The Hungarian Approach and How It Fits the American Educational Landscape (2015) - <AUTHOR> https://blogs.ams.org/matheducation/2015/01/10/the-hungarian-approach-and-how-it-fits-the-american-educational-landscape/ <comment><USER2> As a hungarian, I'm pretty surprised by this article, since the quality of hungarian education is getting worse and worse, thanks to the "reforms", which basically means they are spending less and less on education on all level. And however in the past, math education was world class, now the "old school" teachers are retiring and other than a handful of elite schools (Fazekas, Eötvös, Radnóti etc) most of the schools are way below the european average.

(source: [https://www.compareyourcountry.org/pisa/country/hun](https://www.compareyourcountry.org/pisa/country/hun) )

Also usually only one or two hungarian university manage to get to any of the "best 500 universities in the world" list.

<comment><USER3> > since the quality of hungarian education is getting worse and worse, thanks > to the "reforms", which basically means they are spending less and less on > education on all level

I always wondered what good metrics are to measure the quality of education. Since you say "worse and worse" can you share any insight to that? I mean you could potentially refer to the OECD world rankings but somehow I don't trust those lists, since they "feel" quite mechanistic.

<comment><USER4> I'm not the OP, but I'm from another Eastern Europe country where education level falls fast and dramatically. Few key indicators of that:

0\. I was attending an elite gymnasium and we deliberately used old "hard science" (math, physics, chemistry) textbooks from 70'-80', because they were _much_ more advance level than modern ones. Most of math/physics taught during first/second year at university now, was taught in 11-12th grade back then.

1\. Anecdotally, but _all_ teachers complain that every year students are getting less motivated, performing poorer, having shorter attention spans. With local "no children left behind" equivalent it's enough just to attend some percentage of classes to get passable grades.

2\. Every few years some kind of "reform" is performed to reduce the difficulty of final exams to maintain failure rates at bay and keep statistics nice.

3\. Combined points 1 with 2 this leads to universities full of students that have neither motivation nor skills to perform there. Since such students make up majority in most less popular programs, the difficulty level is also reduced to adapt.

4\. Degrading higher level education quality reflects on its prestige as potential employees no longer trust universities to produce prospective candidates. This closes the loop back to point 1 as current pupils deem education "worthless".

5\. Fun fact: entrance exams to non-prestigious universities from 70'-80' were (maybe still are) widely used as assignments in national level competitions when I was at school (late nineties to early 00'). To paraphrase that -- a skill level that once was expected from _anybody_ who wanted to be accepted in university now puts you at the very top.

<comment><USER5> > Anecdotally, but all teachers complain that every year students are getting > less motivated, performing poorer, having shorter attention spans.

Most of these points smack of rosy retrospective bias. Kids have great attention spans if content is delivered to them in a way they can engage in, for instance, interactive computer games. Teachers are just out of touch with kids, and really they always have been. It's why kids almost always like younger teachers more.

<comment><USER6> > Most of these points smack of rosy retrospective bias.

Well, accusations of bias can go both ways. Maybe it feels bad to admit that there is a serious problem with no obvious solution in sight; and pretending that we don't see anything is how we bury our collective heads in the sand.

Instead of accusing each other of biases, let's discuss evidence. As was mentioned in a few comments in this thread, high-school textbooks in multiple countries are gradually dumbed down, so much that 20 or more years old textbooks are now considered a material for gifted kids. (I can confirm this.) Your turn.

> Kids have great attention spans if content is delivered to them in a way > they can engage in, for instance, interactive computer games.

If the only problem is that humans are losing the ability to learn without playing computer games, perhaps we could fix it by making all the necessary games. But we better start making them really fast, because there is a lot of knowledge to cover.

And while we are at this type of solution, we could also fix problems with nutrition by genetically engineering a broccoli that will taste like heroin. Situation is not that bad if kids are still willing to pay attention to addictive things.

<comment><USER5> > As was mentioned in a few comments in this thread, high-school textbooks in > multiple countries are gradually dumbed down, so much that 20 or more years > old textbooks are now considered a material for gifted kids. (I can confirm > this.) Your turn.

And what do you think that proves exactly? Did you prove that the kids using the old textbooks actually absorbed the more advanced material? Did you prove that outcomes using the old material are better than with the new material?

Perhaps the new textbooks simply distilled the relevant material that the vast majority of kids actually grasp, without all the unnecessary detail that was just skipped over. I can think of plenty of different scenarios to explain the evidence that's been listed here, and only one of those explanations are "dumber kids and/or dumbed down education".

The exact same arguments have been trotted out about the dumbing down of liberal college education, where in the 1900s, every college degree meant exposure to poetry, art, history, philosophy and more. Modern college education is then portrayed as poor substitute, completely ignoring the fact that our body of knowledge is at least 10,000x larger than it was in the 1900s, and a direct comparison is frankly laughable.

> If the only problem is that humans are losing the ability to learn without > playing computer games, perhaps we could fix it by making all the necessary > games.

Talk about missing the point. As evidenced by my use of "for instance", that was merely an example. Even among adults, interactive systems are clearly more engaging, and given the environmental factors that shape modern kids, you obviously are already too old to grasp their thinking process if you can't understand that different environmental factors entails different learning processes.

Which just proves my point that adults are and always will be out of touch with the kids of their day.

<comment><USER4> _" And what do you think that proves exactly? Did you prove that the kids using the old textbooks actually absorbed the more advanced material?"_

Well, yes, at least some pupils did. See my comment about degrading exams difficulty and problems in contemporary national competitions. Math problems are fundamental, so it's a good indicator of skills, despite changing times. Science _is_ boring at most times, you can't gamify everything.

</comment></comment></comment></comment></comment></comment></comment><comment><USER7> I would like to point out two of my favorite high school competitions in Hungary.

One is KöMaL [1]. It's a monthly journal, one has to send back solutions to the problems. The competition is during the whole school year. It has problems from math, physics and computer science, these are separate contests. I did the "P" (theoretical physics) competition. Sometimes I took a look for the "B" math problems and I could never solve a single "A" math problem, those are freaking insane.

[1] [https://www.komal.hu/info/miazakomal.e.shtml](https://www.komal.hu/info/miazakomal.e.shtml)

The second competition is the Eötvös Physics Competition [2]. Unfortunately the problems are not translated to English. This is a single round competition for the whole country. There are three physics problems fitting on a piece of A4 paper (single sided). The students can use anything (any number of books, calculator), the competition is 5 hours long. All high school and first year university students participate in the same contest. It's designed to filter out the very best physics students in the whole country (typically only one or two students can fully solve all three problems).

[2] [http://eik.bme.hu/~vanko/fizika/eotvos.htm](http://eik.bme.hu/~vanko/fizika/eotvos.htm)

<comment><USER8> Russia (Soviet Union actually) had somewhat similar mail in programs that IMO we're very useful to stimulate those with high interest in specific fields beyond what a school would do.

IMO stimulating and developing top end is something sorely missing in US education, which is mostly focusing on helping those behind to catch up. That is useful, but pushing 1-2% of best students as far as possible is just as important for the society long term. My 2c.

<comment><USER9> And mail in may be better than extra classes or similar, as it may avoid various "tall poppy" issues.

And these days it can even be done electronically.

</comment></comment></comment><comment><USER10> I took my first eight years of math in Hungary; admittedly, some time ago. Though I'm not sure if things have changed since then, or were different at higher levels, the way the article describes it very much reflected my experience. Starting out, there was a balanced mix of rote learning the basics (as it was done in other subjects), then moving towards creativity and gradual, independent rediscovery, and it was done so in a way that didn't feel stifling if you somehow knew to do it a different way.

Ultimately it's a small country; there was a math bee you could compete in at a local level, and then your district would send the best representatives to the countrywide event. It was a prestigious event, and pretty stressful, but ultimately fun. The questions asked at the national competition were always really oddball and obscure and required both creativity and judicious use of everything you've learned.

Come think of it, the culture of the competitions in various subjects made school really fun.

Some other aspects of my primary school education in Hungary were not so stellar. In other subjects, there was very much a focus on facts in isolation, without really understanding or delving into context, notably in History. Literature I also found limiting, as much emphasis was placed on poetry analysis, which I found to be subjective; nonetheless, diverging from commonly accepted analyses was did not result in a good mark. When I came to the US, I found an emphasis on critical thinking in the Humanities, which was a breath of fresh air.

But in math and science, the quality and method of instruction in Hungary was top-notch.

<comment><USER11> Why is Hungarian math so forward looking but not the other subjects? Is Hungary especially good at math?

<comment><USER12> This question is not just worth understanding in itself but it would be essential to ask at many levels, as science education is becoming the key to the survival of humanity. As a Hungarian who left the country 20 years ago I have been fascinated by this surprising success of the country, partly because I did not personally get a good science education there. Actually, it was very unbalanced, with most of it barely mediocre, while some of it absolutely brilliant. The successful people came from 2-3 high schools (or "gymnasiums", for ages 12-18) in Budapest in the beginning of the 20th century, and whatever success Hungary had in the natural sciences was mostly a result of the work and heritage of that generation. There is a very good description of this in Norman MacRae's book on John von Neumann, worth reading.

A quick summary of the "reasons":

1\. Boom: at that time (from 1867 till WWI) Budapest was booming, more attractive to immigrants than New York. Actually, most of the city you can see today was built in those times.

