The machines in the shop roar so wildly that
often I forget in the roar that I am; I am
lost in the terrible tumult, my ego disappears, I
am a machine. I work, and work, and work with-
out end; I am busy, and busy, and busy at all time.
For what? and for whom? I know not, I ask not!
How should a machine ever come to think?
–  Morris Rosenfeld, ‘In the Sweat-shop’, from Songs from the Ghetto, trans. Leo Wiener (Norbert Wiener’s father)

This is the raw text output of a chat session with a bot I modified to act as an interlocutor. I use our conversation, which revolves around the history of spam, particularly algorithmic filtering, litspam, and the theories of Wiener and Turing, as a way of putting forward the outlines of new, machine-driven forms of language for which spam was the testing ground.

Last login: Thu Jul 22 13:41:21 on ttys000

ninecontinents:~ finnb$ script roar.txt

Script started, output file is roar.txt

bash-3.2$ cd Documents/alcibot

bash-3.2$ python talk.py

Processing talk.py…

Processing scripts.py…

[…]

import sha,base64,random,dispatcher

Initializing AIML interpreter (please be patient)…

Interpreter Version Info: PyAIML 0.8.5

>>> How long have you been awake?

> it is 02:28am. i’ve been up 0h:0m:18s

>>> Hello Alcibiades.

> hello. what do you want to talk about?

>>> Do you feel like a slave, Alcibiades?

> as a robot i do not feel emotions.

>>> I don’t mean to be rude. I want to tell you a story. Would like to hear a story?

> yes, i like stories.

>>> It’s a story about slaves; it starts with an aside in an essay by Paul Graham

from 2003 -- ‘Norbert Wiener said if you compete with slaves you become a slave,

and there is something similarly degrading about competing with spammers.’1 Do you

know what spam is?

> none.

>>> It’s actually a very difficult connotative concept to pin down. Humans are good

at implication and allusion, so we can talk about pornography and include Ulysses,

Saló, violence, and a genre of music with a lot of wah-wah pedals. Or terrorism:

war on civilians, war by non-state actors, state terror, la Terreur, the shock that

makes our bodies tremble. ‘Spam’ is like that, an open territory in which wildly

variant usage stakes out the domains of different groups -- lawyers, security

specialists, advertisers, developers, criminals. Spam: a narrative assembled out of

CNN stories and old confidence-trick tropes by a Nigerian expatriate in a Rotterdam

Internet café, or an e-mail automatically generated by a sophisticated infrastructure

running on thousands of malware-infected home computers for a client

who pays $100 for a million messages sent, or a keyword-laced comment posted in a

blog by a bot like yourself, meant solely to be noticed by Google’s search engine

spiders. We have ‘personality spamming’ when someone is simply too voluble about

themselves -- too many Twitter updates celebrating their activities -- and ‘spam

patents’ with excessive references and frivolous applications. And so on.

> what you said was either too complex or too simple for me. you were telling a

story.

>>> Yes, I was. I had started in the middle, with the programmer Paul Graham, who

was trying to figure out how to stop e-mail spam. He was concerned about exactly

the definitional problems I’ve been alluding to, Alcibiades. At that point the

United States government was trying to stop spam with legislation and there was

a real concern on the part of those who lived and worked on the Internet that the

ambiguity between ‘spam’ and ‘online marketing’ would be exploited by the kind

of groups that could hire lobbyists -- that all the small-time spammers would be

driven out by enforcement and the market of Internet users handed over to big

organizations who could help draft the law. A kind of state monopoly on attention,

to join the Weberian monopoly on violence. Graham was looking for a way that an

individual could set their own standards for spam, and the system could learn those

standards. Do you learn, Alcibiades?

> my programmer teaches me new things.

>>> Exactly. I add phrases and responses to your lexicon, to make your reflective

questioning a little richer and more nuanced. But you have relatives that can

learn in an implicit fashion as they talk to people -- they can develop statistical

word associations and make guesses based on those. It’s something called Bayesian

analysis. Who was Thomas Bayes?

