Difference between revisions of "Neural aesthetics"

From Monoskop
Jump to navigation Jump to search
Line 118: Line 118:
 
* [https://ainowinstitute.org/ AI Now Institute], New York University. A research institute examining the social implications of artificial intelligence. Founded in 2017 by Kate Crawford and Meredith Whittaker.
 
* [https://ainowinstitute.org/ AI Now Institute], New York University. A research institute examining the social implications of artificial intelligence. Founded in 2017 by Kate Crawford and Meredith Whittaker.
 
* [https://blog.mozilla.org/blog/2018/06/04/mozilla-announces-225000-for-art-and-advocacy-exploring-artificial-intelligence/ Mozilla Award for Art and Advocacy Exploring Artificial Intelligence], 2018.
 
* [https://blog.mozilla.org/blog/2018/06/04/mozilla-announces-225000-for-art-and-advocacy-exploring-artificial-intelligence/ Mozilla Award for Art and Advocacy Exploring Artificial Intelligence], 2018.
* [http://postdigital.ens.fr/ Research seminar on artificial imagination, Ecole Normale Superieure, Paris since 2017], 2017.
+
* [http://postdigital.ens.fr/ Research seminar on artificial imagination, Ecole Normale Superieure, Pari. Founding by Beatrice Joyeux-Prunel and Gregory Chatonsky], 2017.
  
 
==Literature, data, resources==
 
==Literature, data, resources==

Revision as of 12:16, 14 September 2019

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

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

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