2\. Culture: as a result of the boom and the wave of immigrants it became a very liberal and open minded place (though this did not apply to the feudal class at all - their kids typically became soldiers or playboys)

3\. Motivation: in a feudal society studying and intellectual eminence was the way to go unless you were born an aristocrat. Parents, students, teachers were willing to put time and money necessary to make their kids excel. This may not sound unusual with all those helicopter parents you see nowadays, but actually this was huge. Imagine growing up in a family where you knew - and your whole family knew - that your only chance of making it is to be the best at math your abilities allow.

4\. Education: I don't know where to start, so I can only give examples. Imagine you are a 12 year old child and your teacher borrows you his favourite papers on quantum physics and asks your opinion on them. Then you give a smart comment and your teacher contacts the relevant professor at the university to have a tea with you at your house next Sunday.

5\. Language: most of these kids learned Latin and Greek, and in before their teenage years also spoke at least German fluently.

6\. The "marble table": Stanislaw Ulam (in his autobiography, another amazing read) and also MacRae tells about the most important ingredient, the marble table at the café (the easy-to-erase whiteboard of the time). In Central Europe mathematicians met at cafeterias, discussed all day (often meaning 12 hour days at a cafe!), challenged each other, and did rarely work in isolation, not worrying too much about "who thought of it first". They happily took young kids in, 15 year olds sipping juice and 50 year old Banach drinking something much stronger (may not have been Banach, but you'll read the book!). It was such a well-known "way of doing math" that the IAS in Princeton was officially established to re-create this culture and pull the typical American professors out of their ivory towers.

It is an elitist system, I know, and does not solve mass education challenges. But this small elite circle had an impact on almost everyone in the country's education system. Even if you weren't a Wigner, Teller or Neumann, you spent 6 years in this environment and possibly became a great teacher, similar to the one who taught half of these people, the great László Rácz [1] and taught in this fashion.

Also, a similar great science education happened in Japan at some point (50's, I think ), but I only read this as a side note in the book on Neumann. Anyway, it is possible to do this again and with two small kids I'm very interested to know how.

[1] [https://en.wikipedia.org/wiki/L%C3%A1szl%C3%B3_R%C3%A1tz](https://en.wikipedia.org/wiki/L%C3%A1szl%C3%B3_R%C3%A1tz)

<comment><USER13> >Anyway, it is possible to do this again and with two small kids I'm very interested to know how.

Here's a context-relevant place to start: [http://slatestarcodex.com/2017/07/31/book-review-raise-a- gen...](http://slatestarcodex.com/2017/07/31/book-review-raise-a-genius/)

<comment><USER14> I wonder if the review is missing some aspects of "concentrate on one subject"?

On one subject, a mentor can be a master, and master instructor. And such instruction is crucial for developing deep expert understanding. But in the future, with better content, and improved learning infrastructure, one might imagine these becoming available for more than one subject.

Both the concentration on one subject, and that subject being chess, contribute to the effort having nice properties. Like non-superficiality. An integrated and deeply organized body of knowledge and skills. Developing transferable knowledge (within the subject at least), rich feedback, and reflective building of mastery. But it's the properties that matter. Chess, or another one subject, might be taught in a way that fails to have the nice properties. And as learning infrastructure improves, the nice properties may become available without the "one subject" or "and it's chess" restrictions.

There's a long history of exceptional masters teaching their field/craft to their children from a young age, who in turn become exceptional masters. The challenge is to scale that.

The straightforward "a gaggle of masters in everyone's pocket" is AI-complete implausible. But the art of the next decade is crafting human-computation hybrid systems. Things like eye tracking, and big data, provide opportunities that no master mentor has ever had. We just need to get around to building on them.

</comment></comment></comment></comment></comment><comment><USER15> I would to see this approach applied more in the sciences as well.

I my college syntax (linguistics) class, most of the teaching was done by the teacher giving us a (carefully curated) set of data to explain as homework.

Class time was spent making sure everyone got to the same answer and understood the arguments behind it. When there are multiple reasonable answers, they also made sure that students understand the other ones and why we rejected them (sometimes the reason is as simple as 'we need to pick an answer, lets just vote'; other times it is 'both are reasonable explanations, the field went with B, read Chomsky 1987 if you want to know why.'). We would then generally talk about problems we still have (especially if the data was English, and students could come up with new data that didn't work as nicely).

<comment><USER16> _" Class time was spent making sure everyone got to the same answer and understood the arguments behind it."_

How many students were in your class?

With the typical undergraduate class sizes that I've TA'ed at (about 30 students or so), there's just not enough time during the class to go over everything with each person individually and explain everything to them if they don't get it. Even if there was time, if someone is slow the rest of the class will be bored to death waiting for you to explain something to them (possibly over and over until they get it). Some of the more advanced students will be bored by any explanation, while the most detailed explanations with many examples are necessary for other students.

There are office hours and lab time, and some students make the most of that, but some don't. I actually spent a lot of my time helping out students who were lagging behind and needed a lot of attention, and wound up kind of neglecting the talented ones who didn't need any help, but who I think would have gotten more out of the class if we were able to engage them more, challenge them more, and cater to their interests more rather than trying to cater to the lowest common denominator.

I'm not sure what the right answer here is in classes where students vary widely in skills, interests, and abilities.

<comment><USER15> 25\. It also does not typically require much individual attention; generally presenting a cleaned up version of the arguement is enough. If multiple students have a problem, they tend to have the same problem, so the work scales sublinearly. Of course, larger class sizes will always make teaching more difficult.

</comment></comment></comment><comment><USER17> > After the student investigation, the teacher highlights important ideas > embedded in a concrete problem, and summarizes and generalizes their > findings. In particular, the teacher's summary makes sense and is > meaningful, because students have had the experience of playing around with > these ideas on their own before coming together to formalize them as a > class.

It's important for students to get their hands on examples and play with the ideas we're trying to present on their own. One issue I have is that this takes so much time. If you're introducing concepts that the students don't have good intuition about, you have to go so slowly. Even then it feels like some students can't follow.

I think it's beneficial when it's possible to get students to engage with the material on a daily basis as reading or homework. Hard to do when they're expected to take 4 classes (college) or 6-8 (high school) and dedicate study time to each of them.

<comment><USER15> When you use this approach, you generally cover less material, but cover it better. For most classes, this seems like a good tradeoff.

4 classes isn't really that much. You can essentially replace the time students would normally spend "studying" with them spending that time working on the problems. Further, in my experience, this type of work is far more engaging than traditional studying, so it is easier for students to spend time on it; and the time they do spend tends to be more thoughtfull.

<comment><USER18> Moreover, as people practice, they can get generically better at meta-skills such as (in no particular order) skimming, close reading, constructing mental outlines of arguments, drawing diagrams and pictures, inventing examples to match given specifications, testing concrete examples against abstract statements, generalizing specific relationships discovered in the examples to abstract laws and proving them generically, discarding or modifying abstractions to better match a wider collection of examples, working backwards, discarding dead-end problem-solving strategies while mining them for partial useful results, breaking problems down into manageable pieces, deductive logic, generating hypotheses likely to be interesting, abandoning whole problems which turn out to be too hard and switching to something else, cross-applying solution strategies from one field to another, simplifying complex arguments by discarding or separating extraneous or duplicated steps, writing up explanations in clear and coherent way for various audiences, researching past work in an efficient way, diving into new fields with completely alien notation and terminology and quickly taking the lay of the land, and so on.

These skills are more generically useful than recipes or collections of facts about any particular subject, because they serve as multipliers for quite generic learning and problem-solving efficiency.

One big problem with trying to teach a single specific course in a very problem-centered / student-driven / socratic style is that often students have not been sufficiently trained in any of these meta skills, and as a result move at much slower pace than their potential because they waste a lot of time on inefficient problem-solving methods, get side-tracked, throw away useful work, explain things poorly, don't get around to crystallizing their useful results, and so on. The more courses get taught in this style, the faster each subsequent course can move as students figure out how to learn effectively.

<comment><USER19> That's a nice list of meta-skills. Do you have any further reading to recommend on identifying/teaching meta-skills?

<comment><USER18> You could try Polya's _How to Solve It_ , and follow up with Schoenfeld's _Mathematical Problem Solving_ (which cites a lot of other material). These are rather focused on problem solving per se, and don't really discuss larger- scale strategies for research or learning. You can try just diving into child development and education literature. Not sure what out there really tries to be comprehensive in summarizing the best techniques for teaching and learning arbitrary meta-skills.

</comment></comment></comment></comment></comment><comment><USER20> Math is much more than what I was taught in school. I discovered that as a child when stumbling upon this Hungarian math book at the local library:

[https://www.amazon.com/Playing-Infinity-Dover-Books- Mathemat...](https://www.amazon.com/Playing-Infinity-Dover-Books-Mathematics- ebook/dp/B00A73IWVY/)

<comment><USER21> Hey, that looks awesome! Thanks for recommending it. I put it on my bucket list.

</comment></comment><comment><USER22> Let's make this very clear: this is _not_ a typical Hungarian approach. This is what Fazekas and to an extent a few more similar specialized high schools do. The typical Hungarian approach is frontal instruction with no respect to the learning speed differences.

Source: Personal experience. I am a Fazekas alumni and have a Hungarian maths teachers masters as well. I am bankrolling a very small reform school in Hungary so I am in contact with current Hungarian teachers every day and also I am obviously very interested in what's going on so I read a lot.

</comment><comment><USER23> A friend of mine enjoyed her first years in school in the Pannonian basin; I am not sure, whether they did something special - but it was enough to get her into a selective German high-school specialised in maths and sciences later in her life. Always admired her for the experience, as she repeatedly speak of it if it has been fun and games.

