In lieu of an abstract, here is a brief excerpt of the content:

  • SIGGRAPH 2019 Art Gallery
  • John Wong (bio), Jieliang Luo (bio), Weidi Zhang (bio), Brigitta Zics (bio), Özge Samanci (bio), Adam Snyder (bio), Gabriel Caniglia (bio), Victoria Vesna (bio), Alfred Vendl (bio), Martina Fröschl (bio), Glenn Bristol (bio), Paul Geluso (bio), Stephan Handschuh (bio), Thomas Schwaha (bio), Akira Nakayasu (bio), Ziv Schneider (bio), Caitlin Robinson (bio), Jiabao Li (bio), Honghao Deng (bio), Panagiotis Michalatos (bio), Yoon Chung Han (bio), Praful Surve (bio), Alex Rothera (bio), Christopher G. Thompson (bio), Christopher Baker (bio), Shekpoint Charlie (bio), Rosalie Yu (bio), Charles Berret (bio), and Neil Mendoza (bio)

RuShi
John Wong

Is big data the new superstition now? Will a data scientist or artificial intelligence (AI) be the new fortune-teller?

RuShi (如 是) means "As Is": nothing more or less, but the true colors of something. Every Buddhist scripture starts with these two words to show the scripture has no interpretation by anyone else and totally comes from Buddha.

RuShi is a piece of algorithmic interactive installation art that uses the ancient Chinese fortune-telling algorithm "BaZi" (八字). In English, BaZi means "eight words." BaZi is an application that uses eight words to analyze a person's destiny. Every person's date of birth can be used as data and converted into eight words and the eight words are all translated into five elements (gold/wood/water/fire/earth). The interrelationship of the five elements can predict one's character and happenings throughout his or her whole life, and it has become widely used since China's Song Dynasty.

This work is about big data and prediction, fate and superstition, questioning what we really need or want in the age of big data and AI. While using the ancient data analysis application yet taking out all the extra cultural signs and materialistic interpretations, there remains only the "As Is," i.e. the five elements. Participants type in their date of birth; the fortune-telling algorithm turns out showing only the data visualization of the different audience's flow of colors. We can see no prediction of life from this machine, but only time, changes of color and the beauty of different people's balance of life.

"What do you want to know?" Both data scientist and fortune-teller have the ability to turn the world of uncertainty into quantified numerical existence and then give us an answer with future predictions. What if big data and AI are the new superstitions? What if a fortune-teller or data scientist is only a storyteller? Algorithms are everywhere. We are now over the age of posthumanism. Data is bigger than humans and AI is much more powerful than humans. At the same time, only a very limited population has the ability to understand or control how AI works; indeed, it's the same scenario as the field of fortune-telling. The majority can only choose to be part of it or not or to believe in it or not. We allow ourselves to believe in something that we don't understand, as if we are seeing a fortune-teller and hoping the mysterious algorithm can show us our future and tell us what we should do. Indeed, all of the questions we want to ask the fortune-teller are unconsciously built on fear. But in RuShi, each member of the audience will only see his or her and the others' flow of colors; it goes back to the basic. I want the audience to see only the beauty of balance, different people's balance, without the interpretation of the storyteller. [End Page 400]


Click for larger view
View full resolution

RuShi, LED screen and video projection, 2018. (© Photo: John Wong)

[End Page 401]

LAVIN
Jieliang Luo and Weidi Zhang

The current artificial intelligence (AI) technique allows a deep neural network to recognize over 20,000 different categories of objects. Meanwhile, countless deep neural networks are trained for different applications as the output from each neural network is a singular projection of the neural network's own understanding of the real world. Given the same visual input, for example, neural networks trained for facial recognitions can only interpret the input as a collection of faces, while neural networks used...

pdf

Share