Art, Creativity, and the Potential of Artificial Intelligence - MDPI
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arts
Essay
Art, Creativity, and the Potential of
Artificial Intelligence
Marian Mazzone 1, * and Ahmed Elgammal 2, *
1 Department of Art & Architectural History, College of Charleston, Charleston, SC 29424, USA
2 Department of Computer Science, Rutgers University, New Brunswick, NJ 08901-8554, USA
* Correspondence: mazzonem@cofc.edu (M.M.); elgammal@cs.rutgers.edu (A.E.)
Received: 2 January 2019; Accepted: 14 February 2019; Published: 21 February 2019
Abstract: Our essay discusses an AI process developed for making art (AICAN), and the issues
AI creativity raises for understanding art and artists in the 21st century. Backed by our training in
computer science (Elgammal) and art history (Mazzone), we argue for the consideration of AICAN’s
works as art, relate AICAN works to the contemporary art context, and urge a reconsideration of how
we might define human and machine creativity. Our work in developing AI processes for art making,
style analysis, and detecting large-scale style patterns in art history has led us to carefully consider
the history and dynamics of human art-making and to examine how those patterns can be modeled
and taught to the machine. We advocate for a connection between machine creativity and art broadly
defined as parallel to but not in conflict with human artists and their emotional and social intentions
of art making. Rather, we urge a partnership between human and machine creativity when called for,
seeing in this collaboration a means to maximize both partners’ creative strengths.
Keywords: artificial intelligence; art; creativity; computational creativity; deep learning;
adversarial learning
1. AI-Art: GAN, a New Wave of Generative Art
Over the last 50 years, several artists and scientists have been exploring writing computer
programs that can generate art. Some programs are written for other purposes and are adopted
for art making, such as generative adversarial networks (GANs). Alternatively, programs can be
written that intend to make creative outputs. Algorithmic art is a broad term that points to any art that
cannot be created without the use of programming. If we look at the Merriam-Webster definition of art,
we find “the conscious use of skill and creative imagination especially in the production of aesthetic
objects; the works so produced”. Throughout the 20th century, that understanding of art has been
expanded to include objects that are not necessarily aesthetic in their purpose (for example, conceptual
art), and not created physical objects (performance art). Since the challenges of Marcel Duchamp’s
practice, the art world has also relied on the determination of the artist’s intention, institutional display,
and audience acceptance as critical defining steps to decide whether something is “art”.
The most prominent early example of algorithmic art work is by Harold Cohen and his program
AARON (aaronshome.com). American artist Lillian Schwartz, a pioneer in using computer graphics
in art, also experimented with AI (Lillian.com). However, in the last few years, the development of
GANs has inspired a wave of algorithmic art that uses Artificial Intelligence (AI) in new ways to
make art (Schneider and Rea 2018). In contrast to traditional algorithmic art, in which the artist had
to write detailed code that already specified the rules for the desired aesthetics, in this new wave,
the algorithms are set up by the artists to “learn” the aesthetics by looking at many images using
machine learning technology. The algorithm only then generates new images that follow the aesthetics
it has learned.
Arts 2019, 8, 26; doi:10.3390/arts8010026 www.mdpi.com/journal/arts
Arts 2019, 8, 26 2 of 9
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Figure 11 explains
Figure explains thethe creative
creative process
process that
that is
is involved
involved in in making
making thisthis kind
kind of
of AI
AI art.
art. The
The artist
artist
chooses a collection of images to feed the algorithm (pre-curation), for example,
chooses a collection of images to feed the algorithm (pre-curation), for example, traditional arttraditional art portraits.
These images
portraits. Theseare then fed
images areinto
thena fed
generative AI algorithm
into a generative that tries to
AI algorithm imitate
that these
tries to inputs.
imitate The
these most
inputs.
widely used tool for this is generative adversarial networks (GANs), introduced
The most widely used tool for this is generative adversarial networks (GANs), introduced by by Goodfellow in
2014 (Goodfellow et al. 2014), which have been successful in many applications
Goodfellow in 2014 (Goodfellow et al. 2014), which have been successful in many applications in thein the AI community.
It iscommunity.
AI the development of GANs
It is the that likely
development sparked
of GANs thislikely
that new wave
sparkedof AI
thisArt.
new In wave
the final step,
of AI theInartist
Art. the
sifts through many output images to curate a final collection (post-curation).
final step, the artist sifts through many output images to curate a final collection (post-curation).
