Art, Creativity, and the Potential of Artificial Intelligence

By Marian Mazzone, Ahmed M. Elgammal. Arts, 2019

372 citations recorded by the source corpus.

Our essay discusses an AI process developed for making art (AICAN), and the issues AI creativity raises for understanding art and artists in the 21st century. Backed by our training in computer science (Elgammal) and art history (Mazzone), we argue for the consideration of AICAN’s works as art, relate AICAN works to the contemporary art context, and urge a reconsideration of how we might define human and machine creativity. Our work in developing AI processes for art making, style analysis, and detecting large-scale style patterns in art history has led us to carefully consider the history and dynamics of human art-making and to examine how those patterns can be modeled and taught to the machine. We advocate for a connection between machine creativity and art broadly defined as parallel to but not in conflict with human artists and their emotional and social intentions of art making. Rather, we urge a partnership between human and machine creativity when called for, seeing in this collaboration a means to maximize both partners’ creative strengths.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/ad1a9e389d738635ffaf35e74bcf6bccb6c79b68

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Negar Rostamzadeh
  2. Ramya Srinivasan
  3. Emily L. Denton
  4. Ming-Hsuan Yang
  5. Dacheng Tao
  6. Yoshua Bengio
  7. Sergey Levine
  8. Yann LeCun

In Cognitive Neuroscience

  1. Emily L. Denton

In Human-Computer Interaction

  1. Emily L. Denton

Citations in the corpus