essay on computer arts

Beyond Productivity: Information Technology, Innovation, and Creativity (2003)

Chapter: 4. the influence of art and design on computer science research and development, 4 the influence of art and design on computer science research and development.

I nformation technology (IT) as a medium for the work of artists and designers is discussed in Chapter 3 , which points out that there are many ways for computer science (CS) to support new tools and applications for the arts and design disciplines, in service to cutting-edge and more mainstream practitioners alike. These tools and applications offer the potential for beneficial developments in information technology and creative practices (ITCP). But there are further, more profound implications of the intersection between IT and the arts and design, and these are the focus of this chapter, which views art and design practices as forms of CS research and development. This perspective on CS is more subtle, more challenging, and more fundamental than the tools orientation of Chapter 3 . It involves a non-traditional and perhaps unfamiliar kind of art and design practice. It also involves rethinking CS in ways that many computer scientists would find non-traditional.

BEYOND TOOLS

The information arts.

Writing in 1993 during the take-off of the wired boom of the 1990s, veteran commentator Stewart Brand pondered whether “technology has swallowed art, and so is art gone now?” 1 In fact, if art is understood as the making of unique individual objects—such as paintings, sculptures, and drawings—or the result of traditional approaches to

the performing arts, then, for some new-media artists, the answer may be yes. They take information technologies for granted, but their art is not fixated on the computer as a medium, as if it were paint or a violin, or even the sound artist’s turntables or the scenic artist’s optical instruments. As Stephen Wilson’s recent encyclopedic compendium of contemporary intersections between art, science, and technology shows, the information arts range across the life and space sciences, nanotechnology, robotics, and other new materials, as well as IT itself. 2 This style of practice does not use technology to create new artworks so much as it uses artistic practice to manage and interpret information at the cusp of technological and scientific research.

This new kind of art and design practice looks increasingly like technical research, but it is done from an artistic or design rather than a scientific perspective—it asks different kinds of questions and uses different kinds of methods to search for answers. Generally speaking, technical research focuses almost exclusively on new technical possibilities: What new things can be done? How can they be done faster or more efficiently? By contrast, artistic and design work tends to focus on the social and cultural meaning of the technology that is under development. This aspect differentiates the approach from that of conventional CS, which does not tend to address explicitly such implications of decisions about system design and implementation, and which may look askance at approaches that have a social science flavor. While a traditional work of art can be thought of as a representation of an artistic concept, the information arts often ask what technologies themselves (perhaps unintentionally) express and how they ought to be reconceived. 3

Artists’ questioning can be a powerful, constructive force. In particular, since the mid-19th century artists have often personified the “user to come” for new cultural technologies. Many media technological advances have arisen in the arts and design fields or have been modeled there, a decade or a generation ahead of the industrial-academic curve; see Box 4.1 . For Alvy Ray Smith, the prominent computer graphics expert, artists are most valuable as “explorers at the edge of our culture,” and he looks to them to “tell the rest of us what [computation] really is.” 4 Thus, the information artist functions as an archetypal knowledge worker: someone able to “penetrate conventional organizations to which their continuing attachment to an ‘external’ knowledge community represents a valuable asset.” 5 ITCP

makes apparent the value of the artist as mediator—someone who is increasingly intercommunicating—addressing IT-related process and context and expanding beyond the traditional artist’s focus on content.

For an example from the world of design practice, recall the work of Karim Rashid (presented in Chapter 2 ). The software that controls the variations in each of the napkin rings produced is integral to the creative process. Without such software control, the rings would be identical as the outputs from a mass production process. In both instances (with and without the intervening software), the initial design involves creative practice. However, the introduction of Rashid’s software into the production process offers an additional opportunity for creativity. In this sense, the reach of the information artist extends beyond product design to process design.

The reach of the information artist extends beyond product design to process design.

MODELING DISCIPLINES: FROM MULTIDISCIPLINARY TO TRANSDISCIPLINARY

The relationship between IT and the arts and design as discussed in Chapter 3 can be described using a multidisciplinary model. Each discipline (e.g., architecture) is represented as a circle; the area where the circles overlap is the area of intersection (e.g., IT-enhanced architecture). One implication of this representation is that the non-over-lapping areas do not change. Each discipline provides some piece of its practice or theory that is compatible, useful, or mutually beneficial to other disciplines without any change in the way the discipline itself fundamentally works (see Figure 4.1a ). 6 This kind of representation conveys how aspects of already-existing genres, methods, or theories are applied in different contexts—in the case of this report, how IT can be applied in the arts and design areas.

But in another possible model ( Figure 4.1b ), the circles do not intersect but instead share a common frame. Each discipline maintains its own knowledge and methodologies but is fully, not partially, open to the other disciplines. 7 In this second model, the disciplines not only apply their methods in a new context but also are receptive to fundamental changes in knowledge and methodology based on their interaction. What is crucial to enable this kind of interaction is the “frame” surrounding the disciplines—a mutual awareness and understanding, especially a historical understanding, of one another and their relationship. The shared frame may be only a transient phenomenon—the disciplines may come into contact, engage in some fruitful exchange, and then continue to develop separately and move apart, as contrasted with the multidisciplinary approach sketched in Figure 4.1a . In transdisciplinary research, 8 the point is not just application of given methodologies but also implication —a result of imagining entirely new possibilities for what disciplines can do.

In transdisciplinary research, the point is not just application of given methodologies but also implication —a result of imagining entirely new possibilities for what disciplines can do.

(a) Multidisciplinary Model: New Context of Application

• Mixes knowledge of disciplines A, B, and C for a common purpose, but each discipline keeps its former shape

• Intersected area creates new context for application of already-existing concepts and methods

—IT as a means to replicate, automate, speed up, and/or reduce the cost of prior analog practices

—Art as post hoc “beautification” of separately conceived functionality styling

(b) Transdisciplinary Model: New Context of Application

Transaction space requires understanding of own and other disciplines

• New common space created specifically from interpenetration of disciplines

• Boundaries are shown as perturbed, and they adjust to accommodate the reflection from other circles

• Intensity of communication between disciplines becomes context for implication — expansion beyond context of immediate application to “anticipatory vision” of future possibilities

• Transaction space may be transient, leaving separate circles reconstituted based on a single transaction—or sustained over time, leading to a durable merging

FIGURE 4.1 Models of the relationship between information technology and the arts and design: Multidisciplinary versus transdisciplinary. SOURCE: Adapted from Margaret A. Somerville and David J. Rapport, eds., 2000, Transdisciplinarity: Recreating Integrated Knowledge, EOLSS, Oxford, U.K, pp. 248-249.

The project on Internet data sonification (introduced in Chapter 2 ) involving a Lucent Technologies statistician and a media artist/composer illustrates the differences between the two models. Brought together through an ad hoc, short-term artist-in-residency effort co-sponsored by Lucent Technologies and the Brooklyn Academy of Music, neither the scientist nor the artist had a preexisting hypothesis or conception of how to combine musical gesture and statistical modeling of data. The outcome was a prototype listening station that conveys the dynamic properties of Internet communication flows (e.g., newsgroups and chat communities) through sounds: melody, texture, and rhythm. This common object resulted from reciprocal learning about the implications of each field for the other. Beginning with a less joint or problem definition, a multidisciplinary outcome would have been different from this—and arguably less innovative. For example, a composer or a painter might have interpreted the abstract patterns revealed by the data flows; that kind of approach has been seen in art inspired by data visualization. Or the statistician might have proposed to a designer the production of an elegant display of quantitative tables.

The Listening Post research prototype has spawned several sequels, each realized in the separate worlds of the scientist’s and the artist’s ongoing work. Mark Hansen, the scientist, defined applications relevant to the operation of Lucent network-monitoring facilities; Ben Rubin, the artist, continued to present data-driven network sonification in musical and gallery contexts. The team has, in fact, endured longer than the initial pilot. 9 Two co-authored papers on the results of the experiment address different specialist readers, accounting for the collaborators’ contribution symmetrically. 10 Intellectual property agreements unique to the dynamics of the project were developed after some difficulty; they recognized that the artistic content and the invented intellectual property—together constituting the project outcome—are inextricably merged and are co-owned. 11

In the intersection of multiple disciplines described above, the roles of artists and designers and computer scientists are clear-cut. Artists and designers have needs that computer scientists can fulfill. Engaging in a fruitful exchange requires conversations to identify those needs and to determine how computer scientists can best fulfill

them. In a transdisciplinary situation, however, artists and designers are not clients of computer scientists but instead interact with them as peers. Bringing to the exchange their own disciplinary methodologies and value systems, artists and designers have their own opinions about what research ought to be pursued and how it ought to be done. One result is a fundamental rethinking of how research into information technology might be conceived.

IMPLICATIONS FOR COMPUTER SCIENCE

For computer science work, the advantages of being open to the perspectives of the arts and design disciplines are potentially large. Computer science already has a productive tradition of drawing on other disciplines, from mathematics to physics to cognitive psychology, to advance its own work by exploring new problems and thinking about new potential solutions to those problems. 12 Similarly, responding to disciplines from the arts and design worlds opens the possibility of discovering new methodologies for and solutions to problems that, until now, have been beyond the reach of the computer science field to solve or perhaps even articulate. Often the effects of IT research have proved profound (and sometimes unintentionally so), 13 and ITCP serves as a way for those with primarily technical interests to communicate with those more interested in the social, cultural, and political aspects of technology.

The perspectives of the information arts are particularly interesting in cases where CS research itself is already moving toward the perspective embodied by art and design practices. One example of such a shift is in the field of human-computer interaction (HCI). In the last 10 years, the field has moved gradually from focusing largely on the hardware and software of human-computer interaction (e.g., development of the mouse and graphical interface) to paying more attention to human psychology (e.g., what mental models of software are constructed by users) and social interaction (e.g., how software can support project collaboration). More recently, HCI has begun to draw more broadly on the social sciences, especially ethnography (the rigorous, qualitative study of human use and contexts of technology), in order to design systems that better fit into the lives of human users. Simultaneously, the connections have deepened between HCI and the design community, which approaches human-computer interaction in more open-ended ways. 14 These shifts in HCI as a field bring it closer

One result is a fundamental rethinking of how research into information technology might be conceived.

to the information arts and suggest that there is now a potential for synergy between the two.

One such area where the methodology and attention to social, cultural, and political context typical of the information arts may benefit HCI is the use of technology outside of work contexts. Work applications tend to focus on efficient, problem-solving functionality for which we now have well-understood design and evaluation techniques. Applications for everyday life, however, suggest the importance of aspects that are less understood and are hard to quantify, such as quality of experience, meaningfulness, personal values, identity, and appropriateness to social and cultural context—areas for which the perspectives of the information arts may be particularly appropriate. This concern with the human element can already be seen in consumer electronics, which have a history of drawing on market research and human factors analysis and which often depend on design for competitive advantage; the broader uses of IT envisioned with increases in the embedding of computing and communications components implies a broader and often rather different set of personal and other non-work-focused technologies in the future. HCI researchers are realizing that there is a need to do some fundamental rethinking of HCI methods to understand what these assumptions are, to analyze the extent to which they are applicable to the new contexts of everyday life, and, in cases where they are not applicable, to invent new methodologies that are more appropriate. 15 This kind of rethinking is an endeavor for which the information arts can be helpful; concrete examples where interaction may be particularly fruitful are discussed further in the section “ Non-utilitarian Evaluation ” below.

Similar shifts are occurring in other areas of computer science. In artificial intelligence (AI), for example, there has recently been a focus on lifelike computer characters or believable agents, with a great deal of interest in incorporating approaches from drama and the arts into agent design. 16 The development of algorithms for information retrieval on the Web has underscored the need to combine theoretical

computer science with an understanding of the social structure of the Web 17 and raises potential connections to the cultural politics of Web information, 18 an area in which the information arts are working. For areas like these, in which purely technical solutions do not seem adequate to fully address the problems of interest to computer scientists, interaction and engagement with information arts could be beneficial to computer science.

Because the information arts are inherently transdisciplinary, they hold the possibility of motivating more than just the straightforward use of information arts for computer science’s ends or simple collaboration between information artists and computer scientists. Instead, there can be a mingling and repositioning of each interacting discipline. From the perspective of computer science, this implies a move to more qualitative, rather than quantitative, research methods; a greater incorporation of political, social, and ethical considerations into computer science research; and more focus on intuition and aesthetics. 19 Given the movements that have already taken place on the arts and design side, productive cross-fertilization and a broader base for ITCP will depend on the flexibility and openness of individual researchers, research communities, departments, universities, and professional societies—the institutions and organizations that define academic computer science. 20 As detailed elsewhere in this report, there is both movement in that direction 21 and resistance to such movement.

PROMISING AREAS

During the course of its deliberations, the committee identified a number of promising areas for transdisciplinary work. Several have attracted fairly active interest, whereas others are just emerging. They open the possibility of fruitful discussion and collaboration in these

areas between computer scientists and artists and designers engaged in computer-science-like work. The discussion below focuses on areas that the information arts are particularly well suited to address based on the following factors: The areas involve the social context or politics of computing; they raise difficult ethical issues that need to be addressed in the context of technical research; they have high public or social impact; and/or they suggest fundamental rethinking of computer science. Although the following compilation is neither comprehensive nor predictive of the most promising areas, it does give an idea of the breadth of possibilities for productive engagement between the information arts and computer science.

