Neural aesthetics

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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.

Resource on recent work between art/design and artificial neural networks in machine learning, or AI art, creative AI, art and artificial intelligence

Events[edit]

2014[edit]

2015[edit]

  • 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[edit]

2017[edit]

2018[edit]

2019[edit]

2020[edit]

2021[edit]

2022[edit]

2023[edit]

  • Neural Net Aesthetics, panel, New Museum, New York, 26 Oct 2023. Presented by Rhizome, with Refik Anadol, Maya Man, and Eileen Isagon Skyers, moderated by Michael Connor.

2024[edit]

Artists, designers, writers, musicians, makers[edit]

Initiatives[edit]

  • School for Poetic Computation, founded 2013 in New York. @sfpc.
  • School of Machines, Making & Make-Believe, founded March 2014 in Berlin by Rachel Uwa.
  • Fast Forward Labs, a machine intelligence research company, New York. Founded by Hilary Mason in ca. June 2014. [10]
  • Artists and Machine Intelligence (AMI). A program at Google that brings together artists and engineers to realize projects using Machine Intelligence. [11]
  • OpenAI, AI research and deployment company, San Francisco. Founded in December 2015 by Elon Musk, Sam Altman, et al. Products include DALL·E 2, OpenAI Codex (a descendant of GPT-3), API access to GPT-3.
  • Magenta, a Google Brain project dedicated to generating art and music using machine learning. Started in June 2016. [12]
  • Art and Artificial Intelligence Laboratory, Rutgers University, New Brunswick, NJ. Founding director: Ahmed Elgammal.
  • Creative AI meetup group, since September 2016. Founded by Luba Elliott.
  • Feminist.AI, a community AI research and design group focused on critical making as a response to hegemonic AI, est. 2016.
  • 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.
  • Artificial imagination / Artificielle postdigital, a research seminar on contemporary art and artificial intelligence, Ecole Normale Superieure, Paris. Founded in 2017 by Béatrice Joyeux-Prunel and Grégory Chatonsky. Twitter.
  • AI Art Gallery, a collection of art, music and design using machine learning, since 2017. Curated by Luba Elliott.
  • Mozilla Award for Art and Advocacy Exploring Artificial Intelligence, 2018.
  • Global AI Narratives Project, Leverhulme Centre for the Future of Intelligence (University of Cambridge), since 2018.
  • All Models, international mailing list of critical AI studies, initiated in July 2020. Hosted by the research group KIM at the Karlsruhe University of Arts and Design.
  • Creative AI Lab (Database), an ongoing project to aggregate tools and resources for artists, engineers, curators & researchers interested in incorporating machine learning and other forms of artificial intelligence into their practice. A collaboration between Serpentine R&D Platform (Eva Jäger) and the Department of Digital Humanities, King’s College London (Mercedes Bunz a.o.). Launched July 2020. [13]
  • Polytopal, a 'Human-Centered AI' research and development company, started July 2020.
  • Slow Readers, an informal research group which has embraced deceleration to look at gender inequality & AI, with V2_Fellow Renée Turner, a.o. Started in January 2021. [14]
  • Glaze: Protecting Artists from Style Mimicry, an academic research group of students and computer science scholars interested in protecting Internet users from invasive uses of machine learning.
  • Critical Voices on AI, seminar series, Birkbeck Institute for Data Analytics, started March 2023.
  • Awesome AI for LAM, a curated list of resources, projects, and tools for using artificial intelligence in libraries, archives, and museums.

Literature, data, resources[edit]

Practice and criticism[edit]

