Can Computers Create Art? — TEI-tagged view

Aaron HertzmannpersName (2018date). “Can Computers Create Art?.” Artstitle·j.

Object of study — whether computers using Artificial Intelligenceterm could create artterm, and what it would take for a computer to be credited as the author of an artwork

Research question — could a piece of computer software ever be widely credited as the author of an artwork, and what would this require - a question of the psychology and philosophy of artterm

Contribution — the theory that artistic creation is primarily a social actterm performed between social agentsterm, from which it follows that computers cannot be credited with authorshipterm of artterm in our current understanding, together with scenarios under which this could change

Full text — tagged

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Can Computers Create Artterm?

Abstract Abstract This essay discusses whether computers, using Artificial Intelligenceterm (AI), could create artterm. First, the history of technologies that automated aspects of artterm is surveyed, including photographyterm and animation. In each case, there were initial fears and denial of the technology, followed by a blossoming of new creative and professional opportunities for artiststerm. The current hype and reality of Artificial Intelligenceterm (AI) toolsterm for artterm making is then discussed, together with predictions about how AI toolsterm will be used. It is then speculated about whether it could ever happen that AI systems could be credited with authorshipterm of artwork. It is theorized that artterm is something created by social agentsterm, and so computers cannot be credited with authorshipterm of artterm in our current understanding. A few ways that this could change are also hypothesized. Keywords: artterm and technology; artificial intelligenceterm; photographyterm; painting; computer animation; image stylization 1. Introduction Artificial Intelligenceterm (AI) research has made staggering advances recently, including many publicly-visible developments in web search, image recognition, conversational agents, and robotics. These developments have stoked fear about Artificial Intelligenceterm’s effect on many aspects of society. In the context of artterm, news media hype presents new image and video creation algorithms as if they are automating the creation of artterm. Perhaps they will empower everyday users while putting artiststerm out of work … and, while they are at it, rob us of our humanity?

Beyond the hype, confusion about how technology influences artterm pervades serious discussions. Professional artiststerm are often concerned that computers might put them out of work (Nicholas 2017)—a concern I have heard for decades. Some practitioners present their algorithms as themselves, potentially, artiststerm (Colton 2012; Elgammal et al. 2017; Shein 2017), as do some journalists (Dvorsky 2017; Perez 2018; Pogue 2018). And, I was recently contacted by a prominent social psychologist who, inspired by recent results with neural networks, wished to conduct experiments to assess whether ordinary people might be willing to buy artwork made by a computer, and, if so, why. It was assumed that computers were already happily making their own artwork.

On the other hand, when I have informally asked friends or colleagues the question of whether computers can create artterm, the answer is sometimes a decisive “No.” Artterm requires human intentterm, inspiration, a desire to express something. Thus, by definition, there is no such thing as artterm created by a computer, so why would anyone worry? The concepts of artterm and inspiration are often spoken of in mystical terms, something special and primal beyond the realm of scienceterm and technology; it is as if only humans create artterm because only humans have “souls.” Surely, there should be a more scientific explanation.

In this essay, I tackle the question of “Can Computers Create Artterm?” This might seem like a simple question. Sometimes people ask it as a question about technological capabilities, like asking if a new car can go 100 miles per hour. For this sort of question, someone who understands the technology ought to be able to give a simple yes-or-no answer. But it is not a given that any computer will ever be widely considered as an artistterm. To date, there is already a rich body of computer-generated artterm, and, in all cases, the work is credited to the human artistterm(s) behind the toolsterm, such as the authors or users of the software—and this might never change.

A more precise statement of this essay’s question is: “Could a piece of computer software ever be widely credited as the author of an artwork? What would this require?” This is a question of the psychology and philosophy of artterm; I do not describe the existing technology in much detail. I also discuss the related question of “Will AI put artiststerm out of jobs?” and, more generally, of whether AI will be beneficial to artterm and artiststerm.

Before directly addressing these questions, I discuss the history and current state of automation for artterm. I begin with some historical perspectives: previous moments in history when new technologies automated image and film creation, particularly the invention of photographyterm. In each case, we see that these new technologies caused fears of displacing artiststerm, when, in fact, the new technology both created new opportunities for artiststerm while invigorating traditional media.

I argue that new technologies benefit artterm and artiststerm, creating new toolsterm and modes of expression, and new styles of expression. Sadly, artterm and scienceterm are often viewed as being separate, or, even, in opposition (Snow 1959). Yet, technological development stimulates so much of the continued vitality of artterm, and new artistic technologies create new job opportunities. Our new AI technologies follow this trend, and will for the foreseeable future: new AI algorithms will provide new toolsterm for expression and transform our artterm and culture in positive ways, just as so many other technologies have in the past.

Computers do not create artterm, people using computers create artterm. Despite many decades of procedural and computer-generated artterm, there has never been a computer widely accepted as the author of an artwork. To date, all “computer-generated artterm” is the result of human invention, software development, tweaking, and other kinds of direct control and authorshipterm. We credit the human artistterm as author, acknowledging that the human is always the mastermind behind the work, and that the computer is a simple toolterm.

I then discuss whether this could ever change: would we ever agree to assign authorshipterm to a computer? I argue that artistic creation is primarily a social actterm, an action that people primarily perform as an interaction with other humans in society. This implies that computers cannot create artterm, just as people do not give gifts to their coffee makers or marry their cars. Conversely, any human can make artterm, because we humans are social creatures. However, the boundaries of artterm are fluid, and, someday, better AI could come to be viewed as true social agentsterm. I discuss this and other scenarios where AI algorithms could come to be accepted as artiststerm, along with some of the dangers of too eagerly accepting software as artiststerm.

This essay expresses my point of view, as someone who has developed various kinds of technology for artterm, has followed the development of new technologies affecting artterm and culture over the years, and, in a few instances, exhibited my own work as artworks. I have written in a style that is meant to be accessible to a general audience, since these are topics that many different people care about.

2. History and Current Practice Before making general claims about how computer toolsterm relate to artterm, I begin by first describing several useful historical examples, including the inventions of photographyterm, film, and computer animation. These examples show previous situations in which a new technology appeared poised to displace artiststerm, but, in reality, provided new opportunities and roles for artiststerm. I also discuss how procedural artterm and computer artterm employs automation. In so many of these fields, outsiders view the computer or the technology as doing the work of creativityterm. Yet, in each of these cases, the human artistterm or artiststerm behind the work are the true authors of the work.

I argue that, throughout history, technology has expanded creative and professional opportunities for artiststerm dramatically, by providing newer and more powerful toolsterm for artiststerm. The advent of new technologies often causes fears of displacement among traditional artiststerm. In fact, these new toolsterm ultimately enable new artistic styles and inject vitality into artterm forms that might otherwise grow stale. These new toolsterm also make artterm more accessible to wider sections of society, both as creators and as consumers. These trends are particularly visible in the past two centuries since the Industrial Revolution.

2.1. How Photographyterm Became an Artform For lessons from the past about AI and artterm, perhaps no invention is more significant than photographyterm.2 Prior to the invention of photographyterm, realistic images of the world could only be produced by artiststerm. In today’s world, we are so swamped with images that it is hard to imagine just how special and unique it must have felt to see a skillfully-executed realistic painting (Figure 1a). The technical skillsterm of realism were inseparable from other creative aspects. This changed when photographyterm automated the task of producing images of the real world.

