https://www.bloomberg.com/news/articles/2016-06-23/artificial-intelligence-has-a-sea-of-dudes-problem
Adrian Mackenzie The production of prediction: What does machine learning want? 

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The tension relaxes, now product/proof is on the table. The tension returns when S complicates the solution by the mention of institutient practices.

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"more of the same"

> Ik heb een item met je gedeeld:
> Retreat for the ARTs
> https://drive.google.com/folderview?id=0B8LpLXLpBS3fSjN5a3hEZTA0Wk0&usp=sharing&ts=57768630
> Dit is geen bijlage. Het item is online opgeslagen. Klik op de bovenstaande link om het item te openen.
> Please make your won folder inside of this one in which you sotre your captures - if thay can be stored in this way.

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Relations between human and machinic ways of  learning have entered a new phase. Neural networks have been around for  quite some time (Marvin Minsky built SNARC, the first neural net capable of learning, in 1951); their presence and role in society, however, has  shifted over the last few years. The continued increase of  computational processing power, combined with the explosion of available data, has led to a turning point in the abilities of artificial forms  of intelligence. Machinic varieties of synthetic, qualitative judgment now seem to be developing through the vastly augmented ability to hone observational powers.
This transformation is symbolically represented in the victory of Google’s AlphaGo program over the professional 9-dan Go  player Lee Sedol in March 2016. Algorithmic learning systems are  increasingly doing work beyond the capabilities of human specialists in fields such as medical diagnosis, personal profiling in marketing, security assessment, industrial design, economic, traffic and weather  predictions. Although artificial forms of of intelligence are deployed  in increasingly more areas of society, their significance for research practices remains largely unexplored. [significance? applications? relevance?]

The retreat will serve as a catalyst for the development of new perspectives on the relations between machine  learning and research, including questions of authorship, copyright, originality, as well as the transformed condition of labor under automation.

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https://transmediale.de/content/machine-research
The research/Phd workshop MACHINE RESEARCH contributes to the transmediale festival programme for 2017. Participants participate in closed seminars and talks in Brussels, the generation of online and offline publications, and public presentations at the festival in Berlin. The 2017 transmediale festival focuses on the elusive character of media and technological change and how it is articulated in the contemporary moment of messy ecologies of the human and non human. It explores perspectives of the nonhuman that suggests a situation where the primacy of human civilization is put into a critical perspective by machine driven ecologies, ontologies and epistemologies of thinking and acting.

The workshop aims to engage research and artistic practice that takes into account the new materialist conditions implied by nonhuman techno-ecologies including new ontologies of learning and intelligence (such as algorithmic learning), socio-economic organisation (such as blockchain), population management and tracking (such as datafied borders), autonomous or semi-autonomous systems (such as bots or drones) and other post-anthropocentric reconsiderations of agency, materiality and autonomy.

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Het Nieuwe Instituut
Klaas Kuitenbrouwer – Expert, New Media and Digital Culture, R&D
Tamar Shafrir – Expert, Things and Materials, R&D
Katia Truijen – Expert, R&D
Marina Otero - Head of R&D

2016 Research Fellows
Andrea Bagnato – Epidemiology & Territorial Reorganization http://andreabagnato.eu/
Simone Niquille – Digital Identity, Data Ownership, Imaging Technology & Power http://technofle.sh/
Fusün Türetken – Panmetallism & Alchemy -- Forensic architecture collection

Volume Magazine
Nick Axel – Managing editor
Arjen Oosterman – Editor in chief, alterbating with Lilet Breddels, Editor of Volume Magazine.

Special Guests
Matthew Plummer Fernandez Julien shivenger, mickey mouse
Ben Schouten http://hva.academia.edu/BenSchouten/Papers
Dorien Zandbergen https://dorienzandbergen.nl/ Gr1p + smart cities critique, genderchangers
Femke Snelting 
Luis Rodil-Fernandez http://hackthebody.nl/ + http://algoresearch.systems/
Ade Darmawan http://www.debalie.nl/agenda/programma/vrijheidslezing-%237-met-ade-darmawan%2c-kunstenaar-en-activist-uit-indonesie/e_9756207/p_11664072/

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A personalised history of how we arrived at the stage in which software is not just programmed, but also trained. Cade Metz, 'One Genius’ Lonely Crusade to Teach a Computer Common Sense', Wired, 24 March 2016. At: http://www.wired.com/2016/03/doug-lenat-artificial-intelligence-common-sense-engine.

