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UAL Research Online

Actress + Young Paint Live AI/AV

Cunningham, Darren and McCallum, Louis (2019) Actress + Young Paint Live AI/AV. [Performance]

Type of Research: Performance
Creators: Cunningham, Darren and McCallum, Louis

AI Tools for performance developed by Louis McCallum

For the 2019 CTM/transmediale Collaborative Concert, the two festivals presented the world premiere of the new live AI/AV show from Darren J. Cunningham, better known as Actress, and the AI Young Paint. Continuing CTM's critical engagement with AI, the piece explores the creative and social potentials of machine learning.

Over the course of 2018, the AI-based character spent time programming and arranging Cunningham’s sonic palette. Actress, known for shadowy and unpredictable sounds, often draws loosely on 2-step garage, bass-heavy sounds, and London’s rave heritage. Young Paint has learnt not only how to react to Actress' work, but also to take the lead with the occasional solo. A life-size projection of the sprite working in a virtual studio parallels Cunningham’s performance on stage, visualising their collaboration.

The duo released a mini-album in October 2018 via Werk__Ltd. (a new collaborative label between The Vinyl Factory and Actress), which delivered a sneak peak of these improvisational ventures. The result is a fluid and constantly evolving version of Actress’ murky, UK bass-inspired experimentations, drifting into uncanny and unpredictable traits found in the producer’s 15 year oeuvre.

Your affiliations with UAL: Research Centres/Networks > Institute for Creative Computing
Date: January 2019
Funders: CTM Festival
Related Websites: https://archive2013-2020.ctm-festival.de/projects/commissioned-works/comissioned-works/actress-young-paint-live-aiav/
Related Websites:
Locations / Venues:
LocationFrom DateTo Date
CTM/transmediale highlight performance, Haus der Kulturen der Welt, Berlin1 February 20192 February 2019
Measurements or Duration of item: 60 mins
Date Deposited: 02 Nov 2023 10:39
Last Modified: 22 Feb 2024 15:04
Item ID: 20724
URI: https://ualresearchonline.arts.ac.uk/id/eprint/20724

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