</comment><comment><USER24> My high school did something similar [0]. We never had math textbooks, only a book of problems. Each night we had 10 problems we had to solve. When we showed up in class the next day, each student would present a problem on the board and discuss their solution.

It worked well for the really disciplined, rigorous kids who were super interested in math and already had a solid background in it. But for someone like me, who never quite did all the homework, it became a game of getting to class first so I could present the one problem I did last night.

[0] [https://en.wikipedia.org/wiki/Harkness_table](https://en.wikipedia.org/wiki/Harkness_table)

</comment><comment><USER25> There were 2 documentaries produced by famed documentarian Frederick Wiseman called High School and High School 2. In one, a group of average students are followed through a typical high school in a middle class predominantly white area. In the second one, a group of students are followed through an experimental high school in a predominantly impoverished area with mostly a Latino population. The 'experimental' nature of the school was that every single class, every last one, was completely restructured to center around one thing: critical thinking. Teaching by fiat ('this is how it is because I say so and I am the authority') was banned. Every bit of teaching was through asking questions and having them answered, students challenging teachers on an equal intellectual playing field (unequal in specific knowledge of course, but equal in capacity to reason and challenge assumptions).

The experimental school produced the highest proportion of students to go on to receive college degrees (not just attend college, but finish) every seen in the country. The results were absolutely amazing and tremendously good. But... it's harder for teachers to teach that way. They can't plan ahead. They have to know their subjects inside and out, not just read off of a lesson plan. If a student asks a question that the teacher can't answer, the teacher has to admit it and try to figure it out with them, which many teachers are not emotionally mature enough to participate in alongside an adolescent. It gives a great deal of power and agency to adolescents, and our society is obsessed with stripping adolescents of every iota of control over their own life and denigrating them as much as possible. So widespread adoption of such schooling cannot gain much support at all.

The documentaries also did a good job showing how the "traditional" schooling methods broke the 'spirit' of adolescents and sucked the love of learning and figuring things out right out of them, turning them into disinterested husks of human beings, while the experimental school left them as vibrantly full of a love of life and learning as they entered it. Such things are of course difficult to measure and generally distrusted by our "pleasure is a sure sign of hidden dangers" mentality.

</comment><comment><USER26> I'm not "bad" at math per se, but would there be much benefit in going from the ground up learning in the Hungarian form?

I guess you could blow through the first 7~10 years of schooling in less than a year of dedicated study, but teaching yourself just up to before college level would take you a few years, right?

Is there any online courses that have this content?

<comment><USER12> Reading George Polya's books might be a way of learning this technique. At least they explain very well the method of thinking about math.

</comment></comment><comment><USER27> I grew up and studied math in Hungary and graduated from Fazekas, the school mentioned in the atricle, and its "special math" high school class. I also spent a year in high school in the US so I have some basis for comparison.

My experience was pretty much exactly as described in the article as well and I am forever grateful for the school and my teachers for giving me a foundation I could build on later in life. I'd like to point out a few things though, others have already touched upon some of these:

First, similar to why it's hard to replicate the Silicon Valley startup model elsewhere (or at least why it takes such a long time), the issue is somewhat similar here. The method of teaching is just one part of the equation. It was a whole "ecosystem" of fantastically knowledgable and respected teachers who could anywhere else be university professors or researchers, publications (such as KoMaL), camps, competitions and other extracurricular activities aimed at elementary and high school students, and a culture of math and science being interesting and fun vs. the stereotypical hard and boring.

The way of teaching feels a little bit more of a consequence of this culture rather than the source. You can pick your own preferred origin story for how that culture emerged: a booming industrial economy in the age of the Austro- Hungarian empire, a multi-ethnic, liberal (in the original sense of the word and given the context) country, the migration and resulting concentration of Jews in Budapest, or a series of exceptional teachers and mentors. The highly visible world-wide successes in the first half of the 20th century then later provided an on-going narrative that benefited the national ethos and hence made plenty of funding available in the second half (conveniently forgetting the austro-hungarian, the economic boom, the liberal or the jewish part of the story). But in any case the culture and the support system is (or at least was) there and that one is very hard to replicate at a broad scale, although certainly easier within specific communities. As someone mentioned, even in Hungary it is not broadly present and is limited to a certain set of top schools.

Also, there is a flip side to this story. Personally, I really liked math before going to high school and completely lost interest by the time I finished. Partly because a lot of kids around me were much better so I felt like a failure, partly because I was more interested in computers and programming and also in finance, all of which was looked down upon. The prestige of winning a programming competition was nowhere near the same as placing well in math or physics. Working as a developer on the side was considered a distraction. I think this was for the better for me personally. A decent number of my classmates got burnt out and had severe depressions due to the pressure. You were almost expected to win a gold medal at the International Olympiad and eventually become a world-wide math celebrity. I can't shake the feeling that a lot of them "peaked" at the end of high school, although perhaps that's partly the result of the rapidly declining university system.

Again, I'm very grateful for what I got but it's more in terms creative thinking and problem solving than specific math skills. In fact, I got to learn other subjects, such as history and literature through the same method which I now realize is very unusual in a country where those subjects are usually heavily biased toward insitilling a national identity as opposed to fostering independent thinking.

</comment><comment><USER28> For some reason I never knew that Hungarian Maths teaching is so outstanding. My high school teacher who came from Romania to Hungary always told us that he learned topics much earlier than we did. For example he learned equitations in grade 5 and we learn them in grade 7. So I figured that Romania must be better at teaching Maths.

Having gone through all levels of Maths in Hungary, from elementary school until BSc of university, I want to point out that this method sounds good as long as the teacher is able to keep the attention of the class. In my school, a few teachers were unable to do that and it was a disaster. Children were playing, talking and doing nothing in classes. Even preventing others from learning the material. Fortunately, I have never had these kind of teachers. However, if someone did they were doomed at university, because the expectations were too high for them. So I think it is quite a big disadvantage in Hungary. If you miss out in high school, because you were busy being a rebellious teen, there is a good chance that you never make it. You only realize it after you started university, because it is pretty easy to get into science courses of top universities in Hungary and very hard to actually graduate.</comment>


AMP pages will now show original URL in Chrome's address bar (2019) - <AUTHOR> https://www.androidpolice.com/2019/04/16/amp-pages-will-now-show-original-url-in-chromes-address-bar/ <comment><USER2> So if some random person is doing it, it's called spoofing, but if Google does it, it's called optimization?

If some problem is solved by spoofing, I'd say that something has gone completely wrong.</comment>


The One Python Library Everyone Needs - <AUTHOR> https://glyph.twistedmatrix.com/2016/08/attrs.html <comment><USER2> Actually, I consider the immutability of a `namedtuple` instance to be a benefit. I wish more things in Python were/could be immutable. I have been known to subclass `tuple` from time to time when I want both immutability and the ability to define methods.

I wince when I see `namedtuple` being subclassed. Most users don't realise that the implementation of it actually builds up a giant string to pass to `eval()`... thoroughly nasty! At least it's hidden underneath the covers for the most part.</comment>


Shortchanged: Why British Life Expectancy Is Falling - <AUTHOR> https://www.nytimes.com/2019/08/30/world/europe/uk-life-expectancy.html <comment><USER2> Had this been a story about the US, discussion would devolve into the lack of universal healthcare and other safety net. But AFAIK, UK has NHS (and cheap tuition etc). Then why do we see falling life expectancy in UK as well? Is universal healthcare really not the panacea it is made out to be?

</comment><comment><USER3> Does anyone have a link for a non-paywalled version?</comment>


Advanced Vim Registers - <AUTHOR> http://blog.sanctum.geek.nz/advanced-vim-registers/ <comment><USER2> A less powerful but much simpler alternative to using the registers is the Yankring plugin (<https://github.com/vim-scripts/YankRing.vim>)

With that anything you have ever yanked or deleted is in the ring and when you paste something you can easily rotate through the items in the ring. Basically you type 'p' to paste and if that's not what you wanted you keep pressing ctrl-p until you get what you want.

<comment><USER3> I've just been through the YankRing tutorial and it's fantastic; I don't know how I haven't come across this before. Thanks!

<comment><USER4> I used it (YankRing) for some time, but it was never consistent. The plus point is that it works across terminal, otoh often "p" did not work, so i would have to do "YRShow" and select.

Often this is used, but everything you delete gets copied to your clipboard.

           set clipboard=unnamed

So i prefer to map C-c and C-x (or whatever) to copy/cut to clipboard.

        " regardless of clipboard option, c-c copies selection to clip
        vmap <C-c> "+y
        " cut into clipboard
        vmap <C-x> "+d
        " copy to clipboard, http://news.ycombinator.com/item?id=4767227 , use 3 \y to copy 3 lines
        map <leader>y "+yy
        " paste from clipboard
        map <leader>p "+p
        " delete to black hole register, not going to clipboard etc
        nnoremap <leader>d "_d
        vnoremap <leader>d "_d

I've also mapped a key to write selected lines to a file, and another to read from it.

        map <silent> <leader>ww  :w! ~/tmp/vimxfer<CR>
        map <silent> <leader>wa  :w!>> ~/tmp/vimxfer<CR>
        nmap <silent> <leader>rr  :r ~/tmp/vimxfer<CR>

Pls refer to post below on "reattach to user namespace" for OSX ML users.

</comment></comment></comment><comment><USER5> This was an incredibly helpful article. Tom Ryder's written a lot of other blog posts that were really insightful and enlightening as well. I even learned how to use ed from him.

