嘘とワンダーランド

Science fiction is dead to me. I can not stand the weak ethics, ridiculous predictions, massive biases and total disconnect with science since at least 50 years any more. At first, that depressed me a little. Losing a whole genre is always tragic, but I have survived the death of horror; I will make it without science fiction, too. But actually, I found something to replace it with: Futurology! In fact, retro-futurology. Find some futurologist, at least some years old, and compare their vision of the future with now – it will be hilariously wrong! No exceptions.

Kurzweil is amazing in that he is wrong even after only half a decade! Clarke at least is sometimes correct when predicting stuff half a century away, but Kurzweil couldn’t even design a five-year-plan for a socialist utopia. He even gets his own data wrong in determining the date for the singularity. That’s mind-boggling. It’s really takes a special kind of intelligence to be so stupid.

Based on JBR’s scoring system, I’ll award between 0 to 1 point per prediction, depending on how good it was. Of course, I only rate predictions that can already be judged (and exclude those that are so vacuus that they don’t say anything at all, like “The rate of paradigm shift (technical innovation) is accelerating, right now doubling every decade.”). “Partially borrowed” from Wikipedia.

1. “We will have the requisite hardware to emulate human intelligence with supercomputers by the end of this decade.” Not even a single component of the brain can be emulated. 0 points.
2. Automatic Speech-Recognition Software with good accuracy in 2000. Muahaha. This one is a postdiction and yet, “good”? Yeah, right. Well, it isn’t entirely awful, but “good”?! 0.5 points.
3. Computers will start to disappear as distinct physical objects, meaning many will have nontraditional shapes or will be embedded in clothing and everyday objects. No. I still don’t have a fridge that orders new milk and I’ve been promised one since I was born! 0 points.
4. Full-immersion audio-visual virtual reality will exist. No. In fact, entertainment systems are rapidly moving away from “immersion” (thank gods!). As far as I know, there isn’t any full-immersion for training purposes either. 0 points.
5. Glasses that beam images onto the users’ retinas to produce virtual reality will be developed. Alright, “developed” is correct, but in actual use? Personally, I expect VR to be integrated into phones instead. 0.5 points.
6. Real-time language translation in which words spoken in a foreign language would be translated into text that would appear as subtitles to a user wearing the glasses. Muahahaha! The smartest company on the planet, Google, can’t even correctly generate subtitles in the same language. 0 points.
7. Cell phones will be built into clothing and will be able to project sounds directly into the ears of their users. Yeah, right. 0 points.
8. Exoskeletal, robotic leg prostheses allow the paraplegic to walk. Ok, prototypes exist and work well. The reason they are still rare isn’t so much the robot, but rather the energy source. 1 point.
9. Telephone calls are routinely screened by intelligent answering machines that ask questions to determine the call’s nature and priority. If only! 0 points.
10. “Cybernetic chauffeurs” can drive cars for humans. Even humans can’t drive well, but robots are going to do it before they have mastered vision?! That’s… optimistic. 0 points.
11. The classroom is dominated by computers. No, just no. Laptops might have replaced paper notebooks by now, but it’s very rare for a teacher to be even aware of useful software, like SRS. 0 points. Personal prediction: this won’t be true, ever. School will go extinct before teachers apply science to their job.
12. A small number of highly skilled people dominates the entire production sector. Yes and no. Specialization is going strong, but companies are larger than ever. 0.5 points.
13. Tailoring of products for individuals is common. To a degree and for a price, yes. 0.75 points.
14. Drugs are designed and tested in simulations that mimic the human body. Nope. 0 points.
15. Blind people navigate and read text using machines that can visually recognize features of their environment. Nope. Unless you count “dogs” as machines. Because, face it, that’s how good you have to be to compete on this market. 0 points.
16. PCs are capable of answering queries by accessing information wirelessly via the Internet. 1 point.
17. By 2020, there will be a new world government. While there is still some time, just think of all the paperwork! The UN is breaking apart, the EU is becoming irrelevant and there aren’t any two superpowers speaking with each other. I think we can judge this one. 0 points.

Alright, that’s about it. 3.75 out of 17. I especially like that he is still convinced that translation software is just around the corner.

