Einstein, Bach, Picasso: What Makes These People Special?
2 Seven Hallmarks of Creativity and Two Marks of Genius
3. Focus, Persevere, and Don’t Be Afraid to Make Mistakes
5. Beg, Borrow, or Steal Great Ideas
7. The Need for Experience and Suffering
Intent, Imagination, and Unpredictability
3 Margaret Boden’s Three Types of Creativity
4 Unconscious Thought: The Key Ingredient
The Importance of Taking Time off
Unconscious Thought and Computers
5 The Birth of Artificial Intelligence
The First Inklings of Computer Creativity
Computers That Mimic the Brain
Deep Blue Defeats Garry Kasparov
IBM Watson Becomes Jeopardy! Champion
AlphaGo Defeats the Reigning World Go Champion
II Portrait of the Computer as an Artist
7 DeepDream: How Alexander Mordvintsev Excavated the Computer’s Hidden Layers
Mike Tyka Takes the Dream Deeper
8 Blaise Agüera y Arcas Brings Together Artists and Machine Intelligence
Memo Akten Educates a Neural Network
Damien Henry and a Machine That Dreams a Landscape
Mario Klingemann and His X Degrees of Separation
Angelo Semeraro’s Recognition: Intertwining Past and Present
Leon Gatys’s Style Transfer: Photography “In the Style Of”
10 Ian Goodfellow’s Generative Adversarial Networks: AI Learns to Imagine
Mike Tyka’s Portraits of Imaginary People
Refik Anadol Creates a Dreaming Archive
Theresa Reimann-Dubbers’s AI Looks at the Messiah
Jake Elwes’s Dreams of Latent Space
11 Phillip Isola’s Pix2Pix: Filling in the Picture
Mario Klingemann Changes Faces with Pix2Pix
Anna Ridler’s Fall of the House of Usher
12 Jun-Yan Zhu’s CycleGAN Turns Horses into Zebras
Mario Klingemann Plays with CycleGAN
13 Ahmed Elgammal’s Creative Adversarial Networks
14 “But Is It Art?”: GANs Enter the Art Market
15 Simon Colton’s The Painting Fool
16 Hod Lipson and Patrick Tresset’s Artist Robots
III Machines That Make Music: Putting the “Rhythm” into “Algorithm”
17 Project Magenta: AI Creates Its Own Music
18 From WaveNet and NSynth to Coconet: Adventures in Music Making
NSynth—Creating Sounds Never Heard Before
19 François Pachet and His Computers That Improvise and Compose Songs
20 Gil Weinberg and Mason Bretan and Their Robot Jazz Band
21 David Cope Makes Music That Is “More Bach than Bach”
22 “The Drunken Pint” and Other Folk Music Composed by Bob Sturm and Oded Ben-Tal’s AI
23 Rebecca Fiebrink Uses Movement to Generate Sound
24 Marwaread Mary Farbood Sketches Music
25 Eduardo Miranda and His Improvising Slime Mold
IV Once Upon a Time: Computers That Weave Magic with Words
27 The Final Frontier: Computers with a Sense of Humor
Pablo Gervás and His Poetic Algorithms
29 Rafael Pérez y Pérez and the Problems of Creating Rounded Stories
30 Nick Montfort Makes Poetry with Pi
31 Allison Parrish Sends Probes into Semantic Space
32 Ross Goodwin and the First AI-Scripted Movie
33 Sarah Harmon Uses AI to Create Illuminating Metaphors
34 Tony Veale and His Metaphor- and Story-Generating Programs
35 Hannah Davis Turns Words into Music
V Staged by Android Lloyd Webber and Friends
37 The World’s First Computer-Composed Musical: Beyond the Fence
Creativity in Humans and Machines
39 What Goes On in the Computer’s Brain?
Jason Yosinski and the Puzzle of What Machines See
Mark Riedl on Teaching Neural Networks to Communicate
Margaret Boden and Computer Creativity
41 Evaluating Creativity in Computers
Geraint Wiggins and the Mind’s Chorus
Graeme Ritchie’s Mathematical Criteria for Measuring the Creativity of a Computer Program
Anna Jordanous’s Fourteen Components of Creativity
Rosalind Picard on Developing Machines That Feel
Machines Gaining Experience of the World
43 The Question of Consciousness
John Searle’s Chinese Room and the Question of Whether Computers Can Actually Think