  • Melpomene, Bagabone, Hem ‘I Die Now, New York: Vantage Press, 1980, 136 pp. Perhaps the first novel that was purportedly written by a computer.
  • Digimag 76: "Smart Machines for Enhanced Arts", eds. Silvia Bertolotti and Marco Mancuso, Milan: Digicult, Summer 2017, 74 pp, EPUB. Texts by Memo Akten, Claire Burke, Geoffrey Drake-Brockman, Jerry Galle, Eugene Kogan, Robert B. Lisek, Filippo Lorenzin, Andreas Refsgaard, Liu Yuxi, Alessandro Masserdotti. [16]
  • Algolit, Data Workers, Brussels: Constant, Mar 2019. (English)/(French)
  • Entangled Realities: Living with Artificial Intelligence / Leben mit künstlicher Intelligenz, eds. Sabine Himmelsbach and Boris Magrini, Merian, 2019, 229 pp. Catalogue. Exhibition. Review: Cianciotta (Neural). (English)/(German)
  • Espace 124: "IA, art sans artistes? / AI, art without artists?", Montreal, Jan 2020. Special issue of magazine. Introduction. TOC. (French)/(English)
  • Beyond the Uncanny Valley: Being Human in the Age of AI, San Francisco: de Young Museum, and Cameron Books, Feb 2020, 224 pp. Texts by Claudia Schmuckli, Yuk Hui, Janna Keegan, Matteo Pasquinelli, and Tobias Rees. Publisher.
  • Angie Keefer (ed.), Version Space, Library Stack, 2020-2022. A series of pamphlets transcribing conversations among artists and graduate students in visual art regarding Artificial Intelligence and related topics.
  • K Allado-McDowell, Pharmako-AI, intro. Irenosen Okojie, Ignota, 2020, 152 pp. [22]
  • Ben Vickers, K Allado-McDowell (eds.), Atlas of Anomalous AI, forew. Bill Sherman, Ignota, 2020, 303 pp. [23]
  • Ilan Manouach, Anna Engelhardt (eds.), Chimeras: Inventory of Synthetic Cognition, Athens: Onassis Foundation, 2022, 536 pp. With contributions from 150 researchers and artists. [26] [27]
  • Alexandre Gefen (ed.), Créativités artificielles. La littérature et l'art à l'heure de l'intelligence artificielle, Dijon: Les presses du réel, Mar 2023, 264 pp. Publisher. (French)
  • Francesco D'Isa, La rivoluzione algoritmica. Arte e intelligenza artificiale, Rome: Luca Sossella, Apr 2024, 160 pp. Publisher. (Italian)
  • Dennis Yi Tenen, Author Function: A Literary History of Artificial Intelligence, University of Chicago Press, forthcoming. [34]

Online galleries and collections[edit]

See also exhibitions in the Events section above.

Recent debates on artificial intelligence in the humanities and social sciences[edit]

  • Florian Hecker, Robin Mackay, "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, Sep 2015, pp 279-289.
  • Clemens Apprich, Wendy Hui Kyong Chun, Florian Cramer, Hito Steyerl, Pattern Discrimination, Lüneburg: meson press, with University of Minnesota Press, Nov 2018, xii+123 pp.
  • Lev Manovich, AI Aesthetics, Moscow: Strelka Press, Dec 2018, 57 pp. Excerpt. [39] [40] [41]
    • Estetika umetne inteligence, trans. Tamara M. Soban, afterw. Vuk Ćosić, Ljubljana: Mestni muzej Ljubljane, and Zavod Basic, 2019, 79 pp. (Slovenian)
  • Katerina Cizek, William Uricchio, Sarah Wolozin, "Media Co-Creation with Non-Human Systems", in Cizek, Uricchio, et al., Collective Wisdom: Co-Creating Media within Communities, across Disciplines and with Algorithms, 3 Jun 2019.
  • Gabriele de Seta, "China.ai", in Realtime: Making Digital China, eds. Clément Renaud, Florence Graezer Bideau, and Marc Laperrouza, PURR, Jan 2020, pp 154-169. Book. Book launch.
  • Karen Hao, et al., "AI Colonialism", MIT Technology Review, Apr 2022. Article series.
  • Holo 3: "Mirror Stage: Between Computability and Its Opposite", ed. Nora N. Khan, May 2022, 228 pp. Research notes. Publisher.
  • Josh Dzieza, "AI Is a Lot of Work", The Verge, 20 Jun 2023. As the technology becomes ubiquitous, a vast tasker underclass is emerging — and not going anywhere.
  • Critical AI, journal, ed. Lauren M. E. Goodlad, Center for Cultural Analysis, Rutgers University, Oct 2023 ff. Duke UP.
more

Software[edit]

  • 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[edit]

See also

Courses and textbooks for artists[edit]

Textbooks
Video lectures
Introductions
MOOC
Syllabi
Resources

Research papers[edit]

Scientific introduction into deep learning[edit]

Historization of deep learning[edit]

  • Yann LeCun, Yoshua Bengio, Geoffrey Hinton, "Deep Learning", Nature 521, 28 May 2015, pp 436-444. Critiqued by Schmidhuber recasting the recent advances of deep learning as building on top of prior work with multilayer neural networks, going back decades.