PIIM IS A RESEARCH AND DEVELOPMENT FACILITY AT THE NEW SCHOOL © 2015 PARSONS JOURNAL FOR INFORMATION MAPPING AND PARSONS INSTITUTE FOR INFORMATION MAPPING 68 Fifth Avenue New York, NY 10011 THE PARSONS INSTITUTE FOR INFORMATION MAPPING 212 229 6825 piim.newschool.edu KEYWORDS Collaboration, computer vision, cultural analytics, economy of abundance, interactive data visualization PROJECT DATE 2014 URL http://misharabinovich.com/soyummy.html ABSTRACT his paper describes an interdisciplinary collaboration that began with creative misuse of artiicial intelligence and computer vision algorithms originally developed at McGill’s Centre for Intelligent Machines. We began by analyzing image banks and video with sotware originally designed for surveillance and robotic anomaly detection. We started with basic visual analysis and had only limited success with movie summarization. Gaining Insight Into Films Via Topic Modeling & Visualization MISHA RABINOVICH, MFA YOGESH GIRDHAR, PHD Figure 1: Jay Muhlin Edit We moved beyond misuse when the sotware actually became useful for ilm analysis with the addition of audio analysis, subtitle analysis, facial recognition, and topic modeling. Using multiple types of visualizations and a back-and-fourth worklow between people and AI we arrived at an approach for cultural analytics that can be used to review and develop ilm criticism. Finally, we present ways to apply these techniques to Database Cinema and other aspects of ilm and video creation. INTRODUCTION In the summer of 2013, Misha Rabinovich was an artist in residence at McGill’s Centre for Intelligent Machines working primarily with Yogesh Girdhar (then earning his doctorate degree). Our goal was to stage ordering interventions in an deluge of cultural information. We began by creatively misusing robotic navigation sotware which was originally developed for robotic navigation and anomaly detection. his sotware worked to summarize data by observing a data stream and saving only those observations which would provide an overview of the whole data set. It measured the amount of surprise