Binged a bit. Time to purge.

I’m hoping to roll up all the blogs into something like Middleman (which is personally ironic, since I started using Blosxom way back in 199X for X small), and all the special-purpose dynamic sites and sub-sites into some web projects I’ve been working on.

Including some projects I’ve been working on for a long time now, and hope to announce next week.

Said web work to be done by mid-April.

So it’ll be noisy, is what I’m saying.

And no, I’m not being very careful with “my personal archives and brand”. Fuck ‘em. If you didn’t read them then, there’s not really that much reason to have them around now, is there?

So it’s not so much “Pardon Our Dust!! ” as “Change is Supposed to Hurt. THAT’S HOW YOU KNOW IT’S WORKING.”

[And if you haven’t seen it, I think Brian Kerr’s idea for a premium Pinboard.in service that deletes old bookmarks is excellent.]

And that’s a nerdy in-joke right there. It’s about multiple-objective (or multi-criterion) search. I’ll tell you about it another day. Every time I make a nerdy joke, or you hear a nerdy joke, that you don’t understand, you write it down and I’ll tell you what it means. Then you memorize it and soon you’ll have a decent armamentarium of nerdy jokes. Oh, the adventure of molding little new nerds!

So anyway, the distinction between the names we use is not specious. Very Smart People do Genetic Programming. I am stupid, and also a fool, and forgetful, and have some kind of amnesiac fault in my head that makes it so whenever I do an experiment I somehow forget all the stuff that normal people are able to assume is true about the system and their tools and their habits and their data.

This is a serious handicap. It means I am forced to constantly peek at things everybody else is able to take for granted. I plot charts of measures that nobody else bothers to plot; I write tests for code that everybody knows will just work; I refuse to trust other people’s libraries if they don’t have tests because me, I am so dumb; I emit a series of plaintive “Why?” noises whenever I read other people’s preprints or published papers. And lord help the speaker at any conference I attend, me with my “two questions” I always ask after every goddamned one of them when all they really need is a drink and a chair.

Poor Bill. You’ll have to forgive him—he has a condition.

To the point: In the field of Genetic Algorithms, and by (specious) descent in Genetic Programming and more generally in Machine Learning, the Very Smart People have some habits I’m unable to save in my faulty long-term habit cache.

One of the most common is The Plot of the Best So Far.

In Machine Learning projects (including Genetic Algorithms and Genetic Programming ones), what you often have when you start is just a bunch of examples of something. Then you run a program that takes those examples as inputs and chugs away and finally poops out a model of the structure:function relations to be found within those examples. The algorithm does magic thinking stuff, and it knows what you want, and it looks at the data for you. For cultural reasons Machine Learning (and GA/GP) folks talk about this process as “search” rather than “guessing a lot of times”, but it is nonetheless a fact that most algorithms really are just generating a series of increasingly biased guesses.

With few exceptions, Machine Learning (and GA/GP) algorithms start with a couple of dumb guesses, then “look” at those and try to “learn” how to make more cunning guesses over time. They guess and guess and guess, until eventually they either start being boring, or they “prove” in some very complicated but authoritative way that the final guess is the absolute best that could ever be found.

As a side effect of this iterative process, they produce not only a series of guesses, but of necessity those guesses are associated with a series of scores. No matter what they’re up to or when they quit, Machine Learning algorithms are pretty much all about moving away from the bad-scoring initial dumb guesses, and towards the better-scoring sort of well-considered and elegant and refined models.

Bear with me. Because of my persistent troubles, I have to remind myself of this.

So anyway, this is how Machine Learning works: Very Smart People who don’t have my problem are handed a big pile of examples collected by a “domain expert”, and they know already what it is they want to do for models. (I feel like a blind man explaining color when I say they “know already”.) And then they just download and run and report the elegant and refined models they get at the end of running a “search”. I mean they start a computer running, and move the laptop off their legs so their thighs don’t burn while it’s guessing, and when the guessing is done they will have a very nice model of the examples, that has a good score. This very nice model is handed to the domain expert whose examples were dumped into the hopper. In my experience, the domain expert is typically very happy to have something more explanatory than the huge pile of virtual post-it notes and dumb guesses they started with.

Very Smart People who write Machine Learning algorithms go through a bit more effort. For them, the motivation for writing new Machine Learning algorithms is to show that the series of guesses produced by an old, obsolete algorithm any fool can download as an R library won’t get to the elegant and refined scores as fast as the new one they thought up. So Very Smart People who write Machine Learning algorithms like to run a lot of horse races.

What’s a horse race? Well, first you need a pile of examples from a domain expert. Then you take Bad Old Machine Learning Algorithm Number One, and you start it running, and instead of walking away and waiting for it to be done, you write down the list of scores for the guesses it produces along the way. And then you do the same for Excellent New Machine Learning Algorithm Which You Wrote. And you show that ENMLAWYW is consistently better at reaching the best-sounding scores, or gets better faster, or more often gets better, or maybe has more something else than BOMLANO. And then you publish a paper!

Sucky old BOMLANO. Nobody uses it any more.

Now it’s fine to make the claim; that will almost always get you where you want to be. But in papers, it’s important to show and tell. So you need to make a drawing of some sort, the kind that fits into a two-column layout in a four-page paper without getting too squished. Alas, many Machine Learning algorithms (even that crap BOMLANO) produce a huge number of scores as they guess towards success, and plotting those is messy and confusing.

So in your time-series plots of the horse race, you show the Best So Far. For several reasons: Plotting 50000 points or so is messy and confusing, especially in a 5-cm square. To be honest most people don’t really care about your thing or what you claim to do; they just want to know you algorithm isn’t worse than BOMLANO (which they learned in graduate school so it has a special place in their habit caches), and so for them best at the end is important—hell, if they’re domain experts, they only want one model after all. But best at the end is a tricky thing, and really you want to show one of those other things I mentioned: ENMLAWYW gets better faster than BOMLANO, or ENMLAWYW gets higher scores than BOMLANO, or ENMLAWYW isn’t as good as often as BOMLANO.

Notice that nobody really cares, beyond a few cranks like me, about anything but the One Final Answer a Machine Learning algorithm provides. Some rare advanced engineering professors might sometimes teach a seminar in multi-objective search, which (I said I’d explain) usually return a whole bunch of high-scoring things that are different from one another but “tied” in the sense the domain expert cares about. But even that is the final answer: it’s just a lot of them.

It’s stupid to care about what happens before an algorithm is done. Everybody knows that stuff.

Woe for me. I’m a sick and confused man because of my habit problem. It makes me look. Not only when I write a new algorithm, but whenever I run one. Not only at the best scores so far, but at every score of every guess. Because I just fail every damned time at assuming I know what’s happening in there.

And every time I look, I am confused by what I see. All kinds of stuff, usually.

Here’s an example from yesterday.

I’ve been looking into some bioinformatics data from Jason Moore’s lab. The pile of examples here are SNP genotype data collected from sick and healthy people, and the models one wants are supposed to “explain” the relation between those folks’ genomes and disease state. I’m initially representing the models using a little domain-specific language I copied from some software they use, though I called the models “Snip scripts” for reasons that aren’t important except to explain the plot title.

Now a fully able Genetic Programming practitioner (and surely most Machine Learning ones too) would be able to look at that problem, and say “Aha!” or whatever it is they say, and boot up R and make it go and not burn their knees, and they’d print the best model out on some kind of dot matrix printer (I suppose for dramatic effect) and tear it off and hand it to Jason. And that would do.

But here’s what I do. I worry about how much better one kind of guessing (call it “dumb”) is, compared to another kind of guessing (call it “machine learning”). So I have to check and see.

Here’s what you get, score-wise, when you make 100000 guesses. That is, in this dopey handicapped algorithm, I picked 100000 random Snip scripts, scored them according to Jason’s instructions, and plotted them as a time-series of eeny weeny little Xs. The scores range (in theory) between 0.5 and 1.0. (Don’t ask about the extra space on the y axis, OK? It’s for something else.)

[PDF!][PDF!]

This is just the sort of thing I like to see. I’m so skittish, I need the reassurance that dumb guessing won’t work on a complicated problem like this. And I also like to have some kind of “compared to what?” distribution to compare against, when I do fancy machine learning things, and there you got one. The distribution is: pretty much somewhere between 0.5 and 0.6 is all you’re gonna get. There’s also a little bit of structure in there, like maybe some kind of horizontal striping, but that might just be rounding. Nothing fretful at all.

But I’m still a skittish fellow with a habit problem, and like I said I don’t really “do” Genetic Programming so much as poke around in the world of data and models. Genetic Programming as such is actually kind of complicated and Microsoft Wordish, to be honest: over the years Very Smart People who write new algorithms have thrown a lot of junk in there, in the spirit of “biological inspiration” or something like that. They wrote a lot of papers, so they’re pretty good, whereas I’ve written damn all for papers, so clearly I need to take it slow.

Now Genetic Programming is as a King among Metaheuristics, and Dumb Guessing is a lowly ant in comparison. Being skittish, I decide yesterday to make a little step and see what happens when we approach the noble Genetic Programming. So I try hill-climbing.

Now hill-climbing is a time-honored straw man of a Machine Learning algorithm. If you can’t figure out the algorithm from the name, let me tell it to you right now: make a random guess, then make another random guess and keep the new one if it’s at least as good as the first one. If you want to see dumb hill-climbing in action, look at the plot I’ve already posted, and imagine that instead of throwing every guess away when I make a new one, I kept the best so far.

Him again.

If you peer at the guessing plot, you can see I could maybe have accidentally found a best-so-far score of about 0.6 after 20000 guesses or so, and pretty much not improved that. Dumb hill-climbing is pretty dumb.

So yesterday I think I can check off “dumb hill-climbing” as completely entailed by my “dumb guessing” plot, and I have to ask: What’s my next step towards King Genetic Programming?

That would be “not-so-dumb hill-climbing”. Basically the only difference between “dumb” and “not-so-dumb” is the way I generate new guesses (and that phrase is in itself a deep lesson in Machine Learning you should really take to heart): instead of replacing the entire guess, I’ll use what I already “know” to make an “informed” guess.

In this case, what I did was take these Snip scripts, which are strings of tokens, and replace some of the tokens with new randomly-selected ones. For reasons I don’t need to explain here, any string of Snip language tokens is a valid model, whether or not it’s a good or bad scoring one.

So I’ve got my next tentative step in metaheuristic space towards Genetic Programming (all kneel), which is this: I make a dumb guess, and I score it, and then I make a second one where I change some proportion p of the old script’s tokens to new ones, and I score that, and I keep the new one if its score is no worse than the old one’s.

Ah, but, but… what proportion p should I use? Crap, I have no damned idea—damn this broken brain! If the number is too high, I’m changing every token so it’s back to dumb guessing; if the number is too low, I’m not changing hardly any tokens, and it’s like even dumber guessing where I keep looking at the same guess over and over. Ummm, somewhere in between?

Now there are Very Smart People who have explored this at length, but like I said I find I can’t read their papers without emitting a series of “Why?!” noises, so to avoid waking up the dog from his nap I just tried a bunch. Like, all of them. I just started with a high number (0.5), and cut it in half every once in a while until it got down to pretty much nothing, and then popped it back up to 0.5 again.

Do I know what’s happening? I do not.

So the I look at some runs. Here’s one. Ignore the little tracy lines on the bottom half, which I won’t bore you by explaining.

[PDF!][PDF!]

The eeny weeny “X” marks are once again the scores of every new guess I make. From left to right I’m making a guess, and then making a new guess based on that, and keeping the new guess if it’s at least as high-scoring as the old one, or the old one otherwise. And because I don’t know what mutation rate (which is technically controlling “uniform mutation”, meaning p indicates the chance that I replace any given token in the old script with a new random token) I start it ridiculously high, and gradually drop it to 0.0, and then pop it back up again to a high number. That’s the “oscillation” you can see over time, even though I don’t trace the mutation rate itself on this plot.

You can see when the mutation rate is high, because the distribution of “new mutant guesses” looks an awful lot like the distribution we see in the “dumb guesses” plot we already talked about. And you can see when it’s small, because the distribution of new guesses is pretty much “even dumber” by picking the same damned script over and over—the best I’ve seen so far—so the little Xs cluster up near the plateaus.

But in between, stuff happens.

Now if I weren’t a handicapped fellow, I would have been content to plot the best scores so far. I’d watch the trace pop up to 0.6 or so around 5000 guesses, and I’d be like “hey!”. I’d grin and watch the best score so far pop up again to 0.7! around 10000 guesses, and I’d be like “whoa!”. And I’d see it hop all the way up to around 0.8 and I’d be like “omg!” and I’d print it out on the Okidata and tear it off with a flourish and hand it to the domain expert with a little dusting off of my hands to indicate how well I know it’s a job well done.

But I accidentally plotted every score, not just the best ones. Well, no more accidentally than I accidentally wrote this long-winded exposition: it’s what I am forced to do by my faults. It’s not a choice: I was born this way.

As a result, I am given some puzzles. Not just puzzles, because there are the things I’ve already pointed out to you, like: I can infer something from this plot of everything that you would never see if you just looked at the maximum score over time. I can explain something about the effect of mutation rate on the ability of this guessing process to find improvements.

But there are puzzles, too.

Do you notice, like I do, that there are an awful lot of different high-scoring X marks, ones that are well outside the expected distribution of scores reached by “dumb guessing”? And do you also see that they only appear after a new best score has appeared?

Do you see the horizontal banding, over time, down in the middle of all those scores? Remember that those are still random variations of the “current best”. Somehow random variations tend to be extremely biased, score-wise, and produce quite similar functions even though their structures must be substantially different.

Do you see something qualitatively change, around 25000 guesses? There’s something different about the way the scores are clumped up around the best so far, and in a few other clumps somewhat below that plateau. There’s also something subtle about the way the plateau itself is composed, as though there were a lot of little incremental improvements over the last 20000 guesses.

Neither of us would have seen those details, if I’d plotted “best score so far”. If I were a real, non-handicapped Machine Learning practitioner (note I don’t say Very Smart Person, because that’d be pretentious), that would be fine: my domain expert customers would be interested in the one best-scoring answer at the end, not that wobbly stuff in the middle; my paper-reading customers would be interested in the time-course of best-scoring answers compared to the well-known BOMLANO benchmark, not all that not-quite-so-good junk that muddies the issue.

Hell, I only showed you one of five runs I did. The others look different. Did I do those other runs Because Stochasticity, and I want to be extra-fancy and report a variance? To make the case for my pronouncement of the Best Model Evar?

No chance; that’s the Very Smart way to work. I did five because I don’t even trust that one example is enough to show me everything I might stumble over. I’m a skittish fumble-thumbs when it comes to this stuff, and I want to know what actually is going to happen before I stride on into using Canned Algorithms (aka, “where angels fear to tread).

And I burned my knees, too, by the way. Seriously. This is a “notebook computer”, not a “laptop”, and technically its CPU is not supposed to reach its 180°C sitting on your thighs.

So here’s the interesting thing, to me: What’s hidden away in all that not-quite-so-good stuff? That is, what are the almost-as-good models, and what can I learn from them about the system this data represents?

Very Smart People have written, authoritatively, that the strength of modern Machine Learning algorithms is the degree to which they tacitly embody that knowledge as they efficiently capture information in the examples. Genetic Programming uses the Power of Biological Inspiration; other Machine Learning algorithms use other powers, like the Powers of Definite Convergence and Ergodic Coverage and Gradient Descent to capture the same stuff. They’ve shown this through mathematics (based on only a very few assumptions), and through experimentation (based on a huge number of benchmarks). They trust one another. This stuff is built right into R libraries and Mathematica; it’s no longer subject to question, honestly.

I’m not that smart. So I have to look. And as result I get distracted: Isn’t it interesting that there is so much hidden in that unexplored detail? Who but the broken shall attend to the otherwise unremarked?