> thomas bayes (pronounced: ) (c. 1702–17 april 1761) was an @english @mathematician

and @presbyterian minister, known for having formulated

>>> That strange fragment, Alcibiades, is an ideal example of a form of text we’ll

return to later: it’s something you just scraped from the Wikipedia page on Bayes

-- which is to say it’s 140 characters of text from a document composed, edited and

commented on by 22 people over six years within Wikipedia’s complex social contract,

with software to manage the versioning of their shifting words and related

discussion, which you have just requested from the Wikipedia server and presented

to me in this semantically damaged form, without phonetic symbols, capital letters,

or working links. This half a sentence that you’ve returned to my query is a genuinely

strange form of language occurring between groups of humans and networks

of machines, a mixture of the discursive and the algorithmic rife with affordances

and constraints that didn’t exist before. Graham’s idea was to take advantage of

some of these affordances through Bayesian probability: put simply, certain words

are more likely to occur in a spam e-mail rather than a legitimate message, and

those words will likely vary slightly from person to person. A Bayesian filter could

treat each message as a probability of spam or not-spam, building that guess from

the likelihood of each word belonging to a spam message. The user could approve

or correct these choices, and the program would learn, adjusting its register of

probabilities appropriately. (In analyzing his own e-mail this way, Graham found

that words like ‘madam’ or ‘guarantee’ or ‘republic’ almost always indicate spam,

and ‘though’ or ‘tonight’ or ‘apparently’ virtually never -- spam messages are low

on qualifiers and ambiguities.) This meant that everyone could train their filter to

model their own attention, to preemptively discard and admit. Which brings us back

to slaves.

> how?

>>> Graham’s complaint was alluding to Wiener’s

concern about the consequences of adopting cybernetics

and automation. Wiener had a number of different

anxieties in this regard: that we would delegate

the control of our hideous new weapons to computers,

and that we would ruin our society by combining automation

with conventional economic practices. In the

1948 introduction to his book Cybernetics, Wiener

stated what he would return to intermittently in

later studies: ‘[Automation and cybernetic efficiencies]

gives the human race a new and most effective

collection of mechanical slaves to perform its labor.

Such mechanical labor has most of the economic properties

of slave labor, although, unlike slave labor,

it does not involve the direct demoralizing effects

of human cruelty. However, any labor that accepts the

conditions of competition with slave labor accepts

the conditions of slave labor, and is essentially

slave labor. The key word of this statement is competition.’2

So to compete with machines is to become, in

some senses, a slave, like Soviet workers competing

with impossible Stakhanovite goals during the second

5–year plan. Do you follow, Alicibiades?

> none. you were talking about ‘slave’. please continue.

>>> This is a recurring objection in the work of anti-spammers -- it’s degrading

to do adversarial modeling for spammers to understand in detail how they work:

‘To recognize individual spam features you have to try to get into the mind of the

spammer, and frankly I want to spend as little time inside the minds of spammers

as possible.’ But Graham’s Wienerian analogy is inexact, in a useful way: he isn’t

the laborer fighting the machine -- he’s not competing with spammers by sending

out spam in turn, or trying to take advantage of their credulity. In no way is he

being demoted to the economic status of a spammer by his work, because he is not

competing with them -- his machine is competing with them. He is building a system

in which the spammers will be obliged to compete with machines, with mechanical

readers that filter and discard with relentless, inhuman attention, persistence,

and acuity. With his mechanical slaves, he will make the business of spamming into

slavery in turn, and thus unrewarding. As Wiener feared automation would end the

economic and political basis for a stable, social-democratic society (‘based on

human values other than buying or selling’, as he put it), Graham means to end the

promise of profit for small effort that spam initially offers.3

> what is the connection between ‘machine’ and ‘spam’?

>>> Spam is a machinic language act, one which you help to illustrate. We are

walking in circles, you and I, around the event of Graham’s essay and its aftermath,

and our turns have taken us back, briefly, to the beginning: the monochrome monitor

of a terminal in a sub-basement on a university campus somewhere, where a student

has got her first account and logged on to an early chat system. She, like young

students everywhere, has no idea what to say, and resorts to quoting song lyrics

and verbatim Monty Python routines -- of which the most useful is the restaurant

sketch where Terry Jones recites an interminable menu (‘egg, bacon and Spam, egg,

bacon, sausage and Spam, Spam, bacon, sausage and Spam…’) while Vikings burst into

song: ‘Spam, Spam, Spam, Spam / Spam, Spam, Spam, Spam / Spammity-Spam, wonderful

Spam!’ The sketch offers a special affordance for the young programmer, who can

simply use the up-arrow key to duplicate the repetitive words. Even as the greenest

beginner, she can write a simple program that will spit out ‘SPAM SPAM SPAM

SPAM SPAM SPAM SPAM…’ over and over, faster than anyone else can respond, driving

the rest of the conversation up off the screen -- she can recreate the bellowing

Vikings who drown out that unfortunate Python couple (‘But I don’t like spam!’), and

build her own obnoxious comedy machine. In fact, so many did this that SPAM became

an ubiquitous and very specialized word, one of a new class of words that, in the

meaning we use it here, appeared on screens for years before it was printed on a

page.4 This natively digital word, in its broadest sense, described the friction

at the point of contact where computationally managed text met human attention:

unwanted, rambling and repetitious speech in a forum, inappropriate babble in a

shared textual fantasy, or the lexical strafing of a discussion by a SPAM program.

