Python script + Processing sketch to paint with a palette of Google Image Search results in the primary colors as well as some shades and some neons. More documentation included with the bundle. Low-fi/digital-punk/whatever.

Thanks to Catherine Musinsky, Times New Viking, and Mafia-Hunt for inspiration.

Download ShitPaint beta

>                 reveals
>                 >         >                 >         >         >
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>                 >         >                 futile and you have no
>                 >         >                 >         idea why
>                 >         >                 >         >         you’re
>                 >         >                 >         >         >
>                 sitting there
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>                 >         with
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> –

This piece was composed by plundering spam titles that were themselves plundered from Edward Thomas and Wilfred Owen.

in the forest was a well, there he sat and cried
Against a grimed hand when his own’s quite dust
Hast thou no place in all their heritage
But here I pray that none whom I once loved
My head hangs weighed with snow
as possible beneath the smoke. Presently he saw open space, and the green
is she going by herself, lying in his grave?

The first piece is DJambalaya, a playlist creation application for the iPhone that can be used on its own or to control a remote computer. Cole and I wanted to create an application that would allow a person to dynamically change playlists at a party without having to duck out of an interaction. The goal was to enable the user to queue up about 30 minutes of music in about the time it takes to send a text message – a socially acceptable break in conversation. To do this, we had to create an interface that allowed for fast movements aided by some form of music recommendation to prevent cases of obsessiveness in finding “the perfect track”, an obsessiveness that leads to unfortunate dallying in selections. After a user selects a ‘seed’ artist, we’re going to hit last.fm to provide waves of related artists, speeding the selection process by only presenting the user with a small number of valid choices as opposed to the hundreds or thousands of choices available if an entire library were exposed.

Here’s a screenshot of the iPhone app circa mid-November after a couple days of development after learning the iPhone SDK. We hit some delays due to holidays and beginning-of-the-year business, but we’re preparing to ramp up development once again.

“The best way to predict the future is to invent it.” — Alan Kay

Alan Kay’s famous phrase reads as inspirational. I support the spirit of it entirely as it accords with the American spirit, the hacker ethos, the do-it-yourself revolutionary philosophy. It’s comforting and empowering to think that all it takes is some elbow-grease and ingenuity to make a difference, shape society, right wrongs. And although I certainly don’t intend to discourage cultural participation, I find troublesome implications in that statement.

The first problem is that an invention needs to be adopted before that invention is able to define the future. Alan is proposing a form of technological determinism: he is subtly implying that technology drives society, rather than the reverse. I find this belief dubious. How and why inventions are adopted is controversial; if there were a formula for determining relevant technology, there would obviously be no failed startups, no failed product launches. But we know that the marketplace is a fickle mistress. Sticking to technology, why did CISC win the computer instruction set wars? Why is Outlook a prominent email/personal management client? It’s clear that in the marketplace, sometimes good ideas fail and bad ideas succeed.

Also lacking is any consideration for how the presence of technology affects societal behavior. Taking a cue from Infinite Jest, would it be good to invent a film so entertaining that it permanently paralyses its viewers with the need to endlessly consume it? More practically and perhaps more damagingly: why have we adopted so much technology that we’re living in a fashion that our ecosystem cannot sustain?

Stepping back from computers for a moment, let’s look at inventions and technology as ideas. We tend to look at the physical products, the signifiers of technology, as the technology itself. But technology exists in the idea space; we’re really looking at ideas. Language is a technology. Government is a technology. And so on. Many good social technologies have not been adopted. Say, civil unions. And why is it so hard to deter Americans from creationism? Many wonderful things that are not economically expedient have been invented.

Convincing people to adopt GUIs (One of Alan Kay’s major innovations in the tech-space) is easy. We need to invent a way to get people addicted to ethics, to reason, to justice.

When I expanded my reading in the genre to lesser authors, in part to know my competition, in part to find easily deconstructed text, I began to feel that the mode of hysterical realist writing is easily misused. (I’ll refrain from mentioning any writers lest I prematurely burn bridges.) The primary difference is that these authors appear to provide details for the sake of details. A gun used to shoot someone is described beyond the make, beyond the model, all the way down to the geolocation of its specific production. Yet that history and that location are irrelevant to the story and explain none of the constituent forces driving or holding together the element’s of the author’s world. Unlike the authors mentioned above, this story would map like a branch with sticks jutting out. Though these sticks increase the overall area of the total story, they frequently fail to reconnect and add to the primary narrative.