Figure
Figure 1. AA block
block diagram
diagram showing
showing the
the artist’s
artist’s role
role using
using the
the AI
AI generative
generative model
model in
in making
making art.
art.
Diagram
Diagram created
created by author A. Elgammal.
In this
In this kind
kindofofprocedure,
procedure, AIAI
is used as a as
is used toola in thein
tool creation of art. The
the creation creative
of art. process isprocess
The creative primarilyis
done by the artist in the pre- and post-curatorial actions, as well as in
primarily done by the artist in the pre- and post-curatorial actions, as well as in tweaking the tweaking the algorithm. There
have been many
algorithm. Theregreat
haveartbeenworks
many that haveart
great been
workscreated
thatusing
have this beenpipeline.
createdThe usinggenerative algorithm
this pipeline. The
always produces
generative images
algorithm that surprise
always produces theimages
viewerthatand surprise
even the the artistviewer
who presides
and even overthetheartist
process.
who
Figure
presides over2 the
is an example of what a typical GAN trained on portrait paintings would produce.
process.
WhyFigure
might 2we like
is an or hateof
example these
whatimages,
a typicaland
GAN should we on
trained callportrait
them art? We will
paintings try toproduce.
would answer these
Why
questions
might we from
like ora perception and a psychology
hate these images, and shouldpoint of view.
we call them Experimental
art? We will try psychologist
to answerDanielthese
E. Berlynefrom
questions (1924–1976) studied
a perception andthe basics of the
a psychology psychology
point of view. of aesthetics for
Experimental several decades
psychologist Danieland E.
Berlyne out that novelty,
pointed (1924–1976) studiedsurprisingness,
the basics of complexity, ambiguity,
the psychology and puzzlingness
of aesthetics for several aredecades
the most significant
and pointed
properties
out in stimulus
that novelty, relevance tocomplexity,
surprisingness, studying aesthetic
ambiguity, phenomena (Berlyne are
and puzzlingness 1971).theAlthough there are
most significant
several alternative
properties newer
in stimulus theories
relevance tothan Berlyne’s,
studying we use
aesthetic it in our explanation
phenomena (Berlyne 1971). for Although
its simplicity as are
there the
explanation
several does not
alternative contradict
newer theoriesother
thantheories.
Berlyne’s,Indeed,
we use theit resulting images with
in our explanation forall
its the deformations
simplicity as the
in the faces are
explanation doesnovel, surprising,other
not contradict and theories.
puzzlingIndeed,
to us. Inthefact, they might
resulting images remind
withus alloftheFrancis Bacon’s
deformations
famous
in deformed
the faces portraits
are novel, such as
surprising, Three
and Studiestofor
puzzling us.a Portrait of Henrietta
In fact, they Moraes (1963).
might remind However,
us of Francis this
Bacon’s
comparison
famous highlights
deformed a major
portraits suchdifference, that of
as Three Studies forintent.
a PortraitIt was Bacon’sMoraes
of Henrietta intention
(1963). to make the faces
However, this
deformed inhighlights
comparison his portrait, but thedifference,
a major deformation thatwe ofsee in the
intent. It AI
was artBacon’s
is not the intention
intention to of the artist
make nor
the faces
of the machine.
deformed Simply put,
in his portrait, but the machine
deformation failsweto see
imitate
in thetheAIhuman
art is notfacethecompletely
intention of and,
theasartist
a result,
nor
generates surprising deformations. Therefore, what we are looking at are
of the machine. Simply put, the machine fails to imitate the human face completely and, as a result, failure cases by the machine
that mightsurprising
generates be appealing to us perceptually
deformations. Therefore,because
what weofare their novelty
looking as failure
at are visual cases
stimuli bycompared
the machine to
naturalistic
that might be faces. However,
appealing these
to us “failure cases”
perceptually haveof
because a positive
their noveltyvisualas impact
visualonstimuli
us as viewers
compared of art;
to
only in thesefaces.
naturalistic examples,
However,the artist’s intentioncases”
these “failure is absent.
have a positive visual impact on us as viewers of
art; only in these examples, the artist’s intention is absent.
Arts 2019, 8, 26 3 of 9
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Figure 2. Examples
Examples of images generated by training a generative adversarial network (GAN) with
portraits from the last 500 years of Western art.
art. The distorted faces are the algorithm’s attempts to
imitate those inputs. Images generated at Art & Artificial Intelligence Laboratory,
Laboratory, Rutgers.