This is the sort of thing you’ll get told does not substantively advance the field. You can’t write a paper about it, or even give a talk at a workshop. The field has moved on, way far past this sort of poking, on to other much Smarter things.

Which is one reason why I incessantly claim to have no field. Me? I just answer questions for people, and we explore together. I don’t have the right to tell them stuff.

Also, I have misplaced the dramatic dot matrix printer.

Some years back I was a staunch believer in the importance of fostering community among one’s followers, and I even made suitable noises to point out the risks to small voices when a Big Player “steals” comments from a little blogger by reblogging it somewhere else where there’s a more active comment thread.

Yeah, well, what can I say? One changes one’s mind.

Look at comments. Look—as I’ve been—at the stream of comment-bots that WordFence Security blocks every few seconds from my wordpress installation.

Conversation is awesome. Community is awesome.

But we have those. These holes in our heads make noises and receive them from the very air, and these gelatinous orbs do some liquid crystalline magic and we can, you know, see the shadow of the world in our minds. Or imagine we do so.

What more does community need, really?

But seriously: If you want to respond to something I’ve written here, respond in writing. We all have a place, now. Tweet. Blog. Send a letter. Call me up.

Or better yet: come sit with me a few hours or days, and we will make something new and better.

call
a
sentence
monotonic
if

and read down the columns of letters, all the columns are alphabetical in top-to-bottom order—skipping blanks whenever a letter’s missing from a given column.

In the example, the first letters of the words are CASMI, so that sentence is not even weakly monotonic. But the sentence “Boy dozes” is weakly monotonic: the “columns” are BD, OO, YZ, _E and _S, respectively, and (ignoring blanks) those are all in alphabetical order.

What’s the longest weakly monotonic English sentence?

Call a sentence “strongly monotonic” if you’re not allowed to skip missing letters, after any letter appears. That is, once a letter appears in a given position, there must be at least that many letters in all subsequent words.

So “Boy dozes” is both weakly and strongly monotonic, but “Free fun” is weakly but not strongly monotonic.

What’s the longest strongly monotonic English sentence?

This was my first “real” visit to an academic environment in a few years—the sort where I’m not just lurking in the background and hanging out with my tenure-track friends. Indeed, the last time I did something like this I think it was my 2008 visit to Nic McPhee at the University of Minnesota at Morris. Like Jason, Nic was also nice and helpful, but UM Morris a qualitatively different academic culture from that of the medical school at Dartmouth. Both times I visited mainly to observe the local work cultures, especially looking at the collaborative network that connects students, faculty and staff—within and between their respective labs, departments, disciplines and institutions.

I’ve been building a catalog of cultural and institutional routines and obstacles that side-track—and (often permanently) delay—potentially valuable projects that could otherwise be explored quickly. The same old question I always ask, more or less: What do you wish you had more resources to pursue?

Recently I’ve found a useful way to explore these routines and obstacles is to discuss little counterfactual scenarios and see what bubbles to the surface. It can be an interesting way to surface transgressive behavior without actually, you know, trying it out in real life.

Here’s a variant that came to me as I stared out an airplane window recently:

Suppose a highly-respected but soon-to-retire researcher in Computational Physiology visits the salient department at Large Ivy University to give a seminar. As one comes to expect from a late-career luminary, her talk tends a bit towards the philosophical, but it brings up a number of interdisciplinary questions and unconventional approaches to the construction, use and study of Computational Physiological systems. There’s a lot to think about, and a lot of material that most mainstream colleagues just don’t run into very often.

After her seminar, she spends a day or two visiting her Host’s lab and a few of his collegial LIU labs, chatting with staff, students, junior faculty, and their various Principal Investigators about their ongoing research and technology, and comparing notes on the interesting things that folks in other institutions and disciplines have been doing.

As it develops, she takes an interest in one of the ideas a graduate student brings up in passing. The idea isn’t a part of the student’s thesis research, nor is it even salient to the funded projects in any of the LIU Comp Phys labs. But it’s a good idea, and she decides it would be fascinating to see how it would play out, and (even better) it’s a purely computational project that the visiting scholar realizes could be done in a few weeks… by an agile team of software developers. It wouldn’t need a grant or even a long planning or proposal process to see what happens.

Neither LIU nor the visitor’s home institution has anything like an “agile team of software developers” as a component—hah! Not even a little bit. But in her increasing time spent “out in the world”, the visitor has actually run into folks who have worked in those environments, and started to see the point of the various “agile values and practices”—at least as a kind of Utopian ideal.

Mind you, this idea isn’t anything commercial. But it’s a damned interesting project, and to be frank it would be a pity to see it delayed until the student graduates, and finishes her post-doc(s), and gets done with tenure track, and so on and on.…

So the visitor chats online with a few people she knows, and they agree the project as sketched is a feasible way to spend about a month of work. Obviously the student should have the lion’s share of academic (and other!) credit if it goes forward. But the agile folks she chats with remind her that the point of the “one team” practice is that the student probably needs to be co-located with the team doing the work with her.

Alas, the student has a thesis committee meeting coming up shortly. She’s been asked by her committee to work over the draft bibliography and bring it more in line with the standards expected in the high-impact journals in the field: get rid of those weird references from graph theory and ecology papers and add more from the modern Comp Phys literature, for example.

Nothing like this project has ever been in any of the Comp Phys journals. It may not even catch on in the community, compared with the more obviously receptive audience over in Artificial Mentation. But the AM folks have never even considered Comp Phys as a domain where their stuff might be useful. It’s a blue-sky project, in that sense.

What has to happen to get this work done? Does the student leave for a month? Does everybody wait until “it’s safe”? Does the student’s advisor collaborate with the visitor on a grant, and use the funds to (eventually) fund an in-house (and almost certainly inagile) development project that will take several years to do what might happen in a month under other circumstances?

Who gets credit? The visitor wants the student to get essentially all of it. Does the student’s advisor get some? Under what circumstances?

Who gives permission? Who needs to give permission? The student should be working on her thesis. The advisor should be seeing to his student’s professional track. And so on.

Who is a risk? What sort of risk?

It was the first anniversary the other day of my Mom’s death. It was also about the same time in 2004 when my wife’s Dad lost his leg to a stupid GP who didn’t pay attention to persistent complaints from a knowledgeable diabetic.

And that was also the year when I bought my first Plustek OpticBook. I began buying and scanning Public Domain books when our parents and friends started dying. We’ve run out of parents these days, and our old friends—the ones from the Greatest Generation—are getting scarce on the ground. But that said, I keep scanning a book a day when I can, because it’s the right thing to do.

I try to stay busy. People in my position seem to say that. I try to stay busy.

But my obsession with scanning books isn’t really the focus of this piece. Nor is the observation that people keep on dying, Oh-woe-is-me. I’ll have plenty to say about the former in a few days; as for the dying, they will manage themselves without any advice from me.

No, we gather here today to talk about the overlap between the two, in my life and my wife’s life. And as is my wont I am going to generalize from that dataset of two exemplars, treat us as type specimens as it were. Because I see a pattern coalescing out there in the world, or at least I want to see that pattern.

Even though I don’t know what the actual outcome will be, I know it’s something you might consider “bad” and I want you to have the opportunity to understand before it really kicks in and stuff starts to break. Future historians will always get it wrong, calling it something about wars or computers or classes, when it was in fact the notion of a “career” imploding on itself, and biology beating culture back into place.

But at least it will be written down here for people to ignore.

Right: So first, people die. You might know that bit.

Increasingly, it’s young people who are taking care of their dying older parents. They’re dying in chronic care situations, and long-term home hospice care. And—excepting cases of dementia—caring for your own parents in their last months is something you do for yourself. Nobody who’s ever been in one likes a nursing home these days, outside of dire emergencies, and so if you can afford to care for your loved ones you will.

And so we see a resurgence of family caregivers, working at home, and especially among the middle class.

Now I hate to break it to you, but as it happens when you spend three or four or a dozen years taking care of your dying relatives you won’t get a special-colored lapel ribbon to wear, and there is no magnet for your car. Nobody else really acts like they give a damn, frankly.

But from first-hand experience I’m here to say that in lieu of survivor support networks and redemptive life-affirming-though-glib storylines, you might get mastery. You might not ever be able to explain it to anybody… but it can be there.

More people are taking care of their loved ones, and I think more people all the time are falling into this odd and unsought state of “mastery”. They are in other words un-joining your club, and joining ours. I doubt we’ll ever be a majority, but demographics being what they are, you’re all in for a surprise over the next couple of decades—whether you’re one of us, or one of those left behind….

If you’re among the dying, I’m sorry to inform you that there will be no surprises. Try again next time.

More people are in this situation because parents are having their kids later.

There’s the thing the new hip literate late-career fertility-treated professional Mommies and Daddies don’t seem to have worked out: When your kids age up to around 40 years old, it just works out biologically that at that very same moment you will already be treading down the Road to Being Elderly.

Just because you were old when your kid was young.

In Olden Times, like the first half of the 20th Century and every century before that, Mom and Dad would typically be barely teenagers, and grandma might be as old as you are compared with your career-delayed kid. Hell, there could be four or five generations overlapping in a single lifetime.

But for careerist professional smartypants who wait until a few gray hairs have been accepted upon their brows before the kids? Unless the transhumanist crowd gets crackin’ pretty quick and we all upload our consciousnesses to computers on Saturn like, this week, guess what? You will start your morbidity-and-mortality extravaganza right smack in the middle of your kids’ nascent “careers”.

Like I said, my Dark Witch rant is not on the theme of woooo—you’re going to die!! Get somebody else to point a bony finger at that gravestone for you.

No, it’s far more interesting to me to point out that as your children come of age, your career culture is going to die. And you will have killed it, by waiting to have your kids until your own (final) careers were done.

I like when cultures die. It’s interesting, it’s fun, it suffuses me with glee. It means we get new cultures, and I am the xenophilic kind of guy.

I have evidence. My wife and I both popped into the world at the headwaters of Generation X, specifically in the first year of that nominal cohort (or the last year of the damned Boomers, but I disavow those who claim it). Whoever named “our” generation did so ex post facto; I think it was probably a columnist who had maybe heard of Douglas Coupland but never read his actual book. But it’s a catchy moniker with which to paint the requisite broad strokes: we’re TV-watching, computers-but-not-Internet, Walkman-toting, ironic, ennuitic, selfish, undirected—all that stuff.

Oh, and flag “undirected” for later, for me, OK? Make a little note.

The Web of History is plaited together like bad macramé from scratchy artisanal strands of Named Generations some asshole historian has dyed in bland 70s colors in an old aluminum saucepan. It’s not written by “the winners”, it’s cut and pasted from random Wikipedia articles by their interns, and never copy-edited. In other words, I’m here (again) to say (again): No. Not so fast, Bucko. It’s more complicated than it sounds, and more interesting.

I slice the situation differently.

Despite being born at the advent of Generation X, I’m a crap representative. I’m here to say we—and the point is we’re definitely a “we” now, and not just my wife and me—are something very different from a “generation”. More of a growing subset that’s an echo and a consequence of a quite different externality.

See, unlike most of “Generation X”, my wife and I were born to old parents. For example, my Dad was born in 1908 in a town now submerged beneath the Columbia River; my Mom was born in 1923 and grew up during the Depression. And yet I was born in 1964.

In our day it took a divorce-and-remarriage play to get the numbers to work out this way, and a lot of crossed fingers regarding Down syndrome and the like. But the inevitable actuarial consequence is that our parents started dying when we were in grad school and getting good and settled in our pension-earning “career” tracks.

All the kids of old parents will have that. Doesn’t matter whether they arise from old-fashioned remarriage, or careerist delay, or the miracle of in vitro spooge-mixing. Old people with new kids just plain tend to die when their kids are younger.

I imagine social scientists and psychologists and crap have said we “suffer” more because of this. They’ve probably got a new DSM V category for “Sad surviving children of elderly parents” all lined up. But they also have a category for “argues with physician over diagnosis” and “sasses teachers too much”, so fuck ‘em.

Life is suffering. Dude who sat under a tree said that a long time back. Learn from it.

What happens is that you stop being part of your Generation. My wife and I are not really part of any Generation, X or otherwise—we’re a demographic subspecies.

If you needed to name us, it would be OK to call us the Hobbyists.

Those of you who are not us should fear us. I meet more of us all the time; I like us, because we’re interesting and talking with the others among us suffuses me with glee. I overflow with glee, in fact, until I end up standing in the puddle. Do you have any idea how hard glee is to get out of the rug, by the way? Just remember: club soda.

“Hobbyists”. I wonder if you think that’s a pejorative term, a word you say “mere” in front of, like “mere amateur”. You go right ahead thinking that, Pink Boy.

We chose to stop our lives.

We got off the merry-go-round career cycle.

There is no ladder to climb. We have no pensions, no tenure, no tick-mark job description for the forms you fill out when you join professional societies. I am trained as a molecular biologist and operations researcher, but I scan books, husband communities, do AI research in my spare time, sell antiques on eBay, trade stocks, edit and publishing print magazines. My wife trained as a mechanical engineer and has an MBA from a top-ranked school, but is a film-maker and photographer, and so on.

And we are not old.

What happens when your parents die and you pause to care for them while it’s happening, it isn’t some kind of symbolic rebirth or spiritual vision quest or any of that crap. It’s an interruption. You spend your days waiting. Days, weeks, months on end. Waiting for some particular problem to improve or get worse, waiting to hear results from doctors, waiting to change the sheets again or to serve another pudding cup at 3am.

And while you wait, you play video games for a while, or you do crosswords, or sort socks a few times. You buy a bunch of extra towels and then you get damned good at folding them all.

And then after that’s used up, when you’ve got all the badges in your iPhone puzzle game of choice, you step back into your hobbies. The things that were never allowed in your “career”, the ones you put off back in your (recent) youth so you could focus on the stuff your superiors demanded of you.

You learn again to write, photograph, program, digitize, archive, read widely, weave, sculpt, paint, engage, play, cook. You Make. Mindfully, over and over again, you Make. “On the side”, to while away the time, it may seem. You cannot work in the traditional way; you may as well be retired or in the hospital yourself.

Your career will wait, you say.

But then here’s the other demographic facet that the honored veterans of our Many Recent Wars will already tell you: The wounded and the sick no longer die. The old no longer die.

And so you will keep Making. Your career will wait.

It’s Gladwell-glib, but even a stopped clock is right three times a day: Odds are good that you will spend at least 10000 hours at your “hobby”. You will become a Master of it.

And eventually you realize your career can still wait, and will keep on waiting, and then you’re free.

I hear some thinking out there: Awesome! You’ve discovered a second career!

These are the people who should be embarrassed to hear themselves say that, but who never seem to realize it. Maybe they’re dizzy from the merry-go-round of their own life histories, their careers and the unquestioned sense they’re getting somewhere.

They don’t see the glint in my eye, which you know of course the sign of rising glee. So I ask them, “Why?”

They never have an answer. Often they’ll trot out the old “Somebody has to pay the bills!” canard.

“Why?”

It dawned on us slowly, but I don’t think it will be that way for all those who follow: my wife and I can do what we want. That is not a “career”. There is no “career”, and there never was.

What we’ve mastered is not a hobby, though I think you ought to call us Hobbyists. What we’ve mastered is doing what’s needed.

I don’t think this has a commonly-accepted name any more. “Slacking” is Barbara’s word, and I approve. Or you could call us “retired” and be close. But is it really “slacking” when we work 12 hour days, every day, in our enthusiasm? When we have launched a dozen businesses in as many years, when we talk daily with correspondents around the world, with people who actually see this mastery and recognize it and with whom we are happy to share it for free?