MIXED REALITY

Mixed reality is a new, interactive medium in which computing is taken off the desktop or head-mounted display and linked with real-world objects and places to become part of everyday, physical lives. In these approaches, IT development and other creative practices are synergistic. On the one hand, IT provides a new medium for creative expression, opening up a space of possible developments to be explored. Design and media art practice, on the other hand, offer a broader functional and aesthetic perspective. An art and design perspective introduces a cultural awareness that is essential in the development of devices that not only are functional but also contribute to the quality of life in a less direct, but often more profound, way. 22

In one classic design, Durrell Bishop’s marble answering machine, each message is represented by a marble (see Figure 4.2 ). 23 When a message is taken, the machine produces a marble. 24 The marble can be picked up and put back into the machine in order to play the message. Placed on a matching phone, the marble causes the phone to dial the original caller. Messages are deleted by recycling the marble in the machine. The marble answering machine speaks to humans’ physicality. 25

Approaches to mixed reality include tangible media and augmented reality. In tangible media, physical objects like Bishop’s marbles

For areas in which purely technical solutions do not seem adequate, interaction and engagement with information arts could be beneficial to computer science.

FIGURE 4.2 A conception of Durrell Bishop’s marble answering machine. When a new message is left, the machine deposits a marble in the upper tray (A) for the recipient to find. The marble can then be placed on the lower indentation (B) to play the message. When the message is no longer needed, the recipient recycles it by dropping the marble into the hole (C). Illustration created by Jennifer M. Bishop, Computer Science and Telecommunications Board staff.

have computational properties. Augmented reality is an alternative to virtual reality in which virtual images and data are projected onto and thereby incorporated with the physical world. For example, one can look through augmented-reality binoculars mounted in the atrium lobby of the Center for Art and Media (ZKM) in Karlsruhe, Germany, to see the heart of the building, overlaid with labels explaining what is done on the different floors: reality plus. 26 Both augmented reality and tangible media have their roots in Mark Weiser’s vision of ubiquitous computing. 27

Technical issues in mixed reality include the maintenance of corre-spondence between real-world and virtual objects, standards for interobject communication, perception (including vision processing, video tracking of objects, plan recognition, and integration of multiple forms of sensory data), spatial reasoning, and learning and adaptation. But designing and constructing mixed-reality devices that are functional, useful, interesting, and desirable are not only technical challenges, but also artistic and practical challenges. University envi-

ronments are one venue where the relevant expertise and aspirations are brought together.

At Georgia Institute of Technology’s Graphics, Visualization, and Usability Center, for example, computer scientist Blair MacIntyre and media theorist Jay David Bolter collaborate on the Sweet Auburn project, cross-informing augmented-reality technology and content development. They are developing applications to support tours of Atlanta’s historic Auburn district, in which “ghosts” from Auburn’s past appear superimposed over the landscape and address users with their stories. 28 At the MIT Media Laboratory, Hiroshi Ishii’s Tangible Media group integrates art, design, and human-computer interaction to generate pre-market speculative applications such as music bottles that can be uncorked to release the music inside and “curlybots” that record and play back physical gestures. 29 A playful, speculative design approach is taken at the Computer-Related Design program at the Royal College of Art (London), home of Bishop’s marble answering machine and whimsical applications ranging from a telepathic Tamagotchi 30 to a bird feeder that use principles of reinforcement learning to teach songbirds new tunes.

COMPUTER GAMES

Computer games, having long ago left their roots as playful experiments for academic computer scientists, are emerging as a contemporary topic of computer science research because advances in many component technologies have driven burgeoning interest in “games” for serious contexts as well as entertainment. 31 Thus, for example, in the mid-1990s the Department of Defense (DOD) began to explore prospects for research collaborations among people doing modeling and simulation in defense and entertainment (including games) contexts, 32 and the Defense Advanced Research Projects Agency

has begun to explore the potential of games for decision support. The DOD exploration gave rise to an Army-funded center at the University of Southern California, the Institute for Creative Technology; related work had already begun at the Naval Postgraduate School in Monterey, California.

Computer games offer a unique playground for serious research, not only because of the underlying allure of fun and competition, but also because important new questions arise. For example, what is a body, a surface (when infinitely malleable), or a space? How does one deal with a changing sense of time given that one can go back to a saved game? How then does one change the way one plays? How does one convey the essence of person despite screen form—gestures, and so on—varying? Most interestingly, designers of massively multiplayer online games are grappling, with a large degree of success, with the social, political, and aesthetic issues inherent in virtual worlds. What is the social contract between participants, and between the participant and the designer? What are the consequences of conflicts in the virtual world, and to what degree should those consequences be determined by the online population, versus the administration? How should people deal with the distribution of authorship in an environment where narratives are participatory and emergent? How does one foster organic, self-organizing social structures in a virtual world? How does a designer make places people want not only to visit, but also to inhabit for hundreds or thousands of hours over the course of several years? These questions raise various issues for a number of computer science fields, including information retrieval, database management, and computer graphics, to name a few—though such questions are not purely CS ones, but rather questions that are truly transdisciplinary. There is evidence that CS is beginning to address some of these questions (e.g., see the special issue “Game Engines in Scientific Research” in the Communications of the ACM , January 2002). 33

NARRATIVE INTELLIGENCE

In the early 1990s, a group of graduate students at the MIT Media Lab formed a new reading group, which they called narrative intelligence (NI). 34 The group explored issues at the intersection of narrative and both human intelligence and AI, seeking to develop a dialogue between new computational concepts and technologies and the insights of literary theories such as poststructuralism and semiotics. The group came together with an understanding of, and the desire to

reconcile, the contradictions and incompatibilities between these two world views: AI technology focused by and large on formal, logical representation and objectivity, whereas the analytical tools provided by new literary theories focused on subjectivity, multiplicity, and the limitations of formalism. The pragmatics of negotiating the differences between these world views led to a creative foment. The group flourished, exploring issues in the philosophy of mind, media theory, HCI, psychology, social computing, constructionism, and AI, developing theories and applications in all these areas, influencing the direction of the doctoral program at the Media Lab, and connecting to a wider network of researchers who joined in the group’s discussions over e-mail. Narrative intelligence as a field was born.

NI research obviously incorporates influences from a variety of fields. Artificial intelligence, with tools to model human emotion, personality, and narrative abilities, provides a framework from which much of the research grows. Psychology, especially narrative psychology, generates explanations of the human ability to understand the world through narrative, creating a basis for systems that model or support this ability. Art research raises new questions about the nature of narrative representation, keeping the concept of narrative fresh. Cultural studies analyze hidden cultural narratives, including the stories AI researchers tell through their research. Literary studies examine the nature of narrative in traditional and interactive forms. Drama provides understanding of the real-time performance of narrative. This emphasis on mixing technology development with artistic and humanistic perspectives is unusual in AI. It has supported the generation of new research fields within AI, such as lifelike interactive computer characters, as well as an increase in cross-disciplinary engagement between AI and other fields. 35

At the same time, narrative trends took on importance in related fields. The concept of supporting human narrative understanding through the interface of human and computer began to gain ground in the field of HCI. Work in media studies on hypertext and interactive fiction was inspiring a generation of systems that support narrative in new ways. Within AI, this interest began to spur research in AI for interactive fiction and entertainment, 36 including interactive computer

characters and interactive plots. Many of these research areas explicitly draw on the arts and drama as a source of inspiration. With the growth of the computer game industry has come an interest in new game forms that support narrative in more complex and interesting ways than a stereotypical shoot-and-kill form.

Research in NI is flourishing, with applications in a variety of areas. Narrative interfaces explore possibilities for making interfaces more usable by incorporating elements of story, for example by embodying interaction in a storytelling character. Artificial agents can themselves be designed to use narrative, as humans do, to make sense of the world and each other (see Box 4.2 ). Researchers are developing systems to support human storytelling, as in the case of plush toys that children can program to tell their stories to families and friends. 37 Databases of stories allow people to search for and share stories pertinent to their experiences. 38 Stories can be automatically generated, perhaps in response to input from human users. Interactive digital video allows video sequences to be generated interactively, telling

interactive stories. 39 The field of interactive fiction and drama has exploded, 40 including the subfield of interactive computer characters, or characters with emotion and personality who respond to human users in the context of a story. 41 A complementary area of narrative intelligence studies the stories that AI researchers themselves tell about what they are doing. 42 Sometimes, analysis of these stories can lead to new forms of AI technology by building on alternative stories. 43

In this explosion of research, the interdisciplinary engagement begun by the NI group at the Media Lab remains present—in work taking place in traditional computer science departments, in cross-disciplinary arenas like the Media Lab, in humanities and arts departments that incorporate new media such as Georgia Institute of Technology’s School of Literature, Communication, and Culture, 44 and in the computer game industry.

NON-UTILITARIAN EVALUATION

As discussed above, artists traditionally use evaluation techniques that differ radically from those of computer scientists, with little interest in formal user studies and more interest in social impact, cultural meaning, and the potential political implications of a technology. They seek to provoke as well as to understand the user. There is an opportunity to develop hybrid evaluation methodologies to combine the broader concerns of artists with the narrower and more structured methodologies of HCI. For example, Angela Garabet, Steve Mann, and James Fung use strategies that are open-ended and interpretive 45 to evaluate users’ reactions to wearable computing designs. Interestingly, they demonstrate that users are more open to and accepting of new technology that is presented as the product of a commercial venture rather than as art. Jonas Lundberg and colleagues uninten

tionally achieved similar results in their explorations of a provocative technology, a refrigerator that videotaped its users, ostensibly allowing those who shared the refrigerator to find out if someone had stolen their food. Although the goal was to confront users with negative aspects of technology, users who saw the “product” demonstrated in an ostensibly commercial presentation were surprisingly enthusiastic. 46 Such results may motivate some artists to be more interested in collaborations with commercial objectives than they might otherwise be.

Evaluation techniques drawing on both HCI and arts traditions could rigorously examine not only the usability and utility of software and electronic products, but also the meanings they may take on in users’ everyday lives, the background cultural assumptions that underlie them (for example, the assumptions designers make about what users are like), and their potential impact on current cultural issues and debates, such as intellectual property issues. At the same time, standard HCI techniques appropriately adapted to the goals of artists (often far removed from issues of usefulness and efficiency that current techniques can address) may help improve the sometimes opaque design of interactive artwork. To achieve these goals will likely require a fundamental rethinking of the notion of user tests, as well as other evaluations. In an early example of what such work might look like, artist-designers Anthony Dunne and Fiona Raby evaluated the Placebo project, electronically enhanced furniture that makes users aware of activity in the electromagnetic spectrum, through open-ended interviews with users combined with photographic portraits of users with their devices. 47 Such techniques allow designers to do evaluation in a form that is to some extent recognizable and understandable to HCI practitioners, while exploring issues that matter to artists, such as the subjective nature of user experience, the stories that give devices not only functionality but also meaning in human context, and the messages that information technologies intentionally or unintentionally communicate to users. There is a need to develop a repertoire of evaluation techniques appropriate for these more open-ended questions that is as wide and deep as that already available for the relatively well defined problems of usability and efficiency.

EXPERIMENTAL CONSUMER PRODUCT DESIGN

Computing is creating new challenges as it moves into everyday life. The impact of IT on everyday culture is felt particularly strongly through electronic consumer products such as handheld computers,

music storage and playback devices, and electronic toys. Designers Fiona Raby and Anthony Dunne have argued that such consumer products are currently designed much the same way as Hollywood movies: They are generally uncontroversial, focused on socially acceptable needs, and broadly marketed, and they serve an optimistically idealized lifestyle. In the analogy to film, they note that the alternatives to mainstream Hollywood film—such as film noir, experimental film, and independent cinema—consistently develop techniques of narrative and visual style that are later adopted by Hollywood, thus in effect serving an R&D role. 48 Likewise, experimental designers often develop ideas that, while often not immediately marketed, influence and eventually help redirect contemporary design practices in marketed products.

Experimental designers explore a range of issues and ideas that are often different from those of individuals working in specific product fields who are more constrained by the demands of the market. Their work often explores issues at the intersection of product design and social issues. For example, the 2002 show of the Interaction Design program at the Royal College of Art included Pedro Sepulveda’s architectural designs responding to fears and anxieties about cell phone radiation. There is tremendous potential for ITCP not only in reconfiguring existing consumer electronic applications, but also in imagining and building prototypes for new applications and markets.

MOBILE AND UBIQUITOUS COMPUTING

As hardware components become smaller, faster, and cheaper, IT is being embedded into more and more physical devices, linked together through (often wireless) networks. Networked systems of embedded computers (EmNets) will be largely invisible but extremely powerful, allowing information to be collected, shared, and processed in new ways. EmNets promise significant changes in environmental and personal monitoring as well as scientific research. Thousands or millions of sensors could monitor the environment, the battlefield, the home, the office, or the factory floor; smart space containing intelligent surfaces and appliances would provide access to computational resources. 49

However, product development often follows the path of least resistance, resulting in products that are technically new but do not take full advantage of the broad conceptual design space that is opened up by mobile and ubiquitous technologies. For instance, it is not uncommon to simply replace discrete analog or digital electronics with an inexpensive microcontroller, or to imagine faster or smaller

versions of existing applications. These approaches reflect an internal technical logic and a safe approach to introducing new products to consumers in a competitive marketplace. As a result, they can ignore some of the larger challenges in designing devices that could and should meaningfully support everyday quality of life.

