4   Art Appreciation and Creative Skills

Margaret A. Boden

4.1   The Problem

Traditionally, our appreciation of art depends in part on our appreciation of the process of art making. In evaluating a painting, for instance, we commonly consider the skills involved in producing it and the specific difficulties overcome in so doing. We appreciate the finesse with which these methods are applied and respect the dedication needed to acquire them. If we know that the painter had to grind his (usually his) own lapis lazuli and mix his own oils, we admire him somewhat more than we admire someone who could squeeze his colors out of a tube. And if we want to know just how a certain canvas was painted, we are interested to know whether the process involved pestle and mortar or labor-saving plastic tubes.

We are impressed, in particular, by the creative application of familiar skills in surprising but effective ways, and—even more—by the generation of new ones. Indeed, advances in artistic technique form a large part of what art critics study—and of what gets individual artists into the history books.

Arguably, such knowledge about the skills of art making does not affect our evaluation of the picture itself. Suppose we were to discover that Leonardo had bought his paints from some fifteenth-century Winsor and Newton art supplier: would the aesthetics of the Mona Lisa be any less compelling or any less mysterious? Or suppose we found out that he had painted the canvas holding the brush between his teeth; what then? In other words, knowledge of the art making is generally thought to be secondary to strictly aesthetic appreciation. But even so, it is regarded as a valuable part of our experience of art.

This presents a problem for computer art in general—which involves skilled and creative computing as the basis of the creative art. The art making here is typically invisible, leaving no obvious footsteps in the artwork itself. Even when brushstrokes, for instance, are specifically represented in the final artifact (as by The Painting Fool, described in chapter 6; Colton 2012), these are not the footsteps of the program, but quasihuman footsteps generated by the program.

What is more, even if the relevant art-generating program were to be explicitly presented to the audience, it would (for most people) be unintelligible. As for assessing the creativity—the novelty, elegance, and ingenuity (and even the integrity; Boden 2010a, 187–190)—of the program considered as a program, that is beyond all but the technocognoscenti.

Admittedly, only relatively few people can fully understand the technical achievements of traditional artists. Many of us have no more than a primitive knowledge of how they use paint on canvas—for example, providing a white primer (to heighten the final colors), as the Pre-Raphaelites did. One might want to say, then, that artists of computer-generated (CG) art with hidden skills are in a position similar to that of their predecessors. However, although few of us know about the white primer, everyone can remember (or imagine) wielding a paintbrush, mixing colors, and using watercolors, pastels, or even oils. The same is not true of CG-skills. CG-artists do not simply do better what we all did clumsily as children or even what we saw our elders do well; they do something of which most of us have no experience at all.

If the CG-artist’s skill is invisible or mysterious, this does not rule out the possibility that the resulting artwork may interest, disturb, or even enthrall us. And that, of course, is all to the good. It means that no one is completely prevented from appreciating CG-art. Novelty, surprise, and value—in a word, creativity—can be recognized in CG-artworks. But it seems that this type of art cannot command our respect for its creativity, or even its competence, considered as a product of technical skill. At best, it can amaze us much as a magician does who conjures a rabbit out of a hat: that he does this can arouse our awe, but how he does so cannot. Only other magicians can recognize the nature and creative value of the conjuror’s skills. In short, where computer art is concerned, one key dimension of art appreciation seems to be missing.

One might put this point even more strongly. One might say that the program itself is the real artwork. In that case, what I have been calling computer artworks are merely the program’s performances. Analogously, consider the Eroica symphony. One may regard the true artwork as Beethoven’s score. Any actual performance of it is an ephemeral event, unique—and perhaps marvelous—in its details but fundamentally derivative of the written score. Some CG-artists take a closely comparable position, regarding the generative process, or programmed procedure, as the real artwork. If we take either of these views, in which the true artwork is the program or procedure itself, then the problem presented here becomes even more taxing. The nature of the program is now—or should be—the crux of aesthetic attention, not (as previously implied) something of secondary importance worthy of attention only because of our general interest in human artists’ skills.