Yes, futurology will fill the void left by science fiction nicely. *chuckles*

Prof. Argüelles critizes the term “polyglot” for its sound and often misleading implications. While I personally like the pronunciation (but I have been a fan of harsh, guttural languages ever since, with a special fondness for /k/), I never really got the rest of his criticism until today. I mean, polyglottery is really only about learning 4+ languages. You only do what everyone else does to learn your second language, then do the same thing a few more times. At best, you use a few techniques to organize the whole effort, but fundamentally, it’s just more of the same.

But when I looked at my notebook (the paper one, that is), I realized what he really meant. The languages start to merge and the families and cultures start to develop new patterns. It’s the difference between a single switch and millions of them that make up a computer. You begin to understand people not in one particular mindset that your first language (and its culture) imposed on you, but soon develop alternatives (the second or third language) and then begin to see general patterns. You go from the perspective of the Romans, who realized that Greek had something to do with Latin, but they couldn’t quite figure out what exactly, to a more general view that understands the development of the Indo-European language as a whole, to maybe a global perspective.

Anyway, enough “make me one with everything”, I actually just realized I used German (using both the modern and old German Kurrent writing), English, Japanese, Ouwi and some ad-hoc pictograms on the same page in a coherent manner. You know you are a polyglot once you run out of space for all the languages to use.

There is a major philosophical schism in language study. Well, learning any skill in general. Let’s explore them via an example that I just today made a final decision on. If the following sounds to specific, then this is a misconception. It applies to any process of learning.

What’s the problem? Say you learn vocabulary via a SRS. That means adding the given item (be it character, word or sentence) as a two-sided card, with a question on the front and the answer on the back. In the case of Chinese characters, the question would be a keyword (or short phrase) containing the meaning of the character (e.g. “to guard” or “にち”) and the answer would be the actual character (e.g. “守” and “日”). You would see the meaning, remember the character and write it out (mentally or with a pen) and check the answer. So far, so good.

However, here comes the schism: Should you add hints to the question side, i.e. a picture, additional sound, any mnemonic you use or whatever kind of hints you can think of short of giving the answer away? There are two schools of thought here:

One group believes that you should study thoroughly and face hard goals, so that you will be able to get good results in the wild. In this case that means not giving any hint at all. You should have internalized these hints already and rely only on your memory. You wouldn’t have access to these hints in a real texts after all. The alleged advantage is a better retention rate, with the drawback of being slower and being slightly less fun. But in the end, it pays off. I will call this group the Nietzscheans because “what doesn’t kill me only makes me stronger.”

The other group thinks you should make the process of studying as easy and fast as possible. In the given example they would always give their mnemonic and maybe additional material on the question side. The alleged advantage is the ability to cover more material in the same amount of time and less exhaustion. Your real retention rate might be lower, but it will pay off anyway. I will call this group the LaFargueans because the exemplify the Right to be Lazy.

So, which group is right? Let’s make the example more concrete. I will be intensively studying a total of roughly 3500 characters so I can get up to speed on all recent Japanese literature. I learn about 100 new characters per day and do daily reviews, so this will take a significant of my time in the near future. So I am very much interested to decrease my workload.

I have read several reports and my own statistics to derive three basic approaches:

a) Use no hints. This make the card a bit harder to get right at first (80% retention rate), but after you have one good answer down, you will be fairly consistent (90% retention rate).
b) Use hints. The first review is somewhat easier (85% retention rate), but still involves some work to get it right. After this, you will almost always be right (95% retention rate).
c) Use hints at first, but drop them later. This is similar to b), but after 3 correct answers you switch off the hints. This will make you fail a lot more cards at first (70% retention rate), but you improve fast.

Now, how much reviews would you have to do daily with each approach? Graphs to the rescue!

daily reviews

same data, logarithmic scale

As you can see, using hints some or all of the time makes almost no difference with regards to reviews, but both approaches are quite a bit better than using no hints. How much better? Using no hints, the Nietzscheans will do about 47,000 reviews in 2 years. The LaFargueans will do about 37,000 reviews. That’s 10,000 reviews less, i.e. 20% less work! And because they are easier, they will also be faster. In my experience up to twice as fast.

