Wait, I’m probably missing the humor here. (These are the problems that led to the rise of smiley faces in network communication.)

Via Matt Kirschenbaum.

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BTW, my only worry about post-modernism is that it exists :)

Meanwhile, related to the rest of the conversation, I thought I’d drop a link to the Dictionary of Algorithms and Data Structures.

That notion of the Numerist Fallacy is intriguing. What with all this accursed post-modernism (sorry if my bias is showing) running around the academic world, not to mention the artistic world, it’s now commonly accepted for every art form that isn’t delivered through a computer that the text doesn’t actually EXIST anywhere except inside the reader’s/viewere’s/listener’s/user’s/whoever’s mind.

It’s very strange that those same people who have no problem with the idea that a novel isn’t a pile of ink-stained paper and a film isn’t a bunch of celluloid would find it difficult to understand that a program isn’t a great heap of 0’s and 1’s stored away on some hard drive. Could it be that of all areas of human thinking in modern times, the most important one – the computer – is the least touched by post-modernism?

I hope this digression is within reason, but I also think that a commonly misunderstood notion is that narrative isn’t a bunch of casual dramatic events. Like any text, narrative only exists inside the audience’s mind, and it is our experience of it that makes it what it is. Chris Crawford’s work, for example, seems, in theory, to be excellent at recreating narrative “externally”, that is, in generating from algorithms that respond to user input a narrative which might be acceptable as that of a story or play. However, the user might not experience it as such, for example, because he might be seeing himself as an actor in a play, which immediately eliminates his ability to experience it as a narrative. I haven’t yet read anything addressing that issue.

I’m arguing that knowledge about how things get made is more useful than knowledge about how they are stored.

Sure, in hindsight, I must acknowledge that there are more elegant ways to learn what I learned there – it’s just that those ways were not visible from the trajectory that I was coming in on. And it worked. And it’s there where my belief that low-level computer knowledge – as ideas which are portable between otherwise unrelated applications – can be useful for artists is grounded.

An alternative is to start with Scheme, probably by reading “How to Design Programs”, the textbook to PLT Scheme .

Then there’s “Instant Hacking”, an introduction to Python for written for non-(or not-yet)-programmers that’s part of the larger Python for Non-Programmers project.

Yet another way to start would be by learning Squeak, which uses Smalltalk to create a programming environment for beginners.

Before this list grows too long, and too rational, I’ll throw in a text that’s more of a crazy suggestion as reading material for programming novices: Alan Perlis’ “Epigrams on Programming”. At first reading, I understood next to nothing, but just found statements like “Syntactic sugar causes cancer of the semi-colons” aestethically pleasing. I returned to them later on, knowing more, and found that not only did I understand more of them with each visit, but they also sharpened my focus on quite a lot of concepts that other texts only gave me some vague idea about.

Finally, here’s a link to Guy Steele’s “How to Grow A Language” paper that noah mentioned…

Now, perhaps I should start a new thread to ask this, but what else can people suggest in Matt’s vein? That is, what else can people suggest that they think is helpful for people trying to understand these issues who come from arts and humanities backgrounds?

I can start off by saying that someone recently introduced me to Guy Steele’s “How to Grow a Language.” I think it’s a great piece — it’s like an Oulipian writing exercise following a constraint derived from thinking about programming languages, and it’s also a good introduction to (and argument about) some of those issues.

Of course, there are also the connections from our procedural literacy discussion and Matt’s pedagogy of programming page.

Other suggestions?

Because the Roman alphabet only contains 26 letters, the English language can only represent 26 concepts.

Or maybe it’s them.

Miscellaneous comments:

One way in which discussion of Bayesian theory is relevant: I’m often annoyed by people reducing logic and science to making statements that must be true or false; this seems to be a staple for science fiction writers. Bayesian theory provides a principled logic for uncertainty.

In the CS department where I work, there is definitely a culture gap which makes it difficult for the more theoretical faculty to support hiring systems or HCI researchers that aren’t doing a lot of deep math because they usually don’t view their work as “fundamental.”

Although neurons in the brain are binary in one sense (either spiking or non-spiking), the timings and rate of spiking are not binary, and continuous values and continuous levels of probability can be represented by timing and rate of firing.

Publisher: Wow! Is that Xerox paper?

(Gasoline provider, meat provider, content provider)

Matt K, thanks for the further info on Goodman’s LoA. Now that you say more I know this book has come up in a relatively recent conversation — I think at UCSB’s “Digital Retroaction” symposium. It seems like a good way to help students understand what it is that makes digital files reproducible. But I wonder about something like audio tapes, which seemed fulfilled by reproduction (or selective reproduction onto mix tapes) when I was in college — but where the reproductions were far from “lossless” (and therefore didn’t make it over the bar of “sameness of spelling”). Am I missing something?

I very much agree that artists and writers who use computers as an essential part of their practice, and computer creatives of other sorts, should learn more about computers and computation when they’re motivated to do so, as I’ve tried to do myself.

I used the juxtaposition of Boolean/Bayesian values only because it was the first illustration that came to my mind (both types are fundamentally relevant to my current work), not because I think that “Bayesian values” are the same as real numbers in general. It was just a quick example (with a tempting alliteration to boot ;-) to show something you cannot build using digital computers (okay, I’ll specify this – not just any possible digital computer, but the kind that most people seem to use today) if you don’t understand how to represent it using binaries. No generalizations in terms of “Bayesianism” (cute word, that) were intended. Sorry for upsetting you.