What have we become? I don’t know. Whenever we’re in a “go around the table and introduce yourselves” moment, the folks who know me all grin when it gets to be my turn. I never know what will come out, whether it’s one of the projects or a deep philosophical thread that links several. In all cases it’s contingent and meaningless, except that it helps calm down the few who are savvy enough to be concerned.

I try to do what’s needed. Because when you’re caring for the dying, you learn to do what’s needed, and what’s right.

On the Shuhari scale, we’re unquestionably Ha-level whatever-the-fuck-this-is. Probably not Ri, but who knows? I’m loathe to call it “wisdom”, but somebody recently accused me of that. I did my best to prove him wrong, and he got over it eventually. But it might be “expertise”.

We ate our careers. Burned them up. Remodeled and recycled them and gave away the pieces. A “second” career would be like having a second brainwashing, a second kind of cancer.

We sat for a handful of months in a cool, quiet, dark room. Caring, waiting, thinking, loving, despairing. All the things you do. And also along the way mastering a hobby, because there is no work but The Work when you’re caring for a dying parent.

Then a few years later, we did it again. And that time we both mastered some different hobby. I don’t even remember which, now.

And then a year or so ago we did it a third time… and we started to learn what mastery itself is.

We don’t get a pretty ribbon for surviving a commonly-accepted narrative arc. We get no pension, and we have no health insurance or “employer” or retirement funds to speak of. Politicians and public policy folks have no idea that we exist.

A year ago I would wake up in the middle of the night like when I was a startup founder, panicked about how ridiculous this all sounds within the old cultural framework. What will we do? Somebody has to pay the bills.

But you learn to do what’s right. And you learn how to learn what’s right. And that’s OK. We make a living; we have no careers.

People ask me for help all the time, and I am happy to give it. Technical help, advice, just conversation. My wife Barbara and I are surprisingly able to help people with their businesses, with their transitions from employed to freelance, with their social entrepreneurship, with their business plans, with their software architecture and online community management. When they listen, they seem to appreciate it.

But in the end, we are undirected. Just like our Generation X was supposed by its predecessors to be. We sound like “slackers” or “retirees” to folks still on the merry-go-round.

But unlike youths we are also masters, and unlike retirees we are not ourselves old. And I think, looking at the demographic curve, that we’re bellwethers.

What else is there for somebody like us to do, but to tear apart the merry-go-round and see what makes it tick? There’s probably some useful stuff in there that can be salvaged.

Maybe we can try to fix it. Maybe we’ll just break it to see what happens. We could do that.

Or maybe we’ll help your children, the ones who will be sitting in the cool dark rooms in a while watching, loving, caring for you, and learning hobbies. If we wait, then they will do it surely.

And yes, maybe there are other ways to reach this state of career-less mastery, to jump or be pushed from the merry-go-round. I wouldn’t be surprised. This makes you feel better how, exactly?

In any case, be sure we’ll do what’s right. You’re all looking dizzier all the time.

Update: Barbara is sitting in her evening chair and reads me “How Older Parenthood Will Upend American Society”, apparently from the New Republic. Small world. There you go; another face of the same stuff, from the standpoint of an older mother.

And one other.

Back soon.

Mid-book, I find the following lovely little passage. In a sense it says: perhaps finally we can proceed mindfully. Maybe that’s what I’m asking for when I harp so much and often about the lack of science (and please, someday, engineering) books like this one: that it is time now to be mindful of our roles in the world we create or discover.

It was how that transfiguration had happened [from Darwin to Neodarwinism] that perhaps holds the most interest. In concluding the Origin of Species Darwin imagined “a tangled bank” overflowing with living forms yet organized by discernible laws, and while full of “grandeur,” a scene that did not result from a preformed pattern. Yet as Ernst Cassirer has argued, “Man cannot escape from his own achievement.” Darwin’s tangled bank has been replaced by a “tangled web of human experience” that weaves together language, myth, art, religion, and all the other strands of humanity’s “symbolic net.” That peculiar capacity of human thought remade Darwin’s tangled bank into a shelf of braided narratives in which the entwining of genomic and geographic data had to play out over a cultural landscape: that was where, to continue the analogy, the selection would take place. The revival of neo-Darwinian concepts, however, too often brought with it a neo-Darwinian scientism that failed to apply to its own informing conceits the perspective it demanded of others. In particular, it made Darwinian evolution an act of special creation.

It was a simplistic narrative that assumed that ideas could be discovered out of data the way bones could be found in sandstone or tuff, and it viewed the progress of biological science (and archaeology) in a way partisans scorned when others applied it to their own fields. They did not appreciate the extent to which their explanatory ideas, even the theory of organic evolution, had a long history, and that, like Equuus caballus within the equids or Homo sapiens among the hominins, the idea was not the intended end product towards which all research had trended but the selected survivor of ancient stock, a product of happenstance, historical contingency, and usefulness. Disciplinary histories tended to be teleological, as narrative must be; the history of the idea of evolution was thus orthogenic in ways the theory’s advocates denounced when applied to nature.

Darwinian evolution was less a special creation, the spark of a divine insight, than it was the rough, imperfect, best adapted, useful, and cantankerous outcome of a tedious and often errant chronicle of observations and imaginings. It was a powerful idea, and once discovered, destined (so it seemed to many) to ramify across whole continents of learning. It offered a promised consilience, which could seem the apex to which all prior study had tended. But such apparent inevitability was an inherent construct of narrative, and just as an organism’s traits are not intrinsically better or worse but better or more poorly adapted to its setting, so it is with ideas. The evolutionary paradigm achieved much of its power and reach because it tapped into very old traditions of thought. Far from being a radical innovation without precedent, Darwinian evolution had itself evolved by fits and starts out of one of the hoariest concepts in Western civilization, the Great Chain of Being.

So imagine him. Imagine him alive or as a ghost, and in particular speaking about the Myth of Progress. I don’t honestly recall whether he talked about the Myth of Progress when he was speaking in Columbus that night; it might have been about Creationism (which was hot in those days) or some other irrationality. But today you and I, we’re going to chat a bit about the Myth of Progress, and I’m invoking Gould as its stalwart foe.

That said, this isn’t going to be about how Science (done right) can help people think in the world. Indeed, it’s about how Science (as she is done) is fucking up how people think. Including, I should emphasize, the majority of Science. And Engineering. And more or less all public policy therefrom derived.

So as it exists in my head, the Myth of Progress lives somewhere between being a folk heuristic, a bad habit of visualization, and a formalized misunderstanding of how the biological world actually works. The Great Chain of Being is one facet: the notion, expanded and locked in since the Neoplatonists, that the world is organized along a lovely axis leading (in some static sense) from imperfectly primitive on up to perfect and holy. The March of Progress illustration you’ve seen parodied in so many advertisements is another facet: the gamboling monkey, the crouching ape, the slumped cave man, the dude striding purposefully ahead, and so on. And so on.

Now Stephen Jay Gould, he wrote a lot on the subject of the Myth of Progress. I am not him, and heaven forfend I tread even near his eminent footsteps, so I am going to summarize what he said on the Myth of Progress for my own purposes here: It is not just bullshit, it’s dangerous bullshit.

Evolution doesn’t collapse down into a linear anything, and it’s not just wrong but misleading to take the branching network of descent and cross-breeding and abuse it that way. Evolution doesn’t proceed by discrete steps, and it’s not only wrong but misleading to imply that species replace one another. Evolution doesn’t even proceed when you get right down from it. It is always happening, everywhere, all over: all the things that ever happen among organisms and their environments are in a real sense “evolution going on”.

There are subtleties in this, of course. A lot of the more insidious ones even biologists fall prey to. There’s the tendency towards calling bacteria “primitive” because they were first. And don’t get me started on the swarm of philosophical traps that comprise the ill-formed notions of beginnings and endings, births and extinctions, even the nature and definitions of “individuals” and “species”. Some day when we’ve both got more time, we’ll do a bunch of onerous mythbusting on these themes.

Maybe. Who knows? But this morning I’m here to talk about writing.

Ah, tradition. You start with an idea or two, and maybe you outline or you blast out a textual draft, and then maybe you print it or you step away and come back to the top of the file, and work through it all, and maybe restructure it, and then there’s this phase where you see inconsistencies on a number of scales or notice shortcomings in references or links or illustrations, and you converge and you refine and you stay up late and then you’re done.

You write a little gamboling monkey of a draft, and you work your way up to the cocky dude with a spear on his shoulder. You slap him into an email attachment or a manila envelope, drop a cover letter on him, and off he goes, done. In the Record. Your baby.

Your property, among other things. Your reputation, your name, your ideas or at least your re-presentation of carefully acknowledged other people’s ideas.

OK, maybe it’s not that simple. Maybe you’ll get it back with some suggested corrections, and you’ll do a couple of further steps of the dance to move it to a more advanced state, and then that’s what’s done. Or maybe you realize later than is typical that there’s a different qualitative structure of your written thing called for, perhaps something that became apparent only after you’d mostly-completed an earlier version, and you revise, but then the refinement process starts again and… well, done is done.

You own that done thing. If you’re collaborating, maybe all of you own it. But it’s owned, right?

Now, some Unthinkable Things:

1. You return to an earlier version of a written work in progress, and start refining that—without abandoning the other branch. In other words, you take a “written work” two different directions at once.
2. You keep changing a work after it has been delivered without explicitly indicating that it has been changed.
3. Somebody else takes an earlier (private) version of your work, and revises that in a different direction, without supplanting or replacing your completely personal one.
4. You don’t give credit to other people’s ideas that appear in your work. No links, no citations, not even any block quotes.
5. Somebody else builds on a “final” version of your work, and doesn’t explicitly acknowledge your contribution.

Bad, bad stuff. Icky squicky stuff for writers and scientists and photographers and more or less anybody in the Life of the Mind.

And yet, and yet, and yet.

Stuff happens, doesn’t it? All of them happen. Students of course do this stuff, mere primitive Students who have not yet been corrected. And insidious scoundrels, who have not yet been brought to justice. Oh, and the absent-minded sometimes will do some of those, especially the ones where they forget to give credit where due. And there’s unconscious plagiarism of course, and self-plagiarism, and there are some ritualized ways one can (for example) take a prior work and bundle it up in a new work by expansion or repurposing.

So those happen.

And then there’s this odd thing about the Public Domain: It permits all those things. You could put zombies in Pride and Prejudice if you wanted, without mentioning the lady who wrote it, but it might not be as widely read without the invocation of her reputation. If instead you took an obscure magazine-serialized anti-Catholic novel of the 1850s and set it in space, and nobody had ever seen it besides maybe ever-watchful Miriam Burstein… but it might [well] end up being a better book.

That’s just to speak of intention. There’s also just noise, isn’t there? What is “done”? You write until the writing is done, but you don’t count typesetting: there are a dozen other people sometimes between you shipping off your “finished” dude with his spear and the discounted book on the remainder shelf at the side of the book store. There are copy editors (hopefully), and copy-and-paste errors, and typesetting, and printing and imposition… all sorts of variation can crop up from “the system”.

And a librarian or a bookseller or an Information Scientist or maybe even just that rare English student who pays attention will spend as long as you like speaking on the subject of “versions” and “works” and “editions” and “variants”.

So, then. What is a book or a paper or a blog post or an invention, that a species is not? You cannot make the argument that a species is “real” where a work is not, because of course the notion of species is just as fraught. Which is why Stephen Jay Gould is in the room, his ghost watching and grinning (I hope), ready to jump in with a long well-formed sentence to remind you: None of these things is a thing. They can be useful. They can help us do work, and tell better stories about the world. But “species” has a history, and has bad edges; “individual” and “descent” even have bad edges, when you get right down to maternal effects on development and microflora and epigenetics and all that other stuff that’s useful in speaking of exceptions in biology.

Those exceptions are evident to any advanced practitioner. They leave the biologist a choice: to either steer well clear of problematic areas, or to jab a finger down inside to see what happens.

jab

In software we have the beginning of a very different notion of the bounds of things. There is collaboration, there are anastomoses in the “tree” of software, and a growing cultural norm that reuse and reusability are not only permitted but rewarded. There are licenses, and there are reputations and livelihoods made by sharing. Great Minds of our day, like Clay Shirky and Steve Johnson, they’re poking at these very holes in the context of property, progress, ownership and collaboration. The law and the Internet, the social and the academic.

Me, I’m no Great Mind. I’ve got a monotonous boring old thing I always always do: I ask why? You might be tempted to spank a five-year-old who plays too often with the iterated “Why?” In my case… well, consider that astute observation of George Bernard Shaw’s before you try that approach.

What might happen if you let one of those Unthinkable Things happen? Just let it go. What might happen?

Why?

I don’t know. That’s why I ask. But there’s one thing I learned a long time ago about life in the world: Eventually everything happens. There is a niche, a context, a way of life in every approach.

I use that phrase “drawing a circle” often. It’s Charles Fort’s, originally. Go look it up if you need reminding. He was a crazy old crackpot of a fellow, Charlie was. Amusing, though: he could after all tell an engaging story.

Anyway, a decade is a foolishly long time to draw a circle; fifteen years, twenty years, a lifetime even crazier. You get wild sometimes along the path, or jittery, or buffeted off course, or just plain bored. Eventually you don’t end up with anything quite so precise or Zen as a “circle”. But you draw, and what you draw you call a circle because that’s what the metaphor demands: it’s always a circle.

I think 1997 it was I had a chance to visit Trinity. The place with the bomb, and the place about which I wrote my first blog-ramble thing about my father’s stories of working on high-speed cameras capable of taking photographs of nuclear explosions, and how (because surely the records are gone, or at least unremarked) those photos of the growing blossom of Trinity’s explosion must therefore be his, mine, ours, that camera’s. I called it “nanohistory” back then; it was a bit of a gag on nanotechnology I thought, and the future, and also multiscaled phenomena which we were all doing back in the day, but that was the thing I saw and wrote.

By now some of the folks who ate Green Chili Cheeseburgers with me at the Owl (best in the world) and rode the Trinity bus that day have had strokes. Some Ph.D.s are granted; some wives are dead. They’ve moved out of their well-preserved Moderne homes along the Turquoise Trail, they’re still living in Santa Fe, they’re where they went. And so on. Years of little accumulated drifts have piled up around the line they drew collectively. Unremarked (at least by me) since the day I filled a roll of 35mm film with their portraits lit by White Sands sun.

And in that time I’ve emptied out the house where my father’s and mother’s material stories, their “memorabilia”, was stockpiled, and picked up new habits and careers, had deaths and all kinds of wiggles of my own. Even the rambling essay I wrote in Santa Fe is nearly gone from the world. Its earlier versions are surely gone, as I never have been bothered to keep older versions, early edits, that sort of thing, and I just threw away the diskettes a week or two back. In the trash. There is no machine in my house that could read them, after all, so why bother with drafts of unread rambles?

This is normal stuff. Mundane; entirely of the world. But it’s about remembering. Being reminded.

We were inventing Big Data back there in the late 1990s. Have I told you that? I think I’ve apologized for it already. But some of the very people at Trinity with me that first Saturday in October fifteen years back were the founders of bioinformatics. Some of us are the data miners who wrestle piles and reams of ASCII and pixels into cobbled-together contraptions we built from folk wisdom and jury-rigged repurposed components we dragged out from the garage. We were discovering how to render data down into clarified, burning utility: models, predictions, and above all controls.

Control was a big one, and I think the most ironic. After all, we were complexologists: for fuck’s sake we were the End of Science, with our hand-waving anecdotal subjective contingent agent-based models. We were about emergence, the not just uncontrolled but inexplicable.

