience generalizes -- that is, why do you think that your methods will work for people who are not you.
I took your advice as well as estimator's into account and added two paragraphs at the beginning to offer 1. Some research showing that many systems follow a distribution where a small portion of work accounts for a large portion of results, and 2. and explanation as to why it's generalizable.
Also, I'd like to compare your system against common sense reasoning baseline. What do you think are the main differences between your approach and usual approaches to skill learning? What will be the difference in actions?
I'm asking that because that your guide contains quite long a list of recommendations/actions, while many of them are used (probably intuitively/implicitly) by almost any sensible person. Also, some of the recommendations clearly have more impact than others. So, what happens if we apply the Pareto principle to your learning system? Which 20% are the most important? What is at the core of your approach?
As I mentioned in another comment, the difference between this and the "common sense" approach is in what this system does not do.
As for what the 20% of this system that gives you the most bang for your buck? That's a good question. Right now my "safe" answer is that it's dependent on the type of skill you're trying to learn. The trouble is that the common threads among all the skills ("Find the 20% of the skill that yields 80% of the results") doesn't have a lot of practical value. Like telling someone that all they need to do to lose weight is eat less and exercise more.
Let me think about it some more and I'll get back to you.
So, after some cursory thought, naturally the part of the system that gives you the most bang for your buck are the first 4 steps. The last 3 steps are designed to help you improve, which is a much slower process than just learning the basics.
So, now to figure out how to recursively apply the the skill of learning a skill quickly to the skill "learning skills quickly".
Okay, so I made a significant revision of the post. The original ideas are all there, just written in a much less obtuse manner.
One piece of information you can use to determine what is most important is the number of other skills which require a certain skill as a prerequisite. Prerequisites should obviously be learned first, and it makes sense to learn them in order of how many doors they open. This is how I prioritize at the moment if I'm not considering subjective measures of "usefulness".
For my learning goals, I've started making concept maps, partly as it helps me understand a subject by understanding how concepts are related, and partly to identify what to learn next as described above. It becomes fairly obvious that I should learn X if I want to learn Y and Z and X is a prerequisite for both.
In my experience, in math/science prerequisites often can (and should) be ignored, and learned as you actually need them. People who thoroughly follow all the prerequisites often end up bogged down in numerous science fields which have actually weak connection to what they wanted to learn initially, and then get demotivated and drop out of their endeavor. This is a common failure mode.
Like, you need probability theory to do machine learning, but some you are unlikely to encounter some parts of it, and also there are parts of ML which require very little of it. It totally makes sense to start with them.
I'm thinking more specifically than you are. Rather than learning probability theory to understand ML, learn only what you determine to be necessary for what ML applications you are interested in. The concept maps I use are very specific, and they avoid the weak connection problem you mention. (It's worth noting that I develop these as an autodidact, so I don't have to take an entire class to just get a few facts I'm interested in.)
It sounds like both you and estimator are actually both on the same page: estimator seems to be talking about the "prerequisite" in the sense of, "systematic prerequisite", as in, people say that you should learn X before you learn Y. You seem to be talking about "prerequisite" in the sense that, "skill X is a necessary component of skill Y"
Both of you, however, seem to agree that you should ignore the stuff that is irrelevant to what you are actually trying to accomplish.
On the other hand, if you don't have a solid grasp of linear algebra, your ability to do most types of machine learning is seriously impaired. You can learn techniques like e.g. matrix inversions as needed to implement the algorithms you're learning, but if you don't understand how those techniques work in their original context, they become very hard to debug or optimize. Similarly for e.g. cryptography and basic information theory.
That's probably more the exception than the rule, though; I sense that the point of most prerequisites in a traditional science curriculum is less to provide skills to build on and more to build habits of rigorous thinking.
Read what is a matrix, how to add, multiply and invert them, what is a determinant and what is an eigenvector and that's enough to get you started. There are many algorithms in ML where vectors/matrices are used mostly as a handy notation.
Yes, you will be unable to understand some parts of ML which substantially require linear algebra; yes, understanding ML without linear algebra is harder; yes, you need linear algebra for almost any kind of serious ML research -- but it doesn't mean that you have to spend a few years studying arcane math before you can open a ML textbook.
That depends on whether you're doing research or purely applied stuff. For applied use, domain expertise trumps knowing the internal details of the algorithms which you usually just call as pre-build functions -- as long as you understand what do they do and where the limits (and the traps) are.
Not many people can invert matrices by hand any more and that's not a problem for a higher-level understanding of linear algebra. Similarly, you don't necessarily need to understand, say, how singular value decomposition works in order to do successful higher-level modeling of some domain.
Can I give a counterexample? I think that way of learning things might help if you only need to apply the higher-level skills as you learned them, but if you need to develop or research those fields yourself, I've found you really do need the background.
As in, I have been bitten on the ass by my own choice not to double-major in mathematics in undergrad, thus resulting in my having to start climbing the towers of continuous probability and statistics/ML, abstract algebra, logic, real analysis, category theory, and topology in and after my MSc.
There's a big difference between the fundamentals, and the low-level practical applications. I think the latter is what estimator is referring to. You can't really make a breakthrough or do real research without a firm grasp of the fundamentals. But you definitely can make a breakthrough in, say, physics, without knowing the exact tensile strength of wood vs. steel. And yet, that type of "Applied Physics" was a pre-requisite at my school for the more advanced fields of physics that I was actually interested in.
You're right; you have to learn solid background for research. But still, it often makes sense to learn in the reversed order.
Nice, but beware reasoning after you've written the bottom line.
As for the actual content, I basically fail to see its area of applicability. For sufficiently complex skills, like say, math, languages or football decision-trees & howto-guides approach will likely fail as too shallow; for isolated skills like changing a tire complex learning approaches are an overkill -- just google it and follow the instructions. Can you elaborate languages example further? Because, you know, learning a bunch of phrases from phrasebook to be able to say a few words in a foreign country is a non-issue. Actually learning language is. How would you apply your system to achieve intermediate-level language knowledge? Any other non-trivial skills learning example would also suffice. What skills have you trained by using your learning system, and how?
Also, when you say "intermediate level language knowledge", what exactly do you mean? One of the key steps is defining exactly what you want to accomplish and why. I don't want to create a whole write-up, only to realize that you and I have two different definitions of "intermediate level language knowledge".
So if you'd tell me the "what" and the "why", I'll do the rest.
I meant something like this.
I'll give a more in depth breakdown soon but for now, I'd probably take a similar approach that I took to learning to read Japanese : learn basic sentence structure, learn top 150ish vocabulary words, avoid books written in non-romaji. Practice hearing spoken word by listening to speeches and following their transcriptions. My exception protocol for unrecognized words was to look them up. And for irregular sentence structure, to guess based on context. It worked for watching movies and reading, mostly but as you can tell, yoi kakikomu koto ga dekimasen*. I'd have to do some thinking on the writing part, it would most likely involve sticking to simple sentences.
*thats terrible Japanese for "I cannot write well". I think. I hope.
But these are the things pretty much everybody does while learning languages.
Basketball is an example. I'm about to head home so I'll do the ultra-abbreviated TL;DR version:
I would say basketball is fairly complex. One thing I didn't mention in the original post (mainly because it starts to get into the "how do individual people learn") but for me, I don't get good at a competitive skill by competing against people who also suck. By getting good enough to be able to play with people who are actually good, it made it easier for me to learn the advanced part of the game faster.
Also, this post has a list of (at least what I think to be) fairly non-trivial skills that I have trained using this method.