The views of social scientists, designers, and artists are needed to address the potential implications of EmNets. Imagine living in a world where it is impossible for anyone to get lost anywhere. Cars, wireless devices, laptops, TVs, and even articles of clothing will always be able to tell you and others exactly where you are. Imagine also living in a world where devices recognize a person’s speech, emotions (through physical expressions and tone of voice), and likes and dislikes, and respond accordingly. Now, many who use e-mail and the Web enjoy relationships that involve real, but digitized, humans. Will technology make so much available without complaint that the need for interpersonal relations dissolves? Will that relationship with technology generate an illusion of satisfaction, knowledge, creativity, and life? These are the kinds of questions that influence IT research, and they are also the kinds of questions that inspire artistic exploration via ITCP 50 —both activities where government-funded academic research or philanthropically supported arts-based activity can explore ideas not likely to flow from conventional commercial efforts. 51

The development of smart appliances for the home provides an interesting case study. Many of the gadgets being developed today— from refrigerators that can determine when to order more milk and scales that monitor users’ weight gain and suggest low-calorie recipes to home entertainment systems that remember the preferred settings of different users in the home—are obvious extensions of already-existing technologies. The approach of the information arts, in which technical questions are seen as interrelated with social and cultural questions, lends itself well to a more fundamental shift in the design of smart appliances to support not only new technologies but also new, better, and/or more interesting ways of living in the home. The Domestic Environments project of the Equator research collaboration, 52 funded by the U.K. Engineering and Physical Sciences Research Council, is one model of how computer scientists collaborating with artist-designers and social scientists can develop appliances that are interesting both technically and socially. One of the appliances is a “drift

table” designed to promote daydreaming and reflection. A “window” built into the table shows images of the British countryside. As objects are placed on the table, the window begins to drift slowly over the countryside.

The focus on security resulting from the September 11, 2001, terrorist attacks may well be a theme worthy of exploration. As technologists push ahead an agenda for more robust computer security, the information arts would promote an agenda of how to improve security with the least harm to society (or even allowing for the possibility of improved security and a net positive gain to quality of life). 53 Additional areas of interest include work inspired by the implications of intellectual property law and policy for the digital environment (see Chapter 7 for a discussion of digital copyright) and bioinformatics.

Two different kinds of intersection between IT and creative practices are presented in Chapters 3 and 4 . On the one hand, Chapter 3 looks at the use of information technology as a medium for art and design practices, suggesting that computer science can support ITCP. On the other, Chapter 4 looks at ways in which art practice and design and computer science can become fused, leading to new fundamental insights into the nature of computer science itself. In practice, these two kinds of intersection are not disjoint. For example, imagine the development of new word processors with input from professional writers (see Box 4.3 ).

In policy circles, a vigorous debate has been taking place in recent years about whether knowledge production at large is shifting from discipline-bound, strongly bounded, and relatively stable models to transdisciplinary, loosely coupled, and transient ones. 54 There is little to be gained by preferring either multidisciplinary or transdisciplinary exchanges; both have their place and are capable of generating useful and interesting results.

There is little to be gained by preferring either multidisciplinary or transdisciplinary exchanges; both are capable of generating useful and interesting results.

But both the multidisciplinary and the transdisciplinary models make clear that there is a continuing need to maintain the integrity of the traditional disciplines, both in the arts and in the sciences. Without a disciplinary frame, the richness of disciplinary practices, methodologies, and concepts can become lost, leaving an oversimplified cross-disciplinary knowledge domain. This danger exists when any practice is digitized in the absence of an appropriate model, as for example in arts education when young people have become wedded to the prescripted options of packaged applications and are only capable of creating PhotoShop art. What Paul David and his co-authors fear would become “cut-price research motels” in scientific research 55 corresponds closely to the degeneration of artistic quality that is possible where electronic art forms (or media art or the modish “new media”) have been cut loose from their deep connections to older and richer art practices.

Finally, it should be noted that the transient, loose coupling of transdisciplinary creativity runs an ever-present risk of premature bureaucratization. 56 A single successful outcome is a necessary but by no means sufficient reason to continue cross-disciplinary work in the same vein. In some cases, the outcome of a rich experimental device is best evaluated and further developed in the separate but transformed disciplines that contributed to it. In other cases, however, the committee has found persuasive evidence of the need for the sustained bridging of disciplines, involving the development of both individual practices and a community of researchers in the cross-disciplinary area with correspondingly innovative institutional structures (and these are discussed in Chapter 5 ).

The Application of Computer Technology in Art Creation

• Conference paper
• First Online: 01 May 2023
• Cite this conference paper

• Ying Wang 40  

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1045))

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The application of computer technology in art creation is a research field that pays attention to the application of computer in art. It is used to produce works of art such as music, visual arts and movies. Like all research fields, it has its own history and development. In 1956, John McCarthy coined the term “artificial intelligence” at Stanford University; However, it was not until 1959 that alanturing proposed the famous Turing test of machine intelligence (Turing 1960). The computer provides a new technical means for the development of traditional art, expands the field of art design, facilitates art design methods, improves work efficiency, and enriches art creation.

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Wang, Y. (2023). The Application of Computer Technology in Art Creation. In: Hung, J.C., Chang, JW., Pei, Y. (eds) Innovative Computing Vol 2 - Emerging Topics in Future Internet. IC 2023. Lecture Notes in Electrical Engineering, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-99-2287-1_69

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Programmed Graphics in Computer Art and Animation

Dr. Mike King

Visual Outcomes in Computer Graphics

Algorithmic vs. Arbitrary: In looking at the work of artists, designers, and animators using computer graphics systems I have found it useful to invent the terms arbitrary and algorithmic synthesis from primitives. (I have introduced these terms in previous papers, [1] and [2].) In any computer graphics system one is synthesising an image from primitives, which may range from the pixel to 2D geometrical shapes to 3D solid forms. In arbitrary synthesis from primitives the system has no control over the placing and attributes of each primitive: the artist is following their own intuition and reasoning, to which the computer is not party. In algorithmic synthesis from primitives the artist or designer yields up control to some degree by imparting to the computer a set of rules (an algorithm) by which it should incorporate or manipulate successive primitives, which may include a random element. The artist will initiate the starting conditions, and allow the computer to take over.

Most computer-generated or computer-manipulated images will be the result of a balance between the two approaches of arbitrary and algorithmic synthesis, as defined above, but it is my strong belief that the computer offers something radically new to the artist when they explore the algorithmic side of image generation. As well as a range of imagery not realisable through other methods there is also the attraction of serendipity: there is the possibility of an unpredictable but satisfying outcome. The photographer knows this feeling for example, as he or she watches an image develop in the dark-room; as does the bronze-caster when the mould is broken and the new-born surface is revealed. In computer graphics an image may slowly develop, or only be made visible after an over-night ‘batch’ render; even with the promise of ever more powerful systems it seems that there will always be some waiting involved, giving rise to a somewhat addictive sense of anticipation. The anticipation arises in part from an aspect of programmed graphics (or any non real-time graphics) which is that the outcomes are deterministic but to some degree unpredictable. They are deterministic because the same starting conditions produce the same end-result (unless we input truly random data, for example from a Geiger counter), but unpredictable in varying degrees because of the complexity of the process.

Existing Software: Most off-the-shelf software packages offer some algorithmic features, but to really exploit the potential of this approach it is usually necessary to program the computer to a greater or lesser extent. It may however be helpful to consider the types of software available to the artist before looking at the necessity of programming. In my PhD thesis [3] I outlined a simple taxonomy of computer graphics software, and elaborated on it in some later papers. I list it here with a few changes to bring it up to date:

Paint bit-mapped graphics (paint systems) Draw 2D vector (object-oriented) graphics (draw systems) 3D including modelling and rendering Page layout formatting of text and graphics (bit-mapped and vector) Animation time-based versions of paint, draw or 3D systems.

The distinctions made between the first three categories are the most important: software may attempt to integrate two or more of these categories, but in practice this is rare. The three types of package: paint, draw and 3D, are used for quite different visual outcomes, and are quite different to use. Page layout is not directly the concern of this paper, but is included for completeness; it is as well-defined an entity as the previous three. In terms of animation systems we see examples of time-based paint or 3D packages, but there are no widely-available draw or vector animation systems for PCs or Macs, the existing ones being high-end systems for the cartoon industry.

Paint systems, draw systems and 3D are used for different visual outcomes, though each one may be ‘bent’ towards the imagery more normally associated with one of the others. Paint systems are good for photographic treatment, and for painterly effects; generally for subtle effects. A draw package is good for diagrammatic imagery and more technical illustration work: it is resolution-independent, so output quality can be very high, but the visual outcomes usually lack the expressivity or subtlety of paint-system work. 3D packages are used to simulate ‘reality’ through the processes of virtual sculpture (or virtual fabrication) and virtual photography. Using these systems we see mainly the visual results of arbitrary synthesis from primitives: the results are highly dependent on the artist or designer. The algorithmic synthesis provided by these systems are limited to the provision of geometrical shapes, and variants on ‘inbetweening’ – routines that allow one shape to change into another over time or space or both.

User-Written Software: If an artist, designer, or animator is writing a program for generating an image (rather than writing a complete software package) the resulting program does not easily lie in any of the previous software categories. However, the data generated by the program will fit into one or other category, for example a piece of code may generate a bit-map (for a paint-package), a vector file such as PostScript (for a draw package), or 3D data for a 3D modelling package. The user-written software will generally be written to exploit an algorithm, and hence will involve algorithmic synthesis from primitives. It is possible however to write a purely arbitrary ‘shopping list’ of primitives, but in most cases it would be easier to use an existing software package to do so. (One of the early programming exercises we give our students is to do just that: a piece of ‘C’ code to draw a figure – the resultant code looks similar to the PostScript output of a draw package used to generate the same drawing.)

With programmed graphics, where algorithmic synthesis from primitives prevails, it is easier to classify the visual outcomes than with software packages. Many algorithms are based on simple mathematics, or more often geometry, in the sense of geometry being that branch of mathematics with a visual basis. Herbert Franke has this to say about mathematically based images [4]:

‘If one is engaged, as we are, with an inventory of all mathematical branches and with an interest in visualising all forms that come to light, one can obtain plenty of forms, shapes and structures never seen before – an expansion of our treasury of forms. Many of these forms have considerable aesthetic charm. According to the usual criteria we cannot call them original works of art. But they can be considered elements available for new creations and can be used to develop artworks.’

In this quote we have a basis for looking at visual outcomes from programming: a mathematical taxonomy. I have explored this idea to some extent in [1a] and [2a], making a binary distinction between ‘classical’ and ‘fractal’ geometries, the latter having developed only in the last century, and their full importance only made visible through the use of the computer in this century. Briefly the taxonomy I have proposed looks like this:

Classical Geometries ¨ geometrical primitives such as circles and rectangles; ¨ mathematical functions such as roots, squares, exponential curves, parametric curves and surfaces; ¨ Lissajou’s and related figures, involving sine and cosine calculations, including waves; ¨ Patterns, including nets, bands and tessellations. Recursive Geometries ¨ Iterative functions (recurrence relations); ¨ random numbers (these are usually based on iterative functions); ¨ fractals and graftals; ¨ particle systems; ¨ growth models; ¨ linear and array grammars; ¨ Markov chains.

A great number of artists have used variations of sines and cosines to generate imagery of waves or to manipulate 3D solids and textures. Many software packages are providing variations on waves or ripples in 2D, 3D, and also for texture maps. Patterns, often classified under linear repeats (friezes), area repeats, and tessellations (interlocking outlines) are another very common use of programming techniques. I discuss these further in [1b], and also describe some of their computer implementations in a system called ICAS in [2b].

Image Processing: Image processing differs from the previous methods of algorithmic synthesis from primitives, in that it does not involve the continued addition of primitives under algorithmic control, but the re-computing of pixel values in an existing bit-map (for example in a scanned photograph). Image processing involves such a wide range of techniques that one can fruitfully sub-divide them into categories:

Point processes – each dot or pixel is processed regardless of its neighbours Area processes – neighbouring pixels are taken into account Frame processes – more than one image combined Geometric processes – pixels are moved from their original positions.

I have taken these categories from [5]. PhotoShop, a typical image-processing programme for the Mac and Windows on the PC, offers a wide range of these processes, covering all four categories. The geometries involved in the last category are often simple displacements of the image based on waves or spirals, but in principle any of the algorithms in the taxonomy outlined above could be used. Graham Harwood [6], whose work is discussed below, has extended the point process with the replacement of pixels by line segments, whose attributes depend on the pixel intensity (see fig. 1).

Artists using Programming The first computer artists could be said to be people like Franke and Ben Laposky, who experimented with images generated with oscilloscopes as far back as 1950. Laposky called these early experiments ‘oscillons’ or ‘electronic abstractions’ according to Franke [7]; they were generated initially with analogue computers. These techniques readily translated into digital computer graphics, and have become part of the visual language of computer imagery ever since. Similar mathematical techniques were exploited by John Whitney Sr. in his pioneering animations of the sixties [8]. Whereas Whitney’s animations were abstract, and based on the ‘classical’ geometries described earlier, another pioneer, David Em, worked with 3D imagery based originally on programs written to simulate planetary bodies [9]. By the time that he entered computer graphics, in 1975, simple paint systems were available, and his work is a mixture of paint system and programmed techniques. Much of the programming was done by James Blinn at the Jet Propulsion Laboratory, and Em was able to use and modify pioneering code that give 3D surfaces rich textures.

Whitney and Em are examples of a tradition of artists working in large institutions (IBM and the Jet Propulsion Laboratory respectively) whose aims and environment are far removed from that of the painter. William Latham, whose work I discuss in more detail below, is a print-maker and sculptor who is a modern exponent of that tradition at IBM [10]. There are other examples of artists working collaboratively with programmers: John Pearson employed under-graduate programmers to produce a series of combinations of arc segments used in his paintings [11] and Harold Cohen, discussed below, works with artificial intelligence experts [12]. Other artists have worked largely outside of institutions and programming help, using their own machines and skills: Mark Wilson [13] and Edward Zajek [14] are examples.