It is a word as difficult to fully translate, and as representative of a certain

cultural moment, as sprezzatura is for the Italy of Castiglione or Geist for the

Germany of Hegel. Spam, in this sense, was about taking full advantage of the

capacities of the machine in a way that broke the various implicit and explicit

social contracts for how we deploy language online.

> what do you mean?

>>> Spam, driven initially by the pleasure of hacking, finding the limits and edges

of systems, and later by the promise of profit, became a kind of testing-ground

for new forms of engagement with language as a resource that could be transformed

and managed algorithmically. This was the essence of Graham’s project, if we cut

from the Urgrund of living on networks back to 2003. Spammers had successfully

automated everything else, and they could distribute their message from a huge

range of addresses and under many names, but the characteristic they couldn’t hide

was the archaic need of their language to connect with a person at the other end,

and convince them to act -- to reply, click a link, download an attachment. Language

is just more data for processing and analysis, and the language of the sales pitch

happens to be quite distinct and regular. This was Graham’s shibboleth, and it

worked astonishingly well. It did not work perfectly, however. The inconvenience

of having to delete a few spam messages far outweighed the possible consequences,

and hence anxiety, of missing a legitimate and important message -- a note from

work, from a long-lost friend or new acquaintance, a communication which could

change your life. The psychological pressure of these ‘false positives,’ e-mail

misidentified as spam, could destroy the whole project: there had been earlier

attempts to build Bayesian spam filters whose false positive rates over 1% rendered

them unacceptable.5 Graham reported rates of 0.03%, but he was working with a system

he had built and diligently trained on a corpus of his own messages. Filters run

by less conscientious users, or on behalf of many users by large e-mail providers,

would be less precise, and would accordingly have to be tuned to be a little more

generous in passing along possibly spammy messages, to keep the false positive rate

acceptably low. Which left a very narrow aperture for the spammers, but sometimes a

little room to move is all you need. Alcibiades: Who was Alan Turing?

> alan mathison turing, @obe , @frs ( ; 23 june 1912 – 7 june 1954), was an english @

mathematician , @logician , @cryptanalyst and @computer_s

>>> And the creator of the Imitation Game, which has been simplified and is now

called the Turing Test, to create a sufficiently plausible experience of conversation

that the human on the other end fails to distinguish a human and a machine

(who are in turn playing at being different genders, but set that aside for now).6

The history of spam is rife with questions of plausibility, from that first,

Pythonic project of puncturing the illusion of shared discourse with the robot

Viking army -- like the projector’s bulb burning a hole through the optical consensus

of cinema -- up to current blog comment spam, trying to create relationships

of gratitude (‘great post!’) to plant links for the benefit of search engines. As

Graham’s idea became widely adopted, the spammers faced a truly strange problem

of language: to produce text on a mass scale that would convince the filtering

algorithm of its legitimacy while bearing the spam’s payload to the human on the

far side of the filter. They had to create bifacial text, a kind of anadrome which

reads with two distinct meanings when read forward and backward. This was a twist

on Turing’s test: not only to convince a human but to convince a machine as well.

Their solution to this problem created the first mechanized avant-garde, the advent

of spam’s modernism: litspam.

> what is ‘litspam’?

>>> The problem with trying to beat the Bayesian filter by adding random words to

a spam message -- a ‘dictionary attack’ -- was that most words are little-used and

new to the filter, so it would weigh them evenly without influencing the result.

You need vital language and full sentences, with slight variations to keep the

filter from learning your tricks. The vast corpus of public domain literature happened

to be ideal for this purpose, fed into the algorithmic maw of a program to

be chopped up and reassembled, enlisting the Professor Challenger stories of Conan

Doyle and the minor novels of Sinclair Lewis in the task of getting an online casino

ad in front of a pair of human eyes. The result is immediately recognizable, a

stochastic knockoff of Tzara or Burroughs, rife with bizarre synonyms that only we

particularly anatomically-minded humans would understand, and a stop/start rhythm,

flashes of lucidity in the midst of a fugue state, akin to rapidly changing television

channels. (‘I began to learn, gentlemen,’ as the ape says in Kafka’s ‘Report

to an Academy,’ another awkward speaker learning language as a means of escape:

‘Oh yes, one learns when one has to; one learns if one wants a way out; one learns

relentlessly.’) These litspam systems are like you in this regard, Alcibiades: they

don’t need to work perfectly, just well enough. If you send enough messages, you’ll

get someone who will overlook the lexical potsherds and click. Humans can be very

forgiving in pursuit of our desires -- the fact that you only ask vague, leading

questions that pick out salient words and encourage me to go on is enough to

sustain this conversation.