In 1839, the first two commercially-practical photographic processes were invented: Louis-Jacques-Mandé Daguerre’s dagguereotype (Figure 1b), and William Henry Fox Talbot’s negative-positive process. They were mainly presented as ways to produce practical records of the world. Of the two, the daguerreotype was more popular for several decades, because Talbot’s process was restricted by patents. Improvements to Talbot’s method eventually made the daguerreotype obsolete and evolved into modern film processes.

Portraiture was a main driver for early adoption. Then, as today, people enjoyed possessing pictures of their friends, loved-ones, and ancestors. Portrait painting was only available to aristocrats and the very wealthy. In the 18th century, several inexpensive alternatives were developed, such as the silhouette, a representation of an individual’s outline (Figure 2a), typically hand-cut by an artisan out of black paper. The daguerreotype offered an economical way to create a realistic portrait (Figure 2b). It was very slow and required locking the subject’s head in place with a head brace for several minutes, while the subject tightly gripped their chair, so as not to move their fingers. Nonetheless, numerous daguerreotype studios arose and became commonplace as technologies improved, and many portraitists switched to this new technology. Figure 3 shows one cartoon satirizing the situation for painters. By 1863, a painter-photographer named Henri Le Secq said “One knows that photographyterm has harmed painting considerably, and has killed portraiture especially, once the livelihood of the artistterm.” Photographyterm largely replaced most older forms of portraiture, such as the silhouette, and no one seems to particularly regret this loss. As much as I appreciate the mystery and beauty of looking at old etchings and portraits, I would rather use my mobile phone camera for my own pictures than to try to paint them by hand.

Another early use for the daguerreotype was to produce souvenirs for tourists: by 1850, daguerreotypes of Roman ruins completely replaced the etchings and lithographs that tourists had previously purchased. As the technology improved, photographyterm became indispensable as a source of records for engineering projects and disappearing architectural ruins, as well as for documentary purposes, such as Matthew Brady’s photographs of the horrors of the American civil war.

“Is photographyterm artterm?” This question was debated for many decades, coalescing into three main positions. Many people believed that photographyterm could not be artterm, because it was made by a mechanical device rather than by human creativityterm. Many artiststerm were dismissive of photographyterm, and saw it as a threat to “real artterm.” For example, the poet Charles Baudelaire wrote, in a review of the Salon of 1859: “If photographyterm is allowed to supplement artterm in some of its functions, it will soon supplant or corrupt it altogether, thanks to the stupidity of the multitude which is its natural ally.” A second view was that photographyterm could be useful to real artiststerm, such as for reference, but should not be considered as equal to drawing and painting. Finally, a third group, relating photographyterm to established forms like etching and lithography, felt that photographyterm could eventually be as significant an artterm form as painting.

Photographyterm ultimately had a profound and unexpected effect on painting. Painters’ mimetic abilities had been improving over the centuries. Many painters of the 19th century, such as the Pre-Raphaelites like John Everett Millais and Neoclassicists like Ingres, painted depictions of the world with dazzling realism, more than had ever been seen before. However, cameras became cheaper, lighter, and easier to use, and grew widespread among both amateurs and professionals (Figure 1c). Realistic photographs became commonplace by the end of the 19th century. If photorealism could be reduced to a mechanical process, then what is the artistterm’s role?

This question drove painters away from visual realism toward different forms of abstraction. James McNeill Whistler’s Tonalist movement created atmospheric, moody scenes (Figure 1d); he wrote: “The imitator is a poor kind of creature. If the man who paints only the tree, or the flower, or other surface he sees before him were an artistterm, the king of artiststerm would be the photographer. It is for the artistterm to do something beyond this.” The Impressionists, who sought to capture the perceptions of scenes, were likely influenced by the “imperfections” of early photographs. In contrast, Symbolists and post-Impressionist artiststerm moved away from perceptual realism altogether. Edvard Munch wrote “I have no fear of photographyterm as long as it cannot be used in heaven and in hell ... I am going to paint people who breathe, feel, love, and suffer.” Vincent Van Gogh, describing his artistic breakthroughs around 1888, wrote to his brother: “You must boldly exaggerate the effects of either harmony or discord which colors produce. It is the same thing in drawing—accurate drawing, accurate color, is perhaps not the essential thing to aim at, because the reflection of reality in a mirror, if it could be caught, color and all, would not be a picture at all, no more than a photograph.” Photographyterm continued to influence modern artterm of the 20th century; one can infer a significant influence of Étienne-Jules Marey’s multiple-exposure photographyterm on Futurism and Cubism, e.g., in Duchamp’s Nude Descending A Staircase.

It seems likely, in fact, that photographyterm was one of the major catalysts of the Modern Artterm movement: its influence led to decades of vitality in the world of painting, as artiststerm were both inspired by photographic images and pushed beyond realism.

Meanwhile, the Pictorialist movement, begun around 1885, was an attempt to firmly establish photographyterm as an artterm form. Pictorialists introduced much more artistic control over the photographs, often using highly-posed subjects as in classical painting, and manipulating their images in the darkroom. Many of their works had a hazy, atmospheric look, similar to Tonalism, that softened the realism of high-quality photographyterm (Figure 1e). They seemed to be deliberately mimicking the qualities of the fine artterm painting of the time, and today much of their work seems rather affected. They pursued various strategies toward legitimization of their work as an artterm form, such as the organization of photographic societies, periodicals, and juried photographyterm exhibitions (Sternberger 2001). Their works and achievements made it harder and harder to deny the artistic contributions of photographyterm; culminating in the “Buffalo Show”, organized by Alfred Stieglitz at the Albright Gallery in Buffalo, NY, the first photographyterm exhibition at an American artterm museum, in 1910. Photographyterm was firmly established as an artterm, and free to move beyond the pretensions of Pictorialism.

This story provides several lessons that are directly relevant for AI as an artistic toolterm. At first, photographyterm, like AI, was seen by many as non-artistic, because it was a mechanical process. Some saw photographyterm as a threat and argued against its legitimacy. Photographyterm did displace old technologies that had fulfilled non-artistic functions, such as portraiture’s social function. Some artiststerm enthusiastically embraced the new technology, and began to explore its potential. As the technology improved, and became more widespread over nearly a century, artiststerm learned to better control and express themselves with the new technology, until there was no more real controversy over the status of photographyterm. The new technology made image-making much more accessible to non-experts and hobbyists; today, everyone can experiment with photographyterm. Furthermore, the new technology breathed new life into the old artterm form, provoking it toward greater abstraction. Wherever there is controversy in AI as an artistic toolterm, I predict the same trajectory. Eventually, new AI toolsterm will be fully recognized as artiststerm’ toolsterm; AI toolsterm may stimulate traditional media as well, e.g., the New Aesthetic (Sterling 2012).

2.2. The Technology of Live-Action Cinema The story of filmmaking and technology has important lessons about how artiststerm and technologists can work together, each pushing the other further. Most of the early photographers were, by necessity, both artiststerm and technologists, experimenting with new techniques driven by their artterm or to inspire their artterm. But, in film and animation, this interaction has been much more central to the artterm form.