How significant is this? Well, 20% less work means you can do 20% more in the same amount of time. The LaFargueans could learn 700 additional characters, not spending a minute more than the Nietzscheans. Of course they would argue that the LaFargueans won’t learn them as well. That’s true. Their real retention rate is lower. Let’s be generous and say the Nietzscheans have a retention rate of 90% after a year, while the LaFargueans have only 75%. But because the LaFargueans could cover a lot more material, they will know the same total of characters (3150), but their knowledge will be a lot more diverse. And they will be done faster (and have more fun). Instead of studying more, they could also stick with 3500 characters. That means they will have trouble with about 525 characters more. They could then spend their 20% more free time on them and would still be done faster (learning 525 cards separately would involve about 5000 repetitions, still 5000 less).

And this totally reflects my experience. Those that go the easier, more playful way always outperform the stricter ones. They can learn a lot more and see results earlier. And that’s why you should always cheat.

Let’s do some blatant propaganda for Spaced Repetition Software aka SRS.

Say, you want to learn something. Something big, like, Japanese or Chinese. Japanese uses 4 different writing system, but the one that stands out are the 漢字, i.e. the thousands of funny symbols. To be literate in Japanese, you need to now about 3000 of those. How would you learn something that huge?

To learn anything, you need two things. First, the information must be sticky. That means it must be represented in a form your brain can actually remember. What that means is: Ever tried remembering a long number? Like, 20 digits long? Impossible, unless you break it down. But ever remembered the whole plot, including all scenes, of a great movie? Totally easy. Your brain can remember pictures and narratives (related things, both by time and cause) easily, but abstract information is very hard. So you need to transform the 漢字, or whatever your learning, into pictures and stories, aka mnemonics. Fortunately, they were designed with that in mind, so that’s very simple.
Second, you need to review regularly. Your memory is leaky and needs constant reinforcment. Fortunately, every time the memory is refreshed, it will stick around a lot longer – roughly 2-3 times as long if you review just on the brink of forgetting. If you know some math, you’ll recognice this as an exponential progression. What does that mean? You only need to review about 7-8 times and the memory will stay for decades! So, that’s manageable. Unfortunately, the brain is a little faulty, so you will forget a few things anyway. The good thing is, though, that with very little effort, you can already reach a retention rate of 90-95%, so on average you only need around 10 reviews per fact to make sure you’ll remember it for a very long time.

That sounds pretty nice already, but still, 3000 漢字? Isn’t that a lot of work? No. That’s 3000 facts, meaning about 30,000 reviews. A review takes 10 seconds, at most. On average, it will take only about 5, but let’s assume 10. Worst case scenario, you know. In total, that’s only about 3.5 days of work. If it were not spaced out so much, you could finish it in a week. Sweet!

Have a look at those graphs.

3000 facts, 20 new facts a day

3000 facts, daily reviews

That’s your work over 10 months. The first shows how much reviews you will be doing per month in total. Yellow is the amount of new (or unseen) facts, red are reviews (or reps) of old facts. Below that is the amount of reviews per day for each month. As you can see, the daily workload is at most 20 minutes and goes does down rapidly. After 5 months, you know all 漢字 and will only be refreshing. And that’s only for a moderate amount of work with 20 new facts per day. You can easily do 50, or even 100 if you are determined. Pretty good, right?

Let’s look at what I’m doing right know. I’ve already learned around 2000 漢字 quite a while ago, but I’ve neglected a lot of them and I found I could only read them, not write them. Which sucks. So I started anew, but while I’m at it, I might as well do some more. :)

Chinese uses the 漢字 exclusively (almost), so you need more to be literate, around 4000 or so. A lot of those overlap (at least 60%) with Japanese, so it’s not a bad idea to learn the superset of 漢字 that both languages use. That would add up to about 5000-6000. I also want to read somewhat older literature and just love obscure sources, so I decided to totally rock the 漢字 and go for over 6000. A realistic upper bound is 8000 or so. After that you’ll have trouble finding any actual sources outside of taxonomy. I will also add 50 per day, on average. Right now, for the first 2000, I’m doing 100, but as they become more obscure, I’ll slow down to focus on more important aspects of the language. Still, 50 a day is maybe an hour of work. Let’s show some graphs.