Rutgers.
So far,
So far, most
most art
art critics
critics have
have been
been skeptical
skeptical and usually evaluate
and usually evaluate only
only the
the resulting
resulting images
images while
while
ignoring the
ignoring the creative
creative process
process that
that generates
generates them. They might
them. They might be
be right
right that
that images
images created
created using
using this
this
type of AI pipeline are not that interesting. After all, this process just imitates the pre-curated
type of AI pipeline are not that interesting. After all, this process just imitates the pre-curated inputsinputs
with
with aa slight
slight twist.
twist. However, if we look look at
at the
the creative
creative process
process overall
overall and
and not
not simply
simply the
the resulting
resulting
images, this activity falls clearly in the category of conceptual art because the artist has the option to
act in the choice-making roles of curation and tweaking. More sophisticated conceptual work will be
coming in the future as more artists explore AI tools and learn how to better manipulate the AI art
creative process.
2. Pushing
2. Pushing the
the Creativity
Creativity of
of the
the Machine:
Machine: Creative,
Creative, Not
Not Just
Just Generative
Generative
At Rutgers’
At Rutgers’ ArtArt & & AI
AI Lab,
Lab,we wecreated
createdAICAN,
AICAN,an analmost
almostautonomous
autonomousartist. artist.Our
Our
goalgoal
was was
to
to study the artistic creative process and how art evolves from a perceptual
study the artistic creative process and how art evolves from a perceptual and cognitive point of view. and cognitive point
of view.
The model The wemodel
built iswe builtonisabased
based theoryon a theory
from from psychology
psychology proposed byproposed by Colin(Martindale
Colin Martindale Martindale
(Martindale 1990). simulates
1990). The process The process how simulates how artists
artists digest prior artdigest
works prior art at
until, works
someuntil, at they
point, somebreak
point,out
they
of
break out of established styles and create new styles. The process is realized
established styles and create new styles. The process is realized through a “creative adversarial through a “creative
adversarial
network network
(CAN)” (CAN)”2017),
(Elgammal (Elgammal
a variant et of
al.GAN
2017),thata variant of GAN
we proposed that
that we“stylistic
uses proposed that uses
ambiguity”
“stylistic ambiguity” to achieve novelty. The machine is trained between
to achieve novelty. The machine is trained between two opposing forces—one that urges the machine two opposing forces—one
that
to urgesthe
follow theaesthetics
machine of to follow
the art the
it isaesthetics of the art itdeviation
shown (minimizing is shownfrom (minimizing deviationwhile
art distribution), from the
art
distribution),
other while thethe
force penalizes other force penalizes
machine the machine
if it emulates an already if it emulates
established an already established style
style (maximizing style
(maximizing
ambiguity). These two opposing forces ensure that the art generated will be novel butwill
style ambiguity). These two opposing forces ensure that the art generated be novel
at the same
but atwill
time the not
same time will
depart not depart
too much fromtoo much from
acceptable acceptable
aesthetic aesthetic
standards. Thisstandards. This“least
is called the is called the
effort”
“least effort”
principle principle in theory,
in Martindale’s Martindale’s
and it is theory, andin
essential it art
is essential
generation in art generation
because too muchbecause toowould
novelty much
novelty would result in rejection by viewers. Figure 3 illustrates a block diagram
result in rejection by viewers. Figure 3 illustrates a block diagram of the CAN network where the of the CAN network
where the receives
generator generatortwo receives twoone
signals, signals, one measuring
measuring the deviationsthe deviations
from art from art distribution
distribution and the and
secondthe
second measuring style ambiguity. The generator tries to minimize the
measuring style ambiguity. The generator tries to minimize the first to follow aesthetics and first to follow aesthetics and
maximize the
maximize the second
second to to deviate
deviate from
from established
established styles.
styles.
x FOR PEER REVIEW
Arts 2019, 8, 26 44 of
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Arts 2019, 8, x FOR PEER REVIEW 4 of 9
Figure 3. A block diagram of a creative adversarial network. The generator explores the creative space
Figure
Figure 3.3.A A
by trying to block
block diagram
generate
diagram of aofcreative
images a creative
that adversarial
maximize style
adversarial network.
ambiguity
network. The generator
The while minimizing
generator explores
explores the from
deviation
the creativecreative
art
space
space by trying
distribution.
by trying to generate
Diagram
to generate images
by author
images that maximize
thatA.maximize
Elgammal.stylestyle ambiguity
ambiguity while
while minimizing
minimizing deviationfrom
deviation fromart
art
distribution.Diagram
distribution. Diagramby byauthor
authorA. A.Elgammal.
