Download high-resolution image

Art in the Age of Machine Learning

Part of Leonardo

Author Sofian Audry

Foreword by Yoshua Bengio

Hardcover

$45.00 US

The MIT Press

7.2"W x 10"H x 0.7"D (18.3 x 25.4 x 1.8 cm) | 21 oz (595 g) | 20 per carton

On sale Nov 23, 2021 | 214 Pages | 9780262046183

Sales rights: World

Add to cart Add to list Exam Copies

View on Edelweiss

An examination of machine learning art and its practice in new media art and music.

Over the past decade, an artistic movement has emerged that draws on machine learning as both inspiration and medium. In this book, transdisciplinary artist-researcher Sofian Audry examines artistic practices at the intersection of machine learning and new media art, providing conceptual tools and historical perspectives for new media artists, musicians, composers, writers, curators, and theorists. Audry looks at works from a broad range of practices, including new media installation, robotic art, visual art, electronic music and sound, and electronic literature, connecting machine learning art to such earlier artistic practices as cybernetics art, artificial life art, and evolutionary art.

Machine learning underlies computational systems that are biologically inspired, statistically driven, agent-based networked entities that program themselves. Audry explains the fundamental design of machine learning algorithmic structures in terms accessible to the nonspecialist while framing these technologies within larger historical and conceptual spaces. Audry debunks myths about machine learning art, including the ideas that machine learning can create art without artists and that machine learning will soon bring about superhuman intelligence and creativity. Audry considers learning procedures, describing how artists hijack the training process by playing with evaluative functions; discusses trainable machines and models, explaining how different types of machine learning systems enable different kinds of artistic practices; and reviews the role of data in machine learning art, showing how artists use data as a raw material to steer learning systems and arguing that machine learning allows for novel forms of algorithmic remixes.

Sofian Audry is an artist, scholar, and Professor of Interactive Media within the School of Media at Université du Québec à Montréal.

Sofian Audry View titles by Sofian Audry

Available for sale exclusive:

• Afghanistan

• Aland Islands

• Albania

• Algeria

• Andorra

• Angola

• Anguilla

• Antarctica

• Antigua/Barbuda

• Argentina

• Armenia

• Aruba

• Australia

• Austria

• Azerbaijan

• Bahamas

• Bahrain

• Bangladesh

• Barbados

• Belarus

• Belgium

• Belize

• Benin

• Bermuda

• Bhutan

• Bolivia

• Bonaire, Saba

• Bosnia Herzeg.

• Botswana

• Bouvet Island

• Brazil

• Brit.Ind.Oc.Ter

• Brit.Virgin Is.

• Brunei

• Bulgaria

• Burkina Faso

• Burundi

• Cambodia

• Cameroon

• Canada

• Cape Verde

• Cayman Islands

• Centr.Afr.Rep.

• Chad

• Chile

• China

• Christmas Islnd

• Cocos Islands

• Colombia

• Comoro Is.

• Congo

• Cook Islands

• Costa Rica

• Croatia

• Cuba

• Curacao

• Cyprus

• Czech Republic

• Dem. Rep. Congo

• Denmark

• Djibouti

• Dominica

• Dominican Rep.

• Ecuador

• Egypt

• El Salvador

• Equatorial Gui.

• Eritrea

• Estonia

• Ethiopia

• Falkland Islnds

• Faroe Islands

• Fiji

• Finland

• France

• Fren.Polynesia

• French Guinea

• Gabon

• Gambia

• Georgia

• Germany

• Ghana

• Gibraltar

• Greece

• Greenland

• Grenada

• Guadeloupe

• Guam

• Guatemala

• Guernsey

• Guinea Republic

• Guinea-Bissau

• Guyana

• Haiti

• Heard/McDon.Isl

• Honduras

• Hong Kong

• Hungary

• Iceland

• India

• Indonesia

• Iran

• Iraq

• Ireland

• Isle of Man

• Israel

• Italy

• Ivory Coast

• Jamaica

• Japan

• Jersey

• Jordan

• Kazakhstan

• Kenya

• Kiribati

• Kuwait

• Kyrgyzstan

• Laos

• Latvia

• Lebanon

• Lesotho

• Liberia

• Libya

• Liechtenstein

• Lithuania

• Luxembourg

• Macau

• Macedonia

• Madagascar

• Malawi

• Malaysia

• Maldives

• Mali

• Malta

• Marshall island

• Martinique

• Mauritania

• Mauritius

• Mayotte

• Mexico

• Micronesia

• Minor Outl.Ins.

• Moldavia

• Monaco

• Mongolia

• Montenegro

• Montserrat

• Morocco

• Mozambique

• Myanmar

• Namibia

• Nauru

• Nepal

• Netherlands

• New Caledonia

• New Zealand

• Nicaragua

• Niger

• Nigeria

• Niue

• Norfolk Island

• North Korea

• North Mariana

• Norway

• Oman

• Pakistan

• Palau

• Palestinian Ter

• Panama

• PapuaNewGuinea

• Paraguay

• Peru

• Philippines

• Pitcairn Islnds

• Poland

• Portugal

• Puerto Rico

• Qatar

• Reunion Island

• Romania

• Russian Fed.

• Rwanda

• S. Sandwich Ins

• Saint Martin

• Samoa,American

• San Marino

• SaoTome Princip

• Saudi Arabia

• Senegal

• Serbia

• Seychelles

• Sierra Leone

• Singapore

• Sint Maarten

• Slovakia

• Slovenia

• Solomon Islands

• Somalia

• South Africa

• South Korea

• South Sudan

• Spain

• Sri Lanka

• St Barthelemy

• St. Helena

• St. Lucia

• St. Vincent

• St.Chr.,Nevis

• St.Pier,Miquel.

• Sth Terr. Franc

• Sudan

• Suriname

• Svalbard

• Swaziland

• Sweden

• Switzerland

• Syria

• Tadschikistan

• Taiwan

• Tanzania

• Thailand

• Timor-Leste

• Togo

• Tokelau Islands

• Tonga

• Trinidad,Tobago

• Tunisia

• Turkey

• Turkmenistan

• Turks&Caicos Is

• Tuvalu

• US Virgin Is.

• USA

• Uganda

• Ukraine

• Unit.Arab Emir.

• United Kingdom

• Uruguay

• Uzbekistan

• Vanuatu

• Vatican City

• Venezuela

• Vietnam

• Wallis,Futuna

• West Saharan

• Western Samoa

• Yemen

• Zambia

• Zimbabwe

1 Introduction
I TRAINING
2 Optimizing Art
3 Curbing the Training Curve
4 Aesthetics of Adaptive Behaviors
II MODELS
5 Beyond Human Understanding
6 Evolutionary Learning
7 Shallow Learning
8 Deep Learning
III DATA
9 Data as Code
10 Deep Remises
11 Watching and Dreaming
12 Conclusion
Glossary
Notes
Bibliography
Name Index
Subject Index

About

Author