Reducing Consciousness to the Sum of Its Parts
44 Michael Graziano: Developing Conscious Computers
Self-Awareness, Introspection, and Perseverance in Computers
Giving Computers Consciousness
Douglas Hofstadter and the Horrors of a Future Controlled by Creative Machines
Pat Langley and Machines That Work More like People
46 Can We Apply the Hallmarks of Creativity to Computers?
The Need to Know Your Strengths
The Need to Beg, Borrow, or Steal Great Ideas, and the Need for Collaboration and Competition
The Need to Focus and Not Be Afraid to Make Mistakes
The Need to Thrive on Ambiguity and the Need for Experience and Suffering
The Ability to Discover the Key Problem and to Spot Connections
List of Figures
Figure 7.1 Mordvintsev’s reference image of a cat and a beagle, 2015.
Figure 7.2 Alexander Mordvintsev, nightmare beast created using DeepDream, 2015. [See color plate 1.]
Figure 7.3 “Dogslug cum puppyslug” created using DeepDream, 2015.
Figure 7.4 Two ibises—the original photograph (left) and as “seen” by a machine, 2015 (right).
Figure 7.5 Picture of clouds before (top) and after (bottom) being transformed by DeepDream, 2015.
Figure 8.1 All Watched Over by Machines of Loving Grace: DeepDream Edition, 2015.
Figure 9.1 Mario Klingmann, Mona Lisa transformed by DeepDream, 2016.
Figure 9.2 A pairing by Recognition of a 2016 photograph with a painting from 1660, 2017.
Figure 9.3 The content image, a photograph of the Neckarfront at Tübingen (upper left). The style image, van Gogh’s The Starry Night (lower left). The image on the right results from combining the style and content images to make The Neckarfront at Tübingen according to van Gogh, as van Gogh might have painted it. [See color plate 2.]
Figure 10.1 One of Mike Tyka’s Portraits of Imaginary People, Ars Electronica, 2017.
Figure 10.2 Another of Mike Tyka’s Portraits of Imaginary People, Ars Electronica, 2017.
Figure 10.3 Theresa Reimann-Dubbers, A(.I.) Messianic Window, 2017.
Figure 10.4 A machine dreams. Jake Elwes, Latent Space, 2017.
Figure 11.1 Chris Hesse, edges2cats, 2017.
Figure 11.2 Chris Hesse, edges2handbags, 2017.
Figure 11.3 Mario Klingemann, Transhancement Sketch, 2017.
Figure 11.4 Mario Klingemann, Transhancement Sketch, 2017.
Figure 11.5 Mario Klingemann, Neurographic Self-Portrait, 2017.
Figure 12.1 CycleGAN translates from a Monet to a photograph-like landscape (top, upper row) and from a photograph to a Monet-like painting (top, lower row), and from a zebra to a horse (bottom, upper row) and from a horse to a zebra (bottom, lower row). [See color plate 3.]
Figure 12.2 Striped Putin astride a zebra, created by Jun-Yan Zhu using a CycleGAN, 2017.
Figure 12.3 Mario Klingemann, computer-generated images created using Pix2Pix and CycleGAN. The original black-and-white photograph (upper left). The same photograph, processed by a CycleGAN trained on 1920s images of men and recent, colored selfies of women (upper right). Previous image, fed into a second CycleGAN trained to turn human faces into doll faces (lower left). The image in turn is processed by a Pix2Pix trained on “glitch images” to build in the element of decay, 2017 (lower right).
Figure 13.1 Images created by a CAN with the style ambiguity function turned off, 2017.
Figure 13.2 Images created by a CAN with the style ambiguity function turned on, 2017.
Figure 14.1 Portrait of Edmond de Belamy, 2018.
Figure 15.1 Happy face (left). As rendered by The Painting Fool, in vibrant pastels, with an “electric” background, 2015 (right). [See color plate 4.]
Figure 15.2 Sad face (left). As rendered by The Painting Fool, in muted colors, with a dull background, 2015 (right).
Figure 16.1 Tear, inspired by Roy Lichtenstein’s Frightened Girl. Lipson attributes the work to PIX18 and himself thus: PIX18/Hod Lipson, 2016.
Figure 16.2 Paul sketching the author, 2017.
Figure 20.1 Haile, the drum-playing robot, 2006.
Figure 20.2 Mason Bretan jamming with Shimon (left) and three Shimis (right), 2015.
Figure 32.1 Albrecht Dürer, Saint Jerome in His Study, 1514.
Figure 37.1 Playbill for Beyond the Fence, February 2016.