Difference between revisions of "Neural aesthetics"

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* ''Espace'' 124: "IA, art sans artistes? / AI, art without artists?", Montreal, Jan 2020. Special issue of magazine. [https://espaceartactuel.com/en/creating-ai/ Introduction]. [https://espaceartactuel.com/en/124-winter-2020/ TOC]. {{fr}}/{{en}}
 
* ''Espace'' 124: "IA, art sans artistes? / AI, art without artists?", Montreal, Jan 2020. Special issue of magazine. [https://espaceartactuel.com/en/creating-ai/ Introduction]. [https://espaceartactuel.com/en/124-winter-2020/ TOC]. {{fr}}/{{en}}
 
* Yair Rubinstein, [https://online.ucpress.edu/res/article/1/1/77/109394/ "Uneasy Listening: Towards a Hauntology of AI-Generated Music"], ''Resonance'' 1:1, Spring 2020, pp 77-93.
 
* Yair Rubinstein, [https://online.ucpress.edu/res/article/1/1/77/109394/ "Uneasy Listening: Towards a Hauntology of AI-Generated Music"], ''Resonance'' 1:1, Spring 2020, pp 77-93.
* ''Art and Machine Learning'', Graz: mur.at, forthcoming. [https://mur.at/post/ml-call-for-papers/ CfP].
+
* Joanna Zylinska, ''[http://www.openhumanitiespress.org/books/titles/ai-art/ AI Art: Machine Visions and Warped Dreams]'', Open Humanities Press, Jul 2020, 176 pp, [[Media:Zylinska_Joanna_AI Art Machine Visions and Warped Dreams 2020.pdf|PDF]].
 +
* ''Art and Machine Learning'', Graz: mur.at, forthcoming. [https://mur.at/post/ml-call-for-papers/ CfP]. [https://mur.at/project/im-netz-der-sinne/]
  
 
===Online galleries and collections===
 
===Online galleries and collections===

Revision as of 22:23, 31 August 2020

Partial results of the "hello world" test of a machine learning algorithm, automated recognition of handwritten digits, showing 125 test cases that the network got wrong. Each case is labeled by the network’s guess. The true classes are arranged in standard scan order. Source: Hinton et al 2006.
Basic structure of a neural network. Several techno-logical forms can be identified in the concept: scansion, that is discretisation or digitisation since the age of radio, TV, etc.; feedback loop, or the basic concept of cybernetics; and network form, here inspired by biological neurons. Diagram by Matteo Pasquinelli with Lukas Rehm, 2017. Source.

A resource on recent work between art/design and artificial neural networks in machine learning.

Related notions: AI art, creative AI, art and artificial intelligence.

Events

2014

2015

  • The Lab at the Google Cultural Institute, Paris, launches a 'machine learning for art' residency, early 2015 - mid-2016. Artists: Mario Klingemann, Cyril Diagne. Talk. Works.
  • DeepDream launched by Google's software engineers Alexander Mordvintsev, Christopher Olah and Mike Tyka, 17 Jun 2015. Reddit post from a day earlier. Vice coverage. Source code.
  • (Artifical Intelligence) Digitale Demenz exhibition, HMKV, Dortmund, 14 Nov 2015-6 Mar 2016. An exhibition exploring the relationship between contemporary art and artificial intelligence. Works by Erik Bünger, John Cale, Brendan Howell, Chris Marker, Julien Prévieux, Suzanne Treister, and !Mediengruppe Bitnik. Curated by Thibaut de Ruyter.

2016

2017

2018

2019

2020

Artists, designers, writers, musicians, makers

Initiatives

Literature, data, resources

Arts practice and criticism

Online galleries and collections

See also exhibitions in the Events section above.

Recent debates on artificial intelligence in the humanities and social sciences

Software

  • Tensorflow, an open source machine learning framework. Developed by the Google Brain team for internal Google use. Released under an open-source license on Nov 2015.
  • ml5.js, an open source machine learning library for the web. Launched Jun 2018.
  • Magenta Studio, a suite of free music-making tools using Magenta's machine learning models. Available as an Ableton plugin or as standalone Electron apps. Launched Nov 2018.
  • Runway, a toolkit that adds artificial intelligence capabilities to design and creative platforms. Built by Cristóbal Valenzuela. First alpha released May 2018.
  • GAN Playground - Explore Generative Adversarial Nets in your Browser, by Reiichiro Nakano, 2017.
  • Magenta demos

Datasets

See also

Courses and textbooks for artists

Textbooks
Video lectures
Introductions
MOOC
Resources

Research papers

Scientific introduction into deep learning

Historization of deep learning