The history of film is filled with artist-tinkerers, as well as teams of artiststerm and technologists. The Lumière Brothers created one of the first films, a simple recording of workers leaving their factory (Figure 4a), but also experimented with a wide range of camera technologies, color processing, and artistic ways to use them. The stage magician George Méliès filmed fantastical stories like A Trip to the Moon, employing a wide range of clever in-camera tricks to create delightfully inventive and beguiling films (Figure 4b). Walt Disney employed and pushed new technologies of sound and color recording, and drove other innovations along the way, such as the multiplane camera. Many of Orson Welles’ innovative film techniques were made possible by new camera lenses employed by his cinematographer Gregg Toland (Figure 4c). The introduction of more portable camera and audio equipment enabled the experiments of the French New Wave, who, in turn, influenced young American directors like Francis Ford Coppola and George Lucas. George Lucas’ team for Star Wars was an early developer of many new visual effects on a shoestring budget (think of Ben Burtt hitting telephone guy-wires to create the “blaster” sound effect), as well as an early innovation in digital film editing and compositing (Rubin 2005). Digital and computer graphics technology, have, obviously, revolutionized film storytelling since then, with directors like Michel Gondry and James Cameron pushing the technology further into unforeseen directions. In each case, we see technologies rapidly adopted by directors to create new storytelling techniques and styles, transforming the mediumterm over and over.

2.3. 3D Computer Animation: A Collaboration 3D computer animation as an artform was pioneered by Pixar Animation Studios, and that success is due to the close collaboration of artiststerm and engineers (Price 2008). It all began with Ed Catmull, an animation enthusiast who received a PhD in computer scienceterm in 1974. In his thesis, he invented several core techniques that every major 3D computer graphics system uses today. During his time in graduate school, he quietly set a goal for himself: to make the world’s first computer-animated film (Catmull and Wallace 2014). Consequently, he founded the Graphics Group at Lucasfilm Computer Division, and hired a team of brilliant engineers to invent computer systems to be used for film-making. However, none of this group could animate, that is, bring a character to life through movement. Hence, they recruited John Lasseter, an animator trained deeply in the Disney tradition. Through tight collaboration between Lasseter and the technical staff, they were able to invent new technologies and discover together how computer animation could start to become its own artterm form (Lasseter 1987). This group, led by Catmull and Alvy Ray Smith, spun out as Pixar, and, over the following years, invented numerous technical innovations aimed at answering the needs set out by Pixar’s artiststerm; in turn, the artiststerm were inspired by these new toolsterm, and pushed them to new extremes. One of their mantras was “Artterm challenges technology, technology inspires artterm” (Catmull and Wallace 2014).

Pixar, by design, treats artiststerm and engineers both as crucial to the company’s success and minimizes any barriers between the groups. When I worked there during a sabbatical, despite my technical role, I had many energizing conversations with different kinds of artiststerm, attended many lectures on artterm and storytelling, sketched at an open life drawing session, watched a performance of an employee improv troupe, and participated in many other social and educational events that deliberately mixed people from different parts of the company. This is the culture that, though it still has some flaws to address, achieved so many years of technical and creative innovation, and, ultimately, commercial and artistic success.

Computer animation is another technology that scared traditional artiststerm. In the early days before they found Lasseter, the Lucasfilm Graphics Group made many attempts to interest Disney animators in their work (Price 2008). Smith later said: “Animators were frightened of the computer. They felt that it was going to take their jobs away. We spent a lot of time telling people, ‘No, it’s just a toolterm—it doesn’t do the creativityterm!’ That misconception was everywhere” (Paik 2007). It is a common misconception that computer animation just amounts to the computer solving everything; a programmer presses a button and the characters just move on their own. In reality, computer animation is extraordinarily labor-intensive, requiring the skillsterm of talented artiststerm (especially animators) for almost every little detail. Character animation is an artterm form of extreme skillterm and talent, requiring laborious effort using the same fundamental skillsterm of performance—of bringing a character to life through pure movement—as in conventional animation (Lasseter 1987).

Traditional cel animation jobs did not last at Disney, for various reasons. Disney Feature Animation underwent a renaissance in the early 1990s, starting with The Little Mermaid. Then, following some changes in management, the Disney animation began a slow, sad decline. After releasing duds like Brother Bear and Home on the Range, management shut down all traditional 2D animation at Disney, and converted the studios entirely to 3D computer animation. Many conventional animators were retrained in 3D animation, but Disney’s first 3D animation, Chicken Little was still a dud. Following Disney’s acquisition of Pixar several years later, they revived Disney’s beloved 2D animation productions. The result, a charming and enjoyable film called The Princess and The Frog, performed so-so at the box office, and, moreover, the animators’ creative energy was focused on the newer 3D artterm form (Catmull and Wallace 2014). Today, traditional 2D animation at Disney is dead.3 Today, computer animation is a thriving industry, and it thrives in many more places than cel animation ever did: at many different film studios, in visual effects for live-action films, in video games, television studios, web startups, independent web studios, and many more. There are now more types of opportunities for animators than ever before. The story here is not the destruction of jobs, but the evolution and growth of an artterm form through technology. This is another story that contradicts the popular notion of artterm and technology operating in conflict, when, in fact, the opposite is usually true.

2.4. Procedural Artwork In the artterm world, there is a long tradition of procedural artwork. Jean Arp created artworks governed by laws of chance in the 1910s (or so he claimed), and, beginning in the 1950s, John Cage used random rules to compose music. The term “Generative Artterm” appears to have originated in the 1960s. Sol LeWitt’s wall drawings are provided as lists of precise instructions; people are still drawing new versions of his paintings after his death (Cotter 2008). Starting in the 1970s, classically-trained painter Harold Cohen began exhibiting paintings generated by a program he wrote called AARON (Cohen 1995). Since the 1980s, many current artiststerm, such as Karl Sims, Scott Snibbe, Golan Levin, Scott Draves, and Jason Salavon, create abstract artworks by writing computer programs that generate either static images, or create interactive artistic experiences and installation works (Figure 5). In Sims’ and Draves’ work, the artwork “evolves” according to audience input. The popularity of the Processing computer language for artiststerm speaks to the growth of procedural artterm.

In each of these cases, despite the presence of procedural, emergent, and/or crowdsourced elements, the human behind it is credited as the author of the artwork, and it would seem perverse to suggest otherwise. The human has done all of the creative decision-making around the visual style, of designing a framework and process, of testing and evaluating alternative algorithms, and so on.

2.5. State of the Artterm in Computer Scienceterm Research Recent developments in computational artistic image synthesis are quite spectacular. But they should not be mistaken for AI artiststerm.

Non-Photorealistic Renderingterm (NPR) is a subfield of computer graphics research (Rosin and Collomosse 2013) that I have worked in for many years. NPR research develops new algorithms and artistic toolsterm for creating images inspired by the look of a conventional media, such as painting or drawing. Paul Haeberli’s groundbreaking 1990 paper (Haeberli 1990) introduced a paint program that began with a user-selected photograph. Whenever the user clicked on the canvas (initially blank), the system placed a brush stroke with color and orientation based on the photograph. In this way, a user could quickly create a simple painting without any particular technical skillterm (Figure 6a). In a follow-up paper, Pete Litwinowcz automated the process entirely, by placing brush strokes on a grid (Litwinowicz 1997). My own first research paper arose from experimenting with modifications to his algorithm: the method that I came up with creates long, curved strokes, beginning with large strokes that were then refined by small details (HertzmannpersName 1998) (Figure 6b). The algorithm was inspired by my experience with real painting, and the way artiststerm often start from a rough sketch and then refine it.

