Frontier Nerds: An ITP Blog

Some of the most interesting mechanical solutions seem to have emerged from the transition period between manual and automated means of production — the process of adapting tasks long performed by hand to operate under machine power for the first time. The initial iterations of the adaptation process usually result in endearingly anthropomorphic machines, since the process of abstracting human motions out of the process isn’t yet complete. (Examples include electric typewriters, sewing machines, automated assembly lines, etc.)

I’m interested in working through this process of converting hand power to mechanical power myself. A Dymo tapewriter represents an unlikely but possibly satisfying platform to turn into an automatic, electronic device.

I’m also interested in unintended and unknown physical consequences for actions taken online. The stream of new tag data on sites like Flickr could provide interesting source text, and would force the idea of a tag into the physical world – e.g. here’s a machine that involuntarily spits out sticky pieces of tape with letters on them that could, conceivably, tag real-world objects.

Thus, the TagBot. A mechanized, automatic Dymo tapewriter which scrapes new tag data from Flickr in real time, and generates labels accordingly.

A slightly more ambitious variant could be built with mobility in mind — you could position it somewhere in the city, and it would spit out tags from photographs taken in the vicinity.

Mechanically, several factors need to be accounted for:

• Rotation and indexing of the character wheel — a stepper motor would probably suffice.
• The space — a light squeeze on the handle advances the tape without printing a letter. A strong motor or solenoid could manage this.
• Character printing — a harder squeeze on the handle.
• Cut — a hard squeeze on another lever.
• Pull — a motor to pull the finished tag out of the printer.
• Tape reloading — Dymo tape rolls are pretty short, some kind of automated reloading system would be great, but probably beyond the scope / time available for the midterm.

Code and control will require the following:

• An Arduino to coordinate the motions of each mechanical element.
• An interface with the Flickr API to fetch the latest tags. (Either serially from a laptop, or directly over a WiShield.)
• Code to reduce the character set to those present on the character wheel.

Homework #2: The digital cut-up. Write a program that reads in and creatively re-arranges the content of one or more source texts. What is the unit of your cut-up technique? (the word, the line, the character? something else?) How does your procedure relate (if at all) to your choice of source text? Feel free to build on your assignment from last week.

I wanted to build a cut-up machine that was as grammatically and syntactically non-invasive as possible, while still significantly munging the source text. So I decided to shuffle the text in a way that treated parts of speech as sacred — the words could fall where they may, but if the first word was a noun in the source text, it had better be replaced with a noun in the output. If the second word was a verb, nothing but a verb from elsewhere in the text should take its place, and so on.

Programmatically deriving a word’s part of speech turns out to be a major issue, so I leaned on NLTK to take care of this. It actually does a pretty decent job. From there it’s just a matter of storing lists of the words in a dictionary keyed to each part of speech, shuffling them, and then reconstituting the text.

I ran Obama’s most recent state of the union address through my algorithm. It’s formal enough and carefully constructed enough so as not to pose a significant challenged to NLTK’s Brown Corpus trained part of speech tagger. Also, I was struck by how much the output resembles the famous Bushisms, albeit with a lingering Obama-esque tinge.

Here’s some output:

Bloody Biden, succeed. Run Constitution, convictions from America, were moments, But great Allies: tested. union. declares during on time to look, the prosperity must time to union. history of the struggle at our For Sunday one leaders, our people have fulfilled all Bull Union chose done so of hesitations despite progress And America and we are turned again on the war that call and midst; in marchers as inevitable anything and fellow state. they’s tempting to move very of the guests and president at our victory distinguished civil, that President were back done to Tuesday and when the Beach was tested back about Our Omaha but these Americans great beaten that duty. Congress, nation crashed forward so of These Madame the future was of certain. tranquility. And first years periods was landed because doubt. Again, the depression was assume and Congress Speaker was rights on destined the market of our moments and the courage in our Black and at this our fears and divisions, our disagreements and our members, Vice prevailed that we have to answer always of one strife And 220 times.

They, It have When and much, we shall give information’s strength.

Sample output from the full text of Obama’s 2010 state of the union is also available here. The original text is also available for comparison.

The source code follows. Pretty simple, NLTK does most of the heavy lifting.

Attachments

Source code: POS Shuffler Python

Source text: State of the Union

Sample output

Assignment: Use a physics library.

Physics libraries like Box2D tend to use extremely rational language in extremely literal ways (mass, friction, gravity, etc.) — I wanted to build on this language by overloading its meaning and taking it in an absurdist direction. Electrons, in the quantities pushed around by our machines, certainly don’t carry much physical weight… how, then, can we weigh a string of characters?

Google seems to have this sorted out… just about any conceivable string of text can be quantified, weighed, and perhaps valued by the number of results it dredges up.

So I whipped up an app that literally uses Google’s search result count to determine how game elements behave — with the intention to pressure a player into testing their own judgment of the worth of a word against Google’s. It looks like this:

How does our understanding of how much a word weighs depart from Google’s absolutism? How much weight can you balance?

The game mechanics are pretty basic… Box2D manages the interactions between the words and the tilting ledge below. The ledge is attached to a joint in the middle of the screen, and if words are distributed unevenly it will tilt and send terms sliding into the abyss.