Though it didn’t really work out that way somehow. Nowadays not many of us are left here in the Proverbial Woods. There’s a fad or a revolution or a war or something, or so I hear, and the vast majority have put on ill-fitting suits and gone down to the City to be hired up by Big Data Distilleries—Big Brother, Data Science, and even a few at the Tower of Words. Those folks stroll the aisles now under suspended ceilings of fluorescent-lit data centers, patting earnest workers in (proverbial) white lab coats on their shoulders. Either that, or they fell to the service of corporations, and their work became the jargon of the Street, which dearly loved our Edges of Chaos and Emergences and Nonlinearities as handy excuses for doing what had already been decided: making this world we live in.

Just a few of us left here Ending Science these days. A little bit at a time, the work goes on until we’re all bought off or dead. We’re not a colony in any sense now of course; more in the role of folksy fogeys in the shadows of the diner downtown, talking up contingency and narrative, while clinging to an obsolete humanistic definition of “emergence” and “path-dependence” nearly all worn to thread like a quilt in a barn.

Yeah, well. At least there’s coffee.

Hey, here’s a funny thing: Did you know it’s no longer obsessive compulsive disorder when you collect a petabyte of data from a particular rat neuron and absorb your months’ attention focused on just the lovely patterns in the spike trains? Or that it’s no longer hoarding when you’re driven to stockpile every digitized book in the entire world? Or that even the old saw about trying the “same thing over and over and expecting different outcomes” doesn’t really come into play nowadays when the things you keep trying are the functional capacities of combinatorial variants of protein sequences? And! And! It isn’t eavesdropping—you are not a scary neighbor lady—when all the phone calls of a city are pressed into your service of knowing what those damned kids are doing over there, with their parents away (it shouldn’t be allowed)!

That’s inference now, not madness.

It’s the frontier we (and others not far out along our social networks) opened up for you all, about the time we rode the dusty road into Trinity. All those things are now new kinds of service. Not a sad lone madness left among them.

[“Isn’t that interesting, isn’t that interesting.” That’s what my sharp old friend Lew Tilney would have said, without a single question mark at all, when I was dazedly walking the halls of Leidy Labs trying desperately to discover what was wanted of me by my superiors. He’d walk up and slap his hand down on your shoulder and say, “Tozier! You know about trees! I was just reading about trees! Did you know there’s absolutely no damned way water can get to the top of trees? Physics won’t handle it! Now isn’t that interesting.” And he’d stride down the hall in his red socks and I’d wish I could see what came of that thread, instead of having to justify the counting of combinatorial proteins’ functions to people who found it mad. I learned years too late that Lew was always right every time he told you, “Now isn’t that interesting.” It was and is always interesting, salient, connected. There is never any question to mark.]

So a point is, that I wouldn’t be surprised if there was a time around 1900 when talking into boxes and expecting an answer stopped being considered madness. Or a time when acting as though you knew what a person far away was doing that very day didn’t make folks laugh. And so on. You get that picture? Now isn’t that interesting.

At any rate, some of the people on that bus to Trinity, and plenty more who didn’t make the trip that day, or who I met later or earlier in my life by a few years one way or the other—they made all these madnesses into stuff you see on magazine covers and RSS feeds.

And I love that. I can’t tell you how lucky I’ve been to fall into this hobby of watching smart people noticing things.

It feels like “madness” periodically becomes the fabric of societies, in turns, as new transformative technologies come online and escape and spread and do their stuff. I could be more focused I’m sure, more journalistic. I could refer to one of those Philosophers of Science you only really see in epigrams these days, Kuhn or Lakatos or somebody. But not this time; this is mere folksy rambling, not observation of a sort that’s useful.

I just noticed, is all. Way I see it, this is me just having fun watching smart people starting to try to realize they ought maybe to notice something again. And undoubtedly I’ll just sit here and watch for a while more, and when nothing’s forthcoming, maybe I’ll just change the subject.

Not worked it out? Well, that’s fine, that’s fine. No reason to stop chatting, is it?

Maybe we ought to shift gears, talk about the humanities for a while. Wikipedia (I smile for some reason whenever I link there these days) says the humanities are disciplines that study the human condition. “Disciplines” is another word that makes me smile nowadays, too, thank Abbott.

You know, I have a fond respect for those poor folks in the humanities. Personal fondness even. When I was a kid, it was decided I was either going to go to Case and be a biologist, or go to Oberlin or what’s that other place’s name that begins with a D—I can’t recall—and be an English major. A writer sort. Senior year it was old Bill Cawley, my high school English teacher (so hard not to say “professor”, isn’t it?) who slapped a hand down on my shoulder and told me people actually still could make a living, if a hard one, writing. But I picked the other, and luckily too because I met my beloved wife of twenty-five years (amusingly enough in a History of Science course, about stories, words, though we barely paid attention at the time for love), and as a pretty good science sort I got eventually to that bus in Trinity, and learned or to some extent made up the skills of Big Data. And here I am. A folkways practitioner of complexology.

Along the way I spent time in various academies and such. Over there sat the archaeologists, writers, the historians and all those other humanities folks (who I swear actually wear tweed sometimes), clinging to shrinking islands of departments in the context-focused Transformed Universities of the Austere Era. Trying diligently to instill a love of letters, or story, or memory or something in the thousands of kids who trooped through the lecture halls.

Kids are still, at least for the moment, expected to get an embedding cultural framework slapped around them, if only to keep them good citizens and informed voters and able to see perspective on the human condition. Though not too much.

Whatever is the “human condition” these days? Surely it’s 2.0 by now. It’s a kind of madness to think it hasn’t changed, that people haven’t been transformed utterly by all this networking and having machine intelligences at hand with which they can sift the raw data of the revolution to produce information, utility, weal and woe of various sorts. I mean: we have a new ubiquitous sensorium! A different world, in which Science didn’t End at all.

And see all of public policy seems now to want to do away with the waste Great Works entail, the distraction from what kids want and what’s best for them. Ideally they should be getting jobs, and learning skills, and preparing for whatever it is Big Data uncovers “automatically”. That’s what I seem to hear. Politically conservative folks want to do away with the thoughts that the humanities provoke; politically liberal folks want to do away with the ties to benighted and inhuman Bad Old Hegemonic Times the humanities rehumanize. In both cases I think it’s maybe the sense of inconsistency you get from read literature and discussed history that’s the biggest threat. We talk of the humanities in terms of waste and inutility, but really they’re seen as a threat.

They’re confusing. They dilute the story of the present and the future.

Letters, you see, are complex. History isn’t glib, it’s really never glib: it’s got folds tucked into its folds, and everything seems to mean something else to somebody else. The humanities are onerous because they’re all so tied together by these confusing personal subjective accidental ramified networks that reach back down into the stacks of libraries we’re emptying, and meanings and usage we’re glossing over these days.

And so they’re dangerous. Lean times call for leanness; what’s needed now is an efficient ability to frame every actionable item and sort it on the basis of delivered value. History doesn’t have a lane on the kanban.

It’s a wasteful kind of madness to dive down too far into old books. And a dangerous kind of madness to force kids who might better be working in the present and building our future to sit quiet and look instead into the past. What could they possibly gather up from that well-trod cemetery soil? Things are different now.

By now you’re thinking I’m bemoaning the end of the humanities departments and the closure of libraries and the loss of all that tweed. Really? You know, that would be a nice simple story you could distill out of this path if you like: “Dagnabbit, wouldn’t it be better if we taught kids Greek again? Why not add Letters and History to STEM, and make it… STEMLH. Crap. We’re going to need more vowels. Get Art on the phone, stat.”

But no, that’s not what I’m encircling. That’s been done, and besides I’m supposed to be Ending Science.

The trick is, Science is all tied and twisted up in the Humanities, Snow notwithstanding. They’re jealous siblings, copying one another in turn. Now isn’t that interesting.

Here’s what I love about the humanities, at this juncture: Just as every family gathering has that memorable crazy Aunt or Uncle, the humanities still insist on coming to our metaphoric Thanksgivings and rambling on about their personal hobby horses.

Brutal frankness: I like them humanities folks much better these days than I like most of my Sciencey Engineery cohort, or most any of the folks who sit with me at conferences of learned societies nowadays when I deign to drag myself down to the City and attend. They’re all good people who compute and sift and train up the Future, but they are nonetheless a boring old bunch. That stereotype is still just as true as the tweed humanists’ trope.

Ah but see, those humanities folks, they can tell a story. And they remember stuff. Crazy stuff, like how to read the shipping manifests of third century Asia Minor, or how some elliptic references to “death” are really horny poet-talk while others are about tuberculosis. And this one is best, as I see it: They’re willing to use the word “remember” to refer to acts of construction.

They apologize a little bit to the rest of us when they “remember”, just to explain the weird affectation they have that telling a story is building a thing. The mode in science these days, and also engineering, is that remembering is paring away mistakes, and disclosing the real truth of the world so it can be shared and consistency may reign on Earth as it does… (well you know the rest of that one). Among themselves the humanities folks all know remembering is a special kind of making, that recalling and recording is constructing novelty, that it’s not computation or reduction or scouring away matrix. And even better: they know how to make this special mad kind of making useful, or at least engaging and entertaining. Often as not they spend most of their time entertaining one another, reading their papers aloud at conferences and such, but sometimes one will be lifted up from their shrinking island preserve and be presented in the popular press, as a kind of Outsider Artist or something.

That thing they do, I like that. I like their mindfulness, that they act as if knowing were making.

Not many of us like it so much any more, though. It’s a mad notion when you look at it from a modern perspective: history and literature, poetry and classics, archaeology and dancing about architecture. “Mad” for the same reasons you’d be put away in a rest home for standing up in a busy public place where people are trying to go off and get their proper work done, yelling and ranting and invoking archaic names in ceaseless demands that they slow down and notice, see what’s there—or more likely what isn’t there.

Crazy people tell folks to slow down in lean times. They question what’s real and known and true all over again, stuff we’ve shipped, the truth we’ve accumulated. As if when you examined it again for the hundredth time, the old photograph of a bomb exploding would this time be more than an image of reality hanging on a fence in a desert. Some kind of story you made up on the spot, different next time.

But of course you and I know remembering is simply looking stuff up.

It’s not making things up. Data access, which is why we’re all so earnest in our recording and curation of the facts. Data access is what drives Big Science now, and marketing and all statistical miracles that have come to pass and are nascent in the world. It’s the real world, the world of data that’s important, not the made-up world of fiction and history. A cancer cure is not a story, nor is the money in the bank you made from high-speed trading, nor even the counts of the number of times the gendered pronouns appeared in our digitized Early Modern books. Those are facts, written down right there on in public.

And yet there are still a few of these other poor folks, sitting down and quietly reading old stuff and acting as if modern statistics and data-driven explanations were anything at all like story-telling. Mad folk. Fiddling in back-country hollers of the academy, little ivy-covered museums and even lone shacks off the beaten track, refusing for whatever reason to move down to the City and get themselves a proper job adjuncting or something.

Ayup.

No, that was it. I was just thinking out loud about the humanities, is all. Sad to see them go, you know. But it’s for the best.

Say, I bet you know about data! I’ve been thinking a little about data lately. Did you know that there’s so much data now that there’s no damned way to consider every model, prediction, or control mechanism—even for one given data stream? Let alone all of them! It makes no sense. Data’s all there, models are simple to build, and so now all the work is boiled down to arguments over technique, concocting various approaches and invoking conflicting proofs, and worrying about utility functions and constraints and contingencies. Hell, it’s like now we have the data, only the hard part is left: figuring out what questions to ask first.

Now isn’t that interesting.

Just sayin’.

A personal history of OuLiPo, from a recent member. The resulting first-person asynchronous faceted work is an honest biography and explanation of the constraint-players’ club, ranging from its prehistory to future. Too many folks confusedly consider OuLiPo to be a rather mathematically-tinted but otherwise mundane facet of Surrealism, or a more reasonable-seeming and obsessively consistent ‘Pataphysics, but as Becker makes clear: it ain’t. And rightly not. A pleasant read, and to be frank a game-changer for the manner of reading among the susceptible: Even now I think back and search for the oulipian constraint Becker must have used in framing the book….

Sure, Byron was weird. But the thing I’ve been learning belatedly about history and the lives of all those old-timey writin’ literary folks is how much of their lives is spelled out and yet remains opaque. I mean, I scan old magazines and as a result end up reading a goodly number of them, and yet that sense of, “WTF?!” as an oblique satire or anonymous homage rolls by remains a constant part of my experience. This book, a focused slice of polished thesis no doubt, clears at least a few cobwebs I’d stumbled into through the years: sure Byron got around. But Catherine Lamb, the crazy minx, comes off in this detailed analysis an awful lot like Sherlock’s Irene Adler: the one from the TV show, I mean, with the nakedness and the extreme smarts and the gift of pubic hairs in blood and all. And then there’s occultists channeling posthumous Byronic verse, and the pastiches that were ragged satire, and… it gets a bit thick, a bit too scholarly now and then. But there’s a costume drama or two tucked in here, with naughty bits and verse and all that good stuff.

I’m a sucker for Delany’s prose. I grabbed this as a “similar work” from something else I haven’t yet read, and am liking it quite a bit (not least because it helps me understand a bit more of Stars in My Pocket Like Grains of Sand, which even a college junior (as I was when it first came out) couldn’t hope to really ever fathom. (And yes, I’ve nested parentheses three deep. (We’re talking about Delany!) Four.)).

Amusingly enough, I’m reading Mina Loy because the editor bought a magazine from me on eBay. What? You didn’t think I Googled the buyers of my personal collection of zines? Feh; fat lot you know. Having spent way too much time lately among the original works of the Progressive Era, I now want to stage an anarchistic shuffle-up: Woolf, Loy, and Voltairine.

Somewhere between Zinn and Holton on a scale of History-isn’t-quite-what-you-were-taught (and Wouldn’t It Be Funny if the Conservatives Actually Knew What They Were Defending), Levinson is about the prospect of reform. Which is to say: Constitutional Convention, to clear up some of those long-standing “difficulties” that remain to date among our hallowed forefathers’ arguments, misunderstandings, and crappy opaque compromises. Yeah, that’ll happen.

I am in love, frankly. Science books that are self-consciously about narrative: not rehashes of the goddamned Great Men in Labcoats trope, but narratives that explain the science itself. How is it we came to be allowed to think of an Ice Age? How is it we came to consider that there could be other “men”, missing links, protohumans, and ultimately the actual hobbits and giants we now accept? And (perhaps most interestingly so far) how is it we’re allowed to call the Pleistocene anything at all, to shift its mode of definition away from the habits and norms of earlier conventions to the point where it’s defined completely differently from other epochs: by ice, and Man. Science books should be more about science, like this one is. Not a popularization so much as well-written literary criticism Of Science!

It’s that time of year. O what might she have wrought, had she survived? Read everything she ever wrote, I’m telling you. I’m re-reading this, and then her short stories, which I have here by my hand, complete.

I’m making books. You’ll see. Hendel’s book comes highly recommended, and I second that height: It’s not advice, nor craftsmanship, but rather a collection of thoughts from many hands on how the text block works (and is worked). Interviews with designers from many places, classicists and outrageous tweakers, with an emphasis on how and why any book looks like it does. And what that look means.

I remember the cudgeling I got years ago when Cliff Pickover asked on a fan list whether he should use Palatino or Times New Roman for his “new book”, and I said he should actually use a real font, and design the pages, and make it nice. I don’t know what Second Culture those folks came from, but they really abhorred the notion that font choice and design was as important as the damned words on the page. I’d post a link, but I can’t recall the names of the books he finally printed in Palatino, alas.

And there you have it.

Everybody Says I Should Read This. And I’m reading it. Slowly, actually, not least because I see, then think. Back up and see, then think. Too easy to have all one’s assumptions and observations brought together and miss the points of failure. So far, I haven’t found those points of failure, so I’m reading slowly, thinking, and reading more. But I knew immediately he was right.