Elgammal.
Unlike the generative AI art discussed earlier, this process is inherently creative. There is no
Unlike
curation
Unlike onthethegenerative
the generative
dataset; AIart
instead,
AI art
we discussed earlier,this
fed the algorithm
discussed earlier, this process
80Kprocess
images is is inherently
inherently5creative.
representing creative.
centuries There isisno
of Western
There no
curation
art history,
curation onthe
on the dataset;instead,
simulating
dataset; instead,
the wefed
process
we fed thealgorithm
of the
how algorithm
an artist 80K80Kimages
images
digests representing
art history, 5centuries
with 5no
representing centuries
special of ofWestern
Western
selection of
art history,
genres
art simulating
or styles.
history, the process
The generative
simulating of
the processprocess how an
of howusing artist digests
CANdigests
an artist art
is seeking history,
innovation.
art history, with
withTheno special selection
outputsselection
no special of
surprise of
us
genres
all the or
genres or styles.
timestyles.
withTheThe generative
of artprocess
the generative
range AICANusing
process CAN
CANisFigure
generates.
using isseeking innovation.
4 shows
seeking The
Theoutputs
the variety
innovation. of surprise
AICAN-generated
outputs surpriseususall
the
art.
all time
the timewith
withthethe
range
rangeof of
artart
AICAN
AICAN generates.
generates.Figure
Figure 4 shows
4 shows thethe
variety of of
variety AICAN-generated
AICAN-generated art.
art.
Figure 4.
Figure 4. Examples
Examples ofof images
images generated
generated by
by AICAN
AICAN after
after training
training with
with images
images from all styles
from all styles and
and
Figure
genres4.from
Examples
the past 500
of 500 years
images
years of Western
of Western
generated art. Images
by art.
AICAN after trainingofwith
courtesy images
the Art Artificial Intelligence
from all Intelligence
& Artificial styles and
Laboratory,
genres Rutgers.
from Rutgers.
Laboratory, the past 500 years of Western art. Images courtesy of the Art & Artificial Intelligence
Laboratory, Rutgers.
We
We devised
devised a visual Turing
Turing test
test to
to register
register how
how people
people would
would react
react to the generated images and
whether
whether they could tell the difference between AICAN- or human-created
would react to art.
they a visual Turing test to register how peoplehuman-created
We devised To make the
the generated test timely
images and
and of high
whether high quality,
theyquality, we
we
could tell themixed
mixed images
images
difference fromAICAN
from
between AICAN
AICAN- with
with works
works from
from
or human-created ArtArt Basel
Basel
art. 2016
To 2016
make the(the
(the testflagship
flagship art
timely
and of high quality, we mixed images from AICAN with works from Art Basel 2016 (the flagship arta
art
fairfair
in in contemporary
contemporary art).
art). We We also
also used
used a a set
set ofof images
images from
from abstract
abstract expressionist
expressionist masters
masters as
baseline.
fair Our study showed
in contemporary art). Wethat human
also usedsubjects
a set ofcould
imagesnotfrom
tell whether
abstractthe art was made
expressionist by a human
masters as a
artist or Our
baseline. by the machine.
machine.
study showed Seventy-five
that humanpercent the time,
subjectsofcould people
not tell in our
whether the study
art wasthought
made by the AICAN
a human
artist or by the machine. Seventy-five percent of the time, people in our study thought the AICANArts 2019, 8, 26 5 of 9
Arts 2019, 8, x FOR PEER REVIEW 5 of 9
generated
generated images
images were
were created
created byby aa human
human artist.
artist. In
In the
the case
case of
of the
the baseline
baseline abstract
abstract expressionist
expressionist
set,
set, 85% of the time subjects thought the art was by human artists. Our subjects even
85% of the time subjects thought the art was by human artists. Our subjects even described
described the
the
AICAN-generated
AICAN-generated images images using
using words
words suchsuch as “intentional”, “having
as “intentional”, “having visual
visual structure”,
structure”, “inspiring”,
“inspiring”,
and
and “communicative”
“communicative” at at the
the same
same levels
levels as
as the
the human-created
human-created art.art.