I continue to refer to the performances (the results) of CG-art programs as the actual artworks concerned, as most people, including many CG-artists themselves, usually do. Accordingly, I continue to treat the writing of the program as an aspect of the CG-artist’s skill—how he or she mixes and manipulates the CG-oils, one might say. But the strong claim of the underlying program to be regarded as the definitive artwork should not be forgotten.

Whichever horn of that philosophical dilemma one chooses to take, CG-artists face a significant barrier to aesthetic appreciation. This obstacle arises not only in the minds of the general public but also in those of most professional art critics and curators, too. Is there anything that CG-artists can do to remove it or at least to minimize it?

4.2   Perhaps Skills Are Irrelevant, after All?

One thing that CG-artists might conceivably do is try to convert us all to the views of the art theorist Robin Collingwood. Collingwood, as explained in chapter 6, insisted that our common assumption about the aesthetic worth of the artist’s skills is radically mistaken. He believed that such skills are utterly irrelevant to the status of the object considered as art.

On his view, the essence of a work of art is its power to elicit a certain emotional experience in the viewer or listener. Strictly speaking, he saw the true artwork as being not the physical artifact itself but the experience constructed in the observer’s mind. The artifact has aesthetic value only insofar as it enables this mental construction to take place. He acknowledged, of course, that artists need certain skills to be able to fashion artifacts that will reliably prompt the relevant experiences. And those skills, like any other hard-won and effective technical practices (plumbing, for instance), are worthy of our respect. But they do not merit specifically aesthetic admiration.

For Collingwood, then, the artist’s skills could in principle remain wholly unknown to the audience without in any way diminishing the value of the artwork as art. If it leads us to construct a particular emotional experience in our minds, that is enough. Just how artifacts having that psychological effect are made by the artist is a technical question—one of some historical and professional interest, no doubt, but not of any aesthetic importance.

Someone might suggest, accordingly, that CG-artists should adopt Collingwood as their patron saint and try to persuade the rest of us to follow him, too. In that case, the invisibility and unintelligibility of their skills would not matter. Ignorance of their techniques would not undermine people’s aesthetic appreciation of their work in any way.

That would not be a sensible suggestion, however. For one thing, Collingwood’s stress on emotion as the key to art raises some difficulties for computer artists, although not nearly so many as is commonly assumed (see chapter 10). For another, his sidelining of skill in art appreciation sits uneasily alongside computer artists’ justifiable pride in their highly unusual skills (see chapter 10).

But there is a third reason that suggestion should be resisted—and this reason is not confined only to CG-art. Namely, Collingwood’s position on the aesthetic irrelevance of artistic skills, although not his prioritizing of emotion, strikes most people as bizarre.

It strikes us as bizarre because, as already remarked, we normally take it for granted that admiration of the artist’s skill is part of art appreciation. Indeed, the art critic John Ruskin (discussed at length in chapter 7) saw recognition of the processes of skilled art making as central to aesthetic valuation. Ruskin was even more widely influential than Collingwood. He still is; countless people who have never even heard his name have adopted Ruskin’s key ideas within their attitude to art.

4.3   Of Course Skills Matter! But Which Ones?

So perhaps CG-artists with a proper pride in their own creative processes should choose Ruskin, instead, as their patron saint?

Well, no. Ruskin—despite his emphasis on the importance of skills—is no more suitable as a champion for CG-art than Collingwood is. Ruskin did not merely say that skills are crucial in genuine art making. He said that skills of a certain kind are essential.

In a nutshell (for more detail, see chapter 14), these are the skills of individual craftsmen, working by hand with brush or chisel, knife or needle, and leaving visible footsteps (or fingerprints?) in the resulting artwork. To respond to a work as art, for Ruskin, was to recognize it as a handmade product of a freely engaged human individual.

One might try to include CG-artists within Ruskin’s deeply admired class of skilled craftsmen by pointing out that programming itself is a highly skilled activity and sometimes also (as explained in chapter 10) a highly creative one. Moreover, although program code does not carry physical traces of the craftsman’s hand, as Ruskin’s beloved medieval gargoyles do, it may well bear stylistic traces of the individual programmer. A person’s programming may be as distinctive as the other personal signatures recognized by connoisseurs of art (Boden 2010b).