Imagine a manic twelve-year-old English [sic] boy was allowed to outline a novel published in installments in the Boy’s Own Adventure Magazine. Lovely fluff, with metatextual stuff sprinkled lightly throughout. Is it sustainable? I’m told it may well be.

So my father’s memorabilia from NACA and the first days of NASA Lewis Research are now bare photographs, snips of glassware blown by the masters in the instruments lab, parts of plaques and trinkets received to honor unknown anniversaries and projects. My mother’s gardening books are reduced to a mere pending book sale, her cards identifying the jumbled garden she kept as useless as the plowed-over drought-purged garden itself. My wife’s parents, with their own accumulated and uncurated precedents, are a genealogical mystery story too baroque for publication: Wait, I thought she was married to him—who’s this? My lost friend Nancy, herself a collector of collections, can no longer tell me the difference between the fancy milk glass and the cheap junk, or help me split the Victorian pyrography from the 1930s kit-work she accumulated in her over-small house. My godfather, who came to this country as if to a frontier, with a patent in hand that made a (small) fortune by stuffing your attics full of pink floss, his few passed-along bits and bobs salvaged from a 1900s Wiener Wekstätte youth adorn our shelves and confound visitors by being so out of place.

There’s a swirl of pop-cultural pop-psychology floating in and around collecting these days, focused on throwing “hoarding” glibly down in front of any cultural variation that shows respect for memory and material culture at the expense of geometric austerity. Yet at the same time we love love love our tumblrs full of scanned ephemera, the RSS feeds filled with snapshots snipped from 1940s girlie rags and punk zines, the free (as in what? “beer”?) books scanned up to the cease-and-desist line of 1923. The past is all the more a foreign country because it’s kept in other people’s houses, in museums and libraries and private collections we not only never visit but we alienate by calling “pathological”.

If the autistic or the over-social, the religious or the ruthless atheist, the capitalist or the volunteer can all make their valid claims for respect in our society, let this be a claim on behalf of rememberers. Not those pundits who resort to big-story macroscopic remembering: where were you when Large Things Happened that Tie Us Together? But the supposedly trivial memories, a.k.a. “the fabric of history”. The baby thrown out with the bathwater of hoarding-abhorrence is the baby of our origins in family and culture, the fine wires that connect the stuff we read in history textbooks to our selves.

Knowing about all this junk is the only way I know to own your own history, the history of your place and your people. Otherwise, anything not in your head is reduced to a cunning science fiction story. When we who breathed leaded gasoline fumes are all dead, it’ll only be the key fobs for lost manufacturers, the uninstagrammed images of gas stations with uniforms, the misfolded road maps and quaint magazine ads that reminds us what that thing meant to the world.

I’m sitting within a few inches of a Chinese checkers board (of Nancy’s, since hers is the stratum we’ve recently uncovered after the purge of a decade’s deaths) and sitting next to it is a little wooden contraption: a block of mahogany-stained oak carved cunningly with channels, decorated with rotating screw-hinged caps, holding marbles for the game. It’s a purpose-built wooden Chinese Checkers marble-holder, manufactured by the Van Raden Product Company of Alter Road, in Detroit. Not by Milton Bradley, but rather by… some dude. You Google it, you’ll find this mention, and some forums somewhere on some woodworking topic where a fellow found another and doesn’t know what to make of it.

The address was 3136 Alter Road, Detroit. Go look it up on Google Maps. Zoom right on in there. Look real close at the house where this man lived. What you see? Zoom out a couple blocks. Look at those blocks, that wide-ranging perfusion of lawn they seem to have. Spacious, yes? Gone. Zoom out a bit more, look at that density. The voids. The holes.

Gone. Gone. Gone.

Tell me the story of the man who made the marble holders, back in the Chinese Checkers craze of the late Depression, in that vacant lot in Detroit. The neighborhood in which he arose is filled with empty blocks, five or six houses left standing on entire city blocks. Residential blocks. Each missing house once filled with things that ended up dead stuff, the chaff of history.

I don’t know what to do about this. It’s no easier to fix than the death of people is, and some days it seems there’s no more point in attributing “history” to key fobs from disappeared car dealerships and framed prints on the wall behind the photographed dead than there is to saving emptied milk containers and screws in a baby food jar. And yet there is in fact something happening, something odd and interesting. I can find my godfather’s name here and there in the growing memory of the world and somehow draw the flimsy links through public records to the point where we can drive up to his Rossford neighborhood and recognize things from photographs he took the day the house was new, in 1927. I see my father’s tiny image standing at the side of photos in the NACA Langley history archives, and that same day he clearly took a picture for himself, standing looking back the other way. And I go to see Van Raden’s street, now, after wars and more wars and abandonment and scouring, and if I want take back his handiwork and make a new (though flimsy) link of sorts.

Not every thing’s a reminder, nor of historical import. But the ability to tell meaningful stories about those things is as far as I know the only way we have to explain them and ourselves—the sort of explanation that’s not merely our strength but also our responsibility.

This just to say that as I sell things off, and purge and lighten and discard, I’m doing all I can to weave as well. Be reminded; that’s all I ask. Be reminded.

I mean we haven’t avoided Greece and Rome; nobody can. We’ve had our share of Great Men, Great Philosophers, Emperors, Tyrants, the world according to Thucydides and Plutarch. Even the “peripheral” [flag that word for a moment, please] histories we listen to—the Celts, Asia Minor, Persia—and the off-brand facets histories like the Arabic Scientists and the Enlightenment and stuff always touch on Greece and Rome, democracy and empire. Wind, fire, all that kind of thing.

Maybe it’s osmosis, or maybe it’s something more akin to repeated slaps on the forehead with a rolled-up scroll while broadly mouthing “LOOK AT THIS AGAIN”, but I’m starting to notice something I never saw before. Like any nerd, I grew up learning about Greece from brightly colored mythology books, and Rome out of Spartacus and such. Our Social Studies classes were all about 1970s Patriotism tinged by that 1950s Dewey-would-lose-against-Marx Cold War citizenship stew and pedagogical style our teachers were raised up in. The Founding Fathers read about Greece and Rome, inspired by the democracies of Athens and the republic of Rome, blah blah. So maybe one needs to have been slapped on the forehead a few dozen times with the actual history before that patina of received wisdom starts to crack.

Viz: it wasn’t that simple.

Now any actual historian will probably be making the Wry Smile Eye-rolling Face now. But of course most of us well-educated liberal-thinking technical folks don’t bother too much, no matter how earnestly and efficiently we pursue knowledge, to dive down the rat-hole of Narrative Construction.

It all starts with Egypt, of course. I remember as a Junior High student I would get up at 6am (for some reason) and watch a television class about Egyptian art on some broadcast Cleveland TV station. And you know they mention this Ptolemy dude, either the Emperor (wait, Egypt didn’t have Emperors, it had Pharaohs) or the Astronomer Who Was Very Wrong (wait, were there Astronomers or just Astrologers before Copernicus?), and it gradually sinks in and it’s only decades later that some other tidbit or two falls into place and Whoa whoa hang on, that was Greek no I mean Macedonian I mean Hellenistic stuff, and Egypt was the southwestern Alexandrine empire, and—hang on—so the Romans were dealing with the remnants of Alexander’s empire?! and so on. Strands congeal, like DNA precipitating in an Eppendorf tube (hey, that’s my heritage).

And then Whoa, hang on again—so all those letters from Bible dudes and Greek Philosophers and Geometers were from Turkey?! and then But but the “democratic” Athenians were total assholes and thank goodness Alexander came along and… well, and so on. Call it “provincialism giving way slightly to paying attention”, or maybe “narrative reconfiguration”, depending on your background.

Clearly it isn’t that history is written by the winners, but rather that they write and distribute the Cliff’s Notes.

OK. That’s the setup. Here’s one point: Seems as though the writers’ guidelines for Cliff’s Notes demand Clear Separating Boundaries. Starts and Endings. First there was Egypt where they had mummies, then there was Greece where people were Democratic, then there was Rome with fuzzy helmets and brass skirts, then there was (in advanced classes) Byzantium [sic] which was pretty foreign and dissipated like Paris or something, then after a bit over there you get your King Arthur, and then after a while somebody turns on the lights and we get telescopes and gunpowder, and here we are. Nice clean starts and finishes, all along the way, like dinosaurs being wiped out so little furry mammals can turn into Baluchitherium [sic] and stuff.

Surely there’s a name for this fallacy. “Consecutivism” maybe? “Discretism”? It is a fallacy, clearly; I’ve been hanging around a half-hour a day with actual historians, the sort who sound as if they fling their arms around as they read, and they’ve managed to get chocolate in my peanut butter all over the place: Greeks in my Egypt, and [Greek!] Asia Minor in my Rome, and Celts in the Bible, and Persians in my Sparta, and cats and dogs living together.

And thence: Self-definition is all about the boundaries. Insert a cunningly-crafted keen insight about boundaries here, one that touches on all the expected things about brainwashing, self-definition, provincialism, cultural pride, homogeneity and diversity, ingroups and outgroups, wind, fire, all that kind of thing. Shorter version: “Hey, you know those are just Cliff’s Notes you’re reading, right?”

All this? All this was crystallized into an anastamosing tissue of rant because I just read Alexis Madrigal talking about the awful awful things that have happened in our American culture and the growing dichotomy and the worries everybody in public policy expresses all the time about jobs and decline and inequality.

It makes me sad, every time I see this sort of thing. Sad because of the box it grows within. It’s the provincial Startups Will Restore Us box, the Economic Development box, the one decorated with fine print that counts how many jobs (asses in office chairs!) and Press Releases From Tech Spinoffs (young people are the only ones who ever do anything interesting!) and with a star-shaped brass sticker that reads “Now with 25% more EARNEST HOPE!”

This box is a special kind of conservatism. Burke would recognize it, because it’s all about not breaking things. Fundamentally it’s a ubiquitous habit of wanting to restore—and more insidiously, to expect change to happen the same way it happened last time—and it relies on the Cliff’s Notes version of economics and history. As though the only people in an economy were a few charismatic megafauna, a corps of earnest and essentially non-profit bureaucrats, and the undifferentiated Classes: upper, middle, poor, from which those others arise now and then by spontaneous generation. All tidily projected into the future by extrapolation: The big charismatic megafauna of the future must be like the ones of the past, technical not artistic, leading not integrating, rebuilding not repurposing. The institutions of the future will be like our recently lost ones (companies, states, all that), the best Mankind has found in the March Forward. And the Classes, well, they are out of balance.

Now see in your Dark Age, which after all is merely a lacuna between a couple of those ex post facto discrete volumes of Cliff’s Notes, change happens. The diversity of what happens, the details of who’s doing what for whom and under what name, that carries on as before. Perhaps moreso. Whenever Empire stumbles, novelty seems more promising out at the unremarked periphery, in the lost provinces and the places where exotic weirdos start trying new stuff out. Not in the core.

Some day, hopefully in a few decades, somebody will realize sustainability is a thing that happens only in places where central planners look away. I wonder whether we ought to stage a “Dark Age” of our own, rather than waiting for all these rebuilding reworking rebooting economic “development” efforts to fail in turn.

Development is exploitation, in Holland’s sense. Let us explore for a while. It’s not merely that the keys aren’t under that light pole, it’s that there are no doors out here in the lovely dark. Let us be better now to one another, and not worry so much about honoring the beloved dead: the factories, the jobs, the state lines, and the habits of empire.

This is not about “revolution”, by the way. This is simply a request. Let us please have a King Cnut of Economic Development: Richard Florida might do fine, if only he was paying attention, because he has conquered our mindset for sure. Let him set himself up on a throne at the shores of our “economic collapse”, and make whatever gestures are called for by his audience to stem the tide of fundamental transformative change, and let him then turn wisely to the fans and lackeys and point out the moral of this lesson: that Emergence is not what you expect and foster.

Sorry. I couldn’t help but laugh out loud there.Richard Florida would never say anything of the sort.

Nonetheless, let us emerge into the darkness, in other words. Everything that has happened here under the lamp has already come and gone. We should totally leave this lone light here, burning, if nothing else to draw the moths and bats it’s always drawn and act out its role as symbol of many sorts. Me, I’m headed over there towards those noises….

I recently updated the published draft; if you’re behind, feel free to go update your copy now. New content includes a description of the iPad game Cargo-bot, and a detailed test-driven re-implementation of the game logic in an emulator we’ll use for GP in forthcoming chapters. I spent a lot of time on the test-driven development, so I’d like some feedback if you’re willing.

• pry/pry

  Pry is a powerful alternative to the standard IRB shell for Ruby. It is written from scratch to provide a number of advanced features, including:
  
  irb ruby software-development interpreter

• Daniel Fischer’s Blog — A Starting Guide to VIM from Textmate

  For about four years I’ve been using Textmate almost every day. I’m very fast with it. I’ve always thought about switching over to VIM or Emacs but I have been scared of losing my speed. In fact, I’ve actually tried Emacs in the past and also wrote a blog post on my experience. I liked it in general, but I ended up coming back to Textmate after a week. Why? I didn’t really feel like I was gaining anything.
  
  textmate vim tutorial habit

• A Question Answered — Credit Slips

  “Over coffee this morning with a friend, I threw out the same question from my original post. How does an organization get itself to the place where it collectively comes to think such strong-arm collection tactics on hospital patients are a good idea, let alone morally defensible? A profile of Accretive’s CEO, Mary Tolan, in Crain’s Chicago Business contains this gem: “My objective is just to be a happy, confident capitalist,” says the devotee of Ayn Rand’s and Milton Friedman’s free-market gospel, which she applies with a combative, survival-of-the fittest management style.”
  
  randism buh-bye-john-galt

• The Humble Origins of the NEXT Global Economy. Don’t Miss Out.

  “It’s simple.  If you want to build a thriving local economy.  A local economy that makes your community resilient to economic failure and shocks, you need to find ways to help the innovators in your community make things.”
  
  resilience sustainability communities-of-practice makers

• How to add Notification Center support to your website or app

  “Originally introduced last year in iOS 5, Notification Center is one of the more useful new features in OS X Mountain Lion. What’s really nice is that the ability to show notification banners isn’t limited to native applications; both Safari and Chrome allow websites to show alerts in Notification Center as well. This is a quick and straightforward guide to adding Notification Center support to your website or web app.”
  
  notifications MacOS javascript tutorial interactivity web-applications

• OAuth 2.0 and the Road to Hell « hueniverse

  “This is a sad conclusion to a once promising community. OAuth was the poster child of small, quick, and useful standards, produced outside standards bodies without all the process and legal overhead. Our standards making process is broken beyond repair. This outcome is the direct result of the nature of the IETF, and the particular personalities overseeing this work. To be clear, these are not bad or incompetent individuals. On the contrary – they are all very capable, bright, and otherwise pleasant. But most of them show up to serve their corporate overlords, and it’s practically impossible for the rest of us to compete.”
  
  openauth standard-setting-play committees speak-with-one-voice antipatterns via:vielmetti

• “Must read” papers in numerical analysis — MathOverflow

  
  nudge-targets

In the early days, I remember a lot of people in software projects would conflate the Customer with the User, even when they weren’t the same person. And then of course there’s the (risky?) conflation of Customer with Bill-payer, or (maybe more dangerous?) Customer with Project Owner (in a Scrum sense). But those are known problems, and the agile coaching community seems to have handled them reasonably well—in the context of traditional professional settings—if nothing else by repetitive correction.

The trouble I’ve found recently is how often the term puts off explorers, those who are tempted or solicited to try an “agile for one” (or “agile for us”) approach to their investigatory work—the just-try-it-and-see-what-happens projects that fill our days as engineers, scientists, artists and Makers more generally. To some extent the problem is the word and the freight it carries, and the diverse languages and cultures of from which those folks come, and the cultural antipathy prevalent between communitarian life-of-the-mind folks and doing-it-for-the-money folks.