The sadness and beauty of watching Google’s AI play Go: machine changes human. http://www.wired.com/2016/03/sadness-beauty-watching-googles-ai-play-go/

Leo Benedictus, journalist for The Guardian,  undertakes a series of experiments to gauge the level of capability of artificially intelligent systems that are claimed to be able to paint, cook, translate or write. https://www.theguardian.com/technology/2016/jun/04/man-v-machine-robots-artificial-intelligence-cook-write

Bratton exposes the challenge of conceiving types of intelligence which are not human, and narrates how the anthropocentric idea of intelligence had hobbled the development of artificial intelligence.  Benjamin Bratton, ‘Outing Artificial Intelligence: Reckoning with Turing Tests” in Matteo Pasquinelli (ed.), Alleys of Your Mind: Augmented Intelligence and Its Traumas. http://meson.press/wp-content/uploads/2015/11/978-3-95796-066-5-Alleys_of_Your_Mind.pdf "This pretentious folklore is too expensive"

Joanna Drucker’s  Aesthetic Provocations in Humanities Computing’  is an historical analysis of how speculative computing and generative aesthetics are the true transformative aspects of the digital humanities and not  textmining and image analysis. => This text is rather dense, and may require some knowledge of the field of computational humanities to be parsed.  However, this analysis does outline  a conceptual space for the ARTs. 
http://digitalhumanities.org:3030/companion/view?docId=blackwell/9781405103213/9781405103213.xml&chunk.id=ss1-4-10&toc.depth=1&toc.id=ss1-4-10&brand=9781405103213_brand  http://www.speculativecomputing.org -> offline. Giving the aesthetics a place in knowledge production. Changing order of visualisation processes: not just a means to an end (the end of a process), but a generative process itself.

Karl Schroeder, ‘Thalience, the successor to Science’, on Karlschroeder.com The term thalience was coined by scifi author Karl Schoeder in his novel ‘Ventus’. Thalience may be understood as a notion of machinic subjectivity, although several definitions of this speculative concept exist.  http://www.kschroeder.com/my-books/ventus/thalience
 
Charles Stross, ‘Lobsters’ the first chapter of his novel Accelerando, in which a crossbred between a digital simulation of the nerve system of tiny lobsters and a language parser trained on Teletubbies and Russian communist propaganda has achieved some helpless form of artificial intelligence.
https://www.jus.uio.no/sisu/accelerando.charles_stross/portrait.a4.pdf
 
Projects
This is a small collection of projects, or documentation of projects that give an overview of recent artistic work and experiments with machine learning and artificial intelligence. 

Driessen & Verstappen’s E-volver. ‘Breeding units’ of software agents produce images that are judged by humans. Only the most interesting images survive. Successful units procreate, their offspring generating new images.  https://notnot.home.xs4all.nl/E-volverLUMC/E-volverLUMC.html  The concept of the breeding units may be inspiring for one possible notion of the ARTs. https://notnot.home.xs4all.nl/E-volverLUMC/machine.html
 
Matthew Plummer Fernandez’ ‘Novice Art Blogger”. An algorithm looks at modern paintings and tells us what is sees. This work directly inspired the notion of the ARTs. http://noviceartblogger.tumblr.com/ In a similar vein http://www.terrapattern.com/ recognises and names patterns in sattelite imagery.
 
Ross Goodwin’s  ‘word.camera’ Take any sort of photo you want, upload it, and the word.camera app will transpose the image into ornate text. A picture of a dead pigeon on a sidewalk might trigger a reflection on mortality; wearing a funny party hat might inspire the app to come up with a joke.  https://www.idfa.nl/industry/tags/project.aspx?id=8334c286-a4b5-402a-b072-c5e8ac899db5 and other works http://rossgoodwin.com/
https://word.camera/
http://ml4a.github.io/index/ Machine Learning for artists

On the effect of training on pattern-recognising algorithms:
Samim Winiger used an open-source neural network that was trained on 14 million passages of romance novels by a Ryan Kiros, a University of Toronto PhD student specializing in machine learning. Called the Neural-Storyteller, the network was trained to analyze images and retrieve appropriate captions from its vast store of sexy and romantic  knowledge, creating “little stories about images,” It demonstrates how the skills of neural nets are influenced by their training data.
http://gizmodo.com/it-was-inevitable-someone-taught-a-neural-network-to-t-1740989017
https://github.com/ryankiros/neural-storyteller
 
Shinseungback Kimyonghun collected images of people that were identified as cats and of cats that were identified as humans by an image recognising algorithm
http://ssbkyh.com/works/cat_human/