Neural aesthetics

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

• DeepDream: The Art of Neural Networks, 26-27 Feb 2016. Exhibition and symposium. Organised by Gray Area Foundation for the Arts and Research at Google.
• alt-AI: exploring the intersection of artificial intelligence and art, School for Poetic Computation, New York, 20-22 May 2016. Symposium and exhibition. Video recordings of talks: Gene Kogan, Golan Levin, Cassie Tarakajian, Hannah Davis, Rebecca Fiebrink, Mike Tyka, Heather Dewey-Hagborg, Brian Whitman, Allison Parrish, Mario Klingemann, Lynn Cherny, Kathryn Hume; performance: Jason Levine. Interviews (15 min), Exhibition highlights (2 min). Website mirror.
• Music, Art & Machine Intelligence workshop, San Francisco, 1 Jun 2016. Jointly hosted by Google's AMI and Magenta groups. Video recordings of talks. Proceedings.
• Recognition exhibition, Tate Britain, London, 2 Sep-27 Nov 2016. An artificial intelligence program that compared artworks with up-to-the-minute photojournalism. Developed by Jolibrain (Alexandre Girard and Emmanuel Benazera) and Fabrica. More.
• Neuronal Creation, exhibition and discussion track at Retune Festival, Berlin, 6-8 Oct 2016. Report: Elliott (Medium).
• Machine Research – Research/PhD Workshop 2016, Brussels, 24-26 Oct 2016. Organised by Aarhus University, transmediale, and Constant. Contributes to the transmediale festival programme for 2017.
• STATE Festival 2016, Kühlhaus, Berlin, 4-5 Nov 2016. Conference, exhibition, performances, screenings and workshops exploring the intersection of current emotions research and artificial intelligence. Director: Christian Rauch.
• How Deep is Your Dream, Merz Akademie, Stuttgart, 8 Nov - 6 Dec 2016. Weekly lecture series curated and moderated by Olia Lialina. Video recordings of talks: Sebastian Schmieg, Frieder Nake, Lauren Huret, Gene Kogan, Simone C. Niquille.

2017

• Machine Fictions exhibition, part of Leverhulme Centre for the Future of Intelligence Annual Conference, Webb Library, Jesus College, Cambridge, UK, 13-14 Jul 2017. Curated by Luba Elliott.
• Ars Electronica: AI. Artificial Intelligence. Das andere Ich / The Other I, Linz, 7-11 Sep 2017. Festival featuring exhibitions, performances, conferences, etc. Catalogue.
• The Age of AI Artisans, State of AI Pop-Up Studio, Los Angeles, 3-29 Oct 2017. Exhibition featuring Unhuman: Art in the Age of AI, the first exhibition on art produced by the AICAN Algorithm.

2018

• Ars Incognita: Poetics of Artificial Creativity: Symposium on Art and Artificial Intelligence, Centro Atlántico de Arte Moderno (CAAM), Spain, 15-17 Mar 2018. Dir. Héctor Rodríguez, associate dir. Tomás Laurenzo, artistic dir. Álvaro Rodríguez Fominaya.
• Encoding Cultures: Living Amongst Intelligent Machines conference, ZKM, Karlsruhe, 27-28 Apr 2018.
• Machine Dreams exhibition, part of CogX 2018 conference, Tobacco Dock, London, 11-12 Jun 2018. Curated by Luba Elliott; commissioned by Tabitha Goldstaub. Artist interviews (Forbes).
• Art Assembly: Machine Learning, Fiber, Amsterdam, 22 Jun 2018. Organised by Fiber as part of the Coded Matter(s): Worldbuilding series.
• Christie's auctions work made by the French collective Obvious, first announced Aug 2018, sold for $432,500 on 25 Oct 2018; sparking debate from established artists in the field, 25 Sep 2018; and prompting further investigation, 14 Oct 2018.
• Seoul Mediacity Biennale 2018: Eu Zen exhibition, Seoul Museum of Art, Seoul, 6 Sep-18 Nov 2018.
• Pierre Huyghe: UUmwelt exhibition, Serpentine Galleries, London, 3 Oct 2018-10 Feb 2019. Exhibition guide. Interview (Guardian).
• Machine Learning for Creativity and Design, NIPS 2018 Workshop, Montreal, 8 Dec 2018. Organised by Luba Elliott, Sander Dieleman, Rebecca Fiebrink, Adam Roberts, Jesse Engel, and Tom White.

Artists, designers

• Memo Akten, @memotv, Istanbul.
• Albert Barqué-Duran, @albertbarque, London, Berlin & Barcelona.
• Robbie Barrat, @DrBeef_, SF Bay Area.
• Anil Bawa-Cavia, @cavvia, Berlin & London.
• Ian Cheng, @eyecheng, NYC.
• Sougwen 愫君 Chung, @sougwen, NYC.
• Sofia Crespo, @soficrespo91, Berlin.
• Hannah Davis, @ahandvanish, Paris.
• Heather Dewey-Hagborg, @hdeweyh, Chicago & NYC.
• Rebecca Fiebrink, @rebeccafiebrink, London.
• Mario Klingemann, @quasimondo, Munich.
• Gene Kogan, @genekogan, Berlin.
• Kyle McDonald, @kcimc, Los Angeles.
• Simone C. Niquille, @s__c__n, Amsterdam.
• Tivon Rice, @tivonrice, Seattle.
• Anna Ridler, @annaridler, London.
• Helena Sarin, @glagolista.
• Sebastian Schmieg, @s_schmieg, Berlin.
• Cassie Tarakajian, @hellothisiscass, Brooklyn, NY.
• Francis Tseng, @frnsys, Brooklyn, NY.
• Michael Tyka, @mtyka, Seattle.
• Tom White, @dribnet, Wellington.
• Samim Winiger, @samim, Berlin.