This usage of excessive details has uncomfortable corollaries – one could argue that detail for the sake of detail, that is, meaningless maximalism, cheapens the art of writing and devalues the reader. A strength of writing is that the author has extremely fine-grained control over how much information the reader is given, arguably moreso than in other forms of expression. Constantly writing with detail at full bore (pun intended) is like composing music without rhythm or dynamics. Worse still is that it allows the reader less agency. Law and Order, by its philosophy and its medium, is intended to be as representational as possible and allow no questions on the part of the viewer – the viewer is allowed to stop thinking and witness melodrama with absolute resolution and justice. Maximalism ideally should give the reader more to consider; misused it thinks for the reader and replace’s the reader’s imagination. Consider the reader. Consider the reader’s mind.

The basis of Radoff’s arguments are all things that games can do, fictive best-case scenarios where every gamer takes an interest in military history and experimental physics and creates based on their game experiences. It ignores the reality that the majority of gamers take a passive stance towards entertainment, using it merely as a pastime in the place of broadcast television. This premature switch from the subjunctive to indicative mood is more than intellectually dishonest – it draws attention away from questions that we necessarily need to ask of the videogame medium to help it advance and to have its inevitable place in peoples’ lives become meaningful.

For games to grow into a medium that is, on average, more than a primarily passive consumerist entertainment, we must avoid making disingenuous laudatory statements and administer a thorough and honest interrogation of what sort of behaviors games currently inspire, what we’d like them to inspire, and how we can bridge the inevitable gap between the reality of the first answer and the ideals of the second. Only then will we be able to take a focused look at how we can actually engineer games to live up to the potential of what they can inspire in their fanbase.

Below are brief point-by-point rebuttals:

#1: Games can make you smarter
Everyone learns through discovery and exploration, but discovery and exploration is bounded in games.

#2: Games can excite people for high-paying careers

Doesn’t, um, high pay excite people for high-paying careers? When is a high-paying career a Good in itself?

#3: Games Inspire Tangential Learning
Games inspire shallow tangential learning the same way non-interactive media inspire tangential learning, and I’d love to see numbers showing that games inspire a larger number of their consumer base. How many fans of the show Rome took a serious interest in Ancient History? Fans of Rome: Total war?

#4: Games can enhance creativity
How may couch potatoes make their own movies? How many gamers follow through produce meaningful mods? Game mods are also generally bounded by in-built and in-bred game mechanics and themes.

#5: Games can foster advanced social skills
Granted, leading a clan can foster advanced skills for dealing with teams/organizations. This is a limited band of the social spectrum and, again, not every gamer gets a leadership position in a guild/clan, nor does every gamer play games that facilitate that sort of organization.

#6: Games could help end war
What?

Wired’s Media Pyramid

You’ll notice that it totals 9 full hours of media intake which is disturbing for several reasons:

1 — Is the appropriate response to a limitless amount of media to consume it in as broad a spectrum as possible?

Given the amount of media being churned out every day, it would be entirely possible to fill nine hours with any one of the constituent groups of Wired’s pyramid: gaming, social networking, microblogging, news, and entertainment. Does it make sense to divide one’s time between arbitrary classes of media, or to divide those 9 hours by subject – visual arts, music, literature, philosophy, news, new media, etc? Living the Wired way, it is entirely possible to play an hour of games, then read, listen, and watch media also related to games, albeit on TV, in front of a computer, from a magazine, and hooked up to an iPod. I’d hardly say device diversity compensates for diversity of subject.

2 – Why doesn’t a balanced media diet include creating new media and responding to consumed media?

More disturbing to me, however, is that this pyramid is entirely passive. Assuming 8–10 hours of work plus necessary quotidian duties such as sleeping, eating, and showering, all of one’s free time would be spent on this proposed 9 hours of media consumption. Aside from the obvious concerns that a person should, say, leave the house from time to time, the pyramid depicts a problematic relationship with media. Where is room for expression? Where is a person supposed to consider what this media means, what this media means to him or herself? And it completely undermines the democratic nature of publishing on the internet – no longer is broadcasting limited to those whose day jobs involve media as it is now possible for any internet user to also be an internet publisher. I propose that the pyramid should allocate at least 1/3, if not 1/2 or more, of its time to media production. Researching things that tickled one’s whimsy. Making one’s own blog posts, youtube videos, music tracks, etc. One can levy a serious argument that inspiring everyone to publish content is going to result in avalanches of crap; my rebuttal is that the quality of output is irrelevant as this is about respecting human agency and creativity. It figures that professionals at the top of the mass-media food-chain would not deign to acknowledge that the ability to produce and communicate is not a god-given right to a select-few, but rather an evolutionary trait distinguishing homo sapiens sapiens from the rest of the animal kingdom.

