define:’d
Volume II
Volume I is here.
I’ve listed this month’s “define:” searches below. Same format, word followed by comments in parentheses:
Celerity (Think Acceleration)
Censorious
Conglobation
Denouement (French is weird)
Frisian (Neal Stephenson giving an analogy.)
Germane (NOT Teutonic! That association wouldn’t be germane.)
Judas Cradle (Eh?)
Lye (Fight Club!)
Patternless (It exists! Apparently, saying “Random” doesn’t cut it anymore)
Perdition
Posit (Opposite of Deposit?)
Reductionist
Travail
Trilby (Fedora, now Trilby. Any more hats I should know of?)
XY problem
Baroque (17th century art, type of pearl, stained glass, and more; Meanings overload!)
Callused (Eww)
CMB (Yeah. Of the “Science. It works” fame)
Collude
Diddle (Um, yeah. I thought it was a musical instrument.)
Homounculus (Full Metal Alchemist)
Indictment
Prosaic (as opposed to esoteric?)
Recondite (is itself recondite)
Seditious (as opposed to subversive)
Trenchant (Trenchant? Trenchant?)
October, then.
Link round-up: Aug 08
Experimental feature: A round up of interesting links that have squirmed into my unwieldy bookmark collection this month. For reference and evangelism.
The mandatory interesting game videos, webcomics, and link-blog posts (like kottke.org) are appended to to the Feed Feed. The rest is custom:
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A short write up about Neal Stephenson [wired.com] including a review of his latest, Anathem.
In every Neal Stephenson novel, there are characters who regard the world with an insatiable yet bemused curiosity; they are fascinated with the way things work and are forever eager to lay on hands, tinker, tweak, and obsess. In other words, they’re hackers. In Anathem, the narrator, Erasmas, though not a techie, shares this trait. So does the author.
*
Emacs v. vi is rooted in the love of Lisp. [stevengharms.com] Apparently, Emacs is a text editor underneath a Holodeck:
As one uses Emacs / learns to edit eLisp, it becomes apparent that you’re meddling with the powers of the Gods. You become aware, over time, that you’re messing with the powers that could create amazing web applications or make SkyNet.
*
Videogame Concept art books [sidtheturtle.co.uk]:
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An infinite square grid of 1 Ohm resistors [geocities.com/frooha]. This one’s is a hard nut to crack. The only other way I’ve seen of solving this problem is on Archiv, and involves Lattice Green functions. (I don’t even know what that means.)
*
Engineer’s Dreams [edge.org], by George Dyson. A hard science fiction(?) piece detailing the rise of Internet sentience.
Only one third of a search engine is devoted to fulfilling search requests. The other two thirds are divided between crawling (sending a host of single-minded digital organisms out to gather information) and indexing (building data structures from the results). Ed’s job was to balance the resulting loads.
When Ed examined the traffic, he realized that Google was doing more than mapping the digital universe. Google doesn’t merely link or point to data. It moves data around. Data that are associated frequently by search requests are locally replicated—establishing physical proximity, in the real universe, that is manifested computationally as proximity in time. Google was more than a map. Google was becoming something else.
*
Where Are the Geniuses of Today? [alexpetrov.com] An excerpt from James Gleick’s Genius, positing that even as the human race grows smarter, the standard deviation’s going down; we’re not much smarter than each other.
Is it only nostalgia that makes geniuses seem to belong to the past? Giants did walk the earth — Shakespeare, Newton, Michelangelo, DiMaggio — and in their shadows the poets, scientists, artists, and baseball players of today crouch like pygmies. No one will ever again create a King Lear or hit safely in fifty-six consecutive games, it seems. Yet the raw material of genius — whatever combination of native talent and cultural opportunity that might be — can scarcely have disappeared. On a planet of five billion people, parcels of genes with Einsteinian potential must appear from time to time, and presumably more often than ever before. Some of those parcels must be as well nurtured as Einstein’s, in a world richer and better educated than ever before. Of course genius is exceptional and statistics-defying. Still, the modern would-be Mozart must contend with certain statistics: that the entire educated population of eighteenth-century Vienna would fit into a large New York apartment block; that in a given year the United States Copyright Office registers close to two hundred thousand “works of the performing arts,” from advertising jingles to epic tone poems.
