Difference between revisions of "Neural aesthetics"

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* Andrey Kurenkov, [http://www.andreykurenkov.com/writing/a-brief-history-of-neural-nets-and-deep-learning/ "A 'Brief' History of Neural Nets and Deep Learning"], [http://www.andreykurenkov.com/writing/a-brief-history-of-neural-nets-and-deep-learning-part-2 Part 2], [http://www.andreykurenkov.com/writing/a-brief-history-of-neural-nets-and-deep-learning-part-3/ Part 3], [http://www.andreykurenkov.com/writing/a-brief-history-of-neural-nets-and-deep-learning-part-4 Part 4], ''Andrey Kurenkov'' blog, 24 Dec 2015.
 
* Andrey Kurenkov, [http://www.andreykurenkov.com/writing/a-brief-history-of-neural-nets-and-deep-learning/ "A 'Brief' History of Neural Nets and Deep Learning"], [http://www.andreykurenkov.com/writing/a-brief-history-of-neural-nets-and-deep-learning-part-2 Part 2], [http://www.andreykurenkov.com/writing/a-brief-history-of-neural-nets-and-deep-learning-part-3/ Part 3], [http://www.andreykurenkov.com/writing/a-brief-history-of-neural-nets-and-deep-learning-part-4 Part 4], ''Andrey Kurenkov'' blog, 24 Dec 2015.
  
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===Recent debates on artificial intelligence in the humanities===
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===Recent debates on artificial intelligence in the humanities and social sciences===
 
* Matteo Pasquinelli (ed.), ''[https://monoskop.org/log/?p=15585 Alleys of Your Mind: Augmented Intelligence and Its Traumas]'', Lüneburg: Meson, 2015, 212 pp.
 
* Matteo Pasquinelli (ed.), ''[https://monoskop.org/log/?p=15585 Alleys of Your Mind: Augmented Intelligence and Its Traumas]'', Lüneburg: Meson, 2015, 212 pp.
 
* Florian Hecker, Robin Mackay, [[Media:Hecker_Florian_Mackay_Robin_2015_On_Sound_and_Artificial_Neural_Networks.pdf|"On Sound and Artificial Neural Networks"]], in ''Writing and Unwriting (Media) Art History: Erkki Kurenniemi in 2048'', eds. Joasia Krysa and Jussi Parikka, MIT Press, 2015, pp 279-289.
 
* Florian Hecker, Robin Mackay, [[Media:Hecker_Florian_Mackay_Robin_2015_On_Sound_and_Artificial_Neural_Networks.pdf|"On Sound and Artificial Neural Networks"]], in ''Writing and Unwriting (Media) Art History: Erkki Kurenniemi in 2048'', eds. Joasia Krysa and Jussi Parikka, MIT Press, 2015, pp 279-289.
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** [http://www.glass-bead.org/article/machines-that-morph-logic/?lang=frview "Des machines qui morphent la logique: les réseaux de neurones et l’automatisation déformée de l’intelligence comme inférence statistique"], ''Glass Bead'' 2, 2017. {{fr}}
 
** [http://www.glass-bead.org/article/machines-that-morph-logic/?lang=frview "Des machines qui morphent la logique: les réseaux de neurones et l’automatisation déformée de l’intelligence comme inférence statistique"], ''Glass Bead'' 2, 2017. {{fr}}
 
* Adrian Mackenzie, ''Machine Learners'', MIT Press, 2017, 272 pp. [https://mitpress.mit.edu/books/machine-learners]
 
* Adrian Mackenzie, ''Machine Learners'', MIT Press, 2017, 272 pp. [https://mitpress.mit.edu/books/machine-learners]
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* Eirini Malliaraki, [https://medium.com/@eirinimalliaraki/toward-ethical-transparent-and-fair-ai-ml-a-critical-reading-list-d950e70a70ea "Toward ethical, transparent and fair AI/ML: a critical reading list"], ''Medium'', Feb 2018.
 
* [https://monoskop.org/log/?tag=artificial-intelligence writings on artificial intelligence on Monoskop Log].
 
* [https://monoskop.org/log/?tag=artificial-intelligence writings on artificial intelligence on Monoskop Log].
  

Revision as of 03:24, 28 February 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


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