Of the many young artists David Salle has mentored, none
were ever as challenging as his latest student, who cannot hold a paintbrush or
a conversation.
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“The mountain looks too airbrushed,” Salle informed the
algorithm that lives inside his iPad. The landscape painting it had produced,
based on hundreds of his own artworks, was typically generic, lacking in depth.
But the next one succeeded, depicting a valley stream with expressionistic
wisps and a sense of volume.
“The way it has rendered water looks more deliberate,”
Salle, 70, said. “But it’s funny to call something deliberate when it has no
consciousness, isn’t it?”
For nearly a year, the painter — known for edgy images
appropriated from art history and popular culture, as well as juxtapositions of
voluptuous nudes and ham sandwiches — has attempted to defy conventional
thinking about generative artificial intelligence by testing an AI program’s
capacity to become a sophisticated creator of art.
The partnership has grown through weekly meetings with two
technologists, Danika Laszuk and Grant Davis, who tailored a text-to-image
model to Salle’s requirements, relying on descriptive prompts that generated
images in the artist’s style. The New York Times observed three of their work
sessions, tracking the algorithm’s progress over several months as it adopted
more of Salle’s techniques and abandoned the bland photorealism that often
limits other generative programs.
“We are sending the machine to art school,” Salle quipped,
before expounding on the principles of light, shadow, depth and volume that
good painting requires. The algorithm wouldn’t need eyes to achieve greatness,
but it would need to hone the robotic equivalent of intuition to spark
inspiration and fool a gallerist.
And first, it would have to learn to mimic his style.
The experiment was a mutually beneficial arrangement. Laszuk
runs a program called E.A.T__WORKS, for the venture capital firm Betaworks,
that pairs artists and engineers on projects where her company might earn a
percentage of the profits. Davis is building Wand, an AI platform for artists
that promises to help them streamline their operations with faster imaging
through text prompts and sketching. Salle was something like a guinea pig for
Wand, teaching its program how to paint while developing his own series of digital
images.
With permission from Ben Lerner, a friend of Salle’s, the
group has been feeding bits of poetry from his new book, “The Lights,” to evoke
more fantastical images of cities growing within organic cells, and patterns of
interlocking barbules. Prompts also have been sourced from another friend,
writer Sarah French.
“Our process starts with very imaginative prompts,” Davis
said. “And we generate lots of images before selecting the ones we like. Then
David starts drawing on top of them. The process can repeat itself like that
until he’s satisfied.”
Salle is one of the first traditional artists to embed on
the front lines of artificial intelligence. He, in turn, was trained by
conceptualist John Baldessari at the California Institute of the Arts in the
1970s and has a style that absorbs a diverse set of influences, from Italian
painter Giorgio de Chirico to New Yorker cartoonist Peter Arno.
The results have sometimes been described as memories that
barely hold together, and as attempts to ascribe significance to the foggy
afterimages of art history. He is often grouped with the appropriation artists
of the 1980s, including Richard Prince and Cindy Sherman, who have questioned
the primacy of authorship in contemporary culture. He has also juxtaposed photography
with painting.
“Every major artist is an amalgamation or synthesis of
diverse sympathies and influences,” Salle wrote in his 2018 book “How to See”
about making and viewing art. He recalled asking painter Alex Katz to make a
list of his own influences; Katz said the list started with Jackson Pollock and
ended with “the guy who made Nefertiti.”
On another page of his art treatise, Salle delivered a grand
theory of creativity: “Form is the raw material, and style is the forge.”
Artificial intelligence has a limitless vault of forms,
thanks to the billions of online images it studies through a process called
diffusion, in which the algorithm learns the structure of an image — and then
learns to create variations. Its knowledge is then stored in the parameters of
the model, which is translated to the AI through a short sequence of numbers
known as “latent space.”
But learning artistic style requires going beyond simple
pattern recognition. Experts say that increased matchmaking improves accuracy
but also stymies the machine’s ability to produce the unexpected. A balance
must be struck.
The algorithm’s “training” to become the next David Salle
started with a diffusion model to develop a general understanding of visual
images based on hundreds of the artist’s paintings. Davis, the engineer, then
introduced dozens of detailed snapshots of Salle’s paintings to the program so
it would learn to “think like a painter.”
Some of the first experiments were underwhelming: blobby
landscapes, figures drawn without brushstrokes, flat abstraction. But the
critiques that Salle offered did improve the machine’s intelligence enough to
surprise the artist.
“As a painter you only have time to create a painting, but
each painting contains within it all the paintings you don’t have time to
make,” Salle said. “AI is a great tool because it allows me to see thousands of
combinations; things that I would manually sift through in years are made with
5,000 versions in an hour.”
What will become of his own identity, as the algorithm
continues to produce more Salle paintings than he could ever imagine? Some
days, it seems like the algorithm is an assistant. Other days, it’s like a child.
When asked if the AI would replace him entirely one day, the artist shrugged.
“Well,” he said, “that’s the future.”
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