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Project Euler [projecteuler.net]:
Project Euler is a series of challenging mathematical/computer programming problems that will require more than just mathematical insights to solve. Although mathematics will help you arrive at elegant and efficient methods, the use of a computer and programming skills will be required to solve most problems.
I’m still looking for the right language. The Perl (familiar devil) vs Python (Unknown horror) battle rages in my head.
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Gears of War: Destroyed Beauty [gearsofwarforum.net]
A concept art coffee table book for people who like detailed images of muscle-ripping locust hordes and macho soldiers on their coffee tables.
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And now a touch of frivolty:
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Save the earth. Don’t breed. (Brownie points for killing yourself). These folks are serious.
Church of Euthanasia [churchofeuthanasia.org] (Also, VHEMT [vhemt.org])
From the FAQ:
5. Do I have to kill myself?
Of course you don’t have to kill yourself! If you really want to, though, wait until after you’ve joined the Church. That way, you automatically become a saint, without any additional paperwork. Don’t forget to leave a note thanking and/or blaming the Church, and feel free to will us your estate, if you have one.
*
Where the Hell is Matt? [youtube.com]
Extra points for recognizing the lyrics in the background score.
*
Splendid, splendid score. And a cutesy game to go with it: Sunny Day
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Roll over, September.
Hypocrisy
“You know, when I was a young man, hypocrisy was deemed the worst of vices,” Finkle-McGraw said. “It was all because of moral relativism. You see, in that sort of a climate, you are not allowed to criticize others—after all, if there is no absolute right and wrong, then what grounds is there for criticism?”
“Now, this led to a good deal of general frustration, for people are naturally censorious and love nothing better than to criticise others’ shortcomings. And so it was that they seized on hypocrisy and elevated it from a ubiquitous peccadillo into the monarch of all vices. For, you see, even if there is no right and wrong, you can find grounds to criticise another person by contrasting what he has espoused with what he has actually done. In this case, you are not making any judgment whatsoever as to the correctness of his views or the morality of his behavior—you are merely pointing out that he has said one thing and done another. Virtually all political discourse in the days of my youth was devoted to the ferreting out of hypocrisy.
“You wouldn’t believe the things they said about the original Victorians. Calling someone a Victorian in those days was almost like calling them a fascist or a Nazi.”
Both Hackworth and Major Napier were dumbfounded. “Your Grace!” Napier exclaimed. “I was naturally aware that their moral stance was radically different from ours—but I am astonished to be informed that they actually condemned the first Victorians.”
“Of course they did,” Finkle-McGraw said.
“Because the first Victorians were hypocrites,” Hackworth said, getting it.
Finkle-McGraw beamed upon Hackworth like a master upon his favoured pupil. “As you can see, Major Napier, my estimate of Mr. Hackworth’s mental acuity was not ill-founded.”
“While I would never have supposed otherwise, Your Grace,” Major Napier said, “it is nonetheless gratifying to have seen a demonstration.” Napier raised his glass in Hackworth’s direction.
“Because they were hypocrites,” Finkle-McGraw said, after igniting his calabash and shooting a few tremendous fountains of smoke into the air, “the Victorians were despised in the late twentieth century. Many of the persons who held such opinions were, of course, guilty of the most nefandous conduct themselves, and yet saw no paradox in holding such views because they were not hypocrites themselves—they took no moral stances and lived by none.”
“So they were morally superior to the Victorians—” Major Napier said, still a bit snowed under.
“—even though—in fact, because—they had no morals at all.”
There was a moment of silent, bewildered head-shaking around the copper table.
“We take a somewhat different view of hypocrisy,” Finkle-McGraw continued. “In the late-twentieth-century Weltanschauung, a hypocrite was someone who espoused high moral views as part of a planned campaign of deception—he never held these beliefs sincerely and routinely violated them in privacy. Of course, most hypocrites are not like that. Most of the time, it’s a spirit-is-willing, flesh-is-weak sort of thing.”