8000 facts, 50 new facts a day

8000 facts, daily reviews

Less than an hour per day on reviews for half a year, then only up to 20 minutes. Half of them done in 2.5 months, meaning already pretty much 95% literacy. A total of 9 days of work for the reviews and 6 days for the initial learning. And that’s why I love my SRS.

I must be insane. I’ve finally decided to learn Chinese (Mandarin, specifically), too. As if I wasn’t busy enough. Oh well, at least it makes me feel better about my Japanese. And more 漢字 is always fun.

Alas, let’s learn some Prolog first. I’m not sure if I’m intrigued or disgusted by logic programming. During the last few months, I have come to dislike mathematics (and especially mathematicians). It is not scientific (because the axioms are arbitrary and not chosen to reflect a particular world) and, worst of all, I feel it’s useless. Ok, that’s too harsh a statement. I will have to elaborate on that, but I don’t have any time right now. In a nutshell, pure mathematics seems to me to produce only barely useful results (outside of mathematics, of course). What good is Fermat’s Last Theorem in the first place anyway?
The biggest problem I have with logic programming (and mathematics, and prescriptivists in linguistics) is their worldview. They believe that their ontology, their categories and grammars actually apply to the world, or anything at all. They don’t. There is no consistent description of the universe. Pretending there were one isn’t a good idea, even though it can get you a few good results in the short-term.
(You only need to look at the great results of computer linguistics and that there isn’t a single grammar book for one language that can hold up 5 minutes in a normal dialog among 12-year-olds. After all those years you’d think they’d give up. Alas, I hope no one listens to them anymore once we have hardware-implemented neuronal networks and machines passing the Turing test.)

It all reminds me of String Theory. Maybe it’s elegant, maybe not, one thing is sure – it is useless. 40 years and not a single experiment. That’s not science, that’s wanking.

This is a rant. Don’t take it too seriously. I’m sleep deprived and needed something to do, ok?

Neo 2 claims to be an ergonomic keyboard layout. I have been using it until about a month ago when I forked it and created my own layout. (I’ll upload it here once I get around fixing the last few bugs.) I don’t contend its claim as far as the letters are concerned. However, level 3 aka Mod3, containing the punctuation characters, is totally fucked up. How fucked up? Let me demonstrate.

First, let’s get some data. What’s a typical use for Mod3? Programming and normal texts. I decided on analyzing C (representing curly-bracket languages), Python (representing pseudo-code and list languages), my shell history and shell files (representing Unix usage) and Perl (representing line noise). Additionally, I included my local Usenet cache to represent normal texts. I took several thousand files each and in the end averaged them all together. (data: freq-c freq-pl freq-py freq-history freq-news average)

Second, we do a little experiment. Say, if Neo 2 is actually ergonomically optimized, we would expect the most frequent characters to be on the easiest to reach positions. Of course, it depends a little on personal taste, keyboard form and hand size how easy each position is, but let’s go with the arrangement the Neo devs chose for their letters. If the layout is optimized, the most frequent letters and the most frequent punctuation characters should be on the same physical keys.

Let’s see if this is true. Here is a little table, showing the letters sorted by (German) frequency and the corresponding Mod3 character. I’ve put the keys with “.” and “,” according to their left-hand equivalent. Green means 5% or more, yellow down to 1.5% and the rest is red (which is often around .01%). Have a look.

Great fucking job, guys. The 7 best keys get only 2 frequent characters. 6 frequent characters are in the middle of fucking nowhere. You decided to place an obsolete character, the long S, that no one in my fucking news cache even used at all, in a better position than ,, ., “, ‘[3] and #, which together make up a good 40% of all punctuation. [0] The one character that is more frequent in many cases than any letter except the friggin’ E, and more frequent than 2/3 of the letters in general, the underscore, isn’t even anywhere near a good position.[1]

Way to go, Neo, way to go.[2]

