Difference between revisions of "Neural aesthetics"

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* [http://magenta.tensorflow.org/welcome-to-magenta Magenta], a Google Brain project dedicated to generate art using machine learning. Started in June 2016. [http://research.google.com/pubs/author39086.html]
 
* [http://magenta.tensorflow.org/welcome-to-magenta Magenta], a Google Brain project dedicated to generate art using machine learning. Started in June 2016. [http://research.google.com/pubs/author39086.html]
  
==Literature, resources==
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==Literature, data, resources==
 
===Courses and textbooks for artists===
 
===Courses and textbooks for artists===
 
; Textbooks
 
; Textbooks
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; Resources
 
; Resources
 
* [http://aiexperiments.withgoogle.com 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. [https://twitter.com/alexanderchen/status/798677118473281537 Launched] on 16 Nov 2016.
 
* [http://aiexperiments.withgoogle.com 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. [https://twitter.com/alexanderchen/status/798677118473281537 Launched] on 16 Nov 2016.
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===Data sets===
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* Luke de Oliveira, [https://medium.com/startup-grind/fueling-the-ai-gold-rush-7ae438505bc2 "Fueling the Gold Rush: The Greatest Public Datasets for AI"], ''Medium'', 11 Feb 2017.
  
 
===Scientific introduction into deep learning===
 
===Scientific introduction into deep learning===

Revision as of 10:18, 8 December 2017

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.

Data sets

Scientific introduction into deep learning

Historization of deep learning

Recent debates on artificial intelligence in the humanities

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. Visual art, Contents, Index, About.

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. Art and culture, Contents, Index, About.