Matthew Plummer-Fernandez: The Art of Bots: A Practice-based Study of the Multiplicity, Entanglements and Figuration of Sociocomputational Assemblages (2018)

“This thesis examines and analyses an emerging art practice known as artbots. Artbots are internet-based software applications that are imbued with character and configured to engage and entertain online audiences. This form of practice, and the community of practice leading it, was found to be underrepresented and misunderstood. I argue that this artform is original and warrants a more thorough understanding. This thesis develops a conceptual framework for understanding artbots that focuses on and enables questioning around pertinent aspects of the practice.

A wide range of literature was reviewed to provide theoretical underpinnings towards this framework, including literature on algorithm studies, science and technology studies, and software architecture. The devised framework examines artbot case studies through the notions of multiplicity, entanglement, and figuration, having understood artbots as heterogenous sociocomputational assemblages comprised of software components and human intraactivity.

The research followed a varied methodology that encompassed participant observation and my own practice-based experiments in producing artbots. The study resulted in several original works. In addition, a showcase titled Art of Bots brought together key proponents and artbots, further providing material that is analysed in this thesis. The study helped identify and discuss artbots with attention to how they utilise modular software components in novel arrangements, how normative human and nonhuman relations of interaction are being eschewed in favour of entangled interrelations, and how artbots challenge common narratives dictating technological constructs by inventing unique characters and figurations.”

Doctoral thesis
Publisher Goldsmiths, University of London, 2018
Creative Commons BY-NC-ND License
218 pages

Publisher

PDF, PDF (10 MB)
ZIP (Supplemental material, 20 GB)

The Techno-Galactic Guide to Software Observation (2018)

“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

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

“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)