[TRAILER] Deep Time Machine Learning

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What can structuralist film strategies offer in the age of machine vision?

TRAILER for the project
Deep Time Machine Learning
by Jol Thomson
2017-2019
Single & Multi Channel
Various durations

DTML discovers geospeculative histories of the digital, through the octagonal 'mirror staging' of an intelligent robots genealogy. It locates the ongoing troubled relationships between obsolete human societies within an archeo-computational axis that has failed to produce justice, health or equality in the inflated and precarious 21st century's breakdown.

"With a view to developments in robotics and artificial intelligence, the Committee on Legal
Affairs deemed it time for the European Union to take action in respect of the legal and
ethical issues raised by these new technologies".[1]

DTML's approach is neuroethological. Its a developmental psychology of superficially intelligent machines. Please see: schloss-post.com/rotating-divinatory-hexagrams/

Incorporating various resolutions through a wide array of image producing technologies, (including LIDAR which captures spaces rather than 2 dimensional image-surfaces), tools, law, logic, and visuality coalesce in (hyper) media historialities and structuralist film techniques.

1st exhibited in Zentrum für Kunst und Medien (Centre for Art and Technology), Karlsruhe, Germany in the exhibition 'Open Codes: Living in Digital Worlds' 2017-2018: zkm.de/en/deep-time-machine-learning

In 2018 DTML was exhibited in two EU Commission Meetings through the STARTS program; in Sophia, Bulgaria and in Vienna, Austria.

In 2019 DTML is part of the programming in Recontres Internationales Paris/Berlin: Contemporary Moving Images: art-action.org/site/en/index.php

This is a TRAILER for an ever evolving moving image project. Feel free to contact me to see 1 or two channel full length versions.

1. europarl.europa.eu/RegData/etudes/STUD/2016/571379/IPOL_STU%282016%29571379_EN.pdf

2.
europarl.europa.eu/sides/getDoc.do?pubRef=-//EP//NONSGML+TA+P8-TA-2017-0051+0+DOC+PDF+V0//EN

Deep Time Machine Learning is crucially punctuated with excerpts from the EU Commission to Civil Law Rules on Robotics, 2017 (which is available in every European language)[2] and was developed in spring of 2017 at the BOSCH Gmbh Campus for Research and Advanced Engineering with the support of Akademie Schloss Solitude's 'Art, Science, and Business' program, and as a 'Wimmelforschung' residency project.

* See a behind the scenes making of this project in the Robotics lab of BOSCH Campus here, from Hagen Betzwieser: schloss-post.com/deep-time-machine-learning/

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