The first edition of Franke’s ‘Computer Graphics – Computer Art’ [7a] gives an introduction to work of the programming pioneers, and also presents an early discussion of the aesthetic of ‘Computer Art’. In ‘Digital Visions’ [15], a more recent publication that includes imagery from software packages, Cynthia Goodman gives a good non-technical coverage of the early computer artists: the bulk of their work was realised through programming because nothing else was available until the early paint and 3D packages came out.

William Latham, as mentioned above, has followed in the footsteps of pioneers like Whitney, Em, and Cohen, by collaborating with a large institution, in this case IBM. He is presently full-time Research Fellow at the UK Scientific Centre in Winchester UK, and works in close partnership with Stephen Todd and a team of programmers and researchers. He has developed a unique 3D computer graphics system that allows him to mutate 3D forms and animate them. At the heart of the creative process is a system component called ‘Mutator’ which allows the generation of basic forms, their evolution through random mutations of gene ‘vectors’, and their breeding and selection, giving Latham the opportunity to act as ‘creative gardener’.

The 3D models that Latham originally works with are the result of combining fairly traditional 3D computer graphics primitives (cube, sphere, cone and so on), using Boolean modelling controlled through a scripting language called ESME. The ESME programs are created with a text-editor, and give algorithmic control over the placement of primitives, including the use of a programming technique called recursion, which in this context corresponds to the recursive geometries mentioned earlier. Fig. 2 illustrates an ESME code fragment. Mutator has now taken over this ‘low-level’ text-driven algorithmic control by the use of the metaphors of evolution and breeding, as in fig. 3, which shows evolving forms and the main Mutator menu. This is an interesting example of algorithmic synthesis from primitives controlled at one level by writing code, and at another level by an interactive user interface. Fig. 4 illustrates a ‘still’ image as a final piece generated by the system, and chosen by Latham for exhibition in the form of a large photographic print. The illustrations come from the book ‘Evolutionary Art and Computers’, by Todd and Latham [10a], which describes the systems in detail. The question posed by Latham’s use of evolutionary algorithms is – what takes the place of natural selection? His answer is not any form of ‘survival of the fittest’ algorithm, but in his approach of a flower gardener – he simply selects according to his own visual sensibilities.

Karl Sims, a computer artist with a science background, has used a similar approach to Latham in his ‘Panspermia’ series, creating scenes with 3D plants controlled by genetic parameters [16]. These are ‘bred’ together, using a ‘survival of the prettiest’ to select the final set of parameters – the results are quite different to Latham’s work however, tending to a kind of realism involving plausible vegetation. Note that Sim’s selection criteria are similar. Sims is artist in residence at Thinking Machines Corporation – the computing power needed for his work, as with Latham’s, is probably too expensive at present for an artist working alone.

Harold Cohen, a successful British painter in the mid-’60s, wrote his first computer program in Fortran in 1968, at the age of 40. This lead to the evolution of a single artificial-intelligence program called ‘Aaron’ which became capable of generating figurative drawings by the mid ’80s. Cohen’s work is pioneering, unique, and important, and illustrates one major attraction of learning to program: you can achieve something unique, pioneering and important. This is meant quite seriously, although his work is often overlooked, despite one-man (or should it be one-man and one-computer) shows around the world, including at the Tate Gallery in London, England [17]. Cohen has used programming to incorporate rules of painting or composition, derived from an analysis of his own work and others, including children’s drawings. The imagery is not based on maths, geometry, or algorithms in the sense of previously described work, but on decisions, or at a more basic level, the if-then-else statement. The work does however fall into the category of algorithmic synthesis from primitives, where the algorithms are not so much derived from maths or geometry, as from what Cohen terms cognitive primitives. In Pamela McCorduck’s book ‘Aaron’s Code’ [12a] Cohen says: “The program [Aaron] has always been structured in terms of cognitive behaviour, not in terms of form.” (This is in contrast to Latham’s work, which is based on form – ‘evolution of form’.) The cognitive primitives in the early versions of Aaron relate to the subdivisions of a surface in a series of entirely abstract works entitled “Three Behaviours for Partitioning Space”, leading much later to the representations of rocks, plants and people in the explicitly figurative “Eden” series. Cohen says of the direction of his work:

“Aaron … does not do naturalistic pastel drawings, not because it would be difficult, but because it would be inappropriate. But I would also not have it in mind to draw those interminable geometrical figures popularly identified by now as ‘computer art’.”

According to Pamela McCorduck, Aaron was written in the C language up to 1990, after which it was translated, or re-written, in Lisp, a language more commonly associated with artificial intelligence. Cohen has benefited from collaboration with DEC, with the loan of DEC VAX machines for his shows, and eventually an outright gift of a machine from the corporation. As with Latham’s and Sims’ work, a lot of computing power is needed for Cohen’s work – in 1979 his machine had 64K RAM, while his present machine has 34 MB, a factor of about 500 which probably reflects the overall increase in power available to him over the period.

Graham Harwood, a colleague at London Guildhall University, is founder of ‘Working Press’ and originator of many counter-culture publications in the UK [6a]. He began to use computers as a direct result of his use of photocopiers, and to achieve some of the aims set out in the ‘Festival of Plagiarism’ [18]. In 1987/88 there was little software available for affordable computers that could do the kind of image manipulation that he was interested in so he wrote his own software in ‘C’ on a PC compatible. Although he says now that he could probably manage without his own code, he still intends to continue programming for specialised image-processing effects, in particular effects that could give the sense of appropriation, corruption and degradation, as shown in fig. 1. Graham rarely uses maths or geometry as a starting point, preferring to start with a visual effect and work backwards to the required code using common sense. Although he makes little use of the algorithms developed by computer graphics researchers and published in places like the SIGGRAPH conference proceedings, he does like to feel part of a programming community, partly as a counter to the traditional ‘precious’ status of art and artist.

For the future Graham is looking at the programming of plug-ins for existing packages like PhotoShop, and also to writing interactive works where the user can make their own way through one of his stories. His preferred medium is the broadsheet or booklet or magazine, on the basis that there are currently not enough potential readers equipped with the multi-media technology for interactive works. As that changes however he envisages distributing his work on CD-ROM.

Richard Wright, also a colleague at London Guildhall University, is known for his computer animations ‘Superanimism’ [19] and ‘Corpus’ (see fig. 5), the latter being jointly produced at the NYIT and London Guildhall University, and for his writings on computers in culture. Richard was Research Fellow at IBM UK before Latham took the post, and developed his interests in computer graphics as a visualisation tool in science [20], writing a ray-tracer and other software that has been used both in his animations and in the renderer for my ‘Sculptor’ images [21]. His original interest in computer graphics programming was as an alternative to manual methods of production, but also for the new imagery available through mathematical techniques. Although much of the software that he has written is now commercially available, he still finds many techniques only realisable through programming. Richard also makes the point that familiarity with computer graphics programming journals means that he is much more aware of leading-edge techniques that take many years to reach commercially available packages, and which he can take advantage of. Another interesting point that he makes in favour of programming is that it tends to allow the artist to impose their ‘mark’ in a more consistent way than when using a range of commercial packages. (I have also found this to be the case with the ‘Sculptor’ series, where attempts to continue the work in commercial 3D systems has lost a certain individuality.) His current project ‘Heliocentrum’ has attracted an Arts Council of Great Britain award, and involves use of an advanced liquids visualisation technique. He has used fluid dynamics software adapted from the SIGGRAPH paper by Cass and Miller [22], illustrating the previous point regarding leading-edge techniques.

Writing Programs Having looked at programmed graphics in general, and some case studies in detail, we can better ask the question: how has the rationale for programming for the artist changed over the last forty years, and in particular just recently with the availability of good art software running on affordable hardware? What other factors are there?

A feature of much early computer graphics was its vector orientation: output devices such as screens and plotters drew lines, and many algorithms made use of this. With the replacement of the vector technology with raster technology, the capacity for continuous-tone imagery arrived, and also, more recently, the appropriate software packages. Raster graphics has probably made writing software more difficult, firstly because of so few standards concerning graphics displays and colour output (in the old days you had lines and that was it – if you wanted you could change the pen!), and secondly because one is drawn into much more ambitious projects. This problem – of more ambitious projects – leads also to the need for more powerful equipment, as we see in the cases of Latham, Cohen and Sims. More powerful computers can leave one with a smaller range of commercial art software, but this paradox is easily understood if one considers that few artists and designers are likely to own top-end Unix boxes or supercomputers, so there is a very small market for good art and design packages for this type of hardware.

Historically most programming ventures by computer artists have taken this form: the writing of small stand-alone programs for certain visual outcomes – Cohen’s Aaron is a rare exception in being a single program that grew over a period of nearly 25 years. The early programmers would have used Fortran or Algol, and possibly machine languages to interface with plotters and so on. Nowadays the more likely languages are C and Basic, with compilers and interpreters readily available for Macs, PCs and Unix platforms. In each case a library of basic graphics commands is included within the compiler, but with no commonly agreed standards: the chances of a command to draw even a filled rectangle the same way on the three types of system are small. However, with such a range of commercial software packages now available on the Mac and PC, each offering a huge functionality, some of the reasons for writing programs have gone. Some of the reasons are still with us however, and many software developers are aware that their package, however huge it grows, will never satisfy the needs of all their customers. Hence the introduction of programming ‘hooks’ into the system, allowing a third party, or even the customer, to write specialised bits of code.

An example of software with programming ‘hooks’ for the user is Ani Pro, a 2D animation package running on PCs, at present restricted to 8-bit colour (256 colours), and produced by AutoDesk. It has a programming interface for the user consisting of a C-language interpreter (called Poco) built into the package, which the user accesses from one of the menus. The programmer has access to a well-documented library of functions that represent the systems interface, drawing tools, inks, and time-based commands. Poco seems best suited to interaction programming, as its execution of code is approximately ten times as slow as compiled code; however, the code can be compiled to run faster with specialist compilers. 3D Studio is a professional-level 3D animation package also produced by AutoDesk, and uses C-language plug-ins, called external processes. Unlike Ani Pro these are externally written and compiled code generated by a protected-mode compiler (a protected-mode compiler produces code that makes use of the more powerful PCs, in particular the large amounts of memory needed). The plug-ins are divide into four types:

Image processing Procedural modelling (e.g. waves and spirals) Procedural animation (e.g. particle systems) Procedural textures.

These plug-ins allow the incorporation of algorithmic synthesis of primitives into an interactive package, an ideal opportunity for the artist/programmer to extend a package.

The computer artist may start by writing some specialised pieces of code, and find that it may be worth developing it into a package. The end-result may be just for their own use, or may find a wider use, either as share-ware (FRACTINT is a good example), or as a commercial venture. Some artists may not wish to allow others to use their software at all, on the basis of protecting the ‘originality’ of their work. We also see examples where the artist adds an interactive front-end to programmed graphics, or just some interaction for specifying parameters that would be tedious to type in as numbers, for example co-ordinates. On older systems you had to write all the code for any interaction yourself, and each software developer did this in a different way, resulting in a wide range of user interface styles on the same machine. A large part of the development effort for a software package would go into the user interface, with the minor advantage however that you could build the interface to suit your perceptions of its design.

Windows 3, the various Macintosh systems, and WIMPS (WIMPS is an acronym for Windows, Icons, Mouse, and Pull-down menus) user interfaces on UNIX platforms such as X-Windows and Motif provide the developer with a standard way to build interfaces, and with libraries of code to incorporate into the software. The disadvantages? Firstly you have to use the interface routines provided, and secondly there is a steep learning curve: Microsoft Windows and the Mac are reckoned each to have about 1000 library functions, and these take time to sort through and learn. With System 7 on the Mac and with Microsoft Windows there is also a whole new philosophy of programming to learn, summed up by ‘don’t call us, we’ll call you’. This means that you have to take into account that the user may wish to use any other part of the system, not just your program, so your program has to be ready to accept messages from the mouse and keyboard that belong to other programs and pass them on. In practice this means that you program has to wait for messages that do concern it, and allow the processor to do other things if the messages don’t.

In my own work as a computer artist and educator, I have written about 100,000 lines of code, most of which lies within packages: PolyPaint (an 8-bit broadcast paint system) ICAS, the Integrated Computer Art System, Smart 3D, a simple 3D modeller, and Sculptor, a 3D system based on spheres as the sole primitive. PolyPaint is a system that has been in daily use by myself and my students at London Guildhall University for over five years, and was able to take advantage of locally-available user feedback. I have used one-off pieces of code for special purposes, for example in the backgrounds of some ray-traced Sculptor pieces – see fig. 6 (colour plate) for an example. How though, does the range of affordable new software affect the rational for building software packages in my case? Admittedly, one of the motivations was for research into interface design, but in fact the packages, written from a personal perspective do things that available packages still don’t, for example there is little around that makes pattern-making as important part of a package as ICAS did.

In order to collect a range of small maths-based programs together, and also to learn the programming of Windows, I have built a Windows framework for these routines, called ‘Mathspic’. The advantage of doing this, apart from being able to collect the maths-based routines together that I like working with as an artist, was to take advantage of the interfacing routines provided by Windows (and also for potentially re-writing the older packages for this environment). Many maths-based programs will take a number of parameters to control, and it is much easier to explore a piece of maths if one changes these interactively, rather than edit one’s code, re-compile, and run the program again (consider for example Latham’s interactive front-end to Mutator). I have used the Borland Resource Workshop to create dialogue boxes for custom control of my routines (see fig. 7). Fig. 8 shows a custom dialogue box in use to enter parameters for the generation of an image, along with a text panel in a background window with C code showing the calling routine for the dialogue box.

