week5_midterm_post

The first half of Desma 9, a class entitled “Art, Science & Technology”, has dealt largely with, unsurprisingly, art, science and technology and the connections that exist between them. The very first week, we discussed the “two cultures” - the concept that there exist disparate cultures for art and science. We discussed the merits of C.P. Snow’s Rede Lecture and whether the same holds true in todays society. The second week -entitled math, perspective, time and space - demonstrated the significance of science in arts, while the third week - during which we discussed robotics - demonstrated the influence art has on science. Finally, the fourth week dealt with the human body - an inspiration for many works of art and technology alike - and medicine.
In one of my other classes, the professor made an interesting comment in passing. He said, “the interface is where the interesting things happen”. He was specifically referring to fluid flows and he generalized a little by mentioning inter-disciplinary engineering, but it does not stop there. Professor Vesna has shown us numerous examples of this in class. Casey Reas’s interpretation of “Process 10″ is a perfect example of this. “Process 10 is a text that defines a process” but the interpretation is what makes his work an interface. Reas uses programming to interpret the text to generate the art described by the text. As a result, Reas is creating an interface between software programming and art. This is just one of many examples shown in class.
My project proposal also describes an interface. More specifically, an interface between human behavior, mathematical analysis and performing art. The human behavior I am targeting is bias. Bias is something that we all have. It is molded by our experience and it shapes our experiences. The idea is to create a mathematical model that quantifies bias and then to create a robotic performance based on the results. It is impossible - as far as i know - to numerically describe all bias in all people, so I restricted the demographic to news outlets and the bias to politcal bias.
In order to effectively assess political bias, the methodology would have to be free of bias, therefore my quantitative analysis would be programatic and would rely solely on the behavior of the news sources being assessed. The assessment of bias would be done by analyzing the linking and refrencing behavior of a news outlet compared to that of other news outlets. Two news outlets who frequently reference and link to one another using a positive tone are most likely of a similar political bias. It can also be inferred that two news outlets that only reference and link to one another using a negative tone, would be on opposite sides of the political landscape. Using the neural network of refrences and links and the push/pull forces of the tone surrounding said links, the algorithm would calculate the political bias of each news source.
The robotic performance gives these results a tangible aspect. Hearing that one news source received a political bias score of 7 is worthless on its own. Using a simulator, a person could experience the leanings (literally) of their favorite news source in real time.

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