I ask that everyone who reads this post take at least one hour this week to create something and share it (perhaps in the comments below).

Other competitors in this field include Powerset, a startup recently acquired by Microsoft, which retrieves Wikipedia articles using algorithms based on fuzzy logic. Unfortunately, this is currently hardly better than searching for the given keyword on Wikipedia yourself. Another service, lexxe, discovers clusters of similar answers among the websites indexed by Google; a neat idea, but the results it produces are vague and still require human interpretation. Finally there is MIT’s START, which uses a small corpus of websites it knows how to parse and delivers definite, accurate answers within a limited ontology.

WA takes a more ambitious approach. Its corpus is a database of millions (!) of human-curated entries in a pre-defined, computable format. These are likely supplemented with ontological metadata. That its corpus is human-curated turns out to be WA’s biggest strength as well as its strongest limitation.

Until WA’s launch, I had no idea what to expect. I’m too cynical and practical to expect that I could type in “universal physics theory” and have WA analyze its corpus and come up with an answer. I also understood that a human-curated corpus could not be unlimited. I was left not knowing what sorts of queries would be answerable. Wolfram’s explanatory screencast got my hype-machine running: it demonstrated a tool that could answer questions like “how many internet users are there in Europe?” and “weather in champaign when mathematica released”. Although limited in scope, this made WA appear to be a great asset in understanding the world around us in new contexts. And at times it works very well, even with the simplest of searches.

WA on One Trillion

With a minimum of human prodding, it can produce exciting connections and results.

Where WA falls short in practical use, however, is in its user interface and its limited corpus. The limitations of its corpus are no surprise – every bit of data from which it draws is human-curated. To its credit, the examples page lists all the topics on which it has information. Although the human touch is wonderful in that the data is so well structured and more accurate than wikipedia, it’s clearly not scalable if the goal is to approach the limits of all available quantitative data.

The interface problems, however, are less excusable. In addition to having the same limitations we’re used to in natural language processing, it gives almost no useful feedback – did it have problems with keywords? What were the problematic aspects of the query that it could not parse? Could it parse the query but lacked relevant information in its databases?

Whereas Google assumes you have the brain and it has the knowledge, WA tries to cover much of the ground in between. It’s great when it answers “what is the gdp of france / italy” in one query, as opposed to two google queries and some mental math, but it ultimately is frustrating to work with since its ‘brain’ is less capable than our own. This makes using WA like interacting with an autistic savant – it only understands you some of the time, can’t tell you why it can’t understand you, and doesn’t have any regard for things outside of its corpus.

The execution of this project reminds me of Wolfram Tones, Stephen Wolfram’s attempt to make a generative music algorithm in over a dozen music styles. Wolfram Tones was ambitious, yet so over-simplified that its output was almost completely uninteresting. It defined genre by the most rudimentary properties (e.g., guitar+drums instrumentation on a 4/4 beat = rock) and music as any repetitive sequence of notes that followed basic musical theory. The resultant sounds were as offensive to the concept of art and music as they were to the ears. Wolfram Alpha, despite its uses, is similarly offensive to the concept of knowledge.

This has interesting implications regarding the progress of AI. Granted, WA makes no claims of having any AI whatsoever, that developing an AI or using an existing AI system was deemed inappropriate is telling. What this does show, however, is the result of a team of hotshot programmers with the resources of a large bank account and a finite corpus under a defined ontology working towards computer understanding, or a simulation of it. And in that realm, WA is impressive only to people who appreciate how difficult it was to implement what is there. On a purely practical level, it’s a nice tool that can do interesting things with numbers, but it is obvious it does not know why it is doing anything that it does. WA is one of the more ambitious projects in computer understanding, and it makes incremental improvements. Wolfram acknowledges this, admitting that it “almost gets us to what people thought computers would be able to do 50 years ago”. What does that say about Kurzweil’s estimation that we’re only 10 years away from the Singularity?