Beginning
Beginning in October 2017, we started exhibiting AICAN’s
in October 2017, we started exhibiting AICAN’s workwork at at venues
venues in in Frankfurt,
Frankfurt, Los
Los
Angles, New York City, and San Francisco, with a different set of images for
Angles, New York City, and San Francisco, with a different set of images for each show (Figure 5). each show (Figure 5).
Recently, in December 2018, AICAN was exhibited in the SCOPE Miami
Recently, in December 2018, AICAN was exhibited in the SCOPE Miami Beach Art Fair. At these Beach Art Fair. At these
exhibitions,
exhibitions, the
the reception
reception of of works
works was was overwhelmingly
overwhelmingly positive
positive on
on the
the part
part of
of viewers
viewers who
who had
had nono
prior knowledge that the art shown was generated using AI. People genuinely
prior knowledge that the art shown was generated using AI. People genuinely liked the artworks liked the artworks and
engaged in various
and engaged conversations
in various conversationsaboutabout
the process. We heard
the process. one question
We heard time and
one question timeagain: Who is
and again: the
Who
artist? Here, we
is the artist? positwe
Here, that the person(s)
posit setting upsetting
that the person(s) the process
up the designs
processa conceptual
designs a and algorithmic
conceptual and
framework, but the algorithm is fully at the creative helm when it comes to
algorithmic framework, but the algorithm is fully at the creative helm when it comes to the elements the elements and the
principles of the art it creates. For each image it generates, the machine chooses the
and the principles of the art it creates. For each image it generates, the machine chooses the style, the style, the subject,
the forms,
subject, theand composition,
forms, including
and composition, the textures
including and colors.
the textures and colors.
Figure 5. Photographs from AICAN exhibition held in Los Angeles in October 2017. Photographs by
author A. Elgammal.
3. AI in
3. AI in Art
Art and
and Art
Art History
History
The
The CAN
CAN study
study provoked
provoked a a number
number of of concerns about AI
concerns about AI as
as aa threat
threat oror rival
rival toto art
art made
made by by
human
human beings. Yes, the study is interested in the process of art creation, and the more problem
beings. Yes, the study is interested in the process of art creation, and the more abstract abstract
of what creativity
problem is and does.
of what creativity is and However, AI focuses
does. However, AIon developing
focuses a machinea process
on developing machine andprocessmachine
and
creativity, not merely
machine creativity, aping
not and aping
merely trying and
to pass as human-made.
trying Our work is focused
to pass as human-made. Our work on understanding
is focused on
the process of creativity
understanding such that
the process a means can
of creativity such be that
found a to modelcan
means thatbeprocess
foundtotogenerate
model athatcreative result.
process to
One way to do this, and what this study has chosen, is to model the process
generate a creative result. One way to do this, and what this study has chosen, is to model the process by which art is taught
and then stimulate
by which art is taughtAICAN to synthesize
and then stimulatethatAICAN styletoinformation
synthesize and that next
stylecreate something
information and new. To do
next create
this, the machine was trained on many thousands of human-created
something new. To do this, the machine was trained on many thousands of human-created paintingspaintings in a process parallel
to
in aa human
processartists’
parallelexperience
to a human of looking at other artists’
artists’ experience works,atlearning
of looking by example.
other artists’ works,The AICAN
learning by
system was then designed to encourage choices that deviate
example. The AICAN system was then designed to encourage choices that deviate fromfrom copying/repeating what had been
seen (the GAN function)
copying/repeating what to hadencouraging
been seen new (the combinations
GAN function) andtonew choices based
encouraging new on a knowledge
combinations of
and
art
newstyles (thebased
choices CANon function).
a knowledgeIf theof creation
art stylesprocess is modeled
(the CAN successfully,
function). art may
If the creation result.
process is modeled
One barometer of
successfully, art may result.whether art has been successfully created through the chosen process is whether
human Onebeings appreciate
barometer it as artart
of whether andhasdobeen
not necessarily
successfullyrecognize it as AI-derived.
created through the chosen AICAN
process wasis
tasked with creating works that did not default into the familiar psychedelic
whether human beings appreciate it as art and do not necessarily recognize it as AI-derived. AICAN patterning of most
GAN-generated
was tasked with imagescreatingasworks
a testthat
of itsdid
creativity
not defaultfunction.