This type of artistic algorithm design reflects the majority of computer graphics research in this area (HertzmannpersName 2003; Rosin and Collomosse 2013). The algorithms are automated, but we can explain in complete detail why the algorithm works and the intuitions about artistic process it embodies. This mathematical modeling of artistic representation continues the investigations begun in the Renaissance with Filippo Brunelleschi’s invention of linear perspective, a viewpoint I have written about elsewhere (HertzmannpersName 2010).

At some point, I found it very difficult to embody richer intuitions about artistic process into source code. Instead, inspired by recent results in computer vision (Efros and Leung 1999), I began to develop a method for working from examples. My collaborators and I published this method in 2001, calling it “Image Analogies” (HertzmannpersName et al. 2001). We presented the work as learning artistic style from example. But the “learning” here was quite shallow. It amounted to rearranging the pixels of the source artwork in a clever way, but not generalizing to radically new scenes or style (Figure 7). (A related style transfer method was published concurrently by Efros and Freeman (2001).) Since then, other researchers have improved the method substantially, making it much more robust (Fišer et al. 2016).

In 2016, Leon Gatys and his colleagues published a new breakthrough in this space, Neural Style Transfer (Gatys et al. 2016). Based on recent advances in neural networks, their method transfers certain neural network correlation statistics from a painting to a photograph, thus producing a new painting of the input photograph (Figure 8). The method is still “shallow” in a sense—there is no “understanding” of the photograph or the artwork—but the method seems to be more robust than the original Image Analogies algorithm. This paper led to a flurry of excitement and new applications, including the popular Prisma app and Facebook’s Live Video stylization, as well as many new research papers improving upon these ideas. This work is ongoing today.

Another development which received considerable attention in 2015 was the invention of DeepDreams by Mordvintsev et al. (2015), who, developing a visualization toolterm for neural networks, discovered that a simple activation excitation procedure produced striking, hallucinatory imagery of a type we had never seen before. There are many other current projects, particularly those around Generative Adversarial Networks (Goodfellow et al. 2014) and Project Magenta at Google (Metz 2017), that also show promise as new artistic toolsterm. For example, Figure 9 shows images that we generated by visualizing trends learned by a neural network from a large collection of artistic images in different styles. A variety of related images are produced by Creative Adversarial Networks (Elgammal et al. 2017); it is a visual style that seems familiar but not the same as what we are familiar with (and is probably driven, in part, by the biases of the convolutional neural network representation).

In each of these cases, the artworks are produced by a human-defined procedure, and the human is the author of the imagery.

2.6. Artificial Intelligenceterm is Not Intelligent Unfortunately, there has been a considerable amount of media hype around AI techniques. In the news media, algorithms are often anthropomorphized, as if they have the same consciousnessterm as humans (e.g., (Wilson 2017)), and sometimes they are described as artiststerm (Dvorsky 2017; Perez 2018; Pogue 2018). In fact, we do not really know what consciousnessterm is (despite many theories), or what it would mean to embody it in an algorithm.

Today, the most-successful AI and machine learning algorithms are best thought of as glorified data-fitting procedures (Brooks 2017). That is, these algorithms are basically like fitting a curve to a set of datapoints, except with very sophisticated ways to fit high-dimensional curves to millions of datapoints. When we as researchers speak of “training” an algorithm, or an algorithm that “learns”, it is easy to misinterpret this as being the same thing as human learning. But these words mean quite different things in the two contexts. In general, “training” a model to learn a task involves careful human effort to formulate the problem, acquire appropriate data, and test different formulations. It is laborious and requires considerable expertise and experimentation. When a new task needs to be solved, the human starts over.

Compared to human intelligence, these algorithms are brittle and bespoke. For example, image recognition algorithms have undergone breathtaking breakthroughs in the past decade, and are now widely used in consumer products. Yet they often fail on inputs that suggest bizarre misunderstandings; the existence and robustness of adversarial examples (Szegedy et al. 2014) and procedurally-evolved images (Nguyen et al. 2016) demonstrates that these algorithms have not really learned anything like human-level understanding. They are like tourists in a foreign country that can repeat and combine phrases from the phrasebook, but not truly understand the foreign language or culture. These systems have no autonomy except within the narrow scope for which they were trained, and typically fail-safes must be put in place as well, e.g., Google Photos no longer classifies anything as a “gorilla” because of one high-profile failure (Simonite 2018).

There are some fascinating parallels between human learning and machine learning, and it does seem likely that humans are, in some way, optimized by evolutionary principles (Alexander 1996; Gopnik and Wellman 2012; Koerding and Wolpert 2006; Tenenbaum et al. 2011). But going from these high-level analogies to actual machine intelligence is a problem for which the solution is not even on the horizon.

3. How Technology Changes Artterm Based on this history, I now make several specific claims about how technology changes artterm. Far from replacing artiststerm, new technologies become new toolsterm for artiststerm, invigorating and changing artterm and culture. These claims apply equally to current developments in AI as they did to previous developments like photographyterm and animation.

3.1. Algorithms Are Artiststerm’ Toolsterm In every technology that we currently employ—whether photographyterm, film, or software algorithm—the technologies and algorithms we use are basic toolsterm, just like brushes and paint.4 The same is true for the new AI-based algorithms that are appearing. They are not always predictable, and the results are often surprising and delightful—but the same could be said for the way watercolor flows on the page. There is no plausible sense in which current systems reflect “true” artificial intelligenceterm: there is always a human behind the artwork.

Applying the same standard to the current research in neural networks and neural style transfer, it would seem equally perverse to assign authorshipterm of their outputs to the software itself. The DeepDream software was authored by a human; another human then selected an input image, and experimented with many parameter settings, running the software over and over until good results were obtained. Indeed, in a recent artterm exhibition meant to promote these methods and their exploration (Tyka 2016), human artiststerm were credited for each of the individual works.

The same process of selection of toolsterm and inputs, adjusting settings and even modifying code, and iterating until a desirable output is produced, occurs in all current and foreseeable computer artworks. Computer-generated artworks result from considerable time and effort from human artiststerm, from conception of the idea, to painstakingly guiding the execution, to selecting from among the outputs.

There have been a few cases where AI algorithms have been presented as artiststerm or potential artiststerm (Colton 2012; Dvorsky 2017; Perez 2018; Pogue 2018; Shein 2017). For the above reasons, I think this claim misunderstands the nature of procedural artterm.

In short, in our present understanding, all artterm algorithms, including methods based on machine learning, are toolsterm for artiststerm; they are not themselves artiststerm.