Fig. 9 shows a work entitled ‘The Car Crash of G. I. Gurdjieff’ which was generated mainly using the C-code routines in Mathspic. The curved patterns are a visualisation of an inverse-square law attraction between gravitational bodies or electrostatic charges, and have been used to show the force-fields between a central body (symbolising Gurdjieff, or any charismatic figure) and those either drawn towards or repelled from him. The six frames are symbolic of the chaos a Master may bring to a community, and also of a final harmony. Other programmed routines allowed the graduated ’embossing’ of the image to the top of the frame, and the selected darkening to the left, neither of which processes would have been easily carried out with PhotoShop (for example), though this package was used in the composition of the program-generated imagery and the scanned images of Gurdjieff and the car. The road was generated in 3D Studio.

In eight years of teaching programming to artists and designers only a small proportion of students have pursued rather than using software packages. Those who have seemed to have had some innate aptitude and sympathy for the approach, but interestingly it seems to have been impossible to predict from interviewing potential students. Perhaps a clearer picture of the visual outcomes could help students decide whether they wanted to take it up or not. I used to consider as a rule of thumb that it takes about 3 years to become a competent programmer, and this is a long period compared to the time it takes to become competent with, for example, the use of PhotoShop as an electronic collage system. One might think that with the advent of plug-in programming things would be easier because others had written the bulk of the code, but in practice the level of competence required to interface with these programs is pretty high. There are also other overheads: in the case of AutoDesk (for 3D Studio and Ani Pro) and Adobe (for PhotoShop plug-ins) one is required to register with the developer’s association for a fee, and to buy specialised compilers. The writing of code for Windows is not for the faint-hearted either; as mentioned before one has to wade through a library of about 1000 routines for the ones that one wants and learn a new programming paradigm. The attractions of Windows, apart from the interfacing libraries, lies with the ability to be independent of graphics hardware (any Windows-compatible graphics card will run any Windows program), cheap platforms, and with the huge potential market for developing a package with commercial application.

Conclusions The sophistication and complexity of modern computer graphics software means that the need for artists and designers and animators to programme is significantly reduced. However where a set of visual outcomes related to algorithms are required, the writing of programs may be necessary, either in the form of stand-alone code, or as a plug-in to another package. These pieces of code require an understanding of maths and geometry, and considerable programming skill, though the artist may be able to commission the code from a programmer. The idea however that artists universally approach programming through a mathematical or geometrical basis as Franke’s comment (quoted earlier) might suggest, is not born out by the examples looked at in this paper, with perhaps Cohen’s work the furthest removed from this approach. Cohen’s work also challenges the concepts of ‘originality’ in art, a challenge that could not have been made in the same way without the use of programming. Conversely the use of programming can enhance the computer artist’s claim to originality, as in the case of Latham, where the software is not intended for distribution. Latham and Sims have used programming to simulate an evolutionary approach to image-generation, Cohen has used it for an AI approach, while Harwood and Wright, probably representing a wider range of artist/programmers, have used programming for effects not available through existing packages. In my own work, I have tended to build packages for my own use and others, with occasional writing of one-off software. As a group, the artists discussed in this paper have therefore a wide range of motivations for using programming, which have not been greatly affected by the availability of new packages. However, the group does, in all probability, only represent a small number of artists using computers, most of whom will be well-served by commercial software. We will have to leave history to judge as to which type of computer artist makes the greater contribution to new artforms in this and the next century.

References [1], [1a], [1b] M. R. King, “Development of an Integrated Computer Art System”, in N. Magnenat-Thalmann and D. Magnenat-Thalmann, eds., New Trends in Computer Graphics, Proceeding of the CG International 1988 (Berlin: Springer-Verlag, 1988) pp. 643–652. [2], [2a], [2b] M. R. King, “Towards an Integrated Computer Art System”, in R. J. Lansdown and R. A. Earnshaw, eds., Computer in Art, Design and Animation, Proceedings of the 1986 conference at the Royal College of Art (London: Springer-Verlag, 1989), pp 41– 55 [3] M. R. King, Computer Media in the Visual Arts and their User Interfaces, unpublished doctoral thesis, Royal College of Art, London, 1986. [4] H. W. Franke and H. S. Helbig, “Generative Mathematics: Mathematically Described and Calculated Visual Art”, in Leonardo, 25, Nos. 3/4 (291–294) 1992. [5] C. Lindley, Image Processing in C [6], [6a] G. Harwood, IF Comix Mental, Working Press, London 1989. [7], [7a] H. W. Franke, Computer Graphics – Computer Art, Phaidon, 1971. [8] J. Whitney Snr., Digital Harmony Peterborough, NY McGraw-Hill 1980. [9] D. Em, The Art of David Em, New York: Abrams 1988. [10], [10a] S. Todd and W. Latham, Evolutionary Art and Computers, Academic Press, 1992 [11] J. Pearson, “The Computer: Liberator or Jailor of the Creative Spirit”, in Electronic Art, supplement issue of Leonardo, 1988, pp. 73–80. [12], [12a] P. McCorduck, Aarons Code – Meta-Art, Artificial Intelligence, and the work of Harold Cohen, Freeman, New York 1991. [13] Mark Wilson, entry in Electronic Print, an International Exhibition of Computer Art, Arnolfini Gallery Catalogue, Editor Martin Reiser, Bristol 1989. See also the inside cover of Digital Visions, reference [15] below. [14] E. Zajek, “Orphics: Computer Graphics and the Shaping of Time with Color”, in Electronic Art, supplement issue of Leonardo, 1988, pp. 111-116. [15] C. Goodman, Digital Visions, Abrams, New York, 1988. [16] K. Sims, “Panspermia”, in Eds., C. Schopf and M. Knipp, Der Prix Ars Electronica, International Compendium of the Computer Arts, Veritas Verlag, Linz, 1991, pp. 76-83. [17] Harold Cohen, Tate Gallery catalogue, Tate Gallery Publications, London, 1983. [18] John A. Walker, Glossary of Art, Architecture and Design, 3rd Edition, London Library Association, 1992, London. [19] R. Q. Wright, “Superanimism: the practice of formalised imagery” in Computers in Art and Design, Editor Isaac Victor Kerlow, Association for Computing Machinery, New York 1991, pp 85-88 [20] R. Q. Wright, “Some Issues in the Development of Computer Art as a Mathematical Art Form”, in Electronic Art, supplement issue of Leonardo, 1988, pp. 103-110. [21] M. R. King, “Sculptor: A Three-Dimensional Computer Sculpting System”, in Leonardo, 24, No. 4 (383-387) 1991. [22] Cass and Miller ‘Rapid Stable Fluid Dynamics’ in SIGGRAPH conference proceedings Vol 24, No. 4, August 1990, pp. 49-57.

This paper is published in Leonardo, Vol 28, No.2, pp. 113-121, 1995. Permission to reproduce this paper is kindly granted by the author and Leonardo.

Dr Mike King is Reader in Computer Art and Animation at London Guildhall University.

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“How Artists Can Bridge the Digital Divide and Reimagine Humanity”

By agnes chavez.

Photo courtesy of Agnes Chavez

A 10,000-square-foot inflatable  Space Cloud with a floor made of ten tons of white salt drifting like beach sand, illuminated with programmable LED lights, lands in a park in Taos—a small, rural, multicultural community in New Mexico. Inside, plankton as large as whales drift on the fabric surfaces of the dream-like cloud, while participants wearing virtual reality headsets paint in three-dimensional space.

The inflatable pavilion designed by Espacio La Nube transformed into a learning space for the integrated STEMarts youth program at the 2018 PASEO Festival. We welcomed 700 students from across Taos County, giving them the opportunity to look under the hood and hear from the artists how the magic is created through the merging of art, science, and technology. As one student participant put it, “Now I know what is possible!”

The STEAM (science, technology, engineering, art, and mathematics) movement has been an important catalyst to develop digital literacy skills in education. To achieve an inclusive and equitable digital society, however, we must broaden the definition of STEAM even further to include the development of humanistic skills and sustainable “ futures thinking ” through community-engaged projects. This is especially critical in rural and underserved communities where we are facing a gender, race, and culture gap in the field of science and technology. These gaps in digital arts continue to be a challenge in curation.

The STEMarts Lab, founded in 2009, designs installations and artist-embedded curricula that focus on the intersection of the arts, humanities, and philosophy with science and technology. Through immersive and educational sci-art experiences, students work directly with artists whose work imagines what can be achieved with digital technologies. In contrast to an approach that assumes that any social problem has a technological solution, this collaboration empowers our youths and communities to understand the ethics behind new technologies and their impact on nature and humanity, while giving them the tools they need to creatively engage in and with society.

New media artists can play a pivotal role

New media artists have much to contribute to closing the “ digital divides .”  We live in a society that is fast-changing and increasingly reliant on digital technologies. To fully participate in this new society and reap its rewards, it is crucial to not only bridge the "first-level” digital divide of access to and affordability of information and communication technologies (ICT), but also to address the “second-level” digital divide, called the “production gap.” Filling this gap would give people around the world the necessary knowledge and skills to move collectively from being consumers of digital content to producers of it. Currently, the majority of user-generated content is created by a small group of elites. Therefore, it is critical to provide the diverse sectors of our communities with the skills to produce their own content.

But providing access alone—or even closing the production gap—will not help us solve our most complex problems. In order to be effective, as Jan A.G.M Van Dijk notes in The Digital Divide , “policies must simultaneously reduce existing social and digital inequalities.”

Bridging the multiple divides is an essential part of this process.  The United Nations’ Roadmap for Digital Cooperation outlines “key areas for action.” I focus on three of them below—digital capacity-building, digital public goods, and digital inclusion—and I show how collaborating with digital creatives through transdisciplinary educational initiatives can offer exciting new approaches and strategies to address these unique challenges.

The National Endowment for the Arts’ report, Tech as Art: Supporting Artists Who Use Technology as a Creative Medium , affirms that “Tech-centered artists are admirably poised to grapple with larger societal and sectoral challenges—whether engaging with audiences during the COVID-19 pandemic or responding to calls for greater equity and inclusion in the arts and technology. They can be invaluable partners for policy-makers, educators, and practitioners in arts and non-arts sectors alike."

• Digital capacity-building: Harnessing wonder

SPACE was the concept I explored for the 2018 PASEO Festival with curatorial advisors, Ariane Koek and Dr. Anita McKeown . The festival’s free youth program investigated inner and outer space, artistically, socially, and scientifically, and highlighted the role of art, science, and technology in contemplating our place in nature and re-imagining society. In YouthDay@Space Cloud , students from each of Taos County’s schools visited virtual reality stations by NoiseFold and Reilly Donovan where they experienced otherworldly landscapes, and a GPS interactive installation by Parker Jennings that transported them into outer space and back. Through Victoria Vesna ’s hacked gaming technology, they also learned of the destructive power of noise pollution on our oceans.

Through this artist-led experiential learning, students gain insight in the artists’ ways of knowing—sensory, embodied, visual, kinesthetic—which prioritize the creative and human connection to technology. Teachers visited the online STEMarts Curriculum Tool in advance to learn more about the artists so that they arrived ready to ask questions. Follow-up surveys showed that students found the experience positive and fun, and were curious to learn more about art, science, and digital technology. Years later, they still ask if “the bubble” is coming back.

The power of fun should not be underestimated. The Digital Divide affirms that fostering a positive attitude for using digital media is an important first step for closing the digital divide. Another strategy is making a long-term commitment to the community. Annual festivals create more impact than one-off events. Over six years of producing the PASEO Festival, we watched student, educator, and local government engagement grow. Community members stepped up to volunteer at the festival and learn from the artists. Teachers became proactive and asked for artists to come into their classroom to do hands-on workshops. Students continued to use the free digital tools.

One teacher was so excited by artists/activists Illuminator Collective ’s urban projection workshop that, a year later, she and her students created a protest to save the Arts Endowment in Taos Plaza. Students and teachers who have been participating since the first STEMarts programs at ISEA2012: Machine Wilderness , are now furthering their skills as content creators, teaching or mentoring others, pursuing new media arts fields, or simply walking away with a greater understanding of how they can become creative participants in this new digital society.

• Digital public goods: Creating shareable resources

New media artists are at the forefront of inventing and adapting what the UN roadmap calls “digital public goods”— such as open-source software or open data — to create new digital creation tools. Through the STEMarts youth programs built around artist installations, we are cultivating a new pool of creative thinkers who see the possibilities of these open tools and how to use them. These strategies help participants in underserved communities move from being passive consumers of technology to cultural producers, empowered with the technology to tell their own stories.

As an example, Space Messengers is an immersive and educational sci-art exhibition as part of an international youth exchange program in partnership with U.S. embassies. Students in participating countries use artist-created tools to contribute content for the exhibition that travels to festivals around the world. For this installation, artist and openFrameworks programmer Roy MacDonald wrote the code for the web-based Space Board platform and integrated the (x)trees algorithm. This allows students to co-write their science-informed messages and the audience to respond in real time with their own messages from their devices. Both students and the public experience the excitement of learning science and contributing content as part of a real or virtual reality environment. These open-source tools are available for artists to adapt on GitHub .

In another science collaboration, Taos students learn from artist Markus Dorninger how to use his free Tagtool app, which transforms an iPad into a live visual instrument to tell their stories. With this tool, anyone can connect their iPad to a projector and paint with light, create animated graffiti, or tell improvised stories in multiplayer sessions using their fingers or stylus, eliminating the need for computers and mapping software.