I know, having spent so much time among the uppity agile rebels, that this “Customer” can be you, or all of you together (as long as you can arrange to Speak with One Voice), or nobody in particular. I understand that the point of the role is some combination of (1) identifying the most valuable thing to deliver next so you can focus on that and deliver it next, and (2) avoiding the Mission Creep and Babbage’s Disease that keeps projects from ever delivering anything of use at all, and (3) learning the (surprisingly rare) skill of Not Producing Unintelligible Crap.

But the word itself, and the freight it carries, seems to put off people—not least students and other academics—who I have to say are among the most “at risk” for failing to ship any useful work at all, ever. The deep usefulness of the Customer concept, as far as I’m concerned, is that somebody ought to be able to dynamically and adaptively sort the many things you might do next into an ordered list that reflects current perception of value.

To me, “being Customer” in my projects—whether I’m doing open-ended research or targeted software development—is a visceral change I make in my beliefs, desires and intentions. If I’m sorting stories or planning the next day or week, I try to look not at the subjective experiences I’ll have when making progress and doing work, but instead at the value I expect (right then) to obtain from the statement of purpose each story represents. It doesn’t matter than I’m the Customer and the “Development Team” on certain projects, as long as I can differentiate the stance I take when making decisions in those two roles. It doesn’t matter that the “story” is “make one and see what happens”; as a Customer I see that as a time-boxed deliverable, and demand it change to “spend one day making one, and publish a result at the end of that day”.

(What I try never to do as “Customer” is assign expectations of how long something will take. I am there, at that moment, only to see the value in various stories as they’re presented, and add new ones, and rearrange their order. If I need to decide how much might be done before the end of the iteration, or even whether one single “story” won’t or can’t fit in the allotted time, I’ve got to consciously switch over to “Maker” role. Oh, and also I try to avoid ever saying, “Go away, and never come back!” to myself….)

That’s how I try to run my research, and what I want to see in others. It’s safer exploration. It’s the opposite (as far as I can tell) of how academics and STEM folks think most often, and absolutely the opposite of how schoolwork and most technical professional work is planned, because those are simply given lists of comprehensive requirements. Bit three-ring binders full of “it must do all these things”.

Maybe the problem is that these folks, starting off as they do with such deep antipathy to the culture that led to the term, think the “Customer” role is some sort of permit for premature assessment, some kind of master:slave relationship, a debt and an onerous promise to fulfill that debt. But in practice, sorting tasks by value does not entail punishing failures to deliver on those tasks (and this is the deepest reason why measuring “velocity” is so fraught). “Let’s spend a day getting this better!” is not the same as “Get this done by Friday!” The former implies that no matter what happens—we get distracted, we had an emergency, we couldn’t think of a way to make it better, we make it better enough that it goes away entirely—there’s always another round of planning in which we can do the same thing again, another day somewhere down the line where there is perceptible value in working on that thing a bit more. And by the same argument, it means that progress on other fronts is (for the moment) more valuable than working on that thing until it’s “done”!

It saves huge amounts of time to use the Customer role explicitly. That saves my life, some days. It lets me have something to show for my work every day. Me, the “Customer”. Otherwise me, the “Developer” would wander off somewhere doing whatever random crap caught his attention, and end up rushing the useful work at the end.

I don’t know a better word than “Customer”. But I don’t know a better word than “agile” yet, and it’s getting close to the day when that also will need to change. I don’t think I can use the word in print any more, so I’m looking for a replacement soon. Spend a day thinking about it and send me whatever you find by the end of that day, OK?

… there it is.

• KineticJS — HTML5 Canvas JavaScript Library Framework

  “Greetings fellow webonauts! KineticJS is an HTML5 Canvas JavaScript library that extends the 2d context by enabling high performance path detection and pixel detection for desktop and mobile applications. You can draw things on the stage and then add event listeners to them, move them, scale them, and rotate them independently from other shapes to support high performance animations and transitions.  Served hot with a side of awesomeness. ”
  
  javascript animation graphic-design html5

• Tim Brown — More Perfect Typography on Vimeo

  At long last, designers can use real fonts on the web. But what now? Where do we go from here? Tim Brown has been studying type on the web for seven years, and has lots of ideas to share. In this talk, Tim will guide you through using typographic tools and perspectives that will change the way you design websites. Typography is an ancient art and craft; we are merely its latest practitioners. By looking to our tradition for guidance, we might once more attain our finest typographic achievements in this new medium.
  
  via:trek typography graphic-design typeface video

• Twitter / tsuomela: My response to http://t.co …

  
  cultural-assumptions academic-culture wry-smile

• BOOKTRYST: American Rare Book Trade Ads From 1902, Part III

  Part III of an absolutely fascinating nanohistory series at BookTryst, examining each of the ads in a 1900s bookman’s magazine. “On August 10, 1915  Ralph Randolph Adams filed for, and on July 10, 1923 was granted a U.S. Patent for “Radioactive Spray Material.“ “The object of this invention is to provide a radio-active substance for the purpose of stimulating plant growth. A further object is to provide a radio-active substance for the prevention and destruction of insects, larvae, eggs, bacteria and fungi which are injurious to plants or animals. A further object is to provide a material having these properties which can be efficiently applied by spraying, and which will adhere to the parts of plants above ground…or to the fur, feathers or skin of animals [our emphasis] which are bothered by pests…(U.S. Patent No. 1461340). In short, Adams invented a radioactive insect-killer to spray on the leather he used for binding as a preservative to prevent pests from harming his work. Adams “Viennese” bindings prior to 1910 do not, presumably, require use of a Geiger counter, and, having one from 1902 recently pass through my hands, I am relieved. It is unknown to this writer whether Adams’ post-patent bindings glow in the dark.”
  
  books nanohistory digitization culture

Let me try to be telegraphic:

Nelson’s tale is written in a voice that rings surprisingly true to the (shadow) 19th Century’s own voice: language, metaphor, idiom and framing are all spot-on for a suppressed Twain tale from a little-known literary magazine Editor’s secret papers, discovered in a shuttered attic lap desk among a firebrat-infested stack of ledgers and correspondence. This in itself is a fun and lovely act of artistry; you can’t just talk “old-fashionedy” and get away with it. This is words done good, and every one.

The slipstream, steampunk, and otherwise fantastical elements are no more or less jarring than those we muddle ourselves through every day out here—no here, on the three-dimensional side of the screen, in daily life. What happens to our narrator and cunningly perceptive protagonist and the town they live in (all poised at the edges of their respective transitional cliffs) is no more science-fictional than the phantom vibrations I get in my leg when I have no phone, or the habit I’ve gained of tapping words on a paper page expecting to see a definition.

And this, most of all and with no little risk of seeming provincial to some more worldly reader: This is a story about Americans and the awful wonderful thing we’ve accidentally done to one another and the rest of you, liberally mixed against our types’ historical preferences, rebelling against and egging on the emergent change that arises from that mixing, and in our very particular ways watching in wonder as entire worlds find ways to fit snugly inside a single story together.

By which of course I mean your story and mine.

So: Get this, read this, recommend it.

• The future of scientific publishing: Open post-publication peer review « The future of scientific publishing

  “Post-publication: Reviews are submitted after publication, because the paper needs to be publicly accessible in order for any scientist to be able to review it. Post-publication reviews can add evaluative information to papers published in the current system (which have already been secretly reviewed before publication). For example, a highly controversial paper appearing in Science may motivate a number of supportive and critical post-publication reviews. The overall evaluation from these public reviews will affect the attention given to the paper by potential readers. The actual text of the reviews may help readers understand and judge the details of the paper.”

  peer-review open-science collaboration publishing to-do coscience

For some definition of “we” that seems to be growing quickly in our culture: Craftsmen, folks with adaptive lifestyles, those with “grit”, Editors.

To imagine there is no aesthetic sense in the experience (that is, the making or learning) of science and engineering is ridiculous. Worse, I think the prevalent academic denial of the importance of such aesthetic experience in science and engineering is what’s ruining it: pushed it out of reach, attenuated it into a specialist esoteric “advanced” pile of disparate things, enabled its makers to treat laymen as boors and enabled laymen to treat makers as unintelligible nerds, and privileged the incessant novelty of concepts over reproducibility and applicability in the professions.

Have you ever noticed how each specialty, each discipline in the academy tends to find the smallest number of competent specialists, and grow the field of boorish laymen to include not just the uneducated but the differently educated specialists of other fields? Yes, we need to advance the frontiers of theory and practice. We also need to ship. That’s our job too. You need to ship your work not to a journal where it will sit on a shelf behind a firewall, but to people who will act upon it.

It needs to be experienced. Like a work of art on a shelf on a museum store-room, it’s dead until it’s experienced.

In other words, I nod a lot reading this book.

For example, last week’s conference reminded me that the wall between “Theory and Practice” is an important cultural distinction in my “field”. Shusterman on his “field”, and the need to coalesce “theory” and “practice” into a single experiential whole:

Thus, though our theoretical imagination is always largely constrained by established practice, it is not confined to slavish conformity and reactive repetition. For changing circumstances and encounters with other practices can provide new nourishment and alternative orientations. Since no practice is defined for all possible situations, there will always be a need of imaginative projections and creative decisions as to which of the possible projections should actually be pursued, decisions which are apt to be contested and which again raise second-order problems of how to justify those decisions. Since no practice exists in utter isolation, unaffected by others, there remains the need to relate, coordinate, or arbitrate between different practices. As long as our practices present us with such problems and admit of improvement, theory will not only be possible but necessary.

Conceived in this pragmatist fashion, which recognizes the primacy but also the problems of practice, theory is not exterminated but revitalized by the loss of its traditional status as transcendental cognitive privilege. For, once we give up the foundationalist view of theory as revealing the invariably necessary principles for practice, and further relinquish its hope of apodictic, incontestably final justification; once we instead see our practices (and our theories) as contingent products whose encounter with changing situations has necessitated continual adjustment, clarification, justification, and improvement; then theory’s abiding role as critical reflection on practice is secure and seemingly ineliminable.

I spent the week watching people negotiate the various fields they say they “work in”. Watched them talking and arguing, enlightening and redefining one another through their descriptions of their own work, their geography and family trees (the Germans made a big showing; the Virginians not so much), their social strata (students, post-docs, professors, corporates, and then the strange inexplicable escapees like myself). Some of us acknowledged and honored the 20th anniversary of the most influential work in the subject, John Koza’s Genetic Programming: On the Programming of Computers by Means of Natural Selection, and I watched as we lined ourselves up (this “field” we are) along the expected lines and ranks all over again.

I sat for an hour or so after the poster session until the hernia pain I was having subsided, and a little crowd of enthusiastic Computer Scientists got caught in the eddy I made and sat down to chat about Artificial Intelligence and science fiction and what makes Genetic Programming the last best hope for the future of Strong AI, and so on. Now and then I’d open my mouth and say something about how the notion of AI has become a fragile social shred of Cold War hubris, how even the idea of designing software is subject to interpretations these friends and colleagues have never really understood, about the ways that statisticians and software developers and sociologists do their work without stepping into these ontological traps… but those went over like a lead balloon.

I was watching a “field” folded up into itself, addressing itself. A closed field. And that’s OK. Fields are ubiquitous and ephemeral. They’re what we make of them, and the use we derive from being able to tell simpler stories is more than enough to compensate for the obstacles they can occasionally create.

It was thrilling to watch my conference’s “field” being born, twenty years back. There is no less thrill in seeing the little cracks and folds, the seams splitting and the periphery falling away, as it falls apart.

Soon all sorts of raw materials will be exposed and made available again. All sorts of possibilities are stored already there in Res Potentia (as an old friend would say)—in the echoes of what was said along the way but ignored and forgotten in the rush forward, and the glimpses folks have had (but kept to themselves) of other fields’ storylines. Components, parts and passages, the stuff nobody has ever done a close reading of, the unrecorded histories and the things nobody even bothers to say “I thought of that first!” about.

Turnover. Progress. A network unstitched and rewritten.

Something like fifteen years before the tower conference, I’d been asked to leave my position as a graduate candidate in Biology at the University down the road from where I was now looming. I went to the conference thinking I might find time to be melancholy or bitter with that shadow down ’tother end of Walnut Street, like the other survivors of Grad School Culls I’ve met. Graduate School and the academic life are so important-feeling when you’re that age.

But there were no pangs or twinges. Graduate School—and the Academy more broadly—are no less stories than my “field” is a story. We use them as excuses for the embarrassing mad thoughts and trivial affectations we entertain while we fill our days with life. More broadly, they’re memories of the Cold War and its resource limitations, and little mirrors of the states in which we house them: imperial, familial, collegial, or ruthless. We speak of them as though they’re tools, and in a sense they are. But their utility comes not from what they do directly, but the boost they give the scansion of our lives after the fact.

It all reads better when you’ve done what’s expected, don’t you think? Contra common usage, you don’t do Graduate School. It’s a thing that explains what you’ve been doing, why you look that way or act that way, why your enthusiasms and naiveté are so refreshing or enraging. Graduate School is itself a “field”.

I realize I never did tell anybody at the conference what it is I really do. What my “field” “really” is.

They tend to just assume, when you’re at a technical conference. A few old friends and colleagues know a bit better, but they still can’t quite connect the dots. Beyond some jokes about me being a spy of some sort because I was so cagey about my plans and scope and affiliations, I don’t think many folk really noticed. We talked about the things I’ve done of course, but that’s how this sort of thing works, and it’s the common ground for any conversation.

I confess that I look forward to the day we all meet at some other future conference and compare notes, and end up frowning and smiling about the different stories we’ve told about the same stuff. I’ll be there, smiling and frowning and shrugging right along with them. Pick a plane or a cave wall to project the shadow of the Real World onto, and tell a story about the outlines it makes. The trick is to shrug and smile and pick another plane and do it all again to get a completely different shadow, until you find the one most useful for the day. It’s a magic trick for most folks. Now and then I try to share the secrets, but when I start to explain the habits and practices and assumptions that make this stuff feasible and interesting… those go over like a lead balloon.

That story—itself about stories—isn’t simple yet.

At the Walnut Street University fifteen years ago, I was asked to quit making a scene by begging my thesis committee to treat my computational whatever-it-was (“research”?) as Biology proper. The story I can tell now is that I was freed to do the work in the proper “field”, the one that brought me back to this conference in the tower fifteen years later. But in the context of the day it was a blow. Look at the young enthusiasts in schools around the world, learning and eager and listening to the stories we build towers out of. Ivory towers, conference towers—all kinds.

It’s good to fit. To have a simple story everybody knows, and use our stories of similar work and similar life to shore up the walls of the place we all work together. Our “field”, our “University”, our “discipline”.

The story I tried to tell when I was in Graduate School down ’tother end of Walnut Street, before I knew how to do this sort of thing, was about Biological Engineering, and Maker Culture, and explaining things by changing the world. It was all the start of something happening in some other tower, I realize. The story I end up telling now is how doing that same work has nearly broken my old friend and advisor (or at least made him sound like a crackpot to our peers), how the world has caught up and it pleases me to see people in places besides the other end of Walnut Street doing the very work we wanted, and how much pleasure I take in knowing people who knit DNA and create jellyfish from rat cells and threaten to cure not disease but a worldview.

Different plane and a different projection of the same real stuff. The world doesn’t give a damn what we say about it, so we’re free to make new stories on demand. There are always new towers being built, and raw materials getting freed up as older ones are disassembled.

I wave down Walnut Street, and never really think about it again except with a smile. I wonder where those people live now, and what it’s like in the world for them. Is it the same? Is it transformed yet?

Always be willing to wait for one of the Elevators that Doesn’t Get Stuck.