“That we occasionally violate our own stated moral code,” Major Napier said, working it through, “does not imply that we are insincere in espousing that code.”
“Of course not,” Finkle-McGraw said. “It’s perfectly obvious, really. No one ever said that it was easy to hew to a strict code of conduct. Really, the difficulties involved—the missteps we make along the way—are what make it interesting. The internal, and eternal, struggle, between our base impulses and the rigorous demands of our own moral system is quintessentially human. It is how we conduct ourselves in that struggle that determines how we may in time be judged by a higher power.”
From “The Diamond Age” by Neal Stephenson.
The notion that it is a moral failing to say one thing and do another is bizarre.
It might be less than ideal (whatever that means), but it certainly beats preaching nothing and lacking morality altogether.
Hypocrisy is overrated.
A remarkable idea
Two short excerpts from “The Value of Science” by Richard Feynman:
Another value of science is the fun called intellectual enjoyment which some people get from reading and learning and thinking about it, and which others get from working in it. This is a very real and important point and one which is not considered enough by those who tell us it is our social responsibility to reflect on the impact of science on society.
Is this mere personal enjoyment of value to society as a whole? No! But it is also a responsibility to consider the value of society itself. Is it, in the last analysis, to arrange things so that people can enjoy things? If so, the enjoyment of science is as important as anything else.
Not a point of view you hear often. (As a side-note, I am reminded of the science power-dream alluded to in Fred Hoyle’s “The Black Cloud” [spoiler alert]. One of the reasons you don’t hear this point of view being expressed often is perhaps because the ones in charge of allocating resources towards science are often ignoramuses who lack the capability to comprehend this argument.)
When we read about this in the newspaper, it says, “The scientist says that this discovery may have importance in the cure of cancer.” The paper is only interested in the use of the idea, not the idea itself. Hardly anyone can understand the importance of the idea, it is so remarkable. Except that, possibly, some children catch on. And when a child catches on to an idea like that, we have a scientist. These ideas do filter down (in spite of all the conversation about TV replacing thinking), and lots of kids get the spirit — and when they have the spirit you have a scientist. It’s too late for them to get the spirit when they are in our universities, so we must attempt to explain these ideas to children.
An interesting thing about R.P.F’s essays* is that they convey the profundity of their ideas in the simplest of language. Try as I might, I cannot think of anything relevant to add to the excerpt.
R.P.F says it all.
define:’d
An interesting indicator of what I’ve been up to during any given week is the “define:” history of my Google toolbar. As an experiment, I’m posting snippets from every week’s history to see if a pattern will emerge over the weeks. The entire definitions history for this week has over a hundred phrases (and this week was a slow one, too); This is my primary way of picking up new words as I encounter them.
Below, the most recent ones with comments in parentheses:
Copacetic (Seriously?)
Consortium (no plural)
Pugnacious
Symbolise (s vs z)
Initialise (s vs z)
Scilon
Google bomb
Artifice (Ruse; why art?)
Shoestring (more to it than meets the eye)
Eclectic (shades of meaning)
Rhizome (Biology vs Philosphy)
Ostentatious (As opposed to ostensible)
Autocracy (vs. monarchy. Apparently, there’s no difference.)
Talos
Precocious (Adj. used to describe the young Feynman)
August (Knew all the meanings, for a change)
Co$ (This one’s hilarious)
And I can never get past level 48 on Freerice. Not even when I cheat!
Obscurantism
Seinfeld (the sitcom) was filled to the brim with self-reflexive scenarios. On a number of occasions, Costanza and Seinfeld engage in a conversation on their conversation- but the one occasion this was particularly striking was when the two brainstorm to find an idea for the sitcom Jerry. (The irony.) The series nested itself in a self-deprecating public display of self-awareness, exemplified by the instance where Costanza utters in a moment of ennui: “This, this is what the show’s about! The show, is about nothing.”- breaking the fourth wall in the process (and, some might add, exposing it to be the tripe it is).