The problem, of course, is that only a tiny handful of people are knowledgeable enough to be able to identify the creativity, still less the personal authorship, of the code underlying a CG-artwork (which, in any event, is not usually made available). As an attempt to persuade the general public that such art really is art, the close analysis of programming skills is a lost cause. Not-so-close analysis can sometimes be helpful, however; see sections 4.3–4.4. Nor can this approach enable just anybody to try to produce some CG-art themselves: programming, no matter how simple, does not come as naturally as using a paintbrush or needle and thread. As Ruskin might have put it, computer art cannot satisfy his demand that art “should both admit the aid, and appeal to the admiration, of the rudest as well as the most refined minds” (Ruskin 1854, 34).

Furthermore, Ruskin was fiercely critical of the then-new machine age, whose factory technologies he saw as insidiously dehumanizing. Were he alive today, he would regard computers in general and computer art in particular as further steps on this dehumanizing road and, as such, to be deplored.

He would not even have accepted computer-aided art—such as David Hockney’s recent work, for example. But why not? If chisels are allowed as art-making tools, why not computers? And if the individual human artist is making all the important art-making decisions, as Hockney certainly is, why cavil at the use of digital technology rather than the blacksmith’s hammer and tongs? The answer (see chapter 7), is that Ruskin explicitly outlawed the use of instruments that hugely increase our physical or mental powers. Titian or Michelangelo, he said, would have rejected “electricity” or “steam-hammers.” In general, great artists would “imperatively and scornfully refuse, either the force, or the information, which are beyond the scope of the flesh and the senses of humanity” (Ruskin 1875, chap. 2, sect. iv). So computers, clearly, would not be acceptable to him even as convenient aids to art making.

As for generative computer art, in which crucial choices are made not by a human being but by the program, that would be even further beyond the pale. He would not have regarded CG-art as true art at all. So notwithstanding his celebration of creative skills, CG-artists would be most unwise to suggest Ruskin as their aesthetic champion.

Our original question, then, remains: If art appreciation is largely the recognition of artistic skill, how can the arcane, elitist skills of computer artists become widely recognized and respected? How can they be made accessible to the rudest as well as the most refined minds?

4.4   Making CG-Artists’ Skills Directly Visible

Making CG-skills accessible could mean making them more directly visible in the artwork itself, or it could mean making them more intelligible. Both strategies are highly problematic, as we will see. But the latter is more likely to be attempted than the former.

In principle, a CG-artist could try to make his or her CG-graphics or CG-music bear some visible or audible footsteps of the underlying program. But because such traces would affect the perceptible nature—the aesthetics—of the artwork itself, the artist very likely would not want to do this. Only if the footsteps formed part of the original aesthetic intent would they be artistically acceptable.

Artistically acceptable footsteps could arise in those cases of evolutionary art (see chapters 2 and 5) in which part of the artist’s prime intent is to celebrate evolution as such. There, visible traces of the history of the art making would have some intrinsic aesthetic interest. Evidence of the execution of the (evolutionary) program would not merely lie alongside the artwork but would form an integral part of it. The artist concerned might even regard the artwork itself as a continuing process or as a sequence of displayed images drawn from successive generations.

Where evolutionary art, or Evo-art, is concerned, it is relatively easy to make some aspects of the program visible to the audience (see section 4.4). It is much more difficult to indicate this or that aspect of the CG-art program to the observer when that program is a traditional symbolic affair (like all but the most recent versions of Harold Cohen’s AARON, for instance).

Burdening the gallery visitor with successive images generated by a (symbolic) program that was designed by the artist to produce one or more static artworks seems less aesthetically defensible than doing this for Evo-art. What is more, the earlier stages of the program might not actually produce visible images at all; instead, they might merely make decisions that will affect the visible artwork when it comes to be generated later. Even if (some of) the earlier stages were capable of producing visible images, very few CG-artists of this kind would want to present such preparatory phases as part of the artwork as a whole. Nor is it clear how they could succeed in leaving telltale visual traces of the program within the final static artwork even if they wanted to.