into the Our inclusion
familiar of viewerpatterning
psychedelic surveys toofgaugemost
peoples’ responses did not aim to prove that the AICAN artifacts were better
GAN-generated images as a test of its creativity function. Our inclusion of viewer surveys to gauge than human creations,
but ratherresponses
peoples’ to gaugedid whether
not aim thetoAICAN works
prove that thewere
AICAN aesthetically recognizable
artifacts were better than as human
art, andcreations,
whether
human viewers liked the AI-generated works of art. It seemed most pertinent
but rather to gauge whether the AICAN works were aesthetically recognizable as art, and whether to have viewers assess
the
humanAICAN images
viewers in the
liked a group with other
AI-generated contemporary
works imagesmost
of art. It seemed rather than historical
pertinent to haveones,
viewershence the
assess
the AICAN images in a group with other contemporary images rather than historical ones, hence theArts 2019, 8, 26 6 of 9
choice to select these from Art Basel. The objective was to learn whether AICAN can produce work
that is able to qualify or count as art, and if it exhibits qualities that make it desirable or pleasurable to
look at. In other words, could AICAN artifacts be recognized as quality aesthetic objects by human
beings? Because we used Berlyne’s theory of arousal potential, the response of human beings to the
images was a necessary check to evaluate the quality level of AICAN creativity.
There may always be a number of artists and art lovers who resist the idea of AI in art because of
technophobia. For them, the machine simply has no place in art. In addition, many lack understanding
of what AI actually is, how it works, and what it can and cannot be made to do. There is also an
element of fear at work, resulting in an imagined future in which AI will commandeer art making and
crank out masses of soulless abstract paintings. However, as we discuss throughout this article, AI is
really very limited and specific in what it can do in terms of art creation, and it was never our goal is
to supplant the role of the human artist. There is simply and profoundly no need to do that. It is an
interesting problem in machine learning to model the process of image creation and to explore what
creativity might mean within the confines of computation, but these are issues separate and apart from
how a human being makes art, and they are not mutually exclusive in any way. The very best outcome
we can imagine is a fruitful partnership between an artist and a creative AI system. However, we are
in the very early days of developing algorithms for such AI systems.
A comparison with photography is useful because both forms of technology first encountered
resistance in the art world based on the use of a machine in the art-making process. This comparison
has been discussed widely, including in this issue of Arts (Hertzmann 2018) (Agüera y Arcas 2017),
so we will not elaborate on it here. A hopeful sign for AI art is that eventually, some photography was
fully accepted as art. A key path towards its acceptance was the dialogue that developed between
two mediums: Photographers worked to incorporate some of the formal and aesthetic characteristics
of painting, while painters were closely looking at photography and shifting painting in response.
Painters were inspired by the compositional flatness, capture of movement, and summary edges of
the photographic viewfinder. Photographers shifted their approach to lighting, focus, and subject
matter as inspired by the aesthetic criteria of painting. Thus, a feedback loop was established between
the practices of painting and photography. In both cases, creators began to see differently based on
their experiences with the other medium. Perhaps this can happen between AI and painting in turn.
Currently, most AI systems are trained on thousands of paintings made by Western European and
American artists over the last several hundred years. In turn, the AIs create images that speak the
language of painting (color choices, form elements, arrangement of forms on a 2-D surface) and depose
their elements before the eyes of viewers in a way similar to how we look at paintings. Already, we have
contemporary practitioners such as Jason Salavon or Petra Cortright, whose practice demonstrates a
lively exchange between the processes of painting and those of computation. Photography did threaten
to supplant some of the functions of painting, particularly in those instances when a high degree of
naturalistic representation was desirable, such as in portraiture or in topographical representations.
Consequently, photography largely did replace painted portraits and most forms of topographical
imagery, for example. We imagine that AI-produced art could usefully replace some mass-produced
imagery such as decorative art or tourist scenes where repetition of a few pleasing characteristics is
desirable. Consumers would be the drivers of this market, electing for the machine-derived images or
preferring those created by a human.