From mathworld, a corky, off-beat answer to a question from the Google Labs Aptitude Test:
19. ‘Tis known in refined company, that choosing K things out of N can be done in ways as many as choosing N minus K from N: I pick K, you the remaining.
Find though a cooler bijection, where you show a knack uncanny, of making your choices contain all K of mine. Oh, for pedantry: let K be no more than half N.
Answer: ‘Tis more problematic to disentangle semantic meaning precise from the this paragraph of verbiage peculiar.
At this point, some exposition might be in order. What can a satirical in-joke from a TV series have in common with a well known binomial coefficient rule (and sentence construction that would spoof a recursive transition network)?
What, indeed.
“Why are you producing so few red blood cells today?”
A while ago, it was thought that the trick to making a machine play chess well was to extend how far down the branching network of possible moves it could examine. Irrespective of how far they can look ahead, though, skilled human chess players can confidently confound (or at least match) most chess programs of today.
Why?
I found a fascinating account of this puzzler involving AI and human thinking in (where else?) GEB. Apparently, the reason for this was known from the 1940s; If you’ve ever played chess- or play regularly but with skill befitting a two year old, you’ll come to appreciate the reason immensely.
Chess novices and chess masters perceive a chess situation in completely different terms. The results of the Dutch psychologist Adriaan de Groot’s study (from the 1940’s) imply that chess masters perceive the distribution of pieces in chunks.
There is a higher-level description of the board than the straightforward “white pawn on K5, black rook on Q6″ type of description, and the master somehow produces such a mental image of the board. This was proven by the high speed with which a master could reproduce an actual position taken from a game, compared with the novice’s plodding reconstruction of the position, after both of them had five second glances at the board. Highly revealing was the fact that masters’ mistakes involved placing whole groups of pieces in the wrong place, which left the game strategically almost the same, but to a novice’s eyes, not at all the same. The clincher was to do the same experiment but with pieces randomly assigned to the squares on the board, instead of copied from actual games. The masters were found to be simply no better than the novices in reconstructing such random boards.
The conclusion is that in normal chess play, certain types of situation recur- certain patterns- and it is on these high-level patterns that the master is sensitive. He thinks on a different level from the novice; his set of concepts is different. Nearly everyone is surprised to find out that in actual play, a master rarely looks ahead any further than a novice does- and moreover, a master usually examines only a handful of possible moves! The trick is that his mode of perceiving the board is like a filter: he literally does not see bad moves when he looks at a chess situation- no more than chess amateurs see illegal moves when they look at a chess situation. Anyone who has played even a little chess has organized his perception so that diagonal rook-moves, forward capture by pawns, and so forth, are never brought to mind. Similarly, master-level players have built up higher levels of organization in the way they see the board; consequently, to them, bad moves are as unlikely as illegal moves are, to most people. This might be called implicit pruning of the giant branching tree of possibilities. By contrast, explicit pruning would involve thinking of a move, and after superficial examination, deciding not to pursue examining it any further.
If you pause to think about this, it comes across as an utterly spellbinding revelation. Like the proverbial frog in the well, mental models and levels of perception above what we are used to are very hard to digest- but they’re there, as the excerpt explains.
The “chunking into levels” is a predominant theme in all complex systems we see*, at least in the way we seek to understand and analyze them- from computer systems (Hardwired-code->machine language->Assembly language->Interpreters and Compilers) to DNA (Specifying each nucleotide atom-by-atom->Describing codons with symbols for nucleotides->Macromolecules->Cells), and even human thinking.
The last bit requires a little exposition, but first, we note the analogy between the nightmare of writing complex useful computer code in machine language and the terror of reading a virus DNA atom by atom. In both cases, we would miss out on the higher level structures that embody computer programs and virus DNA with their attributes- complex systems possess meaning on multiple levels.
The multi-level description extends to virtually every complex phenomenon. Weather systems, for instance, possess “hardware” (the earth’s atmosphere) which has certain properties hardwired into it (hardwired code) in the form of the laws that flitting air molecules obey, and “software”, which is the weather itself. Looking at the motions of individual molecules is akin to reading a huge, complicated program on the machine language level. We chunk higher level patterns into storms and clouds, pressures and winds- large scale coherent trends that emerge from the motion of astronomical number of molecules.