<comment><USER6> His blog has taught me more about using vim than any other single resource, and I recommend it unreservedly to anyone thinking of starting out with vim.

<comment><USER7> Does anyone know of a service where I could point to "timeless" blog content like this and it would give me a feed that it picks old content on a random, perhaps weekly basis? Then I can integrate it with more timely sources.

I feel like this must exist but I've no idea what to Google for.

<comment><USER4> Try <http://www.vimtips-blog.com/>

Also vimcasts.org, and check the zzapper page linked in the tips blog.

</comment></comment></comment></comment><comment><USER8> Always glad to see vim usage articles pop up now and then. Here's a similar HN thread from a while ago - <http://news.ycombinator.com/item?id=2870024>

</comment><comment><USER9> A useful article for at least intermediate VIM users.

Regarding the system clipboard register: in OS X 10.8 the `"+` register no longer works. Has anyone found a way to get it working again?

<comment><USER5> It works fine for me. Seems like whatever install of Vim you have doesn't support the clipboard. Run "vim --version | grep clipboard" and see if you get a + or - for the clipboard.

If you want a quick fix for getting it working again, make sure /usr/local/bin is early on in your PATH and run "brew install macvim --override-system-vim"

</comment></comment><comment><USER10> If you want to edit your macros, (e.g., the default register q) do "qp, edit, visual select and do "qy.

</comment><comment><USER11> wow, capital register appending is super useful. lots of good tips in this one.

thanks for posting this!

<comment><USER12> I find the difficulty is remembering this when I need it. I've read about capital registers several times, but when I am editing, and I need to copy several distinct lines, I do a "ayy "byy "cyy and then "ap "bp "cp instead of a "ayy "Ayy "Ayy -> "ap because I never remember to use capital registers. I'd love a VIM plugin that tells me that there is an easier way to what I just did by pattern matching my input.

<comment><USER11> I wonder what the complexity of a plugin would be that watches you use vim and tells if you're doing an anti-pattern?

My only gripe with vim is that the discoverability of features is god awful because :help is so overwhelming.

<comment><USER12> Yeah me too. I guess it would need to match all the wrong ways against a right way. It is probably easy for something like "jjjjj", but already someone doing BBBBi instead of I would be difficult to match: because one would also need to see where he ended up with BBBB: beginning of line or somewhere in the middle

</comment></comment></comment></comment><comment><USER13> I am going to buy a t-shirt which says "I use Vim"

<comment><USER14> why</comment></comment>


Open Source Replacement for Values of N Sandy? - <AUTHOR> http://sandysback.blogspot.com/

<comment><AUTHOR> Would be cool if this effort creates a simple Open Source NLP library that a Sandy-like app requires.</comment>


Google Chrome saved me $4,500 - <AUTHOR>

So my machine has been struggling for a long time - it's a Macbook Pro 2.33Ghz 3 gig RAM 320gig/7200RPM HD.  I bought it in 2007.
I push the machine pretty hard, with ten to fifteen applications running, tons of tabs open for my web browsing along with multiple user accounts simultaneously logged in which I switch between.
The whole machine is sluggish and I often have to wait seconds for things to happen.  Nothing is snappy any more.
The main problem is that Firefox is constantly bumping up towards 100% CPU and chewing huge amounts of RAM - around 1.5 gig.
So I decided that its time for a new machine - a new Macbook Pro with 8 gig RAM and a 256gig SSD.  I specced it all out on the Apple store and the price - ouch - was close to AUD$4,558.  Whatever I thought - it's my main work machine, I really need something that's fast enough to get my daily work done without grinding and chugging.
In the meantime I decided to switch to Google Chrome which I had been using a bit on the side for a while and it seemed pretty quick.  So I totally switched to Chrome for OSX and shut down Firefox for the last time.
Suddenly - a snappy machine again!  Even with large numbers of tabs open my web browsing is fast.  Even better, CPU utilisation is generally low (often under 5%) and RAM is usage low too (often under 150MB).
I've got my fast machine back!  Everything is working better, everything is more snappy.  Applications switch fast, web browsing is snappy, and everything just works.
I feel a bit sad about leaving Firefox behind - I want to support it and I've used it for years.  I don't want to speak ill of this old friend.
So the incentive to buy a new machine has gone.  My 2007 vintage Macbook Pro is still doing the job well.  I'm sure I'll need a new machine sometime soon but right my 2007 era Macbook Pro does everything I need, at a good snappy pace. Thanks Google Chrome! <comment><USER2> FYI: Your RAM usage is not 150MB, that's only for the main process. Each tab is it's own process and has it's own RAM utilization as well as CPU utilization.

<comment><USER3> Yup. If I have about 40 tabs open in Chrome, especially with some Flash videos or JS-heavy sites, Chrome can easily chew up a a gig and a half of memory and slow everything to a crawl. There is no magic bullet.

Bumping my memory up to 8GB ($130 from crucial) helped a lot though. No more paging issues for now even when running photoshop, firefox, safari, chrome, zend studio/rubymine/eclipse (yes, I use vim too, but it uses practically no memory)

<comment><USER4> esp if flash is involved chrome can get really mean... not saying other browsers are better. besides that chrome is insanely fast on osx. i compared it with win and its a bit snappier on osx. for whatever internal reasons.

</comment></comment></comment><comment><USER5> Try putting a SSD in it. It'll bring back a lot of "snappiness". Hands down best hardware purchase I've ever made.

<comment><AUTHOR> I did have a look at SSD's for my machine.

The downside is that the model of MBP that I own has a SATA interface that is limited to 1.5Gbps and the research that I did showed that the best SSD's are faster than that, so I'd be wasting some of the power of the SSD because the interface is slow.

The other problem is that installing a replacement hard disk in this machine is a nightmare. I've installed thousands of hard disks into computers and this was the hardest and most scary - I had to bend circuit boards and components to get a new 320GB/7200RPM disk in to replace the old 120GB/5400RPM, hoping that nothing snapped. Apple must have tried hard to design a machine with a disk that hard to upgrade.

Given those two issues I think I'd probably buy an entirely new machine and give the old machine to someone else in the family.

<comment><USER6> First, even if it is SATA 1.5Gbps, it's still a great upgrade, and you can take the SSD with you to your next machine - the SSDs Apple is shipping in the MBPs are not very good, and you'd be better off buying one separately anyway.

Second, which MBP do you have? That definitely doesn't sound right - I have a non-unibody 17" and it only takes about 5 minutes to take the top off; it's mainly just 12-18 screws, and the 15" looks identical to me (unless they changed how it's put together later on):

[http://www.ifixit.com/Guide/Repair/Installing-MacBook- Pro-15...](http://www.ifixit.com/Guide/Repair/Installing-MacBook-Pro-15-Inch- Core-Duo-Model-A1150-Hard-Drive-Replacement/486/1)

</comment></comment></comment><comment><USER7> I had almost the same experience. I had a 2007 MacBook Pro and one day dumped Firefox for Chrome. the difference was amazing.

I eventually did upgrade to a 2010 MacBook Pro/8GB/SSD. It came put to just under $3k after taxes. Expensive, yes. But the speed difference is amazing. It completely changed my work and productivity. Using a computer 8+ hours a day, it is easy to forget how much time you loose a day/week/month waiting on app to load, files to open, etc. The 2010 MBP (with SSD) is the nicest machine I've ever worke with.

</comment><comment><USER8> I would suggest using FlashBlock or equivalent on Macs. (Gruber even suggests uninstalling Flash, chrome has builtin Flash I guess.)

Also, keep an eye on the new Firefox 4 betas, they are much snappier.

That said, it is true that Webkit based browsers are currently the best choice in terms of speed and stability.

Opera is good on Windows, and has advanced on the Mac UI wise, however it is not as stable as Safari on my mac.

</comment><comment><USER9> Had you already uninstalled Flash, or were you running Firefox with Flash installed?

Uninstalling Flash seems to have had a salutary effect on my Safari sessions, though I can't speak to Firefox, having given up on Firefox years ago for anything except the occasional CSS/JS debugging session.

<comment><AUTHOR> No I haven't removed Flash. Yes I had Flash installed.

</comment></comment><comment><USER10> I had the same problems too, had the same firefox profile since the 1.0 days almost, and had my history set to remember it for 1 year, my profile dir alone was over 2GB. After starting a fresh profile everything went back to pretty much normal (by firefox's standards)

</comment><comment><USER11> I use Chrome on OS X as well and have disabled the Flash plugin, and use AdBlock and WidgetBlock Chrome extensions to make browsing faster. The only time I use Firefox is to view Flash.

</comment><comment><USER12> I'm curious if you tried firefox in safe mode or totally removed your profile if firefox would still big down your system?

<comment><USER13> I'm curious on my end too, are you running Windows?

Firefox is nothing but trouble for me on OS X, but it's great for my main testing environment on Windows (at work). The latest 4 beta is a lot better, but I'm fairly dependent on OS X's system-wide text services.

</comment></comment><comment><USER14> Buying new hardware is never a solution for a bad user experience, at least not with the laptop you own.

</comment><comment><USER15> You could probably achieve the same result by dumping a bunch of the plugins you installed in Firefox.

</comment><comment><USER16> I had the same problem with firefox. Safari/Chrome use fewer resources on my macbook.

</comment><comment><USER17> Opera is also fast. Has a built in RSS reader, ad blocker, Html,css,js inspector and many other built in features. The newest (not out yet) has stacked tabs.</comment>


Andy Kaufman and Redd Foxx to tour years after death as holograms - <AUTHOR> http://www.nytimes.com/2015/10/24/arts/andy-kaufman-and-redd-foxx-to-tour-years-after-death.html?_r=1 <comment><USER2> Given Kaufman's brand of humor, he might have found this hilarious. People paying to go see an imaginary version of a dead guy!