And noah, you said:

You have characterized all discrete math as being binary, saying that implementations of Turing Machines need to have “one last bit” and that “‘Bayesian reasoning’ can only implemented in a digital system on a binary foundation.”

Nah… can’t remember characterizing “all discrete math” as anything; sorry to be misunderstood: I was just talking about programming computer systems that are in general use today. I’m serious about that “one last bit” bit, though; it’s a real drag. What is true is that it’s not a problem limited to binary systems; it just so happens that binary systems, due to their commonalty, are where programmers encounter the problem most often today (so often, in fact, that I can’t recall anybody ever telling me that s/he encountered it in a different context). But strictly speaking, it has to do with undecidability, recursion, and infinity, and not with the number base you choose… We digress, though.

What got me thinking was your original post about the alleged “numerical fallacy”, followed by you saying that

I’m arguing that they’ve mis-identified the area in which it would be fruitful for them to “dig deeper.”

Just four years ago, this whole discussion would have meant nothing to me; for about 15 years, I used computer programs to create art for different media, content in not digging any deeper.

A year later, the situation had changed: I had hit a snag, found that I lacked a certain tool, and tried to figure out what it would take me to develop it myself. Had anybody told me that it would not be “fruitful” to dig any deeper, I might have taken this advice, and probably would have waited forever for this tool to magically arrive one fine day. As it were, though, I let my ignorance take the lead, almost blindly vacuuming up information about the inner workings of computer systems and languages for a while (since I didn’t know what I wouldn’t need to know), making a seemingly endless series of mistakes in trying to make sense of it all, but finally winding up with exactly the kind of device that I was looking for at the outset. And in hindsight, even the “frantic” bits of “thrashing around” in order to find a solution make perfect sense. So, with this experience in the background, countering an artist’s inquiries about the bit-level stuff in our programs with something along the lines of “I never needed to know this, so you’ll never need to know this, either” (which was how I understood your post) seemed inappropriate to me – after all, I knew that digging deeper sure can have its gains.

So my question is: is analog thinking possible at all?

Goodman’s an analytic philopsher, and LoA is hardcore. The best known terms he works with are “allographic” and “autographic.” For Goodman, an autographic work is one whose ontology *defies* reproduction, while an allographic work is one whose ontoogy is *fulfilled* in reproduction. Thus, a painting is autographic, a novel is allographic. This dovetails with a familiar question in textual criticism: if the Mona Lisa is in the Louvre, then where is the text of Hamlet? The point is that we make a commonplace/commonsensical distinction between original and reproduction in the case of the Mona Lisa, but we don’t feel we have to go to the British Library and inspect an original Quarto (or recover the Bard’s lost manuscripts) to have a valid experience of “Hamlet.” Closely associated with this is Goodman’s principle of “sameness of spelling.” An allopgraphic work is successfully reproduced if it has the same spelling as its predecessor. Take War and Peace. If you imagine it as one long string of characters, then any identitical string (same spelling) is a valid instance of the work. Here’s where we see the importance of the digital and the discrete: there is no corresponding way in which to break down the “text” of the Mona Lisa. Now, if we digitize the painting, then it becomes subject to formal manipulation with a tool like Photoshop, and lossless transmission of the image: but precisely because the work is now rendered in an allographic, rather than autographic, state.

All of this, of course, elides the kind of argument about materiality that you might expect from a Johanna Drucker or a Kate Kayles or a Jerome McGann or even a Matt Kirschenbaum. Or a Walter Benjamin. I employ Goodman’s ideas strategically, not as the final word on the nature of digital, but as one illustration of why distinctions between digital and analog are worth preserving. The *materiality* of digital media has to rest on its propensity for allographic behavior.

I suspect Kari will have some more to say.

Computers work in a way where you have to find the extremes, the Boolean values, the 1’s and 0’s, – the bits – first, before you can build the up to bytes, with which your able to process Bayesian values: those, then, can open up the infinite universe of values between 0 and 1 for you.

I’m well aware that this will be seen by most as merely nit-picking, but I have to disagree with your use of the term Bayesian values. You seem to be using the term “Bayesian values” to refer to real numbers in general, which really, are nothing to do with probability. True, the ability to represent real numbers necessarily implies that representation of values between 0 and 1 is possible, however, this is something useful for probability in general, and nothing to do with treating probabilities from a Bayesian viewpoint.

“Bayesian reasoning” can only implemented in a digital system on a binary foundation. You have to know what “0″ means and what “1″ means – a bit-, before you can build any system that supports “pseudo-analog” values like 0.3418735, which might be the value of some weight in a Bayesian reasoning system.

Again, I’m bothered by the introduction of Bayesianism into this discussion; these comments no less pertinent to any other set of operations involving real numbers.

Finally, I do agree that one must be aware of the finite nature of the representations used for real numbers on computer systems, and furthermore, that a knowledge of how floating point numbers are represented using binary digits, however, I do not think this is inherently problematic for anyone who has read the IEEE 754 standard.