The Techno-Galactic Guide to Software Observation (2018)

17 July 2018, dusan

“This techno-galactic software survival guide was collectively produced as an outcome of the Techno-Galactic Software Observatory (Brussels, 2017). This guide proposes several ways to achieve critical distance from the seemingly endless software systems that surround us. It offers practical and fantastical tools for the tactical (mis)use of software, empowering/enabling users to resist embedded paradigms and assumptions. It is a collection of methods for approaching software, experiencing its myths and realities, its risks and benefits.”

With contributions from Manetta Berends, Željko Blaće, Larisa Blazic, Freyja van den Boom, Anna Carvalho, Loup Cellard, Joana Chicau, Cristina Cochior, Pieter Heremans, Joak aka Joseph Knierzinger, Jogi Hofmüller, Becky Kazansky, Anne Laforet, Ricardo Lafuente, Michaela Lakova, Hans Lammerant, Silvio Lorusso, Mia Melvaer, An Mertens, Lidia Pereira, Donatella Portoghese, Luis Rodil-Fernandez, Natacha Roussel, Andrea di Serego Alighieri, Lonneke van der Velden, Ruben van de Ven, Kym Ward, Wendy Van Wynsberghe, and Peter Westenberg.

Compiled by Carlin Wing, Martino Morandi, Peggy Pierrot, Anita Burato, Christoph Haag, Michael Murtaugh, Femke Snelting, and Seda Gürses
Publisher Constant, Brussels, 2018
Free Art License 1.3
ISBN 9789081145961
244 pages

Publisher
Project folder
Event

PDF, PDF (44 MB)
HTML (different version)
Git

Taeyoon Choi: Poetic Computation Reader (2017)

27 September 2017, dusan

This online book discusses code as a form of poetry and aesthetic while raising ethical questions associated with it. It is based on Taeyoon Choi’s lectures at the School for Poetic Computation, an independent school he co-founded in New York City.

Edited by Hannah Son
Designed by HAWRAF
Published 2017

HTML
Source code on Github

Gene Kogan, Francis Tseng: Machine Learning for Artists (2016–)

13 October 2016, dusan

“This is an in-development book about machine learning. The first draft is expected early-2017. Some chapters are nearly complete, some are very rough, some are just stubs.

Guides and Demos are being released as we go. Guides are a collection of practical resources for working with machine learning software, including code and tutorials. Demos are are a collection of figures and interactive demos for highlighting important concepts in machine learning, and supplementing the book’s materials.”

Chapters (HTML)
Guides (HTML, Python)
Demos (HTML, Javascript)