Machine Learning

United Kingdom, 2018

Courtesy Universal Everything

Artwork
Universal Everything, Machine Learning (2018). Image courtesy of the artists

In Machine Learning (2018), the human element is rendered in the dancer’s avatar and their mirrored co-player, whose form changes from smart materials to drone swarms. These animated films were a collaboration with choreographer and dancer Dwayne-Antony Simms, and were also made through motion-capture. The duet explores balance, mimicry and challenge as Simms teaches the robot to follow his steps. Although it was initially performed in an empty room, it depicts a surprisingly natural conversation between human and machine. From the abstract, humanity emerges, with the piece asking: Can a robot outperform a dancer? Would a robot dance for fun?

Creative Director: Matt Pyke
Senior Producer: Greg Povey
3D Animation: Joe Street
Sound Design: Simon Pyke
Dancer/Choreographer: Dwayne-Anthony Simms
Motion Capture: Audio Motion

How are these works connected?

Explore this constellation

Content notification

Our collection comprises over 40,000 moving image works, acquired and catalogued between the 1940s and early 2000s. As a result, some items may reflect outdated, offensive and possibly harmful views and opinions. ACMI is working to identify and redress such usages.

Learn more about our collection and our collection policy here. If you come across harmful content on our website that you would like to report, let us know.

Collection

Not in ACMI's collection

Previously on display

6 October 2024

ACMI: Gallery 4

Credits

artist

Universal Everything

Production places
United Kingdom
Production dates
2018

Appears in

Group of items

Presence and Machine Learning

Explore

Collection metadata

ACMI Identifier

LN196365

Object Types

Artwork

Digital

Materials

Single-channel video, stereo sound. Duration: 04:47mins, looped

Collected

10 times

Please note: this archive is an ongoing body of work. Sometimes the credit information (director, year etc) isn’t available so these fields may be left blank; we are progressively filling these in with further research.

Cite this work on Wikipedia

If you would like to cite this item, please use the following template: {{cite web |url=https://acmi.net.au/works/122587--machine-learning/ |title=Machine Learning |author=Australian Centre for the Moving Image |access-date=21 November 2024 |publisher=Australian Centre for the Moving Image}}