EAST-Workshop on Data Art

INTRODUCTION：
The rise of “data society” created the need for new methods to work with data - representing it, discovering patterns, interacting, managing, making decisions. Among various disciplines that developed in response to this need, artificial intelligence (AI) and data visualization play major roles. In cultural sphere, the response to data society diffusion was development of data art - projects that use “big data” as a new artistic medium.
In this workshop we will look at these fields together. We review key ideas and analyze important projects in AI, data visualization and data art. We also consider possibilities and research directions that have not yet been explored.
This workshop will also devote time to discussing the role of AI in digital culture including online services (search, recommendation, communication) and media devices. While algorithmic creation was practiced by individual artists since 1960s, contemporary industrial-scale "cultural AI" is based on gathering and analyzing data about behaviors of many millions of people. The integration of AI into everyday cultural lives of billions of people raises big questions about future of culture and taste. Will AI automation lead to gradual decrease in aesthetic diversity over time, and standardization of taste? Or will AI lead to more diversity and personalization? And how would we go about answer such questions on a global scale?
While learning about computational methods for data analysis and visualization, we will examine them critically. Today our interactions with digital media, access to information and each other in social networks are mediated by software systems. They continuously analyze “big cultural data” ? i.e., content of billions of media artifacts and trillions of records of our online behaviors. In researching contemporary and historical media, we often use similar methods for different purposes ? for example, seeing patterns in cultural history, exploring work of contemporary designers, or content and styles of photos shared by billions of people online. What are the similarities and differences between use of these methods in industry and in cultural research? Are there some assumptions and goals built into methods that are widely used in industry that we need to question before we adopt them? What are the most general ideas behind them? What is left unseen by computational methods so far and how can we use them differently to better capture complexity and variety of digital culture?

The questions to be considered include:
- How does the use of AI today differs from the original AI research in the 1960s?
- How is AI is integrated in cultural services and products?
- What is the effect of AI on aesthetic diversity in digital media?
- How researchers in different fields that study culture (digital humanities, digital art history, film studies, media studies) use AI and machine learning to analyze large cultural and social data?
- What is “data”?
- Why do we still data visualization techniques developed two hundred years ago (i.e. before computers and “big data”)? What are the key new techniques that emerged in the last few decades?
- What is “artistic data visualization”? What are the most influential projects in 20-year history of this field?
- Can we consider selected earlier movements in art and media - such as Impressionism in 1870s, photo collage and film montage in 1920s, Swiss graphic design in the 1950s, post-modernism of the 1980s - as response to “data revolutions” of their time?
- How can we see art of the past differently using contemporary lenses of “big data” and “data visualization”?
- How do contemporary artists respond to the new scale and density of data today? In addition to data art, we also look at selected art installations, architecture and design projects, and sound art.

Practical Work：
In addition to instructor’s presentations of concepts and projects, and class discussions, the workshop also includes a practical part. The students will be given a practical assignment and they will need to present their work at the end of the workshop.

WORKSHOP INSTRUCTOR：
LevManovich is an author of books on new media theory, professor of Computer Science at the City University of New York, Graduate Center, U.S. Manovich's research and teaching focuses on digital humanities, social computing, new media art and theory, and software studies. Manovich is the author and editor of 13 books including AI Aesthetics (forthcoming 2019), Theories of Software Culture, Instagram and Contemporary Image, Data Drift, Software Takes Command, Soft Cinema: Navigating the Database and The Language of New Media . The Language of New Media, was translated into thirteen languages. He is also the Director of the Cultural Analytics Lab that pioneered analysis of visual culture using computational methods. The lab created projects for Museum of Modern Art (NYC), New York Public Library, Google and other clients.

ACADAMIC HOST：
QIU Zhijie,Dean, School of Experimental Art, CAFA; Professor
PRODUCER：
Iris Long,Researcher on Art, Science and Technology, CAFA
WORKSHOP DATES：
January 7th   January 11th, 2019

APPLICATION：
This course is open to CAFA students, researchers and practitioners in the field of media studies, data art and AI. 20 participants will be recruited.
To apply, please email toeast.info@cafa.edu.cnwith email headline:
Data Art Workshop Application + Your Name + Your Department/Institution.
Please kindly attach your CV/portfolio/research papers for our reference.
APPLIATION DEADLINE：
January 1st, 2019