A display of some of the preparatory phases in the execution of a symbolic program might indeed help open the audience’s eyes to the computational skills involved. For it could show that the CG-artist has created a program that focuses on specific aesthetically relevant problems in arriving at the final artwork. One subprocedure, for instance, might enable occlusion by deleting previously drawn lines, another might seek an empty space of suitable size so as to avoid having to deal with occlusion. Understanding how those stages come about would require more technical expertise on the part of the gallery visitor. This presentational strategy is a gamble, however, and one that the CG-artist may not wish to make. For it risks being seen as education rather than art.

In sum, making CG-skills directly visible is easier said than done and, when it is done, may be experienced as pedagogy, not aesthetics.

4.5   Intelligibility through Programming

The tension between pedagogy and art is present in all attempts to heighten people’s appreciation of CG-art by teaching them how it is achieved. Such attempts need not involve visible footsteps in the final artwork or even visible traces of the history of the running of the program. They may be very much more abstract than that. And very much more abstract than the formal rules that underlie some famous examples of Conceptual art (see Boden 2007). In a word, they must deal somehow with programming. They must convey something about what the program does and also something about how it is doing it.

The dilemma here is that making abstract CG skills more intelligible threatens to turn the gallery into a classroom. The audience’s aesthetic experience will be colored (and often diminished?) accordingly. The danger that aesthetics may be elbowed aside to make way for education must be faced, nevertheless. We have already noted that an art lover knowing nothing about programming cannot properly appreciate CG-artists’ work. Knowing something, however, may help. We need to ask just what kinds of “somethings” are relevant and just how they can be communicated to the nonspecialist audience.

Perhaps the first thing to say is that in a country whose schoolchildren are taught information technology (IT) partly by writing miniprograms (in languages like LOGO or BASIC), the potential for the appreciation of computer art will already be heightened. Anyone who has learned to program, even at a Mickey Mouse level, has learned some highly relevant “somethings” that can be acquired in no other way.

They will have realized the clarity and precision required to program successfully. They will have experienced the creativity involved in achieving a desired result in a new way. And perhaps even more important, they will have glimpsed the unsuspected power of superficially simple pieces of code. For these ends, Mickey Mouse programming is enough. There is a greater difference in understanding—and so in the capacity for appreciating CG-artists’ skills—between someone who has never programmed and someone who has than there is between the most fumbling amateur programmer and a dazzling artificial intelligence professional.

When IT was first taught in British schools, miniprogramming was the general approach. Nowadays, most IT lessons focus instead on the use of ready-made computing packages—such as financial spreadsheets, or Hockney’s CA-aid Photoshop, or the ubiquitous search engine Google. The creative activity of trying to compose a program for oneself simply is not made available. Most children, including (so my artificial intelligence colleagues tell me) some with mold-breaking computer scientists as parents, infer as a result that computing is boring and turn away from it accordingly.

In 2012, a senior executive of Google warned (rightly) that the UK is jeopardizing its preeminence in IT by this approach. For our purposes here, what is relevant is not the effect of a country’s IT education policy on its economic potential but the extent to which it prepares its citizens to appreciate computer art or, for that matter, to become CG-artists themselves. Borrowing Ruskin’s words: preparing them to give their aid as well as their admiration. With respect to those goals, too, our children need fewer packages and more programming.

I am glad to say that the British school curriculum has just been altered. From 2014–2015 on, all children are learning some programming. But that welcome change has only just begun. Its effects will take years to become apparent.

At present, then, the potential audience for CG-art is overwhelmingly innocent of programming. The topic is not only abstract (as arithmetic is, too), but—to them—utterly abstruse. So presenting the actual code, or even the logical rules, that generated a given work of CG-art will not help more than a handful of people appreciate the creative skills involved.

This situation is especially problematic for live coding (LC) art, in which the audience is required to match the developing artwork with the lines of code being written by the artist as they watch (see chapter 2). Doing this is far from easy. Indeed, the live code is sometimes written in a programming language (such as LISP) that is unknown even to many IT professionals. Admittedly, the artist will usually attempt to code generative processes that are “succinct and quick to type” (Brown and Sorenson 2009, 17). But that does not necessarily help the audience. The LC-musician Alex McLean (personal communication) has tried to construct a special programming language that will be relatively transparent to nonspecialist listeners. But his example is not widespread; it is hugely tempting—for the artist—to rely on familiar languages that are intelligible only to the cognoscenti.

