10/19/15 // Data Science Event: Quantifying Creativity

Quantifying Creativity: Art through the Eyes of Computation

Monday, October 19, 2015 (5:00 – 7:00 PM) at Humanities 210
Co-sponsored by the Data Science Initiative

From Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, “A Neural Algorithm of Artistic Style.”

Is the experience of art uniquely human? Can algorithms be artistic producers? Or, do machines remove the context and meaning from creativity? As artificial agents generate media and evaluate originality, how will we draw the line between human and machine aesthetics? How will the relationship between art, and humanity, be redefined?

Join us for an interdisciplinary evening as art historians, philosophers, and computer engineers confront these questions and each other. Wine and Cheese will be served.
REGISTRATION REQUIRED.

PROGRAM

>REPORTS from the FIELD: USING ALGORITHMS to UNDERSTAND CREATIVITY

Dr. Ahmed Elgammal (Associate Professor) and Babak Saleh (PhD Student) | Department of Computer Science, Rutgers University

Read about their work using computation to assess creativity and artistic creation: “Which paintings were the most creative of their time? An algorithm may hold the answers“; “Why do we love Picasso? A ‘creativity algorithm’ explains,” Washington Post; “How to Find Out Which Paintings Were the Most Creative of Their Time,” Time Magazine

Chris Smith | Co-founder at BitMesh

Smith has cofounded several startups, plays the piano, and loves cryptocurrency. He has degrees in math and computer science and is currently employed as a Deep Learning Software Engineer at Adatao.

> RESPONSE PANEL

Sabina Tomkins, Moderator (PhD Student, UCSC TIM)

David Cope (UCSC, Music)

Arnav Jhala (UCSC, Computational Media)

Samantha Matherne (UCSC, Philosophy)

Albert Narath (UCSC, History of Art and Visual Culture)

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Want to read more before Quantifying Creativity?

Scholarly Articles

“A Neural Algorithm of Artistic Style,” by Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
“Quantifying Creativity in Art Networks,” by Ahmed Elgammal, Babak Saleh
“Recognizing Image Style,” by Sergey Karayev, Matthew Trentacoste, Helen Han, Aseem Agarwala, Trevor Darrell, Aaron Hertzmann, Holger Winnemoeller
“Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks,” by Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus
“Learning to Generate Chairs with Convolutional Neural Networks,” by Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox
Geraint Wiggins on Computational Music, especially “AI methods for algorithmic composition: A survey, a critical view and future prospects“

Popular Press Coverage

“How Computing Can Help Art Historians,” by Stephen Heyman, New York Times
“Could computers put art historians out of work?” by Matthew Sparkes, Telegraph
“Computers Are Learning About Art Faster than Art Historians,” by Marissa Fessenden, Smithsonian.com