Another sound point of comparison is the replicative process of image production employed by
both the camera and the computer. Like the camera, the computer provides its user with a range of
repetitive and reproductive means to generate multiple images. As noted by Walter Benjamin in the
early 20th century (Benjamin [1936] 1969), the impact of the mass production and reproduction of
imagery has changed how we think about the originality and the legitimacy of reproductions of works
of art, and our viewing experience of art. Most people’s experience of art is now soundly in the realm
of reproductions, and we ascribe meaningfulness to the experience of the reproduction. Although
the singular, original work of art is a paradigm still operational in painting, it is markedly less so inArts 2019, 8, 26 7 of 9
print making or photography, and completely absent in computational art. Computers can produce
many more and varied versions of an image through parameterization, randomizing tools, and other
generative processes than can nondigital photography or prints, but the theoretical principle of the
multiple still applies. The contemporary art world is well able to theorize and accept multiples or
reproductions as legitimate works of art, we believe even at the rate and level of complexity produced
by generative computational systems.
There is, however, one profound difference between AI computer-based creativity versus other
machine-based image making technologies. Photography, and the similar media of film and video,
are predicated on a reference to something outside of the machine, something in the natural world.
They are technologies to capture elements of the world outside themselves as natural light on a plate or
film, fixed with a chemical process to freeze light patterns in time and space. Computational imagery
has no such referent in nature or to anything outside of itself. This is a profound difference that we
believe should be given more attention. The lack of reference in nature has historical implications for
how we understand something as art. Almost all human art creation has been inspired by something
seen in the natural world. There, of course, may be many steps between the inspiration and the
resulting work, such that the visual referent can be changed, abstracted or even erased by the final
version. However, the process was always first instigated by the artist looking at something in the
world, and photography, film, and video retained that first step of the art-making process through
light encoding. The computer does not follow this primal pattern. It requires absolutely nothing from
the natural world; instead, its “brain” and “eyes” (its internal apparatus for encoding imagery of any
kind) consist only of receptors for numerical data. There are two preliminary points to elaborate here:
The first relates to issues in contemporary art, the second to the distinction from human creativity.
First, the lack of referent in the natural world and the resulting freedom and range to create or not
create any object as a result of the artistic inspiration aligns AI and all computational methods with
conceptual art. Like with photography, the comparison with conceptual art has frequently been made
for AI and computational methods in general. In conceptual art, the act of the creation of the art work
is located in the mind of the artist, and its instantiation in any material form(s) in the world is, as Sol
Lewitt (Lewitt 1967) famously declared, “a perfunctory affair. The idea becomes a machine that makes
the art.” Thus, the making of an art object becomes simply optional. And although contemporary
artists in the main have not stopped making objects, the principle that object making is optional and
variable in relation to the art concept still remains. We believe this is at the heart of the usefulness of
the comparison with conceptual art: The idea or concept is untethered from nature, being primarily
located in the synapses of the brain and secondly disassociated from the dictates of the material world.
Most AI systems use some form of a neural network, which is modeled on the neural complexity
of the human brain. Therefore, AI and conceptual art coincide in locating the art act in the system
network of the brain, rather than in the physical output. The physical act of an artist, either applying
paint or carving marble, becomes optional. This removes the necessity of a human body (the artist) to
make things and allows us to imagine that there could be more than one kind of artist, including other
than human.
4. AI Art: Blurring the Lines between the Artist and the Tool
Many artists and art historians resist seeing work created with AI as art because their definition
of art is based on the modern artist figure as the sole locus of art creation and creativity. Therefore,
the figure of the artist is necessary to their definition of art. But understanding art as a vehicle for the
personal expression of the individual artist is a relatively recent and culturally-specific conception.
For many centuries, across many cultures and belief systems, art has been made for a variety of reasons
under a wide range of conditions. More often created by groups of people rather than an individual
artist (think medieval cathedrals or guild workshops), art is often made to the specifications of patrons
and donors large and small, made to order, funded by a wide variety of groups, civic organizations,
or religious institutions, and made to function in an extraordinary range of situations. The notionArts 2019, 8, 26 8 of 9
of a work of art being the coherent expression of the individual’s psyche, emotional condition, or
expressive point of view begins in the Romantic era and became the prevailing norm in the 19th and
20th centuries in Western Europe and its colonies. Although this remains a common motivation for
many artists working today, it does not mean it is the only and correct definition of art. And certainly,
it is not a role that any AI system will ever be able to fulfill. Clearly, machine learning and AI cannot
replicate the lived experience of a human being; therefore, AI is not able to create art in the same way
that human artists do. Thankfully, we are not proposing that it can in our work. Humans and AI do not
share all of the same sources of inspiration or intentions for art making. Why the machine makes art is
intrinsically different; its motivation is that of being tasked with the problem of making art, and its
intention is to fulfill that task. However, we are asking everyone to consider that a different process of
creation does not disqualify the results of the process as a viable work of art. Instead consider that
without the necessity of the individual expressive artist in our definition of art, how we conceptualize
art and art making is greatly expanded.