In short, LC-artists assume that their audience is familiar with programming code. Only if that is so will they be able to appreciate the artwork (usually music) being created in front of their eyes except in the most superficial manner. Such familiarity is highly unusual, however. For most members of the public, LC-art is a closed book.

The other forms of CG-art are less demanding. Even so, special measures may be needed to deepen the audience’s aesthetic experience.

In the very early days of CG-art, the generative code or set of rules was quite often displayed alongside the final artifact. Ernest Edmonds did this for his late-1960s 3-D canvas Nineteen, for instance (Cornock and Edmonds 1973, fig. 2) and so did most of the budding computer artists who presented work in the groundbreaking Cybernetic Serendipity exhibition at London’s Institute of Contemporary Arts (Reichardt 1968). But the (tiny) audience at that time was likely to be au fait with programming or at least with logic and mathematics. Today, the potential audience is very much wider, so other methods are needed to convey the abstractions concerned.

4.6   Intelligibility through Description

One way of communicating the nature of CG-artists’ skills without getting lost in abstractions is to rely on natural language—possibly complemented by visual illustration. So one may try to provide a high-level verbal description of what the program is doing, and how it is doing it, that will be intelligible to virtually any gallery visitor.

The taxonomy of computer art presented in chapter 2 is one attempt to do this, in very general terms. Considering the taxonomy as a whole enables one to understand a given (type of) C-art not only by knowing something about how it was made but also by comparing or contrasting it with other types.

It is not a simple matter, however, to communicate such intangible ideas to the gallery visitor—who, in any case, is normally interested in some particular instance of C-art, not merely in a general taxonomic class. The degree of detail, for instance, will be constrained not only by the audience’s knowledge (or lack of it) of programming or only by their powers of concentration but also by the physical space or time available.

Occasionally, a CG-artist will write an entire book explaining what his or her programs do and how they work (Todd and Latham 1992). Or perhaps someone else will do so on the artist’s behalf—although in this case, the how may be sadly underplayed (McCorduck 1991). But relatively few people will take the time and trouble to read it. More often, the artist has to rely on exhibition catalogs. If the gallery is willing to sponsor a catalog with room for more than a list of names and titles, a written description could be fairly lengthy. If not, then the artist (and the curator) will be limited to what can be realistically presented—and easily read—on the wall nearby. For CG-music, of course, there is no wall; if the description is to be a written one, then some sort of explanatory leaflet is the only way to go.

Sometimes, the CG-art description might be made available not (or not only) in writing but as speech. This would involve an introductory minilecture, presented either at a fixed place and time or (more likely) via a phone app or a portable audiocassette. That project is not straightforward either: the speaker—perhaps a professional actor?—of a prerecorded message would need some understanding of it to communicate it effectively.

The type of CG-art that is easiest to describe in this way is also the one that the general public finds the most surprising: Evo-art. The notion that a program can change itself, as opposed to being rigidly fixed by a human programmer, is highly counterintuitive for many people. But explaining it to them is relatively easy, because the programs that generate Evo-art exploit ideas with which the audience is already familiar. Besides the concept of biological evolution itself, these common ideas include reproduction, generations, siblings, heredity, gene, random mutation, selection, and fitness; and some people will also have come across more technical terms, such as genotype, phenotype, crossover, and point mutation.

As well as describing Evo-art by using words such as these, the CG-artist can provide visible snapshots drawn from different generations of a graphics program’s phenotypic performance. This is more difficult with Evo-music, because one cannot perceive several music snippets simultaneously. And some or all of the visual snapshots can even be presented alongside the relevant (genotypic) code snapshots and with explanatory verbal comments to boot.

These parallel snapshots (with the verbal comments) can convey an idea of the sorts of code mutations that are possible and of their potential graphic results. That deeper mutations (such as crossover or concatenation) lead to deeper, and even less predictable, phenotypic changes than simple (point) mutations do can be illustrated in this way. For examples, compare the deep mutations—and utterly unpredictable genotypic or phenotypic results—that are allowed by Karl Sims’s evolutionary graphics program with the much less adventurous ones allowed by the sculptor William Latham (Sims 1991; Todd and Latham 1992). Latham was less adventurous not because he was less imaginative and not because he (or his code-writing collaborator, Stephen Todd) was less skilled in computing, but because his artistic interest was in exploring the potential of a more limited, yet aesthetically interesting, space of possibilities.

