Capital Cases Resources
In our meeting yesterday, Tripp and I discussed techniques for visualizing clusters of video in relational and geographic space. This issue overwhelmed our other significant challenge: selecting a meaningful analysis of the video. I am convinced that there are two major axes that must anchor our project: the temporal axis and the spatial axis. Without going into a long diatribe, my argument is that after the senses, temporal and spatial perception are the framework of our perspective on our environment. We like to see spatial and temporal representation of information, there is a deep satisfaction and great deal of insight gained through good visualization of data.
We do not (emphasis) analyze our visual experience in terms of brightness and contrast when we are constructing a mental mapping of our reality. Nevertheless, the capability of machines to provide us with data and metadata, extrasensory for humans, can be leveraged for projects where the additional information enriches our view of the environment.
It reminds me of playing a different game called Psychiatrist (or maybe psychologist) in my dorm the first year at UCLA with my garmin approach s1. Again the object of the game is to figure out the rules of the game.
I just thought of this on my lunch break so there is much more to develop. Anyone that is interested is encouraged to participate. I am interested in including photography, film/video, painting, poetry, music, garmin forerunner 405 and computer code… whatever you feel that you can contribute.
When we share a common linguistic and cultural history, we can then leverage that as a database for communication with the garmin forerunner 305 when we work and for obfuscation when we play. In a sort of simplistic generality I would say that when we are “working” we are rewarded for recognizing obvious (or practical at the least) patterns quickly and efficiently but when we are “playing” we are rewarded for recognizing obscure (or impractical) patterns.
My feeling is that the visualization of the video takes precedence and that the more mechanistic and abstract relationships between sequences will be the most meaningful when they are viewed in the context of space and time. So our napkin-based brainstorm produced a model of geographic space along the x,y that leaves a 3D map as a trail as it moves along the z axis. This is a complicated and dense visualization avis consommateur that needs to be seen and manipulated before it can be evaluated. The key would be in the creation of a smooth transition between this higher view and then subsequent closer views that drill down to the full-screen video itself.
Those that organize the game already know the answer and have the twisted satisfaction of watching others struggle to break the code. I hate it. And yet I can’t resist the urge to try comparateur de prix and solve the puzzle. I was the last person in a group of about 15 (everyone sits in a circle) that hadn’t figured out the secret to Psychiatrist. Eventually I just gave up and they told me the answer. It was humiliating. Somehow this silly game had undermined my intellectual confidence.
This all reminds me of a theory of intelligence distilled to mere pattern recognition: humans are good pattern recognizers, thus they are intelligent. Of course the mechanism/s that allow us to recognize patterns across a wide spectrum of sensory experiences are complex, subjective and unique. Most people can do the former well: humans are keen on repetition (so are computer systems). The latter is tricky: it’s rare to find a person (or a computer system) that can consistently recognize the unusual patterns in games (or life). We play pattern games as a way of encouraging elliptical thinking and discouraging linear thinking. Many popular business strategy/training clichés have been hacked together in an attempt to capitalize on this. Of course the irony is that the “elliptical thinking” games become standardized, then all we have is another standard, another regularized pattern that we process ever more quickly and efficiently.
So after having my intellectual ego deflated by such a simple game, I naturally reflect on the meaning of intelligence (more specifically, the meaning of my intelligence: Does my failure to solve one puzzle reveal my brain to be banal and linear?) As much as we admire the mnemonic or mathematical savant, the patterns are trivial. The advances in human thought come not from an ability to repeat the same task quickly and efficiently but from an ability to make leaps of logic. We don’t strive to become more like “computers”, specialized routines unable to deviate from their code, but to make computers become more like us: creative beings.
There is a social value for the word intelligence. It is mostly centered on aptitude for language, math and memorization. Our educational system seems to want, ideally, to forge us into robots with highly specific skills (it doesn’t) so we could participate fluidly in a much larger social machine. Whether the student rises to the top of the class or flunks, the stratification of the population is essential to the system. Sounds rigid rather than intelligent, but it provides us with a necessary hierarchy right? Those that are athletic go one way, those that are “artistic” (a semantic mess) another and those that are intelligent (the social value) take up positions of authority, both strategically and economically (perhaps they’re the same). And what happens to the leftovers? What a sad, elitist way to think of those that don’t fit the traditional patterns for success. There must be a lesson here about conformity… but I’ll leave it this movie.
While discussing the midterm project that Tripp and I are collaborating on, my concerns centered around the capability of current technology to analyze video in a way that is interesting to a user that hopes to discover something about a “character” by viewing a continuous POV shot. The hardware/software that we will use will be off the shelf, meaning essentially a webcam taped to a head and Max/MSP Jitter or SoftVNS to evaluate/manipulate the video. Once a user hands the footage over to the system, there are severe limits on the kind and quality of metadata that can be harvested. I don’t mean to underestimate the value of a digital sort on video content, I am most worried that such a mechanized look at human perspective will tell us more about the system’s capabilities than about the internal mental state of the user.
So I propose that we simultaneously pursue a minimally intrusive “live” user annotation system that would allow points of interest to be marked while in the field. By merely adding a flag at specific points in the timecode, we can learn about the moments that are meaningful to the user. I know that Tripp is primarily interested in what a data management system can accomplish without any hints from the user, but by allowing ourselves the insight into the sequences that the user cares about, we can then isolate those shots and try to figure out what they have in common.
Ultimately I am interested in a model of active capture vs. passive capture for expression, so the idea of recording and storing everything that I see is initially content-less to me. There is immense value however in the capability to record and store everything, a capability that we are inevitably approaching, especially when the user can match his/her thoughts to an image or a sound or any other sensed media.