AI is a set of algorithms designed to function as parallel to human intelligence actions such as
decision-making, image recognition, language translation/comprehension, or creativity. Elsewhere
in this issue of Arts, Hertzmann (Hertzmann 2018) makes a point about art algorithms being tools,
not artists. As we have argued, we would agree that the algorithms in AI are not artists like human
artists. But AI (art generating algorithm in this case) is more than a tool, like a brush with oil paint on
it, which is an inanimate and unchanging object. Certainly, artists learn over time and with experience
how to better use their tools, and their tools have a role in the physical actions by which they make
work in paint. However, the paintbrush does not have the capacity to change, it does not make
decisions based on past painting experiences, and it is not trained to learn from data. Algorithms
contain all of those possibilities. Perhaps we can conceptualize AI algorithms as more than tools and
closer to a medium. The word medium in the art world indicates far more than a tool, a medium
includes not only the tools used (brush, oil paint, turpentine, canvas, etc.) but also the range of
possibilities and limitations inherent to the conditions of creation in that area of art. Thus, the medium
of painting also includes a history of painting styles, the physical and conceptual restraints of the 2-D
surface, the limits of what can be recognized as a painting, a critical language that has been developed
to describe and critique paintings, and so on. Admittedly, we are in the very early days of the medium
of AI in art creation, but this medium might encompass tools such as code, mathematics, hardware
and software, printing choices, etc., with medium conditions including algorithmic structuring, data
collection and application, and the critical theory needed to detect and judge computational creativity
and artistic intention within the much larger field of computer science. At this time, a problem is
the relatively small number of people able to work creatively in this field or judge the role of the
machine in the exercise of creative processes. This will change over time as artists, computer scientists,
and historians/critics all become more knowledgeable. For human artists who are interested in
the possibilities (and limitations) of AI in creativity and the arts, using AI as a creative partner is
already happening now and will happen in the future. In a partnership, both halves bring skill sets
to the process of creation. As Hertzmann notes in his article and Cohen discovered in his work with
the AARON program, human artists bring capacity for high-quality work, artistic intent, creativity,
and growth/change over time. Art is a social interaction. Actually, we think we can argue that AI does
a fair amount of this, and it can certainly all be accomplished in a creative partnership between and
artist and his or her AI system.
Author Contributions: Conceptualization, M.M. and A.E.; methodology A.E. and M.M.; data curation A.E.;
software A.E.; validation A.E.; writing—original draft preparation M.M. (abstract, introduction, Sections 3 and 4)
and A.E. (Sections 1 and 2); writing—reviewing and editing, M.M.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.Arts 2019, 8, 26 9 of 9
References
Agüera y Arcas, Blaise. 2017. Art in the Age of Machine Intelligence. Arts 6: 18. [CrossRef]
Benjamin, Walter. 1969. The Work of Art in Age of Mechanical Reproduction. In Illuminations. Edited by
Hannah Arendt. New York: Schocken, pp. 217–51. First published 1936.
Berlyne, Daniel E. 1971. Aesthetics and Psychobiology. New York: Appleton-Century-Crofts of Meredith Corporation,
p. 336.
Elgammal, Ahmed, Bingchen Liu, Mohamed Elhoseiny, and Marian Mazzone. 2017. CAN: Creative adversarial
networks, generating “art” by learning about styles and deviating from style norms. arXiv, arXiv:1706.07068.
Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville,
and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems.
Cambridge: MIT Press, pp. 2672–80.
Hertzmann, Aaron. 2018. Can Computers Create Art? Arts 7: 18. [CrossRef]
Lewitt, Sol. 1967. Paragraphs on conceptual Art. Artforum 5: 79–84.
Martindale, Colin. 1990. The Clockwork Muse: The Predictability of Artistic Change. New York: Basic Books.
Schneider, Tim, and Naomi Rea. 2018. Has artificial intelligence given us the next great art movement? Experts
say slow down, the ‘field is in its infancy. Artnetnews. September 25. Available online: https://news.artnet.
com/art-world/ai-art-comes-to-market-is-it-worth-the-hype-1352011 (accessed on 3 February 2019).
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