11.06.2019

Uncertainty, Turbulence and Moving Image Archives. UCLondon

https://www.eventbrite.com/e/screen-media-and-theory-workshop-uncertainty-turbulence-and-moving-image-archives-tickets-59710545850

Keynote and screening by Manu Luksch at

Screen Media and Theory Workshop: Uncertainty, Turbulence and Moving Image Archives
7pm, 11  June 2019, University College London, Institute of Advanced Studies, Common Ground, Ground Floor, South Wing
Convened by Dr Annie Ring (University College London) and Dr Lucy Bollington (University College London)

[free registration at eventbrite]

Abstract: Senegalese rappers, an Emirati blogger, Akha elders from the Mekong Quadrangle, and queen of the silver screen Tilda Swinton are just some of the voices that inhabit my work, interrogating archives, regimes, and processes in networked societies. Using excerpts from several films (a hyperdocumentary that establishes the medium it investigates, a manifesto-driven science fiction feature that montages images as ‘legal readymades’, immersive installations shot inside tech industry meetings,…) I will articulate various conceptions of turbulence as it arises at the boundaries of social and technical infrastructures.  
For Dreams Rewired (2015), an archive-driven essay film that interrogates contemporary information politics through the lens of early cinema, the filmmakers worked with 50 international film archives to identify rare material from nearly 200 films that capture our first encounter with media technology. 
Through Faceless (2007) and Empty Quartz (2019), I consider turbulence as restlessness and turmoil. How can network technologies empower citizens against the state and corporate actors, and how do the latter use these technologies to manage political disquiet? 
Agents that are able to predict the future are placed to own it. Turbulence must be removed, smoothed. Third Quarterly Report (2017) and Algo-Rhythm (2018) examine the rise of the predictive (smart) city and algorithmic decision making. Coda: what forms of resistance remain effective in the face of the massive concentration of predictive power?