• Digital inclusion: The STEMarts Model

The STEMarts model (see below) builds youth programming around four pillars:

• 21st-century skills and technology
• Cutting-edge science knowledge
• Real-world application and collaboration
• New media arts and social practice

We explore how an understanding of art, science, and technology expands our understanding of ourselves and our relationship to nature and society. We do this by building partnerships and co-designing with universities, science institutions , community organizations, and all levels of government to integrate diverse perspectives and discover new approaches.  The curriculum, board, and advisors for these projects comprise an international and interdisciplinary team of artists, scientists, and cultural specialists that are actively contributing their knowledge—for example, CoDesRes and its STEAM place-based learning interventions and artist, Andrea Polli with STEAM NM. As another example, Dr. Greg Cajete , consultant and author of the book Native Science: Laws of Interdependence , is instrumental in the integration of traditional ecological knowledge. Deeply engaging diverse community members as collaborators in the creation of the workshop, installation, or festival is key to assuring that a wide range of people have equal access to the knowledge and skills, and a platform to share their stories.

Providing free online resources supports access for rural and underserved communities. The STEMarts Curriculum Tool is an online resource that provides teachers with no-cost content for building STEAM activities around the work of participating new media artists, while providing such artists with opportunities to share their work and knowledge with educators and the community. COVID-19 has been a catalyst to get schools and cultural institutions up to speed with internet and computer access, making these free artist-built resources a powerful way to address diversity in the second-level digital divide.

Why this matters now more than ever

We face unprecedented challenges, and if we are to create equitable responses, we must begin to develop numerous literacies. Students can develop understanding and empathy while exploring the applications of science and technology in our societies. They do not need to end up working in related fields to benefit from acquiring humanistic and scientific literacy—and ensuring that they do so will in turn benefit society and the world.

Creating an equitable and sustainable digital society is an essential process that calls for what the United Nations refers to as “digital cooperation”: an ecosystem approach of multistakeholder collaborations between private sector, public sector, academia, and civil society. The need to bring people together from diverse disciplines and cultural perspectives to create alternative futures is urgent.  In a world of complexity and constant change, no one approach is sufficient. Pioneering artists experimenting with technologies can play an important role fostering literacies and bridging social divides. By supporting these artists creating new digital tools and experiences, we allow our diverse communities to participate in reimagining our humanity.

Agnes Chavez is a new media artist , educator, and creative producer collaborating across disciplines to create data-visualized light and sound installations that seek balance between science and art, and nature and technology. Her most recent work, Fluidic Data , is a permanent installation which visualizes data from the Large Hadron Collider created in collaboration with scientists at the CERN Data Center in Geneva Switzerland. In 2009 she founded the STEMarts Lab , which designs immersive and educational sci-art experiences that empower youth and communities through art, science, and technology. She participated as an artist and as education director for the ISEA2012:Machine Wilderness symposium. In 2014, she co-founded The PASEO Festival and served as co-director/youth program director until 2018.  She created the SUBE , Language through Art, Music & Games curriculum for teaching language to children, which is in its 25th year. She is now developing an international youth exchange program called BioSTEAM International , partnering with U.S embassies to connect classrooms across borders to collaborate on sci-art installations that inspire scientific, artistic and humanistic literacy.

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Home — Application Essay — Engineering Schools — The Art in Computer Science

The Art in Computer Science

• University: The University of Texas at Dallas

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Words: 793 |

Published: Jul 18, 2018

Words: 793 | Pages: 2 | 4 min read

When I was about eight years old, I stole for the very first and the very last time. It was a small drawing of the scrumptious Gingerbread House that Hansel and Gretel, the two lost kids, had been trapped inside by the evil cannibal witch. My ten year old cousin had drawn it perfectly with flawless storybook details and even some details from her own imagination; honestly, I’d never seen a better imitation of that gorgeous fairytale house. My cousin was a superb artist, one who could draw anything and everything. I used to admire her and, at the same time, envy her. I was in desperate need of something that was created and not just printed, and that led me to attempt to steal her work. I never got to actually steal that drawing; I hid it inside my jacket, but somehow my cousin sensed that something was missing from her desk of numerous other drawings and realized, soon enough, that I had taken her missing artwork. This incident taught me a valuable lesson that has stayed with me throughout my life, shaping my character and influencing my decision to pursue a career in computer science.

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I always wanted her to draw me mermaids and superheroes, but she had always refused and told me to learn how to draw myself. I realized that I would never get a hand on those paintings created through careful brush strokes and wooden color pencils, so I started to draw myself, with red and black pens. I wasn't very good at it: I drew blobs and sticks but, for the very first time, I had control. I had control over what I drew, I had control over my supplies and how I used them, and -- best of all -- I had control over my newly found freedom to create absolutely anything on a blank piece of paper.

Today, I thank my cousin for catching me stealing her work. She ignited a passion in me that I had never realized I possessed. I call myself an artist, and my friends and peers are now semblances of the eight-year-old me, craving possession of my artworks created through careful brush strokes and wooden color pencils. When people started admiring my work, I started putting more and more effort into my creations. I spent long hours both outside and inside of school to develop the skill for creating visual art that is one of my greatest sources of pride. I took the most advanced of courses at school to further develop my skills and form an enormous, even unruly portfolio. I am now a holder of multiple medals, won in various art competitions, from Richardson Public Library Art contests to Visual Arts Scholastic Events. I have taught an eight-year-old how to draw and paint as one of my very first jobs. I spent more than 100 hours and still spending more hours volunteering at a daycare center, teaching and helping children create arts and crafts. I had the honor of having one of my artworks, called Man in Purple , be showcased at the Dallas Museum of Art.

My future major and academic goals are geared towards Computer Science as of right now. This fact may come as a surprise now that you have read my earlier paragraphs, but it's in no way a surprise for me. Computer Science, in many ways, parallels what I enjoy about art. The most important parallel that art draws with computer science is control. I have control over what I create, I have control over my language and how I code it, and I have control over my freedom to create absolutely anything on a blank Java file. Computer Science is, after all, about creating something out of nothing. That is exactly what I enjoy about art. Computer Science is itself about details and enjoying the detailed work that is put into creating software, about seeing it gradually form a purpose. The tiny errors I make when I code were an exact reflection of the tiny accidental brush or color pencil stroke I usually make in my drawings. I absolutely love debugging my code, almost as if I’m debugging my artwork to create a more aesthetically pleasing pattern. Being able to draw taught me to make mistakes and quickly fix them without much thought. It taught to me analyze details like a simple shine on the metal end of a shoelace; through this skill of analysis, I’ve developed a more refined eye for detail. This extra sense has succeeded in preventing me from making careless mistakes and in helping me quickly notice errors when I’m coding; it has also helped me write efficient code without much effort.

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Although my extra-curricular activities may suggest a passion for the arts, my main passion lies in creating: creating artwork, creating software, and perhaps creating a new fusion of the two.

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Can Computers Create Art?

This essay discusses whether computers, using Artificial Intelligence (AI), could create art. First, the history of technologies that automated aspects of art is surveyed, including photography 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 artists. The current hype and reality of Artificial Intelligence (AI) tools for art making is then discussed, together with predictions about how AI tools will be used. It is then speculated about whether it could ever happen that AI systems could be credited with authorship of artwork. It is theorized that art is something created by social agents, and so computers cannot be credited with authorship of art in our current understanding. A few ways that this could change are also hypothesized.

1 Introduction

Artificial Intelligence (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 Intelligence’s effect on many aspects of society. In the context of art, news media hype presents new image and video creation algorithms as if they are automating the creation of art. Perhaps they will empower everyday users while putting artists out of work… and, while they’re at it, rob us of our humanity?

Beyond the hype, confusion about how technology influences art pervades serious discussions. Professional artists often are concerned that computers might put them out of their jobs [ 50 ] — a concern I’ve heard for decades. Some practitioners present their algorithms as themselves, potentially, artists [ 10 , 22 , 61 ] , as do some journalists [ 53 , 19 , 54 ] . 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 art, the answer is sometimes a decisive “No.” Art requires human intent, inspiration, a desire to express something. Thus, by definition, there is no such thing as art created by a computer… why would anyone worry? The concepts of art and inspiration are often spoken of in mystical terms, something special and primal beyond the realm of science and technology; as if humans can only create art because only humans have “souls.” Surely, there should be a more scientific explanation.

In this essay, I tackle the question of “Can Computers Create Art?” 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 given that any computer will ever be widely considered as an artist. To date, there is already a rich body of computer-generated art, and, in all cases, the work is credited to the human artist(s) behind the tools, 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 art; I do not describe the existing technology in much detail. I also discuss the related question of “Will AI put artists out of jobs?” and, more generally, of whether AI will be beneficial to art and artists.

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

I argue that new technologies benefit art and artists, creating new tools and modes of expression, and new styles of expression. Sadly, art and science are often viewed as being separate, or, even, in opposition [ 64 ] . Yet, technological development stimulates so much of the continued vitality of art, and new artistic technologies create new job opportunities. Our new AI technologies follow this trend, and will for the forseeable future: new AI algorithms will provide new tools for expression and transform our art and culture in positive ways, just as so many other technologies have in the past.

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

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

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

2 History and Current Practice

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

I argue that, throughout history, technology has expanded creative and professional opportunities for artists dramatically, by providing newer and more powerful tools for artists. The advent of new technologies often causes fears of displacement among traditional artists. In fact, these new tools ultimately enable new artistic styles and inject vitality into art forms that might otherwise grow stale. These new tools also make art 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 photography became an artform

“From today, painting is dead!” — Paul Delaroche, painter, at a demonstration of the daguerreotype in 1839 2 2 2 The historical information from this section is distilled from two texts: Scharf [ 60 ] and Rosenblum [ 57 ] .

For lessons from the past about AI and art, perhaps no invention is more significant than photography. 3 3 3 This connection has been made previously [ 34 , 1 ] , but I explore it much more thoroughly here. Prior to the invention of photography, realistic images of the world could only be produced by artists. 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. The technical skills of realism were inseparable from other creative aspects. This changed when photography 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, 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 1 (a)), typically hand-cut by an artisan out of black paper. The daguerreotype offered an economical way to create a realistic portrait (Figure 1 (b)). 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. By 1863, a painter-photographer named Henri Le Secq said “One knows that photography has harmed painting considerably, and has killed portraiture especially, once the livelihood of the artist.” Photography 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 at looking at old etchings and portraits, I’d 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, photography 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 photography art?” This question was debated for many decades, coalescing into three main positions. Many people believed that photography could not be art, because it was made by a mechanical device rather than by human creativity. Many artists were dismissive of photography, and saw it as a threat to “real art.” For example, the poet Charles Baudelaire wrote, in a review of the Salon of 1859: “If photography is allowed to supplement art 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 photography could be useful to real artists, such as for reference, but should not be considered as equal to drawing and painting. Finally, a third group, relating photography to established forms like etching and lithography, felt that photography could eventually be as significant an art form as painting.

Photography 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. Realistic photographs became commonplace by the end of the 19th century. If photorealism could be reduced to a mechanical process, then what is the artist’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; 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 artist, the king of artists would be the photographer. It is for the artist 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 artists moved away from perceptual realism altogether. Edvard Munch wrote “I have no fear of photography 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.” Photography continued to influence modern art of the 20th century; one can infer a significant influence of Étienne-Jules Marey’s multiple-exposure photography on Futurism and Cubism, e.g., in Duchamp’s Nude Descending A Staircase .

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

Meanwhile, the Pictorialist movement, begun around 1885, was an attempt to firmly establish photography as an art 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 photography. They seemed to be deliberately mimicking the qualities of the fine art painting of the time, and today much of their work seems rather affected. They pursued various strategies toward legitimization of their work as art form, such as the organization of photographic societies, periodicals, and juried photography exhibitions [ 67 ] . Their works and achievements made it harder and harder to deny the artistic contributions of photography; culminating in the “Buffalo Show,” organized by Alfred Stieglitz at the Albright Gallery in Buffalo, NY, the first photography exhibition at an American art museum, in 1910. Photography was firmly established as an art, and free to move beyond the pretensions of Pictorialism.

This story provides several lessons that are directly relevant for AI as an artistic tool. At first, photography, like AI, was seen by many as non-artistic, because it was a mechanical process. Some saw photography as a threat and argued against its legitimacy. Photography did displace old technologies that had fulfilled non-artistic functions, such as portraiture’s social function. Some artists enthusiastically embraced the new technology, and began to explore its potential. As the technology improved, and became more widespread over nearly a century, artists learned to better control and express themselves with the new technology, until there was no more real controversy over the status of photography. The new technology made image-making much more accessible to non-experts and hobbyists; today, everyone can experiment with photography. Furthermore, the new technology breathed new life into the old art form, provoking it toward greater abstraction. Wherever there is controversy in AI as an artistic tool, I predict the same trajectory. Eventually, new AI tools will be fully recognized as artists’ tools; AI tools may stimulate traditional media as well, e.g., the New Aesthetic [ 66 ] .

2.2 The technology of live-action cinema

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

The history of film is filled with artist-tinkerers, as well as teams of artists and technologists. The Lumière Brothers created one of the first films, a simple recording of workers leaving their factory, 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. 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. 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 [ 59 ] . 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 medium 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 artists and engineers [ 55 ] . It all began with Ed Catmull, an animation enthusiast who received a PhD in computer science 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 [ 5 ] . 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 art form [ 43 ] . 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 artists; in turn, the artists were inspired by these new tools, and pushed them to new extremes. One of their mantras was “Art challenges technology, technology inspires art” [ 5 ] .