The skills of the CG-artist in choosing just which types of mutations to allow in the underlying code can be hinted at in this way. Similarly, the fitness function used for selection at each generation can be communicated, up to a point, by displaying sets of snapshot siblings with their differing fitness marked. The artist’s skill in deciding to use that fitness function rather than this one can be gestured at in this way.

Hints and gestures are all that can be provided, of course. A fuller understanding is available only to computer experts. But displaying the bare bones of CG-art as previously suggested can help inform the audience about the Evo-artist’s skills both in coding and in making more obviously aesthetic (i.e., fitness or phenotypic) choices.

This limitation on the communication of CG-artists’ skills is not necessarily a bad thing. Hints and gestures may be all that the CG-artist actually wants to convey. Admiration is often increased by mystery. Cohen waited many years before making the code for any version of AARON available on the Internet. And more traditional artists, too, are not always eager to announce their secret skills to the world.

If Evo-art is relatively easy to explain, connectionist-based CG-art is not. One of the most widely admired pieces in London’s Millennium Dome was Richard Brown’s Mimetic Starfish. This was a continuously changing, and extraordinarily lifelike, image of a multicolored starfish, moving its five arms freely while seemingly trapped inside a marble table (Brown 2000). The image (projected downward onto the table from the ceiling) was generated by a neural network, which made a host of detailed visible changes that were triggered by its perception of the movements and sounds made by the audience nearby.

There was an outline explanation on the wall of how the system worked, which made some highly superficial comparisons with the brain. But just what Brown had to do—just how he had to exploit his coding skills—to construct this marvel was hardly touched on. And that is not surprising. Explaining how a parallel-processing neural network functions involves communicating ideas such as weighted connections, excitation or inhibition, equilibrium, distributed representation, and constraint satisfaction. It is not impossible to do that in reader-friendly terms (see e.g., Boden 2004, chap. 6). But it needs more space and more concentration from the reader than a couple of laminated posters on the wall.

The Mimetic Starfish was an example of computer interactive (CI) art (see chapters 2 and 7). In CI-art, the program interacts with the outside world—sometimes wind or weather but usually the human audience. Accordingly, the detailed nature of the perceptible result depends on just what the program does in response to the outside influence. I say “perceptible result” rather than “artwork” because many CI-artists would insist that the artwork is not actually the resulting artifact but the interaction itself.

In principle, hints about the skills exploited in CI-art—what the program is doing, and possibly even also how it is doing it—could be conveyed by sampled snapshots without compromising the aesthetic intent. The key point of CI-art is that the changing stages of the artwork are constructed on the fly as program and audience interact. If some of these interactions can be picked out so as to convey a sense of what is going on behind the superficial experience, the artistic integrity of the work would seem to remain intact.

It does not follow, however, that these hints can be provided easily. Quite apart from the audience’s general unfamiliarity with programming, there are two further reasons why the CI-artist’s skills may be inaccessible.

The first is that the CI-artist sometimes decides to make what the program is doing invisible. In other words, the human audience is not expected, still less required, to recognize the nature of the interaction. Sometimes the artist is content if they do not even notice that there is an interaction. In such cases, the question of the audience’s appreciation of the CG-artist’s skills simply does not arise.

The second is more general. Because of the theoretical generality of the digital computer, infinite types of computer-generated interaction are in principle possible. To be sure, what actually happens will depend on the CI-artist’s skills (as also on their aesthetic taste and imagination). But just what is going on can vary so much that there is no single answer to the question of what the computer is doing. The programming involved may be of any computational type—evolutionary, connectionist, symbolic GOFAI (good old-fashioned artificial intelligence), cellular automata, hybrid, etc. Even more important, the kinds of interaction involved vary indefinitely. This does not prevent there being a special description of each idiosyncratic system of interaction. But this will have to be prepared anew for every individual case. Whether the CG-artist actually wants to spend his or her time in doing that (an educational task, after all) is a pertinent question.

