NN Cluster

NN Cluster refers to one of the many machine learning techniques called neural network and clustering. NN Cluster is an interdisciplinary initiative by early career researchers in critical technical practices, posthumanities, software studies, artistic practice, digital methods and curating. Founded by artist-researcher Winnie Soon, curator-researcher Magda Tyzlik-Carver and researcher Pablo Velasco from Aarhus University the goal of NN Cluster is to diversify ai practices by introducing, testing, developing, disseminating, non-heterogenous practices of working with and researching ai and ml in the context of arts and humanities. While there is an extensive interest in researching AI/ML, and some concerns about the diversity of inputs that populate/feed its dynamics, a need for a non-heterogenous/interdisciplinary/critical approach is essential.

Projects

A number of different projects are a part of this cluster including:

• Critical Machine Learning and Technical Practice (Winnie Soon): Through examining various (big) datasets, tinkering with open source code libraries, writing computer code, studying neural network models and constructing creative artworks/design, this project/study aims to develop an aesthetic and critical inquiry method to understand, analyze and reflect upon the material infrastructure and processes of machine learning, exploring the aesthetic possibilities and examining the implications of machine learning in wider computational culture.

• Unerasable Characters (Winnie Soon): This is an art project working with the big Chinese dataset from Weiboscope, developed by Journalism and Media Studies Centre, The University of Hong Kong, on erased and censored text (around 10m posts) through utilizing natural language processing and recurrent neural network to inquire the automated practice of internet censorship, examining questions around the poetics of text, cybernetics, authorship and control.

• Posthuman curating/and Affect Data Bodies (Magda Tyzlik-Carver) - Posthuman curating is a project that investigates the relation betwen affect data and bodies in contermporary culture increasingly organised by practices of automation, machine learning, neural networks and user participation in social media and increasing use of affective features employed for computation. Recognising how practice of curating has been automated and distributed across human and nonhuman actors (Tyzlik-Carver 2016, 2018) the focus of this investigation is on neural networks as an organising principle in organising information and data, be it digital, biological or analogue. Posthuman curating as part of NN Cluster proposes a series of seminars on the subject where scholars, professionals and artists are invited to present their practices of data curating with the use of machine learning technologies.

• Procedural Generation as mode of production (Pablo Velasco): Artificial Intelligence (AI) techniques are progressively used to sort complex tasks among big data sets. The AI branch of Procedural Generation (PG) works in the opposite way: it exploits randomness and permutations to generate large amounts of data from a relatively small input. This study observes examples of PG among the entretainment industry and digital cultures, to unravel how PG techniques are used to generate a perception of "infinity" through computation. The study is interested in citically engaging the outsourcing of design and production activities to computation, and in researching the ways in which randomness is operationalised through AI, and how this rearticulates notions of agency, accountability, and logics of rationality.

Activities we have done

• Curating Exhibition at Image Galleri

  Screenshots: Desire and Automated Images

• Hosting workshop:

  Experimental Creative Writing with Natural Language Processing by Allison Parrish

• Hosting performance:

  Performance Drawing with Sound by Anna Ridler

• Hosting talks:

  Machine Learning as Creative Interaction Design Tool by Rebecca Fiebrank 
  Instruments of Creation (Neurography) by Mario Klingemann

• Panels at conferences

  Hard Feelings: Conversation on Computation and Affect at Transmediale festival (2017)

International networks

• Working with Critical Software Thing Collective and participate in AI reading group in 2019

• Co-organizing and contributing to Transmediale research wokshops on Machine Feeling (2019) and Machine Research (2017)

• Co-organising a panel 'Unthinking Photography: cultural value and the networked image' for "Ways of Machine Seeing 2017" organized by the Cambridge Digital Humanities Network, and CoDE (Cultures of the Digital Economy Research Institute) and Cambridge Big Data

• Participated in When Robots Think: Interdisciplinary Views on Intelligent Automation (2018), orgainzed by Franz Hitze Haus/Aachen University

Teaching NN/ML/AI related topics in higher education

• Digital Aesthetics (MA 20 ECTS)
• Software Studies (BA 10 ECTS)
• Aesthetic Programming (BA 20 ECTS)
• Digital Culture (MA 10 ECTS)
• Developing Social Interactions for the Mobile Web (MA 10 ECTS)

Publications

• Automating Desire (forthcoming book in Data Browser series)
• Soon, W & Cox, G., (Forthcoming) Aesthetic Programming: A handbook for Software Studies. Open Humanities Press.
• Soon, W., (2019) ‘The (un)predictability of Text-Based Processing in Machine Learning Art‘. Proceedings of Art Machines: International Symposium on Computational Media Art.

Resources

• Machine Learning Experiments