Artificial Intelligence

"The Imitation Game surveys the extraordinary uses (and abuses) of artificial intelligence (AI) in the production of modern and contemporary visual culture around the world. The exhibition follows a chronological narrative that first examines the development of artificial intelligence, from the 1950s to the present, through a precise historical lens. Building on this foundation, it emphasizes the explosive growth of AI across disciplines, including animation, architecture, art, fashion, graphic design, urban design and video games, over the past decade. Revolving around the important roles of machine" learning and computer vision in AI research and experimentation, The Imitation Game reveals the complex nature of this new tool and demonstrates its importance for cultural production.
Featured artists, designers and architects include *airegan, Stafford Beer, BIG, Ben Bogart, Gui Bonsiepe, Sougwen Chung, Muriel Cooper, DeepDream, Stephanie Dinkins, Scott Eaton, Epic Games, Amber Frid-Jimenez, Neri Oxman, Patrick Pennefather and WETA, among others.

"Unsupervised is a meditation on technology, creativity, and modern art. Anadol trained a sophisticated machine-learning model to interpret the publicly available data of MoMA’s collection. As the model “walks” through its conception of this vast range of works, it reimagines the history of modern art and dreams about what might have been—and what might be to come. In turn, Anadol incorporates site-specific input from the environment of the Museum’s Gund Lobby—changes in light, movement, acoustics, and the weather outside—to affect the continuously shifting imagery and sound."

Centres radical ideas in a world transformed by technology through engaging, collaborative, and accessible media. Uses dialogue and storytelling to probe and advance the field of Artificial Intelligence Ethics.

The last 3 years have resulted in machine learning (ML)-based image generators with the ability to output consistently higher quality images based on natural language prompts as inputs. As a result, many popular commercial “generative AI Art” products have entered the market, making generative AI an estimated $48B industry [125]. However, many professional artists have spoken up about the harms they have experienced due to the proliferation of large scale image generators trained on image/text pairs from the Internet. In this paper, we review some of these harms which include reputational damage, economic loss, plagiarism and copyright infringement. To guard against these issues while reaping the potential benefits of image generators, we provide recommendations such as regulation that forces organizations to disclose their training data, and tools that help artists prevent using their content as training data without their consent.