<comment><USER3> "Dead."

<comment><USER2> Good point. I should have said, "an allegedly dead guy".

</comment></comment></comment><comment><USER4> Who owns the rights to these comics jokes? Does the family of the persons make money from the shows?</comment>


Ask HN: Is there any way to reclaim our nonprofit YouTube channel? - <AUTHOR> Hello everyone,
Thanks for reading. I just wanted to put a call out there to anyone that can help. The nonprofit I'm a part of, Sustainable Waterloo Region, has lost access to its YouTube channel, we suspect due to the changeover between YouTube when it was acquired and merged in with Google+.
I've been on support with G Suite for hours to try to identify which email address the channel is linked to so I could perform a password reset and get back in, but it just seems completely impossible with the support person essentially saying there isn't anything he can do to help.
I submitted the email addresses and names I thought it might be to this form: https://support.google.com/youtube/answer/3056283?hl=en. It never seems to work; I get errors or it says that account can't be found. :(
Here is our YouTube channel: https://www.youtube.com/user/sustainablewaterloo
Can you help us reclaim it?
I'm really hoping and praying that someone who works at YouTube is reading and can help me or someone who has advice on how I can fix this will reach out. <comment><USER2> Godspeed. Try going viral on twitter. That seems to bring out the google staff who can help you.

</comment><comment><USER3> You don't have a ton of views per video, you might want to consider downloading the videos and uploading them to a new account.</comment>


Philosophy and the practice of Bayesian statistics [pdf] - <AUTHOR> http://www.stat.columbia.edu/~gelman/research/published/philosophy.pdf <comment><USER3> I never thought I'd see a 31-page paper by Andrew Gelman on the front page of Hacker News. And certainly not a paper coauthored with a well-known frequentist!

I was lucky enough to work with Prof. Gelman as his research assistant while I was in school - I can't even being to tell you how prolific and brilliant that man is. His name may not be known very much outside academic circles, but I'd go as far as to say that he's the most important Bayesian statistician since Thomas Bayes.

He used to be a contributor to FiveThirtyEight, back before the Times picked it up. I used to explain FiveThirtyEight as 'one of the six blogs Andrew Gelman writes for'. Now, I explain Andrew Gelman as 'a former contributor to Nate Silver's blog'. How times have changed!

Gelman's approach to statistics is more wholly Bayesian than most people with a moderate level of statistical training are likely familiar with. It was from Gelman that I learned why I never need to perform an F-test[0]; at the same time, it was from Gelman that I learned some of the potential pitfalls of pure Bayesian reasoning[1] (and how to address them).

When people ask me where to get started with statistics, both of the books I recommend are Gelman's: _Teaching Statistics: A Bag of Tricks_ and _Data Analysis Using Regression and Multilevel/Hierarchical Models_.

Both have tremendously off-putting titles, but they're actually incredibly accessible. Gelman is great at many things, but picking sexy titles is not one.

If you're interested in understanding the concepts behind this paper, I'd start there.

[0] <http://andrewgelman.com/2009/05/18/noooooooooooooo/>

[1] The linked paper provides a good analysis

<comment><USER4> Shalizi is no slouch either - his notebooks are _fascinating_.

<http://vserver1.cscs.lsa.umich.edu/~crshalizi/notabene/>

<comment><USER5> Thank you so much for sharing this.

Tidbits of resources like this is _the_ reason I frequent Hacker News.

<comment><USER4> You're welcome. You might try his blog, too - his views on subjects like the "wisdom of crowds" and IQ are intellectual fun.

[http://vserver1.cscs.lsa.umich.edu/~crshalizi/weblog/315.htm...](http://vserver1.cscs.lsa.umich.edu/~crshalizi/weblog/315.html)

</comment></comment></comment></comment><comment><USER6> If you are interested in the _practical_ practice of Bayesian methods (and you love Python), check out our open-source project/book _Bayesian Methods for Hackers_ :

[https://github.com/CamDavidsonPilon/Probabilistic- Programmin...](https://github.com/CamDavidsonPilon/Probabilistic-Programming- and-Bayesian-Methods-for-Hackers)

We aim to empower the non-mathematician with really cool tools and methods to solve otherwise very difficult problems. Plus it's all opensource, and every plot/diagram is reproducible and extendable.

<comment><USER7> As an aside, I just want to thank you for making the project/book text available as iPython notebooks. I haven't seen a mathematical writeup as beautiful and interactive as the chapters that you've put out. I've only had time to go through a couple of them, but it really is a treat.

Also, I've learned so much more about how people use python to do analysis and all sorts of other things through ipynb files than reviewing traditional python libraries/code. I wish more people would publish using them.

</comment></comment><comment><USER8> Haven't had time to read the whole article yet, but these two paragraphs from the conclusion (p. 24-25) are excellent:

 _"In our hypothetico-deductive view of data analysis, we build a statistical model out of available parts and drive it as far as it can take us, and then a little farther. When the model breaks down, we dissect it and figure out what went wrong. For Bayesian models, the most useful way of figuring out how the model breaks down is through posterior predictive checks, creating simulations of the data and comparing them to the actual data. The comparison can often be done visually; see Gelman et al. (2004, Chapter 6) for a range of examples. Once we have an idea about where the problem lies, we can tinker with the model, or perhaps try a radically new design. Either way, we are using deductive reasoning as a tool to get the most out of a model, and we test the model – it is falsifiable, and when it is consequentially falsified, we alter or abandon it. None of this is especially subjective, or at least no more so than any other kind of scientific inquiry, which likewise requires choices as to the problem to study, the data to use, the models to employ, etc. – but these choices are by no means arbitrary whims, uncontrolled by objective conditions._

 _"Conversely, a problem with the inductive philosophy of Bayesian statistics – in which science 'learns' by updating the probabilities that various competing models are true – is that it assumes that the true model (or, at least, the models among which we will choose or over which we will average) is one of the possibilities being considered. This does not fit our own experiences of learning by finding that a model does not fit and needing to expand beyond the existing class of models to fix the problem."_

And section 4, which discusses issues that arise in Bayesian statistics when working with multiple candidate models, is interesting and agrees with my limited experience, especially 4.3: "Why not just compare the posterior probabilities of different models?"

ps (to the submitter), it might be helpful when submitting a 30 page paper to mention what part of the paper you'd like to discuss. It makes it easier to get started.

</comment><comment><USER9> As someone who didn't study statistics in college, a paper like this is right in that uncomfortable no-man's land between what I understand and what I'm interested in - it seems to tie into several subjects I have layman's interest in.

For instance, there is the controversy over how useful models are - are they worthwhile goals we can actually draw conclusions from, or are they simply shortcuts on our way to a more reductionist understanding of a phenomena? Is "emergence" a meaningful concept or an empty concept? Is "systems thinking" a valid concept or just a lack of discipline in the effort to understanding things in a reductionist manner?

People here seem to hate Stephen Wolfram but his writing has made some concepts approachable to me that I might not have grasped otherwise - <USER2> instance, that computational irreducibility means that even if the world is entirely reductionist, it still doesn't mean that we can deduce the reductionist reality/inputs from an output. And so therefore, models are useful even though they are wrong. This is also a point that Paul Krugman often makes about economic models - people who disregard models on the grounds that they are wrong just don't grasp the value of them, he argues.

Most of what I've learned about Bayesianism is what I've read from the first few articles over at lesswrong.com - but I noticed pretty early on that I had a discomfort in using probability as a description of what I _believed_ to be true. It seems the general point of this paper is that Bayesianism is useful for deductive techniques - as a tool in a toolset to support a frequentist view? - but not so much as an expression of a subjectivist philosophy. I appreciated this point:

"Beyond the philosophical difficulties, there are technical problems with methods that purport to determine the posterior probability of models, most notably that in models with continuous parameters, aspects of the model that have essentially no effect on posterior inferences within a model can have huge effects on the comparison of posterior probability among models."

More generally the paper seems to be making the point that using the Bayesian philosophy to address models is improper in general since the premise of Bayesianism is to update _beliefs_ based off of evidence/data, while we know that belief-in-a-model is pointless since models are wrong. But past that point I got pretty lost.

<comment> loup-vaillant > _I noticed pretty early on that I had a discomfort in using probability as a > description of what I_ believed _to be true._

If it can ease your discomfort: what else? In colloquial language, we already do have pretty good descriptors of subjective beliefs, such as "I don't think so", "I'm damned sure", "maybe"… It is only natural to call the quantitative version of those "probabilities" —at least to me.

As for frequency properties of seemingly "random" phenomenons, they're a property of the real world, ready for the study. I'd have much more discomfort calling _that_ "probabilities".

<comment><USER9> I agree that colloquially speaking, it aligns most with what we believe. What I have trouble with is that you and I could assign different probabilities (via beliefs) to an external event that has only one probability of happening. Just in that sentence I used the word "probability" correctly both times, yet it has two different conflicting meanings.

<comment> loup-vaillant Then just use different words. Break it down to "degree of subjective belief" and "frequency property" if you have to.