What I do is edit. I’m an Editor. It will be a little while before folks realize what that means, is all. And I’ll be shrugging right there alongside them as we find the words to use when we explain it, and tell that story more usefully, more simply. And maybe a cure will start to come along with these new stories, as more people realize they have trouble telling unfaceted tales, linear tales, stable tales of one thing leading to another.

Being an Editor has a lot to do with salvage, with surfacing and suggesting uses for the raw materials freed up when our stories change themselves. You site yourself at the edges of several shadows, and you squint up at the sun to see what’s really up there, and over time you learn to make some shadows of your own. Every story changes itself in the telling. That’s not merely our work as Editors, but our lives.

Nobody would believe me if I came right out and said that I create the field to suit the work I want to do. On the fly; not from whole cloth, but from the chunks of other fields as needed. Nor will they believe you, when you are cured of your profession and start to merely do what’s called for to make yourself useful.

At least that’s the story I tell myself. It does the job.

Later: Laurent Bossavit has reminded me of Venkatash Rao’s similar essay from a few months back, far less elliptical than mine. Go and enjoy. It’s good to fit.

• BOOKTRYST: American Rare Book Trade Ads From 1902

  ‘Where to begin with Charles Carrington (b. 1867 — d. 1921 of syphilis), who deserves an entire book devoted to his colorful character and career? Of Portuguese descent, Carrington,  born Paul Harry Fernandino, was, arguably, the most notorious publisher of his generation. He began in London. Circa 1893–96 he skipped to Paris; deported from France in 1907, he fled to Brussels. In 1912, he returned to Paris, at times Amsterdam. In short, he operated one step ahead of the law. “Historical, Artistic, Medical, and Anthropological Works,” is certainly one way to characterize the books he published. Erotica, pornography, curiosa, and sexology are other appropriate descriptions. Often, the stated publication locale, publisher, and date on his books were false. Many if not most of his books were “for private subscribers only.” He was active as a publisher for twenty-six years and published approximately 300 books.’

  bookseller bibliomania nanohistory characters-we-have-been-like

• The Ongoing Vigil of Software Security

  “Some of the reasons that we keep seeing these types of exploits are that the “bad guys” are much smarter and more determined than we give them credit for, we’re much lazier and more ignorant than we take responsibility for, and security is difficult to manage properly. As we become more and more reliant upon software, it is imperative that security be taken more seriously.”

  software-development security advice overview

• Economist’s View: “The Sociology of Organizations”

  “It often sounds as though Perrow is faulting these organizations for defects that are inherent in all large organizations. But it seems more fair to say that his analysis does not identify a general feature of organizations that leads to failure in these cases, but rather a situational fact having to do with the power of business to resist regulation and the susceptibility of Congress and the President to political pressures that hamstring effective regulatory organizations. Perrow does refer to specific organizational hazards — bad executive leadership, faltering morale, inability to collaborate across agencies, excessively hierarchical architecture — but the heart of his argument lies elsewhere. The key set of problems spiral back to the inordinate power that corporations have in the United States, and the distortions they create in Congress and the executive branch. … It is specifics of the US political system rather than general defects of large organizations per se that lead to the bad outcomes that Perrow identifies. There are strong democracies that do a much better job of regulating risky industries and planning for disasters than we do — for example, France and Germany. …
  There isn’t much public concern about these risks, and legislators are therefore free to ignore them as well. … So where will the political demand for strong regulation come from? Will we need to wait for the bad news we’ve managed by good fortune to have avoided up to this point?”

  public-policy infrastructure antebellum-America conservatism

• Bachmann, Gaffney, and the GOP’s Anti-Muslim Culture of Conspiracy — The Daily Beast

  “Earlier this month, Rep. Louie Gohmert (R-TX) appeared on the FOX Business show Money Rocks to make the case for depriving the children of immigrants of their 14th Amendment rights. Gohmert claimed that on a recent airplane trip to the Middle East, one of his traveling companions had struck up a conversation with a grandmother who described her family’s involvement in a Hamas plot to send pregnant women to the United States. Gohmert summarized the lesson for viewers this way: “We’re bringing them over here on tourist visas, some illegally, letting them be born here and saying, ‘This is an American citizen. So come back in 20, 25 years when you’re ready to blow us up.’””

  paranoia Republicans conservatism conspiracy-theories political-discourse antebellum-America

• “Now, yawn, darling!”
• “My wife has discovered this hiding place!”
• “Excuse me, dear, I want to remind you that the last buss is at midnight!”
• “He’s unloading perfume for the Harem!”
• SIGHFUL EYEFUL: ROSA DALMAI!
• “It isn’t long after that a man falls into the arms of a woman that he falls into her hands!”
• “Well, I’m not dressed for table tennis. Think of something I won’t have to dress for!”
• “It’s the desk—Our neighbors are complaining because it’s so quiet in here!”
• DOLLS & SENSE: BETTY PAGE!
• “If some folks had known they would live to ripe old ages, they would have had a lot more fun in their youth!”
• “I have just the right prescription to go with the water—and it’s at my apartment!”
• EASY ON THE AHS! JEAN SMYLE!
• “You wouldn’t think it to look at me, but I’m starved fro affection!”
• “I guess I am kinda small, but you ought to see the size of my check book!”
• “Yeah, but I don’t like the way they keep horsing around!”
• “All evening you were a perfect gentleman—now I want you to know that I’m perfect too!”
• SWEET TALK: BIG EYE: CHERRY KNIGHT!
• The average girl seems to think that a flat tire is all right providing that he has the jack.
• “Judkins, get your mind back in the gutter!”
• “Do you mind holding them for a minute—I have to dig for change!”
• “I’m going to have a hard time explaining this to your son!”
• “Separate checks, please—my wife and I just had an argument!”
• “She said she’d do it, if she ever got one, but I never thought she’d have the nerve!”
• “Our skirt blower is out of order, ma’am, I’m just filling in until it’s repaired!”
• SWEET TALK: BIG EYE!
• “When I asked him to give me something that would warm my heart and remind me of him, he presented me with an electric blanket!”
• “On this job, Miss Dibble, you’ll be required to show your ability or you will hear from the front office!”
• “What a shame—just when I get to love you madly, you take a beating in the stock market!”
• “It really is terrific! The rocket boys ought to use it because Bob is still flying!”
• “My heart is yours, Mr. Van Dyke, but ze rest of me belongs to Pierre!”
• “Here’s to tonight—and tomorrow morning!”
• DOLLS & SENSE: IRIS BRISOL!
• “Co-eds,” jests Iris, “are nothing but girls who failed to get their men in high school!”
• “Excuse me, ma’am, are you here on the all-expense tour, or the economy plan?!”
• “You’re almost in the right place, and that’s plain talk!”
• “I should have brought my camera—it might be a photo finish!”
• “My, Helen, you are a little high!”
• SIGHFUL EYEFUL: RANDI RYAN!
• “An election year proves one thing, at least—that politics makes estranged bedfellows!”
• “There’s a man in here! I took this tour on the American Plan!”
• “We would like to present Suzy / Who has beauty to spare; / She’s a frustrated nudist / Who found it was more than she could bare!”
• “Goodness me, does everyone get such good service?!”
• “Are those your views, or those of your sponsor?!”
• “That will teach you to leave a defenseless woman home every night!”
• BE A MEDICINE MAN! When visiting a sick friend, take along our gal below and paste her to his medicine bottle! He’ll now take his medicine like a man!
• “After flying planes all week it’s nice to watch someone else take off down the runway!”
• “Let’s go to your apartment and count your blessings!”
• “…and what’ll we do when he grows up?”
• EASY ON THE AHS: MONA MILLER!
• “The one man who has seen more bathing beauties than anyone else is a busy plumber!”
• “You’re the best boss my boyfriend ever had—He never had all this overtime at his other jobs!”
• “I think he’s an extrovert—he’s always putting out!”
• “It’s the only original I own. All the rest are reproductions!”
• “Inflation or no inflation, the two bucks for a marriage license is still a sound investment!”
• “It’s always fair weather when good friends get together!”
• “Well, you can’t type and you can’t take shorthand—how are you on remembering things!”
• DOLLS & SENSE:
• “The only thing old fashioned about some girls is the cocktail you find in their hands.”
• “He treated me like a baby—kept feeding me a bottle all the time!”
• “She sure gives them a fair shake for their money!”
• “Well, I like this outfit! You’ll either take it or love it!”
• “Anything special you want to tell mother?!”
• “Oh, love is okay, I guess—but Cupid’s dart missed my target!”
• “I save about $2.75 a week by doing my own laundry!”
• “I had to give up the olives—I can’t resist bobbing for them!”
• EASY ON THE AHS: TANYA ROGERS!
• “A ‘bargain’ is something you can’t use at a price you can’t resist!”
• “Sorry I missed seeing Frank… he must be quite busy around the yard. WHAT’S THAT!!!”
• “There’s a girl who comes within inches of going right to the top!”
• “WATCH YOUR STEP!”
• “You know, Alice, if we really wanted to see Italy, we should have come on our twentieth anniversary rather than our honeymoon!”
• DOLLS & SENSE: ZAHRA NORBO!
• “If a woman loves you, all well and good! Otherwise… well… there is no otherwise!”
• “I thought this dress was too flimsy—everyone knew my name is May!”
• “—And what did the sales girl tell you to do after you put on that sexy perfume?!”
• SWEET TALK: BIG EYE: MARSHA JAMES!
• “Blisters can be a badge of character—depending, of course, upon where they are!”
• “Remember you suggested, dear, that I invite some of my old friends over for a little get-together while you’re away—well, I did!”
• “Sign on the dotted line, please!”
• “I hope that character in the front row who burnt his chin on the footlights is all right!”
• “Would you like to sit this one out—or would you want to dance cheek to cheek?!”
• “I have several very important letters!”
• “You’re all tensed up—why don’t you relax!”
• “That’s the way with romance! Last night I was riding high—tonight I’ve hit bottom!”
• “I don’t know why I always have to be the girl with the biggest can in these collection drives!”
• “If he gets to dictating too fast, I put my foot down!”
• “It was nice of you to ask me to become engaged to you, Freddie, but for one wild moment I thought you were going to ask me to marry you!”
• EASY ON THE AHS: DIANE WEBBER!
• “The smart girl,” Diane would have us know, “is one who keeps her eyes closed during and her mouth closed afterwards!”
• “That is literary speaking, of course!”
• DAWN RICHARDS!
• “Married men may not be the most informed people, but they certainly are the most!”
• “A double order of spaghetti and meat balls? Where on earth are you going to put it all?!”
• SHOW OFF! WENDY WELLS!
• “Whoever thought they would be so early!”
• “I was fed up with my saxophone lessons!”
• “Some girls are really lucky; she marries for the first time, and to a millionaire!”
• DOLLS & SENSE: BETTY PAGE!
• “The best solution for World Peace is to spend more money on face powder and not on gunpowder!”

• Scripting News: Un-patents

  “More than ever, we need a pro bono legal function that’s funded as a charity that patents these items, makes a public record of who the inventor is, for kudos purposes only, and thereby prevents a huge company from patenting it. ”

  intellectual-property disintermediation-targets public-policy social-entrepreneurship

• A Tropical Tale Of Tourists, Networks, And A New Kind Of Leadership | Fast Company

  The final stage of Krebs and Holley’s model, and its ultimate aim, is called a core/periphery social network. In this highly stable yet highly resilient social arrangement, which usually emerges after years of effort, a core of strongly affiliated hubs at the center of the social system is connected to a constellation of people and resources on the periphery, through weak ties. This allows for an efficient and natural division of labor: The periphery monitors the environment, while the core implements what is discovered and deemed useful.

  to-do

• Distances, Divergences, Dirichlet distributions « Ben Moran

  There’s an interesting correspondence between unimodal p.d.f.’s on a metric space (like the reals), and distance functions. I will dig through this for amusement below, with some R code to generate the pictures.

  probability-theory metrics high-dimensional-stuff to-read

• Tapestry Central: You Cannot Correctly Represent Change Without Immutability

  “With immutability, changes in state are really new objects; a new version, or “quantifiable set of qualities”, that does not affect the original version. It is possible to compare two different iterations of the same object to see the “deltas”. In Datomic, you even have more meta-data about when such state changes occur, what else changed within the same transaction, and who is the responsible party for that transaction.”

  programming ontology philosophy-of-engineering immutability

• [1206.6866] Stochastic Optimal Control in Continuous Space-Time Multi-Agent Systems

  “Recently, a theory for stochastic optimal control in non-linear dynamical systems in continuous space-time has been developed (Kappen, 2005). We apply this theory to collaborative multi-agent systems. The agents evolve according to a given non-linear dynamics with additive Wiener noise. Each agent can control its own dynamics. The goal is to minimize the accumulated joint cost, which consists of a state dependent term and a term that is quadratic in the control. We focus on systems of non-interacting agents that have to distribute themselves optimally over a number of targets, given a set of end-costs for the different possible agent-target combinations. We show that optimal control is the combinatorial sum of independent single-agent single-target optimal controls weighted by a factor proportional to the end-costs of the different combinations. Thus, multi-agent control is related to a standard graphical model inference problem. The additional computational cost compared to single-agent control is exponential in the tree-width of the graph specifying the combinatorial sum times the number of targets. We illustrate the result by simulations of systems with up to 42 agents.”
  
  coordination agent-based emergent-design nudge-targets control-theory

• [1205.6917] Robust self-triggered coordination with ternary controllers

  “This paper regards coordination of networked systems, which is studied in the framework of hybrid dynamical systems. We design a coordination scheme which combines the use of ternary controllers with a self-triggered communication policy. The communication policy requires the agents to collect, at each sampling time, relative measurements of their neighbors’ states: the collected information is then used to update the control and determine the following sampling time. We prove that the proposed scheme ensures finite-time convergence to a neighborhood of a consensus state. We then study the robustness of the proposed self-triggered coordination system with respect to skews in the agents’ local clocks, to delays, and to limited precision in communication. Furthermore, we present two significant variations of our scheme. First, we design a time-varying controller which asymptotically drives the system to consensus. Second, we adapt our framework to a communication model in which an agent does not poll all its neighbors simultaneously, but single neighbors instead. This communication policy actually leads to a self-triggered “gossip” coordination system.”
  
  network-theory multi-agent-systems mechanism-design emergent-design consensus-systems nudge-targets algorithms

• [1205.2607] Simulation-Based Game Theoretic Analysis of Keyword Auctions with Low-Dimensional Bidding Strategies

  “We perform a simulation-based analysis of keyword auctions modeled as one-shot games of incomplete information to study a series of mechanism design questions. Our first question addresses the degree to which incentive compatibility fails in generalized second-price (GSP) auctions. Our results suggest that sincere bidding in GSP auctions is a strikingly poor strategy and a poor predictor of equilibrium outcomes. We next show that the rank-by-revenue mechanism is welfare optimal, corroborating past results. Finally, we analyze profit as a function of auction mechanism under a series of alternative settings. Our conclusions coincide with those of Lahaie and Pennock [2007] when values and quality scores are strongly positively correlated: in such a case, rank-by-bid rules are clearly superior. We diverge, however, in showing that auctions that put little weight on quality scores almost universally dominate the pure rank-by-revenue scheme.”
  
  game-theory agent-based simulation economics auction nudge-targets

• Graphing the history of philosophy « Drunks&Lampposts

  “Each philosopher is a node in the network and the lines between them (or edges in the terminology of graph theory) represents lines of influence. The node and text are sized according to the number of connections (both in and out). The algorithm that visualises the graph also tends to put the better connected nodes in the centre of the diagram so we see the most influential philosophers, in large text, clustered in the centre. It all seems about right with the major figures in the western philosophical tradition taking the centre stage. (I need to also add the direction of influence with a arrow head – something I’ve not got round to yet.) A shortcoming however is that this evaluation only takes into account direct lines of influence. Indirect influence via another person in the network does not enter into it. This probably explains why Descartes is smaller than you’d think. It would also be better if the nodes were sized only by the number of outward connections although I think overall the differences would be slight. I’ll get round to that.”
  
  visualization philosopher-mining

• [1206.3666] Unsupervised adaptation of brain machine interface decoders

  “The performance of neural decoders can degrade over time due to nonstationarities in the relationship between neuronal activity and behavior. In this case, brain-machine interfaces (BMI) require adaptation of their decoders to maintain high performance across time. One way to achieve this is by use of periodical calibration phases, during which the BMI system (or an external human demonstrator) instructs the user to perform certain movements or behaviors. This approach has two disadvantages: (i) calibration phases interrupt the autonomous operation of the BMI and (ii) between two calibration phases the BMI performance might not be stable but continuously decrease. A better alternative would be that the BMI decoder is able to continuously adapt in an unsupervised manner during autonomous BMI operation, i.e. without knowing the movement intentions of the user. In the present article, we present an efficient method for such unsupervised training of BMI systems for continuous movement control. The proposed method utilizes a cost function derived from neuronal recordings, which guides a learning algorithm to evaluate the decoding parameters. We verify the performance of our adaptive method by simulating a BMI user with an optimal feedback control model and its interaction with our adaptive BMI decoder. The simulation results show that the cost function and the algorithm yield fast and precise trajectories towards targets at random orientations on a 2-dimensional computer screen. For initially unknown and non-stationary tuning parameters, our unsupervised method is still able to generate precise trajectories and to keep its performance stable in the long term. The algorithm can optionally work also with neuronal error signals instead or in conjunction with the proposed unsupervised adaptation.”
  