[0] Some of the reasoning behind this is that any Mod1 key is easier to reach than any Mod3 key. If you think that Mod3+N is harder to type than Ö, then I wouldn’t let you design a wooden stick because it seems your brain is barely able to use 2 fingers at the same time.
[1] “But I don’t program! I rarely need an underscore!” But you constantly use curly brackets, more often than any other bracket?!
[2] To be fair, it’s impossible to optimize for punctuation-heay curly-bracket languages and normal text at the same time. However, this half-assed mess makes it hard for both groups. That’s clearly not a good solution. I chose to optimize for the programming languages because a) I use them more and b) they tend to be so rich in punctuation that they simply overshadow normal text.
[3] Which are not even normal punctuation, if the Neo devs weren’t so inconsistent in their design. If you want to follow official guidelines[4], you should use the German quotation marks „ and “, and the proper accent ’. Which are even harder to reach. Which makes the whole thing even more retarded.
[4] Prescriptionist dipshits are the topic of another rant.

When studying a programming language, I find it useful to look up the background of its creator(s). It will tell you much about its internal design, style and usefulness.

A few examples.

Lisp, and to a lesser degree, Python. You can really tell that John McCarthy and Guido van Rossum have a strong mathematical background. The language is simple, elegant and gives you few, very carefully designed tools. Lisp is maybe the language that is conceptually the most beautiful, yet totally impractical. You probably should write in Lisp, in some ideal world, but you won’t be able to get anything done. It completely clashes with how you actually think and would like to write a program.

C. Ah, a physicist. Simple tools, yes, but completely designed for the task at hand. A good C program may be very powerful, but you always feel you have to study for years until you can get it right. It often feels like black magic. Properly understanding pointers and memory allocation alone can take quite a long time, even just on a conceptional level. You may get the basic idea, but then there are all kinds of traps and exceptions and weird cases were just everything breaks down.
I always feel reminded of quantum physics. It may not be that hard to get the concept of uncertainty at first, but when you look at practical cases, it gets totally messy. You have to use funky equations that bloat and bloat, weird concepts just so you can handle it and get results at all, and when you design an experiment, it will still break down and you’ll spend weeks fixing bugs.
I love the language, but if the universe were written in it, it would have constant leaks, be insanely huge compared to its actually purpose and most of its parts would be all over the place, in seemingly unrelated places. Actually, that does sound quite familiar…

C# and Java. Designed by a committee, led by a software engineer. That really tells you everything you need to know. They are full of cruft (backwards compatibility is one of the worst ideas ever), concepts that sound nice on paper, but have not proven to be really useful and huge, quite often to the point of being slow. Companies and governments favor these, for obvious reasons.

Perl. The only (?) programming language designed by a linguist, which is probably why I love it so much. Actually, it feels a lot like Japanese to me. The set of keywords and basic functions is huge, with a word for each specific idea and fine nuances. Context is important and words can mean very different things depending on how you use them. In fact, because of context, you can often leave out redundant or obvious parts.
It may be the only language to encourage you not to use (explicit) variables unless you think you need to.
Looks reflect purpose, such that weird constructs actually look weird, while common constructs become clean and simple. This empowers you to represent ideas the way you think is best for the job. You can always say things differently, if you want. You can be more explicit when talking to a foreigner, slur and mumble when speaking among friends (or to yourself), emphasize a specific part or just use your own kind of dialect. While this makes it possible to write the most incomprehensible program ever, it also allows you to write poetry.
Of course, over time, this has led to weird exceptions, idioms that may not seem obvious at first and a full-on culture you can not ignore when using the language. But the reason why it’s maybe the most human language is it’s choice of elementary building block: Lisp is build around the list, C focuses on memory and numbers, in Smalltalk everything is an object, but the first thing in Perl is the word. Everything is (kinda) a string and most of it’s features are specifically designed just to handle those in efficient and smart ways. Humans do not think in objects, numbers or lists – our mental stack is way to small for this. We can barely add numbers with more than 4 digits or keep track of more than a few dozen people and our memory is kinda hazy, but we can easily remember and use tens to hundreds of thousands of words in complex structures, with ad hoc grammar and no formal (or even central) definition at all. There is a good reason why you use plain text to explain stuff and only the most twisted of minds would consider a mathematical proof straightforward and intuitive. So why do we build languages mostly like the latter and rarely like the former? I don’t know.