Suitably experienced curators might be able to help here and in other cases of CG-art as well. They might assist in, or even take over, the task of providing a gallery-friendly description of what the program is doing and how. Specialist curators, constantly showing CG-art, might even amass a collection of items—such as posters—that could be used for several different exhibitions. A curator of a normal gallery, one not dedicated to computer art, is unlikely to be able to fill this role. But there might be roving curators, people able to give advice to artists or galleries when the artists are loath to do the job themselves.

Sometimes computer artists will seek feedback from their gallery audience (see chapter 8). This is usually done in the hope of improving early versions of the artwork, taking the audience’s reaction into account—and thereby enabling the audience to be creative participants at one remove (Muller, Edmonds, and Connell 2006). One place where this happens regularly is in the permanent Beta Space section at Sydney’s Powerhouse Museum (Turnbull, Connell, and Edmonds 2011; Turnbull and Connell 2014, 221–228).

A rather different type of audience engagement could help the visitors understand, and so better appreciate, the artwork they are experiencing. Much as children visiting galleries or museums are often given printed quizzes or questionnaires designed to encourage them to focus on certain aspects of the artifacts being displayed, so adults confronted with computer art might be given handouts designed to guide their thoughts to the specific whats and hows of the art concerned. This could become a routine practice not only in designated Beta Spaces (the answers on the handouts might be helpful to the CG-artists in the future development of their work) but also in CG-art exhibitions in general.

Not all CG-art is displayed in galleries, of course. Some of it, especially the interactive variety, is found in streets and city squares. Usually, such art is commissioned by governmental or commercial institutions (Turnbull and Connell 2014, 228–240). The artists and organizers of CG street art face an added difficulty. Not only may there be no convenient wall available for explanatory material but the whole point may be to give the unsuspecting pedestrian an ephemeral surprise to be experienced in passing, not an art system or artifact to be contemplated at length. Rabbits out of hats, perhaps.

4.7   Conclusion

CG-artists may be forever doomed to elicit only a shallow appreciation of their work, responding strongly to the surface aspects but with little or no recognition of the skills involved in their art making. Certainly, they face that obstacle today.

As we have seen, this tendency can be countered in several ways. But most of them risk confusing art with education—an education full of unwelcome abstractions, at that.

Moreover, education will never release them entirely from having to borrow the mantra of philandering husbands everywhere: “My wife [audience] doesn’t understand me!” Traditional artistic skills will always remain more accessible than those underlying CG-art.

All human beings can understand the basic aspects of painting, carving, sewing, and sculpting. Culturally ancient crafts and body arts, as well as the more historically recent (and culturally specific) fine arts, exploit those skills in familiar ways. And all of us can readily learn more about them if we wish, either by practicing them as increasingly competent amateurs or by reading about them in the works of art historians or teachers of art. The skills applied in computer art are different. Even if all children were introduced to programming at an early age, the expertise required would be both less visible and less easily intelligible than the familiar handcrafts that most of us—like Ruskin (and largely following Ruskin)—value so highly.

All is not lost, however. After all, most lovers of traditional art are hugely ignorant of the technical details involved but can respond deeply nonetheless. People faced with CG-art can respond to it deeply too. Indeed, they can be transfixed by it in ways that simply are not available in traditional art (remember the Mimetic Starfish). Knowledge of the computing skills involved can certainly add to the experience, but a valuable aesthetic response can occur without it.

In short, the audience does not have to know the recipe. So maybe the most appropriate—or anyway, the most realistic—injunction for CG-art audiences is the one given by cheerful waiters in down-market bistros, simply “Enjoy!”

References

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Boden, M. A. 2010a. “Autonomy, Integrity, and Computer Art.” In Creativity and Art: Three Roads to Surprise, 175–192. Oxford: Oxford University Press.

Boden, M. A. 2010b. “Personal Signatures in Art.” In Creativity and Art: Three Roads to Surprise, 92–124. Oxford: Oxford University Press.

Brown, A. R., and A. Sorenson. 2009. “Interacting with Generative Music through Live Coding.” Contemporary Music Review 28 (1): 17–29.

Brown, R. 2000. Mimetic Starfish. Video available at www.mimetics.com/starfish.

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