  FOR-SCIENCE brain-machine-interface user-interface adaptive-control emergent-design

• [1206.3980] Visualizing Streaming Text Data with Dynamic Maps

  “The many endless rivers of text now available present a serious challenge in the task of gleaning, analyzing and discovering useful information. In this paper, we describe a methodology for visualizing text streams in real time. The approach automatically groups similar messages into “countries,” with keyword summaries, using semantic analysis, graph clustering and map generation techniques. It handles the need for visual stability across time by dynamic graph layout and Procrustes projection techniques, enhanced with a novel stable component packing algorithm. The result provides a continuous, succinct view of evolving topics of interest. It can be used in passive mode for overviews and situational awareness, or as an interactive data exploration tool. To make these ideas concrete, we describe their application to an online service called TwitterScope.”
  
  visualization data algorithms user-experience

• [1205.3352] Revisiting the effect of external fields in Axelrod’s model of social dynamics

  “The study of the effects of spatially uniform fields on the steady-state properties of Axelrod’s model has yielded plenty of controversial results. Here we re-examine the impact of this type of field for a selection of parameters such that the field-free steady state of the model is heterogeneous or multicultural. Analyses of both one and two-dimensional versions of Axelrod’s model indicate that, contrary to previous claims in the literature, the steady state remains heterogeneous regardless of the value of the field strength. Turning on the field leads to a discontinuous decrease on the number of cultural domains, which we argue is due to the instability of zero-field heterogeneous absorbing configurations. We find, however, that spatially nonuniform fields that implement a consensus rule among the neighborhood of the agents enforces homogenization. Although the overall effects of the fields are essentially the same irrespective of the dimensionality of the model, we argue that the dimensionality has a significant impact on the stability of the field-free homogeneous steady state.”
  
  complexology agent-based externalities simulation evolutionary-economics nudge-targets

• [1206.3421] Linear Latent Variable Models: The lava-package

  “An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation interface covering a broad range of non-linear generalized structural equation models is described. The model and software are demonstrated in data of measurements of the serotonin transporter in the human brain.”
  
  ontology statistics algorithms R-language linear-models model-view-controller design-patterns

• [1205.6129] The Missing Memristor: Novel Nanotechnology or rather new Case Study for the Philosophy and Sociology of Science?

  “In 2008, it was widely announced that the missing memristor, a basic two-terminal electrical circuit element, had finally been discovered. The memristor is the fourth and last such circuit element and thus completes circuit theory. Predicted already in 1971, the eventual discovery of something seemingly so basic needed almost 40 years. However, this discovery is doubted. The predicted memristor has no material memory and is based on magnetic flux, but the discovered devices constitute analogue memory storage that do not involve magnetism. The person who originally proposed the memristor did not reject the discovery but instead changed his mind about what a memristor is. We briefly introduce the history and then carefully memristance and the memristor as such. We discuss its status as a model rather than a device. We discuss the discovered devices, their stability, and how stability relates to the consistency of the theoretical entities. A thought experiment assumes a world without magnetism. Inductors cannot exist there, but memory resistors could still be constructed. On the same grounds as the memristor was historically predicted, an “inductor” could then be predicted. Likely, somebody would also ‘discover’ one. A tentative sociological analysis compares to the flawed detection of gravitational waves but comes to very different conclusions.”
  
  symmetry-is-the-way-things-ought-to-be history-of-science technical-assumptions engineering-philosophy

• [1205.2776] Transport on coupled spatial networks

  “Transport processes on spatial networks are representative of a broad class of real world systems which, rather than being independent, are typically interdependent. We propose a measure of utility to capture key features that arise when such systems are coupled together. The coupling is defined in a way that is not solely topological, relying on both the distribution of sources and sinks, and the method of route assignment. Using a toy model, we explore relevant cases by simulation. For certain parameter values, a picture emerges of two regimes. The first occurs when the flows go from many sources to a small number of sinks. In this case, network utility is largest when the coupling is at its maximum and the average shortest path is minimized. The second regime arises when many sources correspond to many sinks. Here, the optimal coupling no longer corresponds to the minimum average shortest path, as the congestion of traffic must also be taken into account. More generally, results indicate that coupled spatial systems can give rise to behavior that relies subtly on the interplay between the coupling and randomness in the source-sink distribution.”
  
  nudge-targets network-theory emergent-design epidemiology simulation

• [1206.5849] Understanding the complexity of the L’evy-walk nature of human mobility with a multi-scale cost/benefit model

  “Probability distributions of human displacements has been fit with exponentially truncated L’evy flights or fat tailed Pareto inverse power law probability distributions. Thus, people usually stay within a given location (for example, the city of residence), but with a non-vanishing frequency they visit nearby or far locations too. Herein, we show that an important empirical distribution of human displacements (range: from 1 to 1000 km) can be well fit by three consecutive Pareto distributions with simple integer exponents equal to 1, 2 and ($gtrapprox$) 3. These three exponents correspond to three displacement range zones of about 1 km $lesssim Delta r lesssim$ 10 km, 10 km $lesssim Delta r lesssim$ 300 km and 300 km $lesssim Delta r lesssim $ 1000 km, respectively. These three zones can be geographically and physically well determined as displacements within a city, visits to nearby cities that may occur within just one-day trips, and visit to far locations that may require multi-days trips. The incremental integer values of the three exponents can be easily explained with a three-scale mobility cost/benefit model for human displacements based on simple geometrical constrains. Essentially, people would divide the space into three major regions (close, medium and far distances) and would assume that the travel benefits are randomly/uniformly distributed mostly only within specific urban-like areas.”
  
  Levy-flights city-planning multiscale-phenomena self-similarity

• [1206.7011] Ultrathin Terahertz Planar Lenses

  “Conventional optical components shape the wavefront of propagating light by adjusting the optical path length, which requires the use of rather thick lenses, especially for the adjustment of terahertz (THz) radiation due to its long wavelength. Two ultrathin THz planar lenses were designed and fabricated based on interface phase modulation of antenna resonance. The lens thicknesses were extremely reduced to 100 nm, which is only 1/4000th of the illuminating light wavelength. The focusing and imaging functions of the lenses were experimentally demonstrated. The ultrathin optical components described herein are a significant step toward the development of a micro-integrated THz system.”
  
  optics engineering-design nudge-targets

• [1206.3789] Tree decomposition and parameterized algorithms for RNA structure-sequence alignment including tertiary interactions and pseudoknots

  “We present a general setting for structure-sequence comparison in a large class of RNA structures that unifies and generalizes a number of recent works on specific families on structures. Our approach is based on tree decomposition of structures and gives rises to a general parameterized algorithm, where the exponential part of the complexity depends on the family of structures. For each of the previously studied families, our algorithm has the same complexity as the specific algorithm that had been given before.”
  
  RNA structural-biology folding bioinformatics algorithms nudge-targets modeling

• [1206.3403] Topological Measure Locating the Effective Crossover between Segregation and Integration in a Modular Network

  “Computational analysis of time-course data with an underlying causal structure is needed in a variety of domains, including neural spike trains, stock price movements, and gene expression levels. However, it can be challenging to determine from just the numerical time course data alone what is coordinating the visible processes, to separate the underlying prima facie causes into genuine and spurious causes and to do so with a feasible computational complexity. For this purpose, we have been developing a novel algorithm based on a framework that combines notions of causality in philosophy with algorithmic approaches built on model checking and statistical techniques for multiple hypotheses testing. The causal relationships are described in terms of temporal logic formulae, reframing the inference problem in terms of model checking. The logic used, PCTL, allows description of both the time between cause and effect and the probability of this relationship being observed. We show that equipped with these causal formulae with their associated probabilities we may compute the average impact a cause makes to its effect and then discover statistically significant causes through the concepts of multiple hypothesis testing (treating each causal relationship as a hypothesis), and false discovery control. By exploring a well-chosen family of potentially all significant hypotheses with reasonably minimal description length, it is possible to tame the algorithm’s computational complexity while exploring the nearly complete search-space of all prima facie causes. We have tested these ideas in a number of domains and illustrate them here with two examples.”
  
  time-series statistics framework dynamical-systems to-read

• [1206.5959] The Online Replacement Path Problem

  “We study a natural online variant of the replacement path problem. The textit{replacement path problem} asks to find for a given graph $G = (V,E)$, two designated vertices $s,tin V$ and a shortest $s$-$t$ path $P$ in $G$, a textit{replacement path} $P_e$ for every edge $e$ on the path $P$. The replacement path $P_e$ is simply a shortest $s$-$t$ path in the graph, which avoids the textit{failed} edge $e$. We adapt this problem to deal with the natural scenario, that the edge which failed is not known at the time of solution implementation. Instead, our problem assumes that the identity of the failed edge only becomes available when the routing mechanism tries to cross the edge. This situation is motivated by applications in distributed networks, where information about recent changes in the network is only stored locally, and fault-tolerant optimization, where an adversary tries to delay the discovery of the materialized scenario as much as possible. Consequently, we define the textit{online replacement path problem}, which asks to find a nominal $s$-$t$ path $Q$ and detours $Q_e$ for every edge on the path $Q$, such that the worst-case arrival time at the destination is minimized. Our main contribution is a label setting algorithm, which solves the problem in undirected graphs in time $O(m log n)$ and linear space for all sources and a single destination. We also present algorithms for extensions of the model to any bounded number of failed edges.”
  
  operations-research planning algorithms online-algorithms nudge-targets

• [1206.6195] Parrondo games with spatial dependence, II

  “…Again this suggests that the Parrondo region (mu_B is nonpositive and mu_[r,s] is positive) has nonzero volume in the limit. …”
  
  Parrondo-games game-theory counterintuitive-results simulation cooperation

• [1206.4648] Two-Manifold Problems with Applications to Nonlinear System Identification

  “Recently, there has been much interest in spectral approaches to learning manifolds—so-called kernel eigenmap methods. These methods have had some successes, but their applicability is limited because they are not robust to noise. To address this limitation, we look at two-manifold problems, in which we simultaneously reconstruct two related manifolds, each representing a different view of the same data. By solving these interconnected learning problems together, two-manifold algorithms are able to succeed where a non-integrated approach would fail: each view allows us to suppress noise in the other, reducing bias. We propose a class of algorithms for two-manifold problems, based on spectral decomposition of cross-covariance operators in Hilbert space, and discuss when two-manifold problems are useful. Finally, we demonstrate that solving a two-manifold problem can aid in learning a nonlinear dynamical system from limited data.”
  
  statistics inverse-problems system-identification algorithms nudge-targets benchmarking

• [1203.3367] Stochastic differential equations for evolutionary dynamics with demographic noise and mutations

  “We present a general framework to describe the evolutionary dynamics of an arbitrary number of types in finite populations based on stochastic differential equations (SDE). For large, but finite populations this allows to include demographic noise without requiring explicit simulations. Instead, the population size only rescales the amplitude of the noise. Moreover, this framework admits the inclusion of mutations between different types, provided that mutation rates, $mu$, are not too small compared to the inverse population size 1/N. This ensures that all types are almost always represented in the population and that the occasional extinction of one type does not result in an extended absence of that type. For $mu Nll1$ this limits the use of SDE’s, but in this case there are well established alternative approximations based on time scale separation. We illustrate our approach by a Rock-Scissors-Paper game with mutations, where we demonstrate excellent agreement with simulation based results for sufficiently large populations. In the absence of mutations the excellent agreement extends to small population sizes.”
  
  finite-size-effects population-biology noise-in-design nudge-targets

• [1206.5352] The Subword Complexity of k-Automatic Sequences is k-Synchronized

  “We show that the subword complexity function p_x (n), which counts the number of distinct factors of length n of a k-automatic sequence x, is k-synchronized in the sense of Carpi. As an application, we generalize recent results of Goldstein. In contrast, we show that function which counts the number of unbordered factors of length n is not k-synchronized.”
  
  automata strings complexity nudge-targets

• Convex hull algorithms — Wikipedia, the free encyclopedia

  “Computing the convex hull means that a non-ambiguous and efficient representation of the required convex shape is constructed. The complexity of the corresponding algorithms is usually estimated in terms of n, the number of input points, and h, the number of points on the convex hull.”
  
  computational-geometry algorithms nudge-targets

• [1205.6363] What Should Developers Be Aware Of? An Empirical Study on the Directives of API Documentation

  “Application Programming Interfaces (API) are exposed to developers in order to reuse software libraries. API directives are natural-language statements in API documentation that make developers aware of constraints and guidelines related to the usage of an API. This paper presents the design and the results of an empirical study on the directives of API documentation of object-oriented libraries. Its main contribution is to propose and extensively discuss a taxonomy of 23 kinds of API directives.”
  
  digital-humanities documentation text-mining software-development cultural-norms

• Publications Available Electronically

  Godfried T. Toussaint, “The Erdos-Nagy theorem and its ramifications,” Proc. 11th Canadian Conference on Computational Geometry, Vancouver, Canada, August 16–18 1999, pp. 9–12. Long version available at: http://www.cs.ubc.ca/conferences/CCCG. Also: Technical Report No. SOCS-99.2, School of Computer Science, McGill University, June 18, 1999.
  
  nudge-targets geometry genetic-programming-target low-hanging-fruit

• Introduction to Lamina Emergent Mechanisms (LEMS) | Compliant Mechanisms

  “The fact that LEMs can be fabricated from planar layers influences both what processes can be used for their manufacture and what materials may be used in their construction. At the micro level, LEMS can be fabricated using single-layer MEMS fabrication methods and materials, which offers significant cost and reliability advantages. It also provides opportunities for complex out-of-plane motion with only a single layer. At the macro level, manufacturing processes used to make static structures or components for assembly can be used to create mechanisms capable of sophisticated motions and complex tasks. Example processes include stamping, fine blanking, laser cutting, water jet cutting, plasma cutting, and wire electrical discharge machining (EDM). Some of these processes, such as stamping, offer significant cost advantages for high-volume production. Planar fabrication also allows the use of sheet goods to directly create mechanisms. The use of low-cost, high-quality sheet goods has the potential to dramatically reduce cost for high-volume production. It also makes possible the next characteristic: flat initial state.”
  
  manufacturing engineering-design fabrication nudge-targets