The cloud floating above has a text input box which sends search queries off to Google. A bit of code scrapes the resulting HTML to figure out how many results exist for the query. This runs in its own thread so as not to block the rendering / physics work. After the query count comes back from google, the term you entered drops from the cloud onto the ledge. (You can influence where it will land by positioning the cloud with the mouse beforehand.) The more results a term has, the higher its density — this means that major search terms will help you load extra weight on the ledge, but their extra mass also means they’re more likely to tilt the ledge past the point of recovery. This, I hope, forces the player to estimate the weight of a term before they drop it from the cloud.

Here are a few more screenshots from development and testing:

The game doesn’t work very well as an Applet (it uses the net library, which requires code signing), so it’s probably easiest to download the zip below if you’d like to give it a try.

The source is a bit long to embed here, so I’ve attached it below.

Attachments

Weight of Your Words Application

Weight of Your Words Source Code

Interactive Version

Homework #1. Create a program that behaves like a UNIX text processing program (such as cat, grep, tr, etc.). Your program should take text as input (any text, or a particular text of your choosing) and output a version of the text that has been filtered and/or munged. Be creative, insightful, or intentionally banal.

Choose one text that you created with your program to read in class.

Bonus: Use the program that you created in tandem with another UNIX command line utility.

I try to avoid destroying data. I draw upon the usual set of justifications: Storage is only getting cheaper, an empty HD occupies the same physical space as a full HD, yadda yadda.

Whether or not this policy is for the best, it’s left me with over a million lines of instant messenger conversation logs from my earlier years — mundane, personal conversations between myself and a handful of friends. (Running from about Jr. High to end end of High School.) If not of much general interest, the contents of these (often painfully angst-ridden) logs are a personally surreal thing to revisit.

In response to the first assignment, I wanted to draw from this well of text in some way. I’m particularly interested in the idea of accidental art — the daily, unintended collisions with things that might be formally construed as art.

I wrote a quick algorithm to put my variation of the Infinite monkey theorem to the test. Can enough angsty teens, given enough time to type, eventually produce something resembling a traditionally recognized “great work”?

I decided to pit my adolescent conversations against T.S. Eliot’s The Waste Land. I wasn’t interested in simply recreating the poem verbatim, instead I used the first and last words of each line as anchors between the poem and my logs, and anything in the middle would be filled in from my conversations based on nearby words.

So, the algorithm takes the first and last word from each line of the poem, and then looks for matches in my conversation logs. If it finds a match for both words in my logs, then it will walk forward from the first word, and backward from the last word, to create a line of text which starts and ends identically to a line in The Waste Land.

Finally, if the resulting line is too long, it will cut words out of the middle until the length of the output line matches the length of the line in The Waste Land. Currently, lines that are shorter than their equivalents in the original poem are just printed as-is. (It would be nice to find a reasonable way to beef these up to size.)

When matches aren’t found, the line is dropped. Only about 60% of the poem could be reconstructed from my million lines of conversation text. (e.g., words like “abolie” never turned up in my logs, and therefore were not available to reconstruct that line of The Waste Land.)

The code is happy to work with any plain text files. Supply it with a model text (in this case, Eliot’s poem) and a source text (in this case, my conversation logs), and it will do its best to shape the source text into the model text.

The first argument to the program is the source text, and the second is the model text. For example, from the command line, this would use aim.txt as the source and wasteland.txt as the model, and save the results to a text file named aim-wasteland.txt:
python interstitial-wasteland.py aim.txt wasteland.txt >> aim-wasteland.txt

It takes a while to run, and you won’t get decent results unless the source text is huge.

Here’s the full output: interstitial-wasteland-output.txt

And the output with the original poem in parallel: interstitial-wasteland-output-parallel.txt

A small excerpt of the raw output:

I… wasn’t going us, he’s DEAD!

April one is designed for inbreeding,
Memory Lane and stirring
Winter Olympic Games. of recovering
Earth, yet we and feeding
And let me tell for like an hour..by
Individuals can be dangerous though
I”m confused http://winter.squaw.com/html/squawcambig.html

-What are the current problems problems?- it grows
Out man,

The same excerpt in parallel with the model text:

I. THE BURIAL OF THE DEAD
I… wasn’t going us, he’s DEAD!

April is the cruellest month, breeding
April one is designed for inbreeding,

Memory and desire, stirring
Memory Lane and stirring

Winter kept us warm, covering
Winter Olympic Games. of recovering

Earth in forgetful snow, feeding
Earth, yet we and feeding

And drank coffee, and talked for an hour.
And let me tell for like an hour..by

In the mountains, there you feel free.
Individuals can be dangerous though

I read, much of the night, and go south in the winter.
I”m confused http://winter.squaw.com/html/squawcambig.html

What are the roots that clutch, what branches grow
-What are the current problems problems?- it grows

Out of this stony rubbish? Son of man,
Out man,

And the source code:

Attachments

Output

Output inline with original poem

Source Code

Source Poem

The mechanisms class was charged with creating egg-cracking Rube Goldberg machines for the first week's assignment. Rather than breaking the egg shell through brute force, we tossed around the idea of dissolving the egg shell entirely.

Luckily there was some muriatic acid lying around the shop, so we ran a quick experiment. The shell dissolves, but it takes about 20 minutes, and what's left isn't a broken egg, but a layer of internal membrane which keeps the de-shelled egg intact. Interesting, but not well suited to the purposes of our machine-to-be, so we shelved the idea. (Maybe it was too clever by half to begin with.)

(Science music by Boards of Canada.)

I spent an unreasonable portion of my childhood in the back seat of a car, staring out the window. Rainy days, in particular, allowed for one of the more confounding means of passing the time: attempting to predict and understand the movements of water drops on the window.

Attachments

window_drop.zip