3.2. New Technology Helps Artterm Stay Vital Rather than being afraid of the new technologies, we should be enthusiastic about the new artworks that they will enable artiststerm to produce. When we think of artterm as having external influences, we normally think of social or political influences, but ignore the effect of new toolsterm. In contrast, I argue that, especially from the 19th century onwards, technological developments have played a pivotal role in advancing artterm, in keeping it vital and injecting fresh ideas. The stories I gave of photographyterm and cinema include many examples of this. However, the effect is far more widespread.

One of the most important breakthroughs in the history of Western artterm was the invention of oil paint by Flemish painters such as Jan van Eyck in the 15th century (de la Croix et al. 1991). Previously, painting had been done primarily with tempera, which lacks subtle coloration, and fresco, which was very cumbersome to work with. Oil paint had existed in some form for centuries, but van Eyck and others found new techniques that gave them a very practical new mediumterm. It was fast-drying and allowed rich colors and tones, sharp edges, and hard surfaces. The rich light and color that we associate with the Northern Renaissance and the Italian Renaissance are due to this technology (Figure 10).

In each decade since the 1950s, many of our culturally-important works used technology that had only been invented within the previous 10 years. For example, most technology used in today’s feature films did not exist 10 years ago (e.g., widespread use of HD digital cameras; facial performance capture); the same goes for artworks using smartphones and crowdsourcing; artworks involving white LEDs and Arduino controllers; DJs performing on stage behind their laptops; and so on. Even the most vérité-seeming romantic comedies frequently involve recent digital video editing and digital backdrops.

Conversely, artistic styles that fail to change become stale and lose their cultural relevance; the adoption and exploration of new technology is one of the ways that artterm stays vibrant. For example, the introduction of synthesizer music into 1980s pop music created a new sound that was exciting and modern. The sound diversified as the toolsterm improved, until grunge became popular and made the 1980s synthpop sound seem superficial and old-fashioned. Nowadays, a recent revival of 1970s and 1980s instruments by bands like Daft Punk and LCD Soundsystem seems most exciting at times when they are creating new types of music using old instruments. In contrast, the swing music revival of the 1990s never went anywhere (from bands like Big Bad Voodoo Daddy and Squirrel Nut Zippers) in my opinion, because the bands mimicked classic styles with classic instruments, without inventing anything particularly original themselves.

In each era, radical technological innovations are met by artiststerm with both enthusiasm and rejection. For example, when the Moog synthesizer became popular, it was adopted by big-name bands like Emerson, Lake, and Palmer. Other bands felt that twisting knobs to make music was “cheating”: Queen’s album covers proudly state that the band did not use synthesizers. Robert Moog described one New York musician who said of the instrument “This is the end of the world” (Snowden 2012). It now seems silly to imagine that people might have ever categorically objected to synthesized music, or to the scratching and sampling of hip-hop DJs, just as it now seems silly that people once rejected waltzing, the Impressionists, and the Rite of Spring as invalid or immoral.

In addition to stimulating professional artiststerm, new toolsterm make artterm more accessible to larger portions of society. Photographyterm was once accessible only to the most determined early adopters, but has continually become easier, faster, and more compact, to the point where nearly everyone carries a mobile phone camera in their pocket or purse. The same goes for the toolsterm of cinematography (from hand-cranked to heavy cameras to Steadicams to handycams to iPhones), and so on. Modern computers give nearly everyone access to digital equivalents of darkrooms, mixing studios, painting studios, and so on; these were formerly highly-specialized technologies requiring laborious effort.

3.3. New Technology Does Not Cause Net Unemployment Concerns of how technology displaces jobs have been around since at least the 19th century, when Luddite protesters destroyed mechanical weaving machines, and, in folk songs, John Henry competed against a steam drilling machine. These fears are real and understandable. Yet, despite centuries of technological disruption, we do not live in a world of massive unemployment. This is because, as old roles are erased, many more arise in their stead. But these fears keep recurring, because, at any given time, it is easy to imagine losing specific jobs but it requires superhuman imagination to forecast what new opportunities will be created by transformative new technologies. Nowadays, most of us do jobs that would be hard to explain to a 19th century worker.

The real workforce concerns should not be about the technology itself, but about whether the economic system shares the benefits of new productivity fairly across society versus concentrating wealth only among the very richest (Stiglitz 2012) and whether machine learning systems are misused to magnify existing forms of inequality (O’Neil 2016). When displacements due to new technology occur, their effects can be eased by social safety nets and better educational foundations (for employment flexibility and retraining). Conversely, a society which fails to distribute wealth and economic gains fairly has much bigger problems than just the impact of AI.

Fears of new technology seem to be human nature. I suspect many people view the “normal” state of things as being how they were when they came of age, and they view any significant change as scary. Yet, nearly all of our familiar modern technologies were viewed as threatening by some previous generation.

The fear of human-created life has been with us for a long time. Notably, 18th-century scientists discovered electricity. As they searched to understand it, they discovered the life-like effect of galvanism, that the muscles of dead frogs could be stimulated by electrical currents. Had the secret of life been discovered? This inspired Mary Shelley’s novel Frankenstein; or The Modern Prometheus, in which an ambitious university student uses modern scienceterm to create new life (Shelley 1818). Today, the story is vivid and evocative, but, intellectually, we recognize it as preposterous. The fear of AI is essentially the same irrational fear; SkyNet is Frankenstein’s monster, but with neural networks as the Promethean spark instead of galvanism.5 At present, the Terminator’s autonomous AI is only slightly more plausible than its ability to travel backwards in time.

3.4. New AI Will Be New Toolsterm for Artiststerm Some general trends around the evolution of technology and artterm seem quite robust. As discussed above, current AI algorithms are not autonomous creators, and will not be in the foreseeable future. They are still just toolsterm, ready for artiststerm to explore and exploit. New developments in these toolsterm will be enthusiastically adopted by some artiststerm, leading to exciting new forms and styles that we cannot currently foresee. Novices will have access to new simplified toolsterm for expression. It is possible that some tasks performed by human artiststerm will fade out, but these will generally be mechanical tasks that do not require much creativityterm because they fill societal functions other than artistic expression. Some traditional artsterm may fade simply due to seeming old-fashioned. This is the nature of artterm: nothing is fresh forever, which is not to be blamed on technology. Artistic technology is an “imagination amplifier” (Cohen 2000) and better technology will allow artiststerm to see even further than before.

Aside from general trends, it is hard to make specific predictions about the artterm of the future. Les Paul, who invented the solid-body guitar in the 1940s (Figure 11, himself primarily performed light pop, country, and showtunes, and could hardly have predicted how the electric guitar would be used by, say, Led Zeppelin, just as it is hard to imagine Daguerre predicting Instagram. More generally, making predictions about how AI technologies might transform society is very hard because we have so little understanding of what these technologies might actually be (Brooks 2017). Even the scienceterm fiction writers of the 1950s and 1960s completely failed to imagine the transformative power of the Internet and mobile computing (Krauss 2017); for them, the computers of the future would still be room-scale monstrosities that one had to sit in front of to operate. But they did predict moon colonies and replicants by 2018.

In short, we cannot predict what new inventions and ideas artiststerm will come up with in the future, but we can predict that they will be amazing, and they will be amazing because they make use of technology in new, unpredictable ways.