Initiatives

• 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.
• Artists and Machine Intelligence (AMI). A program at Google that brings together artists and engineers to realize projects using Machine Intelligence. [1]
• Magenta, a Google Brain project dedicated to generating art and music using machine learning. Started in June 2016. [2]
• 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.
• Mozilla Award for Art and Advocacy Exploring Artificial Intelligence, 2018.

Literature, data, resources

Art practice and criticism

• Luba Elliott, "The art side of AI at transmediale + CTM", Medium, 10 Apr 2017.
• Luba Elliott, Creative AI Newsletter, since May 2017.
• 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. [3]
• Ars Electronica: AI. Artificial Intelligence. Das andere Ich / The Other I, eds. Gerfried Stocker, Christine Schöpf and Hannes Leopoldseder, Berlin: Hatje Cantz, 2017, 400 pp. Catalogue. Festival. (English)
• Jason Bailey's writing and interviews on Artnome, since Oct 2017.
• Sam Gaskin, "When Art Created by Artificial Intelligence Sells, Who Gets Paid?", Artsy, 17 Sep 2018.
• Ahmed Elgammal, "With AI Art, Process Is More Important Than the Product", Smithsonian.com, 16 Oct 2018.
• Naomi Rea, "Has Artificial Intelligence Brought Us the Next Great Art Movement? Here Are 9 Pioneering Artists Who Are Exploring AI’s Creative Potential", Artnet News, 6 Nov 2018.

Online galleries and collections

• Art Gallery: NIPS Machine Learning for Creativity and Design, a collection of art, music and design using machine learning. A part of the NIPS Machine Learning for Creativity and Design Workshop 2017. Curated by Luba Elliott.

Recent debates on artificial intelligence in the humanities and social sciences

• Matteo Pasquinelli (ed.), Alleys of Your Mind: Augmented Intelligence and Its Traumas, Lüneburg: Meson, 2015, 212 pp.
• 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, 2015, pp 279-289.
• Matteo Pasquinelli, "Machines that Morph Logic: Neural Networks and the Distorted Automation of Intelligence as Statistical Inference", Glass Bead 2, 2017.
  • "Des machines qui morphent la logique: les réseaux de neurones et l’automatisation déformée de l’intelligence comme inférence statistique", Glass Bead 2, 2017. (French)
• Adrian Mackenzie, Machine Learners: Archaeology of a Data Practice, MIT Press, 2017, 272 pp.
• A Peer Reviewed Journal About 6(1): "Machine Seeing", eds. Christian Ulrik Andersen and Geoff Cox, Aarhus: Aarhus University, 2017.
• Eirini Malliaraki, "Toward ethical, transparent and fair AI/ML: a critical reading list", Medium, Feb 2018.
• Agoston Nagy, "Visual Music & Machine Learning Workshop for Kids", Medium, 3 Apr 2018.
• writings on artificial intelligence on Monoskop Log.

Software

• 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.
• Magenta demos

Data sets

• Luke de Oliveira, "Fueling the Gold Rush: The Greatest Public Datasets for AI", Medium, 11 Feb 2017.
• Magenta datasets

Courses and textbooks for artists

Textbooks

• Francis Tseng, "Notes on Artificial Intelligence", started Fall 2015.
• Gene Kogan, Francis Tseng, Machine Learning for Artists, 2016f. Book in progress.

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

• Kyle McDonald, "A Return to Machine Learning", Medium, 7 Oct 2016.

MOOC

• Machine Learning for Musicians and Artists, by Rebecca Fiebrink, Goldsmiths University of London. Launched 2016.

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.

Research papers

• Memo Akten, Mick Grierson, "Collaborative Creativity with Monte-Carlo Tree Search and Convolutional Neural Networks", Dec 2016.
• Memo Akten, Mick Grierson, "Real-Time Interactive Sequence Generation and Control with Recurrent Neural Network Ensembles", Dec 2016.
• Pilar Rosado, "Computer Vision Models to Categorize Art Collections According to the Visual Content: A New Approach to the Abstract Art of Antoni Tàpies", Leonardo, Feb 2017. Based on PhD from U Barcelona, 2015. [4]
• Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, Marian Mazzone, "CAN: Creative Adversarial Networks, Generating 'Art' by Learning About Styles and Deviating from Style Norms", Jun 2017.
• Daniel Berio, Memo Akten, Frederic Fol Leymarie, Mick Grierson, Réjean Plamondon, "Calligraphic Stylisation Learning with a Physiologically Plausible Model of Movement and Recurrent Neural Networks", Sep 2017.
• Ahmed Elgammal, Marian Mazzone, Bingchen Liu, Diana Kim, Mohamed Elhoseiny, "The Shape of Art History in the Eyes of the Machine", Jan 2018.
• Magenta research papers, [5]

Scientific introduction into deep learning

• Ethem Alpaydin, Introduction to Machine Learning, MIT Press, 2004, 400 pp; 2nd ed., 2009, 584 pp; 3rd ed., 2014, 640 pp.
• Christopher M. Bishop, [Pattern Recognition and Machine Learning], Springer, 2011, 738 pp.
• Michael Nielsen, Neural Networks and Deep Learning, Determination Press, 2015.
• Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016.
• Ethem Alpaydin, Machine Learning: The New AI, MIT Press, 2016, 224 pp. [6]

Historization of deep learning

• Jürgen Schmidhuber, "Deep Learning in Neural Networks: An Overview", ScienceDirect 61, Jan 2015, pp 85-117. [7]
• 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.
• Andrey Kurenkov, "A 'Brief' History of Neural Nets and Deep Learning", Part 2, Part 3, Part 4, Andrey Kurenkov blog, 24 Dec 2015.