Pixar, by design, treats artists 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 artists, attended many lectures on art 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 artists. In the early days before they found Lasseter, the Lucasfilm Graphics Group made many attempts to interest Disney animators in their work [ 55 ] . 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 tool—-it doesn’t do the creativity!’ That misconception was everywhere” [ 52 ] . 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 skills of talented artists (especially animators) for almost every little detail. Character animation is an art form of extreme skill and talent, requiring laborious effort using the same fundamental skills of performance — of bringing a character to life through pure movement — as in conventional animation [ 43 ] .

Traditional cel animation jobs did not last at Disney, for various reasons. Disney Feature Animation underwent a renaissance in the early ’90s, 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 art form [ 5 ] . Today, traditional 2D animation at Disney is dead. 4 4 4 Traditional animation styles are still vital in countries like Japan and France that, unlike America, do not believe that animation is “just for kids.” Even so, their visual styles have evolved considerably due to computer technology. 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 art form through technology. This is another story that contradicts the popular notion of art and technology operating in conflict, when, in fact, the opposite is usually true.

2.4 Procedural artwork

In the art 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 Art” 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 [ 11 ] . Starting in the 1970’s, classically-trained painter Harold Cohen began exhibiting paintings generated by a program he wrote called AARON [ 6 ] . Since the 1980s, many current artists, 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 artists speaks to the growth of procedural art.

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 art in computer science research

Recent developments in computational artistic image synthesis are quite spectacular. But they should not be mistaken for AI artists.

Non-Photorealistic Rendering (NPR) is a subfield of computer graphics research [ 58 ] that I have worked in for many years. NPR research develops new algorithms and artistic tools for creating images inspired by the look of a conventional media, such as painting or drawing. Paul Haeberli’s groundbreaking 1990 paper [ 32 ] 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 skill (Figure 6 (a)). In a follow-up paper, Pete Litwinowcz automated the process entirely, by placing brush strokes on a grid [ 45 ] . 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 [ 33 ] (Figure 6 (b)). The algorithm was inspired by my experience with real painting, and the way artists 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 [ 35 , 58 ] . 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 [ 36 ] .

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 [ 21 ] , I began to develop a method for working from examples. My collaborators and I published this method in 2001, calling it “Image Analogies” [ 37 ] . 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 [ 20 ] .) Since then, other researchers have improved the method substantially, making it much more robust [ 24 ] .

In 2016, Leon Gatys and his colleagues published a new breakthrough in this space, Neural Style Transfer [ 25 ] . 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.  [ 48 ] , who, developing a visualization tool 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 [ 29 ] and Project Magenta at Google [ 46 ] , that also show promise as new artistic tools. 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 [ 22 ] ; 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 Intelligence 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 consciousness as humans (e.g., [ 75 ] ), and sometimes they are described as artists [ 53 , 19 , 54 ] . In fact, we do not really know what consciousness 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 [ 4 ] . 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, are now widely used in consumer products. Yet they often fail on inputs that suggest bizarre misunderstandings; the existence and robustness of adversarial examples [ 69 ] and procedurally-evolved images [ 49 ] 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 [ 63 ] .

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 [ 2 , 41 , 70 , 30 ] . 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 art

Based on this history, I now make several specific claims about how technology changes art. Far from replacing artists, new technologies become new tools for artists, invigorating and changing art and culture. These claims apply equally to current developments in AI as they did to previous developments like photography and animation.

3.1 Algorithms are artists’ tools

“my watercolor teacher used to say: let the medium do it. true that — so my sketch provides the foundation and then the network does it thing; i don’t fight, just constantly tweak the #brushGAN toolkit” – Helena Sarin (@glagolista)

In every technology that we currently employ — whether photography, film, or software algorithm — the technologies and algorithms we use are basic tools, just like brushes and paint. 5 5 5 This view reflects, I think, the conventional wisdom in the computer graphics research field, which is my research background. This field has always had close ties with certain artistic communities, especially computer animation and visual effects, and, based on this experience, the field is often resistant to attempts to automate creative tasks. In contrast, artificial intelligence researchers use terminology much more aspirationally, historically using words like “intelligence,” “learning,” and “expert systems” in ways that far simpler than the human versions of these things. 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 intelligence: 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 authorship 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 obtaining good results. Indeed, in a recent art exhibition meant to promote these methods and their exploration [ 71 ] (Figure 10 ), human artists were credited for each of the individual works.

The same process of selection of tools and inputs, adjusting settings and even modifying code, and iterating until a desirable output is produced, occurs in all current and forseeable computer artworks. Computer-generated artworks result from considerable time and effort from human artists, 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 artists or potential artists [ 10 , 61 , 19 , 53 , 54 ] . For the above reasons, I think this claim misunderstands the nature of procedural art.

In short, in our present understanding, all art algorithms, including methods based on machine learning, are tools for artists; they are not themselves artists .

3.2 New technology helps art stay vital

Rather than being afraid of the new technologies, we should be enthusiastic about the new artworks that they will enable artists to produce. When we think of art of having external influences, we normally think of social or political influences, but ignore the effect of new tools. In contrast, I argue that, especially from 19th century onwards, technological developments have played a pivotal role in advancing art, in keeping it vital and injecting fresh ideas. The stories I gave of photography 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 art was the invention of oil paint by Flemish painters such as Jan van Eyck in the 15th century [ 13 ] . 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 medium. 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 11 ).

In each decade since the 1950s, many of our culturally-important works used technology that had only been invented within the previous ten years. For example, most technology used in today’s feature films did not exist ten 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 art 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 tools improved, until grunge became popular and made the 80s synthpop sound seem superficial and old-fashioned. Nowadays, a recent revival of 70s and 80s 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 particular original themselves.

In each era, radical technological innovations are met by artists 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” [ 65 ] . 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 artists, new tools make art more accessible to larger portions of society. Photography 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 tools 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 whether the economic system shares the benefits of new productivity fairly across society versus concentrating wealth only among the very richest [ 68 ] and whether machine learning systems are misused to magnify existing forms of inequality [ 51 ] . 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 science to create new life [ 62 ] . 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. 6 6 6 In fact, Frankenstein is presented as a cautionary tale about the quest for knowledge in general. Victor Frankenstein tells his story as a warning when he learns that Captain Walton is himself driven by an obsessive quest for knowledge that is entirely unrelated to Frankenstein’s. 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 tools for artists

Some general trends around the evolution of technology and art 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 tools, ready for artists to explore and exploit. New developments in these tools will be enthusiastically adopted by some artists, leading to exciting new forms and styles that we cannot currently foresee. Novices will have access to new simplified tools for expression. It is possible that some tasks performed by human artists will fade out, but these will generally be mechanical tasks that do not require much creativity because they fill societal functions other than artistic expression. Some traditional arts may fade simply due to seeming old-fashioned. This is the nature of art: nothing is fresh forever, which is not to be blamed on technology. Artistic technology is a “imagination amplifier” [ 8 ] and better technology will allow artists to see even further than before.

Aside from general trends, it is hard to make specific predictions about the art of the future. Les Paul, who invented the solid-body guitar in the 1940s, 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’s 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 [ 4 ] . Even the science fiction writers of the 1950s and 1960s completely failed to imagine the transformative power of the Internet and mobile computing [ 42 ] ; 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 artists 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 Artist?

So far, I have described how computer technologies are currently accepted as tools for artists, not as artists 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 don’t make art, 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 artists. 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 what are the prerequisites for an entity being an artist, focusing on my hypothesis that art is a social behavior. 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 artist. Most of these alternative hypotheses identify some attribute of human artists, and then hypothesize that that attribute is needed for an AI to be an artist.

4.1 Art is social

“What an artist is trying to do for people is bring them closer to something, because of course art is about sharing: you wouldn’t be an artist if you didn’t want to share an experience, a thought.” – David Hockney [ 38 ]

Why do we create and consume art? I argue that art is primarily a social behavior: art is about communication and displays between people. For example, people often speak of art as being about personal expression, which is an act of communication.

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

I am directly inspired by the theory, going back to Charles Darwin, that art-making is an adaptive product of our biological evolution. Dutton [ 17 ] sets out a persuasive argument for this theory, which I briefly summarize, though I cannot do it justice. Creating art served several functions for our Pleistocene ancestors. Art-making served as a fitness signal for mating and sexual selection. Art can also be used as displays 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 art is social : art arose as forms of communication, displays, and sharing between people. Although art 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: art is an interaction between social agents. A “social agent” is anything that has a status akin to personhood; someone worthy of empathy and ethical consideration. Many of our other behaviors are interactions between social agents, 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 empathy 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 empathy with the bike’s feelings, despite the anthropomorphizing language. We don’t 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 artist. For example, art-making can help practice skills like dexterity and problem solving. Creating art 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 art 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 art is fundamentally social without it. But I believe that the evolutionary view of art gives some additional understanding.

4.2 Non-Human Authors

As we have seen, despite many technological advances, current algorithms are not accepted as artists. 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 processes.

Natural processes, including landscapes like the Grand Canyon or the HuangShan Mountains, are not considered art, 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 art. This indicates that simply creating complex and beautiful outputs is not itself sufficient for art, since there is no creative social communication in these cases.

Some higher mammals, including chimpanzees, elephants, and dolphins, have been trained to paint [ 14 , 17 ] . Many writers are skeptical of animal-made art. 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 [ 47 ] , as US copyright law only allows humans to claim copyright.)

The most interesting aspect of this discussion is not whether animals can create art, but how we decide. Discussions of whether animal-made artifacts are art are not based on a priori rules whether animals can be artists. 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 [ 14 ] , or an artifact that the animal has a special appreciation for [ 17 ] . In other words, we are open to the idea of animals creating art, because they can have social relationships with us. It’s just that we haven’t found any other creature that satisfies our criteria for creating art, whatever they are.

4.3 Judging the work instead

It is tempting to judge whether a computer is an artist based solely on the merits of the work that it produces. In this hypothesis, whether or not a computer can be an artist is a judgment 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 artist. The better computer-generated art becomes, the more we will hear questions about whether computers are artists.

Skill is clearly not the real requirement for someone or something to be an artist. Any human can make art, 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 artist?”, 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 agents. No matter how skillful and surprising a computer’s output is, we will not accept it as an artist until we infer some sort of social being inside.

4.4 An Intent Machine

Another hypothesis is as follows: in the modern art world, the role of the artist is to supply the “intent” and the “idea” for the work; it is not necessary for the artist 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; artists can also employ helpers or crowdworkers, such as in Scott Draves’ Electric Sheep and Aaron Koblin’s Sheep Market [ 40 ] . Consequently, for a computer to be an artist it simply needs to supply an intent.

It is easy to imagine designing a system that creates intent and even coordinates the labor of producing a work. For example, one could write a simple procedural algorithm to generate very basic intents (e.g., “portray ominous landscape”), or sample intents from a Recursive Neural Network trained on artists’ 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 intent, crowdworkers could be used to refine the idea and convert it into a new image, similar to systems like Soylent [ 3 ] . One could also automate steps of the process, e.g., using GANs [ 29 ] 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 intents, and create separate collections around these themes. Artist’s statements could 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., photography blogs) change.

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

I believe that, in general, the consensus would be that the system-builder is the real artist here, and that this is really an artwork about probing the nature of computer-generated art, 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 art algorithm (as in Sections 2.4 and 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 artists just “intent machines”? If art is a social act, then the answer is no.

4.5 Creativity, Growth, Responsiveness, and so on

Finally, three related criteria that have intuitive appeal are the notions of creativity, growth, and responsiveness. They are important for human artists, so perhaps they should be for AI artists as well.

subscript superscript 𝑧 2 𝑛 𝑥 𝑦 𝑖 z_{n+1}=z^{2}_{n}+x+yi requires to reach | z | ≥ τ 𝑧 𝜏 |z|\geq\tau , starting from z 1 = 0 subscript 𝑧 1 0 z_{1}=0 , where τ 𝜏 \tau is a large constant. yet produces dazzling animations of infinite complexity. 8 8 8 For example: https://www.youtube.com/watch?v=PD2XgQOyCCk The Mandelbrot set is very surprising and produces beautiful, unprecedented images, but we do not call its iteration equation creative, or an artist.

All current procedural art 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 “creativity” objectives [ 22 , 49 ] . Unlike human artists, these systems do not grow or evolve over time.

Perhaps an AI artist 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 art, 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 artist is modified in the act of making art. 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 …” [ 7 ]

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 art that is meaningful to people and also significantly evolves over time, that would be truly remarkable. Another missing piece from current systems is an artists’ 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 Art

For guidance about the nature of art, we could have also looked to existing definitions of art. However, there is no Royal Society that prescribes what is and what is not valid art. Instead, art 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 art that include all existing types (music, dance, painting, etc.) and styles of art. The Institutional Definition, originated in the 1960s in response to conceptual art, states, roughly, that art is anything in a style that is broadly accepted as art [ 12 , 15 , 44 ] . A more fine-grained approach is to identify attributes common to many different types of art [ 26 , 27 , 16 , 17 ] . Each of these definitions of art is an attempt to fit the data, and draw a line between those things that we call art (like theatre) and those that we do not (like spectator sports). Understandably, these definitions all assume that the artist is always human, without exploring much whether non-humans can create art, 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 artists, and then hypothesize that AIs with these attributes will be considered artists. Artists make high-quality work; artists supply intent; artists are creative; artists grow.