4. What Is an Artistterm?

So far, I have described how computer technologies are currently accepted as toolsterm for artiststerm, not as artiststerm themselves. Why is this? After all, computers can do other human tasks like speak, search, print, navigate, and, to some extent, drive cars. There are several obvious reasons why computers do not make artterm, including tradition, the incentives involved, and the relatively predictable nature of existing automation. But, still one could imagine an alternate history in which some machines or computer programs had already been called artiststerm. I believe there is a more fundamental reason, which explains not just why this has not happened, but why it is unlikely to happen anytime soon.

In this section, I theorize about the prerequisites for an entity being an artistterm, focusing on my hypothesis that artterm is a social behaviorterm. I will then apply this idea to AI in the next section. I also explore several alternative hypotheses for what could make an AI as an artistterm. Most of these alternative hypotheses identify some attribute of human artiststerm, and then hypothesize that that attribute is needed for an AI to be an artistterm.

4.1. Artterm Is Social Why do we create and consume artterm? I argue that artterm is primarily a social behaviorterm: artterm is about communicationterm and displays between people. For example, people often speak of artterm as being about personal expression, which is an act of communicationterm.

Any human can make artterm, because humans are social creatures. A painter cranking out the same conventional landscapes for tourists year after year is still considered an artistterm. A child’s drawing might only be interesting to their family, but it is still artterm.

I am directly inspired by the theory, going back to Charles Darwin, that art-making is an adaptive product of our biological evolution. Dutton (2009) sets out a persuasive argument for this theory, which I briefly summarize, though I cannot do it justice. Creating artterm served several functions for our Pleistocene ancestors. Art-making served as a fitness signal for mating and sexual selection. Artterm can also be used as a display of wealth and status. Storytelling, music, and dance strengthen social bonds within a group. Storytelling additionally plays a very important role of communicating information that would otherwise be hard to share.

I observe that each of these functions of artterm is social: artterm arose as forms of communicationterm, displays, and sharing between people. Although artterm takes many different forms in different cultures today, each of these forms serves one or more of the same basic social functions that it did in the Pleistocene.

I generalize this theory beyond humans to hypothesize: artterm is an interaction between social agentsterm. A “social agentterm” is anything that has a status akin to personhood; someone worthy of empathyterm and ethical consideration. Many of our other behaviors are interactions between social agentsterm, such as gifts, conversation, and social relationships like friendship, competition, and romance.

In contrast, while we can get emotionally attached to our computers and other possessions, we feel no real empathyterm for their needs, and no ethical duty toward them. Possessions can participate only in shallow versions of these interactions. For example, we frequently talk about our possessions with statements like “The brakes on my bicycle were complaining so I gave it a new pair as a gift and now it’s much happier. I love my bike.” This statement indicates emotional attachment but not true empathyterm with the bike’s feelings, despite the anthropomorphizing language. We do not live in a social hierarchy with our possessions: we do not compete with them for status, or try to impress them. We care about what other people have to say because we care about other people; we care about what computers have to say only insofar as it is useful to us. We generally treat conversational agents (like Siri and Alexa) as user interfaces to software, not like people.

Art-making does also have non-social benefits to the artistterm. For example, art-making can help practice skillsterm like dexterity and problem solving. Creating artterm is often pleasurable or meditative in itself. But these benefits are secondary to their social benefits: they are not the reasons that evolution has produced artterm as a human activity. Similarly, one may also talk or sing to oneself while alone, but talking and singing are still fundamentally social activities.

Note that the evolutionary argument here is optional; one can discuss whether artterm is fundamentally social without it. But I believe that the evolutionary view of artterm gives some additional understanding.

4.2. Non-Human Authors As we have seen, despite many technological advances, current algorithms are not accepted as artiststerm. There are a few other existing examples where objects are created by authors or processes that are not human-driven. These give some support to this theory.

Natural processesterm. Natural processesterm, including landscapes like the Grand Canyon or the HuangShan Mountains, are not considered artterm, even though they may be extraordinarily beautiful and change one’s perspective immensely. Beautiful structures made instinctively by animals, such as honeycombs and coral, are not considered artterm. This indicates that simply creating complex and beautiful outputs is not itself sufficient for artterm, since there is no creative social communicationterm in these cases.

Animals. Some higher mammals, including chimpanzees, elephants, and dolphins, have been trained to paint (Desmond 2016; Dutton 2009). Many writers are skeptical of animal-made artterm. Typically, the animal’s owner or handler steers the process, letting the animal throw paint on the canvas, then stopping the painting when they believe it is done, then selecting which works to show; the animals seem to show no interest in the artwork afterward. Animal artwork has not had any significant cultural impact or popularity; it seems to have been largely the product of media stunts. (People for the Ethical Treatment of Animals have recently tried to claim copyright in favor of a monkey, but failed (Millward 2017), as US copyright law only allows humans to claim copyright.) The most interesting aspect of this discussion is not whether animals can create artterm, but how we decide. Discussions of whether animal-made artifacts are artterm are not based on a priori rules whether animals can be artiststerm. Instead, they are attempts to study the evidence of the animal’s behavior around the artwork, and, from that, to infer whether the artwork is some form of inner expression (Desmond 2016), or an artifact that the animal has a special appreciation for (Dutton 2009). In other words, we are open to the idea of animals creating artterm, because they can have social relationships with us. It is just that we have not found any other creature that satisfies our criteria for creating artterm, whatever they are.

4.3. Judging the Work Instead It is tempting to judge whether a computer is an artistterm based solely on the merits of the work that it produces. In this hypothesis, whether or not a computer can be an artistterm is a judgement of the quality of the work that it produces, independent of the properties of the computer itself. If an algorithm outputs a continual stream of diverse, stimulating, beautiful, and/or skillful outputs, without many duds, we might be quite tempted to call this algorithm an artistterm. The better computer-generated artterm becomes, the more we will hear questions about whether computers are artiststerm.

Skillterm is clearly not the real requirement for someone or something to be an artistterm. Any human can make artterm, including unskilled amateurs and children. Conversely, computers can already be programmed to create infinite sequences of dazzling realistic or abstract imagery, exhibiting technical proficiency way beyond the typical human capacity.

I suspect that, when we look at a computer’s output and ask “is this work good enough to call the computer an artistterm?”, we are not actually judging the quality of the work per se. Instead, we are really looking for evidence that the system itself is intelligent, conscious, and feeling: traits that we associate with social agentsterm. No matter how skillful and surprising a computer’s output is, we will not accept it as an artistterm until we infer some sort of social being inside.

4.4. An Intentterm Machine Another hypothesis is as follows: in the modern artterm world, the role of the artistterm is to supply the “intentterm” and the “idea” for the work; it is not necessary for the artistterm to execute on the work, other than coordinating in its production. For humans, this is clearly true in numerous examples, such as those in Section 2.4, as well as appropriation works like Duchamp’s Readymades and Richard Prince’s questionable Instagram reproductions; artiststerm can also employ helpers or crowdworkers, such as in Scott Draves’ Electric Sheep and Aaron Koblin’s Sheep Market (Koblin 2009). Consequently, for a computer to be an artistterm it simply needs to supply an intentterm.