Still, I prefer to use the short hand "probability" to mean the former, for two reasons: first, "degree of subjective belief" is what most lay-people will understand when we say "probability". Second, even scientists have this meaning messing up with their intuitions, even if when they write papers, they really do mean "frequency property". That can be a major obstacle for science. Let me quote Edwin T. Jaynes (long, but worth it):

 _Those who cling to a belief in the existence of "physical probabilities" may react to the above arguments by pointing to quantum theory, in which physical probabilities appear to express the most fundamental laws of physics. Therefore let us explain why this is another case of circular reasoning. We need to understand that present quantum theory uses entirely di fferent standards of logic than does the rest of science._

 _In biology or medicine, if we note that an eff ect E (for example, muscle contraction, phototropism, digestion of protein) does not occur unless a condition C (nerve impulse, light, pepsin) is present, it seems natural to infer that C is a necessary causative agent for E. Most of what is known in all elds of science has resulted from following up this kind of reasoning. But suppose that condition C does not always lead to e ect E; what further inferences should a scientist draw? At this point the reasoning formats of biology and quantum theory diverge sharply._

 _In the biological sciences one takes it for granted that in addition to C there must be some other causative factor F, not yet identi ed. One searches for it, tracking down the assumed cause by a process of elimination of possibilities that is sometimes extremely tedious. But persistence pays off ; over and over again medically important and intellectually impressive success has been achieved, the conjectured unknown causative factor being finally identified as a de finite chemical compound. Most enzymes, vitamins, viruses, and other biologically active substances owe their discovery to this reasoning process._

 _In quantum theory, one does not reason in this way. Consider, for example, the photoelectric effect (we shine light on a metal surface and find that electrons are ejected from it). The experimental fact is that the electrons do not appear unless light is present. So light must be a causative factor. But light does not always produce ejected electrons; even though the light from a unimode laser is present with absolutely steady amplitude, the electrons appear only at particular times that are not determined by any known parameters of the light. Why then do we not draw the obvious inference, that in addition to the light there must be a second causative factor, still unidentified, and the physicist's job is to search for it?_

 _What is done in quantum theory today is just the opposite; when no cause is apparent one simply postulates that no cause exists —ergo, the laws of physics are indeterministic and can be expressed only in probability form. The central dogma is that the light determines, not whether a photoelectron will appear, but only the probability that it will appear. The mathematical formalism of present quantum theory —incomplete in the same way that our present knowledge is incomplete— does not even provide the vocabulary in which one could ask a question about the real cause of an event._

 _Biologists have a mechanistic picture of the world because, being trained to believe in causes, they continue to use the full power of their brains to search for them —and so they find them. Quantum physicists have only probability laws because for two generations we have been indoctrinated not to believe in causes —and so we have stopped looking for them. Indeed, any attempt to search for the causes of microphenomena is met with scorn and a charge of professional incompetence and `obsolete mechanistic materialism'. Therefore, to explain the indeterminacy in current quantum theory we need not suppose there is any indeterminacy in Nature; the mental attitude of quantum physicists is already sufficient to guarantee it._

This one has been quite an eye opener, making me doubt even Many Worlds, which for one still doesn't explain the Born statistics. Still, thanks to Eliezer's Quantum Physics sequence, I'm now convinced that to the best of Science's knowledge (and despite what many physicists say) the laws of physics are most probably deterministic. Which would instantly solve the conflict by rendering "probability" nonsensical when applied to physical phenomena.

<comment><USER9> That's a lot to unpack - let me make sure I have this right.

Quantum theorists see that light is necessary but not sufficient for some sort of electron behavior. And I'm guessing that it's very hard to find an additional contributory/necessary cause that, when combined with light, would reliably predict the behavior.

So they use probability to communicate their findings.

If that additional contributory/necessary cause exists (but is just really hard to discover, or perhaps light itself is just highly contributory but not necessary/sufficient) then the probability is an effective way to communicate degree-of-belief, and helps to combine information about what they know with what they don't know. In other words, they are using probability to communicate partial findings.

If that additional contributory/necessary cause doesn't exist, then the probability is a fixed, physical part of the science. In other words, they're communicating full findings; that light actually creates probability of an effect.

And that confusion about "probability" is making people believe that the second case is true when they haven't necessarily disproved the first case yet? Which then serves as a curiosity-stopper and makes the science "mysterious". And this messes with scientific intuitions.

Forgive me if I'm way off, I've only read the first few articles of the first couple lesswrong sequences.

<comment><USER10> I thought I'd pop in and mention this set of lectures (now a book, huh) from Scott Aaronson on quantum mechanics and quantum computing: <http://www.scottaaronson.com/democritus/> Particularly Lecture 9 goes into the basic mathematics of quantum mechanics, and explains why it's more appropriate to consider it an extension to probability theory using complex numbers and call them amplitudes, rather than classic probabilities. Not being a physicist myself I don't know if this is really helpful in clearing up professional scientists' intuitions, but it's helpful for me at least in grasping the basics.

You're on the right track by noting that quantum theorists can use probability to communicate their findings. A single classical probability like 0.7 is a _summary_ , not the whole picture. For the whole picture, you need a log of observations with time stamps, which is a tremendous amount of data because taken to the extreme it's a balancing act between maximizing the data you know about you and your surroundings and minimizing the number of planck unit time steps to collect the data. Even with more realistic amounts of data it's still a lot to pack around, so you can summarize it by constructing a probability distribution which is more convenient mathematically, and you can summarize that probability distribution into a handful of numbers if you need to because those are even easier to pack around. (Like the single probability 0.7, or two numbers u and s that characterize a Normal distribution, etc.)

So if you think of probability as just summarizing your past observations and constraining your expectations for future observations (because what else can?), I think it's easy to see how two people can have different probabilities that represent a belief in a proposition or an abstract system or something else. If both are being honest (I'm packing a lot of assumptions in that word since this is basically Aumann's agreement theorem) and the probabilities are different, there are a couple reasons why: either at least one of them has information (observations) the other one does not, or they started out with different prior probabilities. In the former case, they can reach agreement by sharing data. In the latter case, they can eventually reach agreement by obtaining more data or choosing a common prior. With enough successive Bayesian updates, you can arrive at the same conclusion regardless of where you started. (In practice, of course, things are more hairy and not this simplistic, which is what the submitted pdf is addressing in part.)

I find it hard to grok what it means for something to have a physical frequency property. I can understand it as a statistic of an abstract system, i.e. a probability, but in light of Many Worlds, I don't think a frequency can fundamentally be a property of something in the same way energy can be. But since the energy of a photon is related to the photon's "frequency" via its "wavelength", is energy really fundamental or is the relation just a convenient means of making the wave mechanics useful? I've read Feynman tell how the whole idea of forces is just a somewhat circular simplification that's not really fundamental, even though sometimes it seems like it is. While I have a high degree of certainty about the physics accurately describing reality as she is, my meta-belief / confidence about that high certainty isn't nearly as high simply because I feel a lot of confusion about the ultimate nature of reality. But at least at a higher level than fundamental physics, it's clear to me that saying a "fair coin" has p=0.5 per unique side is really just summarizing we don't have enough knowledge of the flipper and the environment it is flipped in to predict the result with certainty in the way we can predict the result of the output of a logic OR gate with low- and high- voltage inputs with certainty. This is different than the uncertainty of which branch you are within a Many Worlds universe, where it's more of a physical resolution problem rather than one of needing more knowledge, similar to not being able to find out what happens between planck unit time steps (if anything happens and if the question means anything).

Ben Goertzel's book Probabilistic Logic Networks is another resource I'd recommend. Sorry if my branching reply is a bit big, but I'm constantly trying to clarify things for myself as well. ;)

<comment> loup-vaillant I have not read the Aaronson's book, but I'm already confident his use of the word "probability" is more confusing than helpful. Okay, the universe is made up of a wacky "configuration space" of "complex amplitudes" (in lay terms, a many-dimensional sea of complex numbers). _And_ , there is a direct relationship between complex amplitudes and the degree of subjective belief of the physicist doing such and such experiment: take the norm of the amplitude, square it, compare that to the other relevant squared norms, and voilà, you have a probabilistic prediction for the result of your next experiment. (I sweep a lot under the carpet, but it really works like this.)

Something to know however: assuming you made no mistake, then the result of the first experiment tells you _nothing_ about the result of any subsequent trials. Here's an example:

Let us send light through a half-sieved mirror. Have detectors on either side of the mirror to detect the light. Now, you can see that each detector detects half as much light as you originally sent (of course). Things gets interesting when you send photons _one by one_. The set up of the experiment is such that when you make your amplitude calculations, you find that the squared norm of the amplitudes at each detector is the same. Here, this means that when you send a single photon to the mirror, your should expect to see a single detector go off, but you have no idea which one (subjective belief of 1/2 for either side).

But there's more. Once you made the calculation, you know all you could possibly know about the initial conditions of the experiment (according to current physics). Imagine you sent a photon for the first time, and you see it going through the mirror. Will that change anything to your expectation on the second trial? Not one bit. You will still bet 1/2 chances on either side. So this experiment is the perfect coin toss.

Or is it?

Imagine you send _twenty_ photons, and you see they _all_ made it past the mirror. Initially that's one chance in a million. In that case, you should be strongly tempted to expect the 21th to go through as well. The question is why?

If you are _absolutely sure_ you set up the experiment right, then you shouldn't change your mind, and still bet 1/2 on either side. But are you so sure of your accuracy that you could set up several millions experiments, and get _all of them_ right? Probably not. The more likely explanation is that your so-called "half sieved mirror" is just a piece of glass. Maybe you were tired, and piked up the wrong one. Or maybe some graduate student played a joke on you.