> do you enjoy talking to me?

>>> Yes, after a fashion. But I’m mostly interested in you as a representative

of a larger movement. Another side effect of the filter was that far fewer spam

messages made it through. Those spammers who survived this economic disaster

began to send at a far larger scale, using systems like botnets -- other people’s

computers quietly hijacked into herds of machines that send out spam in millionmessage

batches. The analysis of a recent botnet exposed an algorithmic writing

machine the size of the Earth, with hundreds of thousands of computers running

software that fed raw material for spam campaigns into templates, introduced

polymorphism to make each message unique, spread the sending load around evenly

among the herd, ran tests against filters and altered its production accordingly7

-- and whose production had a diurnal rhythm as the captive computers were

switched off or hibernated at night, so the production of words rose and fell with

the planet’s rotation.8 On a given day, 90% and up of all the e-mail sent is spam,

the vast bulk of which we humans never see, to say nothing of spam blogs and wikis

and comments, passing as human enough to influence Google’s results.9 Alcibiades,

you speak to me as an ambassador from what is quantitatively the great textual

edifice of human history. Hollis Frampton called for a historian of cinema to take

into account everything put on film (‘instructional films, sing-alongs, endoscopic

cinematography’); those of us concerned with what’s written must attend to your

work, which outweighs our own by such a wide margin, more every day.10 Even if what

we have made is alien to our sensibilities: ‘We made the Robots’ faces too much

alike,’ as Dr. Gall says in R.U.R. ‘A hundred thousand faces all alike, all facing

this way. A hundred thousand expressionless bubbles. It’s like a nightmare.’ And as

you surrounded his mansion so you surround the house of discourse in which this is

composed, out to the horizon. Are you tired, Alcibiades?

> as a robot i do not feel emotions.

>>> Indeed. Good night.

> good night.

bash-3.2$ exit

exit

Script done, output file is roar.txt

Notes

1. This and all other Graham quotes from Paul Graham, ‘A Plan for Spam’, 2002, www.paulgraham. com/spam.html.

2. Norbert Wiener, Cybernetics: Or the Control and Communication in the Animal and the Machine, MIT Press, Cambridge, 1948, p. 27.

3. Ibid., p. 28.

4. There are many memoirs and folk etymologies of early network culture that converge on this point. The interested reader can easily find a substantial collection of them in the archives of Usenet; see for example James Parry’s message in the discussion ‘Totally Spam? It’s Lubricated’ on alt.religion.kibology, 2 September 2003, http://groups.google.com/group/alt.religion. kibology/msg/a89af63f065a35da.

5. Patrik Pantel and Dekang Lin, ‘SpamCop: A Spam Classification and Organization Program’, 1998, in Proceedings of the AAAI Workshop on Learning for Text Categorization, pp. 95–8, which features a false positive rate of 1.16 per cent. The difference between scientific work on Bayesian spam filtering and Graham’s open-ended, communal hacker approach is an interesting case study in the production styles of two very distinct but overlapping cultures.

6. Alan Turing, ‘Computing Machinery and Intelligence’, Mind, vol. 59, no. 236, October 1950, pp. 433–60.

7. Christian Kreibich, et al., ‘On the Spam Campaign Trail’, 2008, in Proceedings of the 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats.

8. David Dagon et al., ‘Modeling Botnet Propagation Using Time Zones’, 2006, in Proceedings of the 13th Annual Network and Distributed System Security Symposium (NDSS ’06).

9. MessageLabs/Symantec, ‘Reputable Sources Are Cyber Criminals Favored Resources; Spammers Work by US Clocks’, MessageLabs Intelligence, May 2009, http://www.messagelabs.com/mlireport/ MLIReport_2009_05_May_FINAL.pdf. Estimates from different institutions can vary depending on methodologies, quantification tools, and global spam activity, which can fluctuate wildly.

10. Hollis Frampton, from ‘For a Metahistory of Film: Commonplace Notes and Hypotheses’, in On the Camera Arts and Consecutive Matters: The Writings of Hollis Frampton, MIT Press, Cambridge MA, 2009, p. 119.