Neutralizer (2017)

Neutralizer is a collision experiment.




– Content

I ask myself, who or what do I want to connect? Or rather, who or what do I think are poorly connected? I thought about ideologies cross culturally and environmentally: how do people from different environment view the same things completely differently? What are the connectors that are malfunctioning here?

Eventually, I found myself blaming the use of language in our cultures, media particularly. I then went on to different news websites and searched for the same headliners. Expectedly, results came back vastly different. Take Feb 22, 2017 as an example, one common headline was found in both Chinese and the U.S. media: “the assassination of Kim Jung Nam might affect the relationship between China and North Korea”. Here are the headlines about the same news from a Chinese news site and a U.S. site, respectively:

Chinese site (People’s Daily):


U.S. site (Bloomberg):

Needless to say, they are different (if not other things). Could I “connect” them?


– Form

I think in most cases, especially when it comes to physical connections, we would like to see connectors acting seamlessly, let it be a conversation with a stranger that just “feels right”, or a plug that fits perfectly into an outlet. However, in an art context, there is also undeniable power in bringing things together by force, intentionally not quite smoothly – juxtaposition, as we call it.

A literal crack on the museum floor could connect immigrants with native citizens, and a fountain installed under Brooklyn Bridge might bring us urban residents closer to nature.

Shibboleth  by Doris Salcedo 

Shibboleth by Doris Salcedo 

Waterfalls  by Olafur Eliasson

Waterfalls by Olafur Eliasson

– Output

I decided to create a python program that merge different texts together by force. Or rather, let them collide into each other. With such force visually, I’m interested to see what results I can bring to fill this communication gap.

I placed the two texts side by side (in this case, People’s Daily on the left, Bloomberg on the right), randomized them by sentences, and let them collide.

Below are a few generated results:

The same program can be applied to other texts as well. I’m looking forward to colliding more words in the future.

View code