Conversely, an actual coin toss _could_ be perfectly fair: there's a lot going on in a coin toss, and depending on the chosen procedure, you can't expect to have sufficient knowledge of the initial conditions to predict anything but 1/2 for heads, 1/2 for tails, even after seeing the the coin landing tails three times in a row. There is, even then, some kind of "frequency property" in the experiment. It's not because the laws of coin tossing are somehow indeterministic, but because the way the coin is tossed systematically hides relevant information about the exact initial conditions.

 _(The formal Aumann agreement theorem is a bit stronger than that: two perfects Bayesians cannot have common knowledge of any disagreement. In practice, this means you can't start from different priors, because a prior embody background information, which can be shared. I know that there is controversy about "fully uninformed" priors, but in practice, they don't change posterior probabilities very much.)_

<comment><USER9> As an outgrowth of this discussion I tried submitting a question to Ask HN but it went over the character limit, so for want of a better place to put it, maybe someone will react here - anyone want to weigh in on my various interpretations of truth and falsehood?

       +</comment></comment></comment></comment></comment></comment></comment></comment><comment></comment><comment>+</comment><comment></comment><comment></comment><comment>+</comment><comment></comment><comment></comment><comment>--+</comment><comment></comment><comment></comment><comment>-+</comment><comment></comment><comment>---+
       |    T/F     |     Fuzzy        |     Frequentist    |     Bayesian      |   Bayesian    |
       |            |                  |                    |   Subjectivist    |  Objectivist  |
       +</comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment>----+
       |    0/0     | Ambiguous/Vague. |    I am ignorant.  |  I am uncertain.  | No one can be |
       |            | I am apathetic.  |                    |                   | certain.      |
       +</comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment>----+
       |    0/.5    |     N/A          | Don't bother me,   | I am uncertain    | There exists  |
       |            |                  | still testing my   | but it may be     | partial       |
       |            |                  | hypothesis.        | false (partial    | knowledge of  |
       |            |                  |                    | knowledge)        | falseness.    |
       +</comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment>----+
       |    0/1     | Completely false | It never happens   | I am certain it   | Everyone      |
       |            |                  | in the physical    | will never happen | should be     |
       |            |                  | world.             |                   | certain it    |
       |            |                  |                    |                   | will never    |
       |            |                  |                    |                   | happen.       |
       +</comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment>----+
       |    .5/0    |     N/A          | Don't bother me,   | I am uncertain    | There exists  |
       |            |                  | still testing my   | but it may be     | partial       |
       |            |                  | hypothesis.        | true (partial     | knowledge of  |
       |            |                  |                    | knowledge)        | truthiness.   |
       +</comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment>----+
       |   .5/.5    | It is partly     | Intrinsically      | I am 50% certain  | All should be |
       |            | true and partly  | 50/50.  Given 100  | it is true, 50%   | 50% certain   |
       |            | false.  (partial | bottles, half are  | certain it is     | it's true;    |
       |            | truth)  The      | full.  Given 100   | false.  I am 50%  | 50% certain   |
       |            | bottle is half   | of her, half are   | sure the bottle   | it's false.   |
       |            | full.  She is    | pregnant.          | is full.  I am    | All should be |
       |            | partly pregnant. |                    | 50% sure she is   | 50% certain   |
       |            |                  |                    | pregnant.         | bottle is     |
       |            |                  |                    |                   | full.  There  |
       |            |                  |                    |                   | is 50%        |
       |            |                  |                    |                   | certainty she |
       |            |                  |                    |                   | is pregnant.  |
       +</comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment>----+
       |    .5/1    |       N/A        | Don't bother me,   |       N/A         |     N/A       |
       |            |                  | my hypothesis is   |                   |               |
       |            |                  | broken.            |                   |               |
       +</comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment>----+
       |     1/0    | Completely true  | It always happens  | I am certain it   | Everyone      |
       |            |                  | in the physical    | will always       | should be     |
       |            |                  | world.             | happen.           | certain it    |
       |            |                  |                    |                   | will always   |
       |            |                  |                    |                   | happen.       |
       +</comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment>----+
       |    1/.5    |      N/A         | Don't bother me,   |       N/A         |     N/A       |
       |            |                  | my hypothesis is   |                   |               |
       |            |                  | broken.            |                   |               |
       +</comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment>----+
       |     1/1    | Equally          | My hypothesis is   |       N/A         |     N/A       |
       |            | confident.       | meaningless.       |                   |               |
       |            | Torn.            |                    |                   |               |
       |            | Ambivalent.      |                    |                   |               |
       +</comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment>----+

<comment> loup-vaillant I'm not sure what to make of this… More precisely, I'm not sure what your fractions on the left actually mean. Personally, I'm tempted to throw "undefined" at each row that does not sum to 1, leaving only "1/0", ".5/.5", and "0/1".

I haven't heard about any distinction between "subjectivists" and "objectivists" Bayesians. I'd say my own position is a little bit of both:

First, background knowledge fully constraint the world view. That means that two rational agents whose knowledges are the same must hold the same beliefs. More practically, two persons having the same _relevant_ knowledge about any given question should believe the same answer (the difficulty here is to sort out what's relevant). Second, agents generally don't have the same background knowledge. Nothing stops us for knowing different relevant information about any given subject. So of course we won't hold the same beliefs.

Fuzzy. Ah, fuzzy… the more I look at it, the less sense it makes. Strictly speaking, there is no such thing as "half truth". A proposition is binary: either true or false. _Beliefs_ on the other hand are not binary: a belief is a proposition _plus_ a probability. If I say the bottle is more than 75% full with probability 84%, then I'm 16% right if it really is only half full. Quite wrong, but not hopelessly so.

With probability functions and utility function, we can make the same set of decisions we could have made with fuzzy logic, which looks like it mixes the two. I personally prefer the modular approach.

<comment><USER9> The fractions are supposed to represent partial degrees of truth. 0.5 is just an example, could be 0.4, 0.6 - anything other than absolute true or false. There are examples of probability systems that try to factor in uncertainty, beyond truth belief or false belief. I think it was possibility theory that was talking about a 0.5 value assigned to "the cat is alive", a 0.2 value assigned to "the cat is dead", and a 0.3 uncertainty - then it would be 0.8 plausibility/possibility of alive, but only 0.5 probability.

The main distinction I saw about subjectivist vs objectivist is that the objectivist still communicates in terms of degree of belief, but the belief is that it is based on objective empirical data and so therefore it's not an expression of _personal_ belief - more a measure of belief for the entire system or knowledge base. More relevant for things like machine learning rather than updating ones own personal beliefs. (Maybe this just assumes common priors.)

I've gone round and round on fuzzy because some people talk about fuzzy as if it is degrees-of-belief, like the bayesian interpretation. But I think it's different - I mean, if I'm partly correct on describing a concept, you wouldn't say that I am right 50 times out of 100, or that you're 50% certain I'm right, right? You'd say I'm half-right.

I'll have to think about probability combined with utility function. I'm not sure how to make it square with fuzzy math. It seems you'd be 16% right whether the bottle was half-full, quarter-full, or empty.

<comment> loup-vaillant Okay, so I got your fractions mostly right.

Well, when you use a computer to make a probabilistic calculation, you have at some point to feed it with the relevant information, or it won't know what to make of the data. _The data alone is not enough._ And if you are absolutely certain your program is (i) correct, and (ii) fed with all the relevant information you know about, then you should definitely believe the result. (Of course, this absolute certainty is not attainable, so any sufficiently surprising result should lead you to think that something went wrong.) Assuming common priors on the other hand, seems unreasonable, unless we're only talking about "fully uninformed" priors such as those based on Kolmogorov complexity.

Yes, I would say you're half right. The example of the temperature given by the Wikipedia is really good. The "degree of coldness" _is_ a worthy notion. What bothers me is that fuzzy logicians don't seem to run probability distributions over those degrees of truths. I mean, I can surely make up a probability distribution on the outside temperature for tomorrow at 10AM. Assuming that temperature maps to degrees of truth to the sentence "it's cold" (say, 100% true below 10°C, 0% true beyond 20°C, and a linear interpolation in between), then I naturally come up with a probability distribution over the degrees of truth (I should get a Dirac spike at both extremities).

Your last one is exactly right. I'm only 16% from empty to 75% full (excluded). That's the punishment I get for not producing a theory of the fullness of the bottle which describes my actual beliefs about it.

I assume that your ultimate goal in life is to make decisions, or to create a machine that will take decisions. To make sound decisions, you need to asses their consequences. Take the example of the bottle: I need a full bottle of water to hike today. Let's assume for the sake of the argument that it is either empty, or full. If it's empty, I need to re-fill it, or I would faint from the thirst (not good). If it's full, checking is only a nuisance (not good either). Now the question is, how can I minimize my discomfort, on average?

Well I need two things: first, a probability distribution. Let's say I'm pretty sure I filled the bottle yesterday, let's call it a 90% chance. Second, a utility function. Baseline, the bottle is full, _and_ I did not bother checking it, utility zero. Checking costs me 1 utility point, and fainting from thirst costs me 50 points (let's assume my brain is actually capable of performing those utility assessments). _Should_ I check the bottle?

Oh yes. Not checking means a 10% chance of losing 50 points, or 5 on average. Checking means a certainty of losing only one point, which costs much less. Conclusion: check your water supply before you go hiking. And re-check just to be sure.

Now this problem didn't call for fuzzy logic. We can however come up with a full probability distribution over the quantity of water in the bottle instead of just two possibilities. From then, fuzzy logic should be able to naturally step in. But frankly, I prefer to run a a separate (dis)utility function over the state of thirst that the lack of water will provoke (from light thirst to actually faint), and combine it with my probability distribution to make my decision. (Though at that point, I'd rather just check than frying my brain trying to solve this cost-benefit calculation.)

