It is widely acknowledged that ubiquitous computing, artificial intelligence, and data-driven media are rapidly reshaping how people think and act in unprecedented ways. As these technologies become incorporated into more aspects of our daily lives, we need new forms of critical awareness and analysis to address the emerging challenges they pose. Working under the conviction that these challenges exceed the reach of individual disciplines and expertise, the Data Science & Humanities Initiative seeks to bring together humanistic researchers with technical practitioners and data scientists to examine our technological milieu in real time.
In addition to informal reading groups and daily interaction, activities of the initiative during the 2019–2020 academic year largely center on collaborative symposia. During the spring 2020 semester, the Center for Data Science is hosting a semi-weekly colloquium that will place historians, philosophers, literary scholars, and artists in conversation with computer scientists, engineers, and data scientists for cross-disciplinary engagement and exchange. Events aim to gather researchers from across the New York University community, leveraging its unique strengths in AI, data science, and the critical & analytical humanities. Topics that will be highlighted include conceptual histories of themes that underlie contemporary computational research, philosophical issues surrounding synthetic minds, human-AI interaction in the arts, and applications of data science in the digital humanities.
Deep Learning Reading Group:
The goal of this reading group is to come to a shared understanding of the technical principles of deep learning and to think through what is distinctive about deep learning as a contemporary approach to artificial intelligence. It is structured primarily with the aim of supporting interest in emerging technologies from researchers in the humanities. The group combines discussions of classic, historical, and contemporary readings of deep-learning-related texts with presentations from researchers from the Center for Data Science.
This group is co-organized by the Data Science & Humanities Workshop and NYU’s Digital Theory Lab.
For more information, contact Joseph Lemelin.
Abstract: What is digital humanities, and how does it relate to data science at NYU? This talk will discuss some of the ways humanists at NYU and elsewhere are using computation in their work, will describe some of the cultural datasets humanists work with at NYU, and suggest some possible avenues for future collaboration between humanities departments and data scientists.
Bio: Benjamin Schmidt is Director of Digital Humanities at New York University. Previously, he was an assistant professor of history at Northeastern University and core faculty at the NuLab for Texts, Maps, and Networks. His research interests are in the digital humanities and the intellectual and cultural history of the United States in the 19th and 20th centuries. His digital humanities research focuses on large-scale text analysis, humanities data visualization, and the challenges and opportunities of reading data itself as a historical source. His current project, Creating Data, explores practices of data collection in the 19th century American state through archival research, visualization, and re-analysis of historical data. He also contributes to popular conversations on topics including higher education in the United States, computational detection of anachronisms in historical fiction, and the “crisis” of the humanities.
Speaker: Kathrin Mauer, University of Southern Denmark & New York University
Time: Tuesday, April 7, 5:00–6:00
Place: 60 5th Avenue, 7th floor Open Space
Title: The Drone Sensorium and Communities
Abstract: This lecture would like to foster a dialogue between scholars from the humanities and data science about remote surveillance technology, machine vision, and algorithmic data processing and its impact on our communal life-worlds today. To this end it investigates civilian and military drones from an aesthetic perspective and examines how drones construct models of communities. In my discussion I aim to show how an aesthetic approach to drone technology can make the drone strange, and, in doing so, reveal its worldmaking and biopolitical power. I work with an understanding of aesthetics as a mode of sensing (aisthêsis), as a mimetic representation of the world, and as a critical discourse. Aesthetic drone imaginaries in visual art, literature, and popular culture often portray communities as network-like, flattened, or swarm-like collectives, which are generated via fluid, decentralized, and ubiquitous sensing processes. How can we interpret these aesthetic drone imaginaries of human collectives? Do their members still share something in common? Do these communities represent the dehumanization of the individual? Or, do they indicate that in the age of drone technology we need to fundamentally rethink essentialist, territorial, and intersubjective notions of communities?
Bio: Kathrin Maurer is Associate Professor of German Studies at the University of Southern Denmark, where she is the main coordinator of the Center for Humanities and Technology Studies. She is currently Visiting Researcher at NYU. Kathrin’s research focuses on cultures of surveillance, drone technology, visual culture, and German literature. At the University of Southern Denmark she leads the research cluster Drone Imaginaries and Communities and the network Drones and Aesthetics (sponsored by the Danish Research Council). Her published articles and books have discussed drone warfare, drone art, the aerial perspective in nineteenth-century culture, literature and migration, and memory discourses. Among her books are Visualizing War: Emotions, Technology, Communities (Routledge, 2018, anthology) and Visualizing History: The Power of the Image in German Historicism (Walter de Gruyter, 2013).
Speaker: Gene Kogan, New York University
Time: Tuesday, April 21, 5:00–6:00
Place: 60 5th Avenue, 7th floor Open Space
Title: AI, Art, and Autonomy
Abstract: For decades, artists have sought to create artificial agents that generate artworks, inspiring debate over what distinguishes mere augmentation of a human artist’s skills from agents exhibiting intrinsic creativity and true autonomy. For the past two years, I’ve been researching an idea to make this “AI artist” not by taking a human out of it, but rather by putting people into it, using machine learning to align their collective intelligence into an emergent, unique, and original style. This idea has stretched me from my familiar terrain of generative art over to more exploratory territory of techniques for private, secure, and decentralized AI. This talk is to introduce the idea in more depth, and make a case for why it’s interesting.
Bio: Gene Kogan is an artist and a programmer who is interested in autonomous systems, collective intelligence, generative art, and computer science. He is a collaborator within numerous open-source software projects, and gives workshops and lectures on topics at the intersection of code and art. Gene initiated ml4a, a free book about machine learning for creative practice, and regularly publishes video lectures, writings, and tutorials to facilitate a greater public understanding of the subject. He is currently an adjunct professor and resident at NYU’s ITP program.
Time: Tuesday, October 1, 4:00-5:30
Place: 60 5th Avenue, 7th floor Open Space
Title: AI Aesthetics
Mercedes Bunz (Digital Humanities, King’s College, London)
On the Misunderstandings of AI
A wide range of different AI systems based on the promising technology of machine learning are rapidly being integrated into everyday life, variously supplanting and delivering institutional decisions. The emerging ubiquity of these systems alternately provokes consternation and enthusiastic acceptance. Taking inspiration from French technology theorist Gilbert Simondon, this lecture considers the boundaries between the meaning-making capacities of technical objects, such as AI systems, and aesthetic objects. It asks what it means that AI systems are ‘integrated’ into the world, and whether this integration could be understood differently. Might existing responses to integration be based on a misunderstanding?
David Bering-Porter (Culture and Media, The New School)
Alt.Intelligence: Generative Media and Deformations of Intelligence
In 2014, the computer scientist Ian Goodfellow and his colleagues developed the idea of Generative Adversarial Networks (GANs), ushering in a new age of generative media. Ranging from Google’s “deep dream” to “deepfakes,” these new forms of media show off a starkly alien style in which neural networks reveal a view on the world that is radically different from our own. This talk looks to examples of generative media produced by GANs to consider how AI functions as both an alternative to and a deformation of traditional views of intelligence. It uses generative media as a touchstone to consider why it is both interesting and important to attempt to articulate the experiential view of artificial intelligence, and how doing so is worthwhile as machine learning continues to pervade our everyday lives.
Co-sponsored by NYU’s Digital Theory Lab and the Dean for Humanities