Difference between revisions of "Neural aesthetics"

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* [https://www.aec.at/ai/ Ars Electronica: AI. Artificial Intelligence. Das andere Ich / The Other I], Linz, 7-11 Sep 2017. Festival featuring exhibitions, performances, conferences, etc. [[Media:Ars_Electronica_AI_Artificial_Intelligence_The_Other_I_2017.pdf|Catalogue]].
 
* [https://www.aec.at/ai/ Ars Electronica: AI. Artificial Intelligence. Das andere Ich / The Other I], Linz, 7-11 Sep 2017. Festival featuring exhibitions, performances, conferences, etc. [[Media:Ars_Electronica_AI_Artificial_Intelligence_The_Other_I_2017.pdf|Catalogue]].
 
* [https://zkm.de/en/event/2018/04/encoding-cultures-living-amongst-intelligent-machines Encoding Cultures: Living Amongst Intelligent Machines] conference, ZKM, Karlsruhe, 27-28 Apr 2018.
 
* [https://zkm.de/en/event/2018/04/encoding-cultures-living-amongst-intelligent-machines Encoding Cultures: Living Amongst Intelligent Machines] conference, ZKM, Karlsruhe, 27-28 Apr 2018.
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* [https://www.facebook.com/events/130216531185870/ Art Assembly: Machine Learning], Fiber, Amsterdam, 22 Jun 2018. Organised by Fiber as part of the ''Coded Matter(s): Worldbuilding'' series.
  
 
==Artists, designers==
 
==Artists, designers==

Revision as of 13:08, 19 June 2018

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.

Events

Artists, designers

Initiatives

Literature, data, resources

Courses and textbooks for artists

Textbooks
Video lectures
  • Gene Kogan, "Machine Learning for Artists", course taught at ITP, New York University, New York, Spring 2016. 6 lectures, ~15 hours.
  • Gene Kogan, "The Neural Aesthetic", course taught at School Of Machines, Making, and Make-Believe, Berlin, Summer 2016. 8 lectures, ~18 hours.
Introductions
Resources
  • A.I. Experiments, a resource on machine learning art developed by Alexander Chen, creative director at Google Creative Lab, with Yotam Mann, C Christiansen, Jonas Jongejan, Gene Kogan, Kyle McDonald, et al. Launched on 16 Nov 2016.
  • People + AI Research, Google Design, 2018ff.

Data sets

Scientific introduction into deep learning

Historization of deep learning

Recent debates on artificial intelligence in the humanities and social sciences

Art practice and criticism


Art and culture

Avant-garde and modernist magazines, Dance, Artists' publishing, Graphic design, Photography, Typewriter art, Multimedia environments, Design research, Video activism, Urban practices, Zine culture, Demoscene, VJing, Live cinema, Art and technology centres, Cyberfeminism, Art and activism, Community television, Hacktivism, Community servers, Hackerspaces, CD-ROM art, Circuit bending, Pure Data, Media archives, VVVV, Maker culture, Glitch art, Live coding, Locative media, Libre graphics, Electromagnetism, Surf clubs, DIY biology, Decolonial aesthetics, Post-digital, Neural aesthetics.
See also Visual art.

Software
communities of practice

Art and technology centres, Circuit bending, Community servers, Copyright activism, Data activism, Demoscene, Digital libraries, DIY biology, Federated networks, File sharing, Free software, Game art, Hacker culture, Hackerspaces, Hacktivism, Internet activism, Libre graphics, Live coding, Live video, Maker culture, Media archives, Net art, Neural aesthetics, Open hardware, Shadow libraries, Software art
See also Art and culture.