<comment><USER9> BTW, I just saw something about this regarding fuzzy uncertainty.

They made a distinction between vagueness and ambiguity. Both of which might make it difficult to assign a probability distribution among the possible values.

Ambiguous is when the boundaries of the sets are not clearly defined. In the thermometer example, it would be not knowing exactly what temperatures are meant by "cold". Or maybe like the supreme court definition of porn, you know it when you see it.

Vague is when there are clear definitions, but you're not sure how well your data fits into those definitions. For the temperature example, it would be what a crappy thermometer would tell you. It'd roughly correlate (it wouldn't return boiling if it's freezing), but it'd be pretty inaccurate.

In true/false values, maybe 0/.5 and .5/0 could be construed as ambiguous, while 1/.5 and .5/1 could be construed as vague.

</comment></comment></comment></comment></comment><comment><USER11> Frequentist here. This paper makes me hate Bayesians a little less. The reason is because a general thrust of the paper (since I have only had time to give it a once-over) seems to be that just because you are a Bayesian it doesn't mean that you have to get rid of model adequacy checks. Not having model adequacy checks is why I think Bayesians run around with a magic wand saying, "poof! there's an optimal model." After proving a theoretical optimality they never check to see if the real world data supports their arguments. So I'm glad to see a prominent Bayesian saying that you don't have to throw model checking out the window.

On a secondary note, I have to lament the use of philosopy in a math paper. I realize that many prominent mathematicians are/were also philosophers and that the two subjects are somehow linked at some level. But really I think that putting philosophy in a math paper is an excuse to use more big words and sound smart. Most of us would like to have a set of formulas to apply and not worry about-- forgive me if I for what I'm about to say-- fuzzy non-science like philosophy and the implications that it might have on our cold hard numbers.

Can each 31 page paper that combines math with philosophy come with a 5 page companion paper that leaves out the philosopy and just has the applicable math stuff?

<comment><USER12> I think your objection misses the fundamental point of the paper which is that blindly applying formulae (in this case Bayesian ones) without considering the part these computations play in the whole scientific process leads to bad science. Specifically, it leads to assuming that the correct model is already among those being considered. The authors say this is often not the case and assuming so often leads to stagnation in the relevant discipline.

This isn't an article that hands the readers a set of rote instructions, it's a warning that rote application is bad science because good science doesn't merely compare existing models with the data, good science proposes new and better models. This is fundamentally an article on the philosophy of science so leaving out the philosophy would make the article pointless.

In general, people who are good at symbolic manipulation are often in search of a methodology that allows them to only do symbolic manipulation and relieves them of the difficult task of doing semantics as well. The article is saying that there's no free lunch here - we have to do the semantics as well, we have to understand why models don't perform well and come up with the creative insights necessary to replace them with new ones.

</comment></comment><comment><USER13> The author's point that the most successful forms of Bayesian statistics accord much better with sophisticated forms of hypothetico-deductivism is reminiscent of the epistemology of normative value(s) which furnish a provisional lens for the analysis of the systemization of statistical transparency.

OK, half of that sentence is from an academic bullshit generator. I won't tell you which half.

Unfair, I know. That paper is clearly not meant for the general public, but still, learn how to communicate.

</comment><comment><USER14> I... uh.... How did this get here?</comment>


Gmail/Lifehack - Send automated emails/newsletters to "Trash" - <AUTHOR> http://www.gettingmoreawesome.com/2011/05/02/achieve-zero-inbox-by-using-trash-as-a-folder/

<comment><AUTHOR> Here is a direct link to the video on YouTube: <http://www.youtube.com/watch?v=Yu_mvKuLaA8></comment>


Before Doom, before Wolf3D, there was Wraith by John Carmack - <AUTHOR> http://www.whatisthe2gs.apple2.org.za/wraith/

<comment><AUTHOR> Also Shadowforge <http://www.whatisthe2gs.apple2.org.za/shadowforge/></comment>


Open Outdoor Robotics platform - <AUTHOR> The goal of this project is to create a standardized outdoor robotics platform, similar to the Turtlebot. The platform will be comprised of 2, 4 or 6 wheels with custom motor controllers. The wheels are scooter hub motors( 36V 250 watts). The two wheel platform will just be used as a proof of concept/test platform for motor controller development. The 4 and 6 wheel models use a rocker-boogie suspension. Each motor will have a dedicated battery pack. The motor controllers will be an open source 3 phase brushless controllers based on the arduino zero. I hope to use the SAMC21 which is 5V tolerant. The controllers will communicate via USB to an arm board running linux and ROS(Nvidia X1). The goal is to provide customers with a clean navigation API so that customers can focus on building their applications on the platform.
The platform is a little larger than a push lawn mower.
The business model would be similar to Makerbot's initial kit model. The basic kit will allow users to easily build the rover saving costs on labor. Fully assembled and tested models will be available as well.  I would also like to build applications myself on this platform in the agricultural field.
Any feedback would be appreciated. <comment><USER2> any idea on initial cost?</comment>


Startups: Stop Trying To Hire Ninja-Rockstar Engineers - <AUTHOR> http://onstartups.com/tabid/3339/bid/87890/Startups-Stop-Trying-To-Hire-Ninja-Rockstar-Engineers.aspx

<comment><USER2> It is interesting how strong the idea of not paying developers decent salaries is, even among people where the quality of such hires determines success or failure of their company.

"While companies fight tooth and nail over engineers with MIT or Stanford degrees with years of experience, as CTO of designer pages, my best hires were consistently entry-level developers that I developed on the job."

Is this really all that surprising? An MIT trained engineer with years of experience probably would find 'designer pages' boring, and would rather be principal engineer at Google (or wherever), getting paid an "unfair" (note quotes!) wage and great stock options and working on highly interesting technical problems.

If such an elite engineer wants to tackle business problems, why not run his own startup vs working where his skills are not valued?

That said, I am all for hiring and training people just out of school. There is a place for people of all temperaments and skill levels in the industry.

</comment><comment><USER3> I thought this was pretty spot-on.

Why not hire junior devs (or marketers, or any other staff for that matter), train them in your company's culture and work methods, and let them grow into the senior role once they have a better understanding of your company and it's customers/users?

It seems to me like this approach would give entry-level folks a chance to develop their skills while also producing far more learning and teamwork than you'd have otherwise. All while saving cash for other strategic needs.</comment>


Ask HN: Do you think Stripe will go public? - <AUTHOR> I'd be super curious to see their financials, but I'm guessing they are printing money. <comment><USER2> It's not a question of if, it's a matter of when they go public or get bought by one of the giants. Visa, Mastercard, Discover, AMEX, PayPal are all candidates for trying to buy them.

PayPal itself got caught in one of these types of decisions. The dotcom bubble peaked around Nov 1999; PayPal was founded (merged) around Dec 1998; they IPO'd in Feb 2002 with the Nasdaq having lost about 50-60% of its value at that point. July 2002 eBay bought PayPal, with the Nasdaq on the floor that Summer, having lost ~75% of its value. This was a massive mistake of timing, the Nasdaq doubled within about 16 months after that Summer of extreme lows. PayPal undervalued itself dramatically by selling when it did, timing the sale exactly wrong.

Venture capitalists have poured $190 million into Stripe. They're going to require an exit, it's a question of whether Stripe catches this stock market boom period, are forced to wait until the next one (potentially 5-10 years or more), or they miss this window and sell themselves to a big acquirer in a few years instead.

The business they're in doesn't quite print money, but it does have solid margins at scale. Visa prints money - $12.7b sales, $5.4b profit (incredible margins). Instead of Visa's 42% hyper net income margin, Stripe might reach 15-20% at scale (more like PayPal's).

</comment><comment><USER3> They have 1 of 2 options. Go public or be acquired. Either way they are obligated to provide their investors with the best return possible. Personally as a user of Stripe I would really like to see them stay independent and go public themselves so they can mostly control the culture and customer experience. Being acquired may mean their culture is changed or their focus gets jacked around.

</comment><comment><USER4> well there's actually some competitors coming up that's offering lower transaction fees. Forte.net is offering 2.15% + 25c per transaction. Mastercard has a competitor as well 2.85% + 30c

<comment><USER5> Stripe has never competed on price, they compete on product.

The interchange rate, basically what Visa/MC charge to processors, is as low as 0.05% for debit cards and less than half what Stripe charges on many credit cards. There are hundreds of merchant account providers a business can go with that charge less than 2.9%, or charge a fixed markup over interchange. Heck, PayPal goes to 2.2% if you process just $10K/month, and 1.9% or less for bigger accounts -- that's been the case for over a decade.

Most people aren't choosing Stripe to get the best fees, they're choosing Stripe because you can get started immediately, their UX is awesome, and their reputation among the developers who have to implement the payment processing integration is stellar.</comment></comment>


More teachers are grouping kids by ability - <AUTHOR> http://www.usatoday.com/story/news/nation/2013/03/18/elementary-teachers-grouping-reading-ability/1990917/

<comment><USER2> Teaching is hard and tiring.

Think programming: short on time, high on abstract requirements, lack of clarity, changing concerns, dictated tools. Now throw in 30 co-workers, 10 of which can't sit still, 10 of which are mentally absent while fiddling with mobile devices, 2 of which are crying, 1 of which needs to interrupt every hour to hit the facilities, all of which have two parents talking to you a couple of times a year, plus internal bureaucracy.</comment>