It is easy to imagine designing a system that creates intentterm and even coordinates the labor of producing a work. For example, one could write a simple procedural algorithm to generate very basic intentsterm (e.g., “portray ominous landscape”), or sample intentsterm from a Recursive Neural Network trained on artiststerm’ statements scraped from the web. Or the method could randomly select some news item, photograph, or historical event, and randomly sample some attitude toward that thing. Starting from this intentterm, crowdworkers could be used to refine the idea and convert it into a new image, similar to systems like Soylent (Bernstein et al. 2015). One could also automate steps of the process, e.g., using GANs (Goodfellow et al. 2014) to generate entirely new starting images from scratch. Crowdworkers could also be used to rate and evaluate the outputs of the system, selecting just the best results and discarding the rest. Final steps of the process could also automatically hire professional designers, e.g., from sites like Upwork or 99Designs. This system could then run continuously, generating new images over time (with payment being automatically made to the crowdworkers involved). Workers could group images with common themes and intentsterm, and create separate collections around these themes. Artiststerm’ statements could be generated around these themes. The system’s preferences could grow and adapt over time as more data are gathered or external data streams (e.g., photographyterm blogs) change.

Suppose someone were to build this system, calling it, say, The Intent Machineterm, and exhibits its work in an artterm show or gallery. Suppose, moreover, they convinced the curator or gallery owner to credit The Intent Machineterm with authorshipterm of the works it had created, but fully disclosed the procedure by which it worked. Would people credit it as the artistterm who had authored its works, or would they say that the system-builder is the real artistterm here?

I believe that, in general, the consensus would be that the system-builder is the real artistterm here, and that this is really an artwork about probing the nature of computer-generated artterm, or the nature of the commercial artworld. Note that the procedure used to define the system is not fundamentally different than any other procedural computer-generated artterm algorithm (as in Section 2.4 and Section 2.5). Even if the work itself ended up being quite good, viewers would ask why it is good, and it is doubtful that the computer’s own contribution would be judged as significant beyond those of the humans involved.

Are artiststerm just “intentterm machines”? If artterm is a social actterm, then the answer is no.

4.5. Creativityterm, Growth, Responsiveness, and so on Finally, three related criteria that have intuitive appeal are the notions of creativityterm, growth, and responsiveness. They are important for human artiststerm, so perhaps they should be for AI artiststerm as well.

Several authors have proposed energy terms or criteria for creativityterm (Elgammal et al. 2017; Nguyen et al. 2016). These are often attempts to express the idea that the system’s output should somehow “surpass” the human programmer and/or the training data. For example, Colton (2008) proposes that we judge the creativityterm of a system, in part, by whether the system’s output surprises the system’s author. I believe that this is too weak a criterion, since many mechanical or algorithmic phenomena may be surprising to their own discoverer or author at first. For example, the basic algorithm that produces Mandelbrot set images can be specified in a single sentence,6 yet produces dazzling animations of infinite complexity.7 The Mandelbrot set is very surprising and produces beautiful, unprecedented images, but we do not call its iteration equation creative, or an artistterm.

All current procedural artterm systems, such as the Mandelbrot set, have a recognizable style, and, after awhile, lose their novelty. I believe that the same will be true for systems specifically designed with “creativityterm” objectives (Elgammal et al. 2017; Nguyen et al. 2016). Unlike human artiststerm, these systems do not grow or evolve over time.

Perhaps an AI artistterm would need to exhibit some form of growth. For example, Harold Cohen, the fine-art painter who began, in 1968, to write software to generate artterm, described the evolution of his views: “Ten years after [1968], I would have said ‘look, the program is doing this on its own’ … Another ten years on and I would have said ‘the fact that the program is doing this on its own is the central issue here,’ denoting my belief in the program’s potential and growing autonomy over the whole business of art-making … It was producing complex images of a high quality and I could have had it go on forever without rewriting a single line of code. How much more autonomous than that can one get? … [But] it’s virtually impossible to imagine a human being in a similar position. The human artistterm is modified in the act of making artterm. For the program to have been similarly self-modifying would have required not merely that it be capable of assessing its own output but that it had its own modifiable worldview …” (Cohen 2014).

In any existing system, it is easy to think of trivial ways for the system to evolve and change over time, e.g., subtly change the color palette or the training data over the years. Superficial growth is easy; meaningful growth is hard to even define for a computer AI. If someone could design a system that produces a sequence of artterm that is meaningful to people and also significantly evolves over time, that would be truly remarkable. Another missing piece from current systems is an artiststerm’ ability to respond meaningfully to their culture, experiences, world events, responses to their work, and other aspects of their environment. It seems hard to imagine achieving these goals without enormous technological advances—they may not be possible without true AI or social AI in some form.

4.6. Definitions of Artterm For guidance about the nature of artterm, we could have also looked to existing definitions of artterm. However, there is no Royal Society that prescribes what is and what is not valid artterm. Instead, artterm is a phenomenon that results from the interplay of cultural institutions and the general population that we can analyze, and it changes over time. Philosophers have attempted to devise concise definitions of artterm that include all existing types (music, dance, painting, etc.) and styles of artterm. The Institutional Definitionterm, originated in the 1960s in response to conceptual artterm, states, roughly, that artterm is anything in a style that is broadly accepted as artterm (Danto 1964; Dickie 1969; Levinson 1979). A more fine-grained approach is to identify attributes common to many different types of artterm (Gaut 2000, 2005; Dutton 2000, 2009). Each of these definitions of artterm is an attempt to fit the data, and draw a line between those things that we call artterm (like theatre) and those that we do not (like spectator sports). Understandably, these definitions all assume that the artistterm is always human, without exploring much whether non-humans can create artterm, and thus do not provide much guidance for this discussion.

4.7. Attribute Theories in General Many of the theories in this section have the following recipe: identify some attributes of human artiststerm, and then hypothesize that AIs with these attributes will be considered artiststerm. Artiststerm make high-quality work; artiststerm supply intentterm; artiststerm are creative; artiststerm grow.

The development of Cohen’s thinking illustrates this. Cohen initially thought that a machine that makes high-quality artterm could be considered an artistterm. The more he observed his own software, the more something seemed to be missing. He noted that human artiststerm grow over time, and his system did not. If he had added some form of “growth” to his system, would it then be an artistterm?

Maybe someday someone will develop a system with enough of these attributes to cross some intangible threshold and thus be perceived as an artistterm. However, it seems very hard to reason concretely about this possibility. How do we define any of these attributes precisely? How much is enough?

Furthermore, many of these attributes are not really required of human artiststerm. Any human can make artterm, even if it is not very original or surprising; the artistterm need not grow noticeably or respond to culture or feedback. We do judge the work by these attributes, but there is no minimum requirement for humans to make artterm.

In contrast, the social theory makes a much more concrete statement. Artterm is fundamentally a social interaction, and thus can only be made by social agentsterm. The social theory has the additional appeal of being based on a plausible evolutionary hypothesis for the reasons we create artterm.

5. Will an AI Ever Be an Artistterm?

With this background, I now turn to speculating about the future. As we have seen, authorshipterm of all current algorithmic artterm is assigned to the human author behind the algorithms. Will we ever say that an AI itself created artterm? Will we ever recognize a piece of software as the author of a work of artterm?