As for multi-level human thinking, it is illuminating to first appreciate that a higher level perception of a system does not necessarily mean an understanding of the lower levels too. One does not need to know machine language to write complex computer programs, nor is one required to be aware of individual molecule trajectories to describe or predict the weather.** In fact, a higher level of a system may itself not be “aware” of the levels it is composed of, such as AI programs that are ignorant of the operating system they are running on. The higher level descriptions are “sealed off” from the levels below them, although there is some “leakage” between the hierarchical levels of science. (This is necessarily a good thing- or people could not obtain an approximate understanding of other people without first figuring out how quarks interact.)
The title of the post is another excerpt from GEB, derived as a somewhat whimsical analogy:
The idea that “you” know all about “yourself” is so familiar from interaction with people that it is natural to extend it to the computer- after all, AI programs are intelligent enough that they can “talk” to you in English! Asking an AI program (which is compiled code) about the underlying operating system is not unlike asking a person “Why are you producing so few red blood cells today?” People do not know about that level- the “operating system level”- of their bodies.
This post owes its existence in entirety to GEB, the Big Book of Big Ideas.
The false positive (in fiction)
It’s not often that I find subtle math lessons hidden in works of fiction; much less blatant, straightforward, undisguised ones. They’re always welcome- especially when they reinforce something I’m already aware of.
An excerpt from Cory Doctorow’s Little Brother:
If you ever decide to do something as stupid as build an automatic
terrorism detector, here’s a math lesson you need to learn first. It’s
called “the paradox of the false positive,” and it’s a doozy.Say you have a new disease, called Super-AIDS. Only one in a million
people gets Super-AIDS. You develop a test for Super-AIDS that’s 99
percent accurate. I mean, 99 percent of the time, it gives the correct
result — true if the subject is infected, and false if the subject is
healthy. You give the test to a million people.One in a million people have Super-AIDS. One in a hundred people that
you test will generate a “false positive” — the test will say he has
Super-AIDS even though he doesn’t. That’s what “99 percent accurate”
means: one percent wrong.What’s one percent of one million?
1,000,000/100 = 10,000
One in a million people has Super-AIDS. If you test a million random
people, you’ll probably only find one case of real Super-AIDS. But
your test won’t identify *one* person as having Super-AIDS. It will
identify *10,000* people as having it.Your 99 percent accurate test will perform with 99.99 percent *inaccuracy*.
That’s the paradox of the false positive. When you try to find
something really rare, your test’s accuracy has to match the rarity of
the thing you’re looking for. If you’re trying to point at a single
pixel on your screen, a sharp pencil is a good pointer: the pencil-tip
is a lot smaller (more accurate) than the pixels. But a pencil-tip is
no good at pointing at a single *atom* in your screen. For that, you
need a pointer — a test — that’s one atom wide or less at the tip.
The “false positive cautionary” rears its head all too often; I’ve seen it on TED, in textbooks on probability, and even in a puzzle column in The Times of India- which is the last place you expect to find common sense.
‘Little Brother’ itself was very… didactic. This isn’t a review, so I’ll let a few more (spoiler-free) excerpts do the talking.
The law didn’t care if you were actually doing anything bad; they were
willing to put you under the microscope just for being statistically
abnormal.
I wasn’t the only one who got screwed up by
the histograms. There are lots of people who have abnormal traffic
patterns, abnormal usage patterns. Abnormal is so common, it’s
practically normal.
And finally, a few lines that really struck a chord, primarily because they’re intimately representative of a teenager’s awe.
If you’ve never programmed a computer, you
should. There’s nothing like it in the whole world. When you program a
computer, it does *exactly* what you tell it to do. It’s like
designing a machine — any machine, like a car, like a faucet, like a
gas-hinge for a door — using math and instructions. It’s awesome in
the truest sense: it can fill you with awe.
(Side note: Little brother is licenced under the Creative Commons licence and can be downloaded for free off the Internet.)