Human-Level AI. If we ever develop AI with human-level intelligence and consciousnessterm, by definition, it would be able to create artterm, since it would have the same capacity for consciousnessterm, emotions, and social relationships. But, as discussed in Section 2.6, this scenario is scienceterm fiction and we have no idea if this is possible or how it would be achieved. Making meaningful predictions about a world with “true AI” is impossible (Brooks 2017), because we have so little idea of how specifically this AI would actually operate. Moreover, this AI would transform society so much as to make it unrecognizable to us. We may as well speculate about what kind of artwork is made by aliens from outer-space—if do we ever meet them, we will have more pressing questions than what kind of music they like.

Hence, the interesting question is whether there could be computer-authored artwork without human-level intelligence or consciousnessterm.

Social AI. If, as I have argued, creating artterm is a fundamentally social actterm of expression and communicationterm, then it follows that AI can be granted authorshipterm when we view the AI as a social agentterm, and it is performing some communicationterm or sharing through artterm.

What does it mean for us to view an AI as a social agentterm? We have to view the AI as deserving of empathyterm and ethical consideration in some way. However, the AI does not need human-level intelligence, just as we have social relationships with our pets. But we do expect that the AI has something to say socially, something that suggests an inner consciousnessterm and feeling.

Short of true intelligence (the scienceterm fiction scenario), I think that the only way this can happen is through “shallow AIterm” agents. People are sometimes “fooled” by shallow AIterm. The classic example is Eliza, a simple text-based “psychiatrist” program developed in 1964, based on simple pattern-matching and repetition of what the user types (Weizenbaum 1966). It was meant as a demonstration of the superficiality of the AIs of the time, but, unexpectedly, many people attributed human-like emotions to the machine. Since then, there are many anecdotes of people being fooled by “chatbots” in online settings (Dvorsky 2016; Epstein 2007), including the recent plague of Twitter bots (Kessler 2014). But, once the veil is lifted, it is clear that these chatbots do not exhibit real intelligence.

Some software and robots have been designed to have relationships with their owners, including talking dolls, Tamagotchi, and Paro therapeutic baby seals. A related effect is that people behave toward their computers as if they were social agentsterm in certain ways (Reeves and Nass 1996), even when they do not believe that they are intelligent. For example, dialogue systems like Siri and Alexa all use female voices by default, based on many findings that male and female users both respond better to female voices (Griggs 2011; Reeves and Nass 1996).

Perhaps, for many users, the system does not need to be truly intelligent, it just has to be perceived as a social agentterm, like a Siri or Alexa that you can ask to make you an artwork (Xu et al. 2017). The day may come in which these agents are so integrated into our daily lives that we forget that they are carefully-designed software. One can easily imagine the development of AI that simulates emotion and affection toward the user; it is easy to imagine, for example, a toy doll that paints pictures for its owner, as one of many behaviors designed to display companionship and affection.

Non-social AI. It seems possible that non-social algorithms could be successfully promoted as artiststerm; there have been a few tentative forays in this direction, e.g., (Colton 2012; Elgammal et al. 2017). For the reasons given above, I am skeptical that such methods will be accepted as true artiststerm without some plausible belief about their underlying social and/or conscious attributes.

Perhaps a curator at a well-known museum would download or otherwise acquire various artifacts from software “artiststerm”, and list the software systems as the authors. There would be controversy, and discussion in newspapers and journals. Perhaps other curators and galleries would follow suit. Perhaps people would find enough value in these computer-generated artworks, while also being convinced that no human could be rightly given credit for their works. This sort of process has happened for things like abstract expressionism, and not for chimpanzee artterm. Could it happen for computer artterm?

Suitcase wordsterm. The term “artistterm” could come to be used as having multiple meanings, just as words like “intelligence” and ”learning” have come to mean something different for humans than they do for algorithms (Brooks 2017). A software program that, say, automatically stylizes your photos, could be called an “artistterm” in the same way that software applications like “calendar” and “mail” programs have replaced their physical-world namesakes. Unfortunately, the use of the same word to mean different things in different contexts causes endless confusion, as discussed in Section 2.6.

Dangers. A continual danger of new AI technology is that human users misunderstand the nature of the AI (O’Neil 2016). When we call a shallow AIterm an “artistterm”, we risk seriously misleading or lying to people. I believe that, if you convince people that an AI is an artistterm, then they will also falsely attribute emotions, feelings, and ethical weight to that AI. If this is true, I would argue that calling such AIs “artiststerm” is unethical. It leads to all sorts of dangers, including overselling the competence and abilities of the AI, to misleading people about the nature of artterm.

It seems likely that some companies will not have any scruples about this. For example, Hanson Robotics has promoted (in many contexts) a social robot as a truly intelligent being, even though it is clearly nothing more than a “chatbot with a face” (Gershgorn 2017) or, in Yann LeCun’s words, a “Potemkin AI” (Vincent 2018).

A related concern is that we deprive the AI’s designer(s) of authorshipterm credit. At present, we credit the author of a piece of automatic software with the output of that software. This usually acknowledges the skillterm and effort required to engineer and iterate with software so that it produces good outputs. Artistic credit is important for understanding the real sources of how something was made.

Outside of scienceterm fiction, I can see no positive benefit to calling a computer an artistterm, but I do see dangers.

6. Conclusions I do not believe that any software system in our current understanding could be called an “artistterm”. Artterm is a social activity. I mean this as a warning against misleading oneself and others about the nature of artterm. Of course, the ambitious reader could take this as a challenge: I have laid out some of the serious objections that you must overcome if you wish to create a software “artistterm.” I do not think it can be done anytime soon, but I also know that proving critics wrong is one of the ways that artterm and scienceterm advance.

One of my main goals in this essay has been to highlight the degree to which technology contributes to artterm, rather than being antagonistic. We are lucky to be alive at a time when artiststerm can explore ever-more-powerful toolsterm. Every time I see an artistterm create something wonderful with new technology, I get a little thrill: it feels like a new artterm form evolving. Danny Rosin’s Wooden Mirror, Jason Salavon’s The Top Grossing Film of All Time, 1 × 1, Bob Sabiston’s Snack and Drink, Michel Gondry’s Like A Rolling Stone, Kutiman’s ThruYOU, Amon Tobin’s Permutation, Ian Bogost’s Cow Clicker, Christian Marclay’s video installations, Íñigo Quilez’s procedural renderings, and Wesley Allsbrook’s and Goro Fujita’s Virtual Reality paintings are a few examples of artworks that have affected me this way over the years. Today, through GitHub and Twitter, there is an extremely fast interplay between machine learning researchers and artiststerm; it seems like, every day, we see new tinkerers and artiststerm Tweeting their latest creative experiments with RNNs and GANs (e.g., @JanelleCShane, @helena, @christophrhesse, @quasimondo, @DrBeef_).

Artterm maintains its vitality through continual innovation, and technology is one of the main engines of that innovation. Occasionally, the avant garde has tremendous cultural impact: electronic music and sampling was once the domain of experimental electronic and musique concrète pioneers, like Wendy Carlos and Delia Darbyshire. Likewise, at one time, computer-animated films could only be seen at obscure short-film festivals. Today, we are seeing many intriguing and beguiling experiments with AI techniques, and, as artiststerm’ toolsterm, they will surely transform the way we think about artterm in thrilling and unpredictable ways.