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

Maybe someday someone will develop a system with enough of these attributes to cross some intangible threshold and thus be perceived as an artist. 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 artists. Any human can make art, even if it is not very original or surprising; the artist 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 art.

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

5 Will an AI ever be an artist?

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

Human-Level AI.

If we ever develop AI with human-level intelligence and consciousness, by definition, it would be able to create art, since it would have the same capacity for consciousness, emotions, and social relationships. But, as discussed in Section 2.6 , this scenario is science 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 [ 4 ] , 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 consciousness.

If, as I have argued, creating art is a fundamentally social act of expression and communication, then it follows that AI can be granted authorship when we view the AI as a social agent, and it is performing some communication or sharing through art.

What does it mean for us to view an AI as a social agent? We have to view the AI as deserving of empathy 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 consciousness and feeling.

Short of true intelligence (the science fiction scenario), I think that the only way this can happen is through “shallow AI” agents. People are sometimes “fooled” by shallow AI. 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 [ 73 ] . 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 [ 18 , 23 ] , including the recent plague of Twitter bots [ 39 ] . But, once the veil is lifted, it is clear that these chatbots don’t 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 agents in certain ways [ 56 ] , even when they don’t 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 [ 31 , 56 ] .

Perhaps, for many users, the system doesn’t need to be truly intelligent, it just has to be perceived as a social agent, like a Siri or Alexa that you can ask to make you an artwork [ 76 ] . 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 along 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 artists; there have been a few tentative forays in this direction, e.g., [ 10 , 22 ] . For the reasons given above, I am skeptical that such methods will be accepted as true artists 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 “artists,” 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 art. Could it happen for computer art?

Suitcase words.

The term “artist” 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 [ 4 ] . A software program that, say, automatically stylizes your photos, could be called an “artist” 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 .

A continual danger of new AI technology is that human users misunderstand the nature of the AI [ 51 ] . When we call a shallow AI an “artist,” we risk seriously misleading or lying to people. I believe that, if you convince people that an AI is an artist, 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 “artists” 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 art.

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” [ 28 ] or, in Yann LeCun’s words, a “Potemkin AI” [ 72 ] .

A related concern is that we deprive the AI’s designer(s) of authorship credit. At present, we credit the author of a piece of automatic software with the output of that software. This usually acknowledges the skill 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 science fiction, I can see no positive benefit to calling a computer an artist, but I do see dangers.

I do not believe that any software system in our current understanding could be called an “artist.” Art is a social activity. I mean this as a warning against misleading oneself and others about the nature of art. 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 “artist.” I don’t think it can be done anytime soon, but I also know that proving critics wrong is one of the ways that art and science advance.

One of my main goals in this essay has been to highlight the degree to which technology contributes to art, rather than being antagonistic. We are lucky to be alive at a time when artists can explore ever-more-powerful tools. Every time I see an artist create something wonderful with new technology, I get a little thrill: it feels like a new art form evolving. Danny Rosin’s Wooden Mirror , Jason Salavon’s The Top Grossing Film of All Time, 1x1 , 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 artists; it seems like, every day, we see new tinkerers and artists Tweeting their latest creative experiments with RNNs and GANs (e.g., @JanelleCShane, @helena, @christophrhesse, @quasimondo, @DrBeef_).

Art 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 artists’ tools, they will surely transform the way we think about art in thrilling and unpredictable ways.

Acknowledgements.

Thanks to Shira Katz, Alvy Ray Smith, Craig Kaplan, Shiry Ginosar, and Dani Oore for valuable comments on the manuscript. Thanks to everyone who shared discussion and/or encouragement online, including Aseem Agarwala, Mark Chen, Lyndie Chiou, Michael Cohen, James Landay, Nevena Lazic, Jason Salavon, Adrien Treuille, and many others.

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• [71] Tyka, M. Exploring the Intersection of Art and Machine Intelligence. Google Research Blog (2016).
• [72] Vincent, J. Facebook’s head of AI really hates Sophia the robot (and with good reason). The Verge (2018).
• [73] Weizenbaum, J. ELIZA—–a computer program for the study of natural language communication between man and machine. Comm. of the ACM (1966).
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Essay on Computer

500+ words essay on computer.

A computer is an electronic device that performs complex calculations. It is a wonderful product of modern technology. Nowadays, computers have become a significant part of our life. Whether it is in the sector of education or health, computers are used everywhere. Our progress is entirely dependent on computers powered by the latest technology. This ‘Essay on Computer’ also covers the history of computers as well as their uses in different sectors. By going through the ‘Computer’ Essay in English, students will get an idea of writing a good Essay on Computers. After practising this essay, they will be able to write essays on other topics related to computers, such as the ‘Uses of Computer’ Essay.

The invention of the computer has made our lives easier. The device is used for many purposes, such as securing information, messages, data processing, software programming, calculations, etc. A desktop computer has a CPU, UPS, monitor, keyboard, and mouse to work. A laptop is a modern form of computer in which all the components are inbuilt into a single device. Earlier, computers were not so fast and powerful. After thorough and meticulous research and work by various scientists, modern-day computers have come up.

History of Computers

The history of computer development is often used to reference the different generations of computing devices. Each generation of computers is characterised by a major technological development that fundamentally changed the way computers work. Most of the major developments from the 1940s to the present day have resulted in increasingly smaller, more powerful, faster, cheaper and more efficient computing devices.

The evolution of computer technology is often divided into five generations. These five generations of computers are as follows:

Uses of Computers

Computers are used in various fields. Some of the applications are

1. Business

A computer can perform a high-speed calculation more efficiently and accurately, due to which it is used in all business organisations. In business, computers are used for:

• Payroll calculations
• Sales analysis
• Maintenance of stocks
• Managing employee databases

2. Education

Computers are very useful in the education system. Especially now, during the COVID time, online education has become the need of the hour. There are miscellaneous ways through which an institution can use computers to educate students.

3. Health Care

Computers have become an important part of hospitals, labs and dispensaries. They are used for the scanning and diagnosis of different diseases. Computerised machines do scans, which include ECG, EEG, ultrasound and CT Scan, etc. Moreover, they are used in hospitals to keep records of patients and medicines.

Computers are largely used in defence. The military employs computerised control systems, modern tanks, missiles, weapons, etc. It uses computers for communication, operation and planning, smart weapons, etc.

5. Government

Computers play an important role in government services. Some major fields are:

• Computation of male/female ratio
• Computerisation of PAN card
• Income Tax Department
• Weather forecasting
• Computerisation of voters’ lists
• Sales Tax Department

6. Communication

Communication is a way to convey an idea, a message, a picture, a speech or any form of text, audio or video clip. Computers are capable of doing so. Through computers, we can send an email, chat with each other, do video conferencing, etc.

Nowadays, to a large extent, banking is dependent on computers. Banks provide an online accounting facility, which includes checking current balances, making deposits and overdrafts, checking interest charges, shares, trustee records, etc. The ATM machines, which are fully automated, use computers, making it easier for customers to deal with banking transactions.

8. Marketing

In marketing, computers are mainly used for advertising and home shopping.

Similarly, there are various other applications of computers in other fields, such as insurance, engineering, design, etc.

Students can practise more essays on different topics to improve their writing skills. Keep learning and stay tuned with BYJU’S for the latest update on CBSE/ICSE/State Board/Competitive Exams. Also, download the BYJU’S App for interactive study videos.

Frequently asked Questions on Computer Essay

How has the invention of the computer been useful to students.

Easy and ready access to information has been possible (internet) with the invention of the computer.

How to start writing an essay on a computer?

Before writing an essay, first plan the topics, sub-topics and main points which are going to be included in the body of the essay. Then, structure the content accordingly and check for information and examples.

How to use the computer to browse for information on essays?

Various search engines are available, like Google, where plenty of information can be obtained regarding essays and essay structures.

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Counselling

Computers can find similarities between paintings – but art history is about so much more

Professor of the Social & Critical Histories of Art, University of Leeds

Disclosure statement

Griselda Pollock receives funding from Arts and Humanities Research Council UK and the Leverhulme Trust, UK

University of Leeds provides funding as a founding partner of The Conversation UK.

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Some computer scientists at Rutgers University in New Jersey have written a computer programme that finds connections between paintings and can even discover influences between artists, they claim. This certainly raises some fascinating questions, but not about art history.

In the paper, Babak Saleh and his colleagues describe how they created a programme to compare paintings so as to establish recurrences of certain features.

They classified more than 1,700 paintings according to various visual features they contained, from simple object descriptions to style and colour. And many striking comparisons and links were indeed thrown back by the programme.

But I’m afraid this is by no means going to help art history. The paper is titled “ Toward Automated Discovery of Artistic Influence ”. And sorry folks, but art history isn’t just about tracing influence and comparing use of things like space, texture, form and colour.

This is what we might call connoisseurial art history, which is what you might have found in the 19th century. Connoisseurs began to compare works scattered across churches and monasteries, classifying them and trying to discern common authorship. The works were identified for certain similarities of technique or ways of painting, for instance, hands or ears. At this point, in the later 19th and early 20th century, the project was somewhat forensic. Indeed, the founder of this method was a doctor, Giovanni Morelli .

But unsurprisingly, the discipline has developed somewhat since then. To study art history, we need to know about economics, politics, literature, philosophy, languages, theologies, ideologies while also studying to understand how art thinks. Art thinks through making, through forms, through materials. And over the past century, art history has been enriched by feminist, post-colonial, queer, and trans-national perspectives. We no longer hunt for connections – we ask questions. We are not diagnosticians seeking for common symptoms. We are not criminologists tracing clues that link a with b.

Even at the most basic level, machines would not be helpful in developing these larger narratives. The idea that machines can see or notice what human beings do not is a fallacy, because the machine is only doing what it is told – and it is the programmers who are setting parameters. But those parameters are based on a woefully old-fashioned and dull misunderstanding of what art historians do, and what they look for.

The big question is not that Caillebotte (one of the examples given) was influenced by Degas. Instead it is what he did with that “Degas-ness”. Did he get what Degas was doing? Was he arguing against Degas by making sure we saw some reference to his work? Why would it be valuable to work with it, to work it otherwise? What does referencing and deferring to another artist make possible for the one who does so?

In one example from the article, the programme “discovered” similarities between French Impressionist Frederic Bazille’s Studio 9 Rue de la Condamine (1870) and American Norman Rockwell’s Shuffleton’s Barber Shop (1950) into which they thought art historians could look further.

It is, of course, possible that Rockwell knew Bazille’s painting from an illustration in a book about Impressionist art, and even liked it. But what would we learn from finding pot-bellied stoves in both paintings, except about how people heated rooms pre-central heating? Rockwell’s art was all about creating an American vernacular style in art in opposition to the European modernism of which Bazille was an early part. Such comparisons are shallow, and overlook time, place, history and art politics.

The real problem is that even in the game of source hunting and influence tracing, ideology is already at work. Influence, linking artists and artworks in a one-way direction, such as family descent, is a dressed-up way of protecting the canon (and the art market), and this machine-aided form of looking for similarity would only reinforce it.

There was, until recently, virtually no art history that ever asked how women or African-Americans, or non-Europeans “influenced” the direction of art, or even traced any kind of links between such artists and the canonised white men. It is the kind of art history practiced in today’s universities, rather than the auction houses, that is asking precisely these bigger questions.

Art history studies cultures, societies, histories, and experiences and how they are given form. All we get from exercises in comparison and influence are superficial resemblances at which any artist would laugh. Art history takes art and artists seriously.

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Essay on Computer

Here we have shared the Essay on Computer in detail so you can use it in your exam or assignment of 150, 250, 400, 500, or 1000 words.

You can use this Essay on Computer in any assignment or project whether you are in school (class 10th or 12th), college, or preparing for answer writing in competitive exams. 

Topics covered in this article.

Essay on Computer in 150 words

Essay on computer in 200-300 words.

• Essay on Computer in 500 words

Computers have revolutionized our lives, becoming essential tools for communication, work, and access to information. They have simplified tasks, increased efficiency, and opened up new possibilities. The internet, accessible through computers, has connected people globally, changing the way we socialize and access entertainment. Industries such as healthcare and finance have been transformed by computers, improving accuracy and decision-making. However, challenges such as cybersecurity threats and privacy concerns exist. In conclusion, computers have profoundly impacted society, enhancing productivity and connectivity. Their role in education, business, and research is undeniable. While enjoying the benefits of computers, it is important to address the challenges they present and ensure responsible and secure use. Computers are a fundamental part of our lives, shaping the way we live, work, and interact with the world.

Computers have become an integral part of our modern world. They have revolutionized the way we live, work, and communicate. A computer is an electronic device that processes and stores data, performs tasks, and enables us to access information from around the world.

Computers have transformed various aspects of our lives. They have simplified tasks, increased efficiency, and opened up new possibilities for creativity and innovation. From personal computers to laptops, tablets, and smartphones, these devices have become essential tools in education, business, entertainment, and research.

The internet made accessible through computers, has connected people globally, enabling instant communication, sharing of information, and collaboration across borders. Online platforms and applications have changed the way we socialize, shop, and access entertainment.

Computers have also revolutionized industries such as healthcare, finance, and transportation, improving efficiency, accuracy, and decision-making. They play a vital role in scientific research, data analysis, and simulations.

However, the rapid advancement of technology has also brought challenges. Cybersecurity threats, privacy concerns, and the digital divide are important issues that need to be addressed.

In conclusion, computers have transformed the world, making tasks easier, connecting people globally, and enabling advancements in various fields. Their impact on society is profound, with both positive and negative consequences. As technology continues to evolve, it is crucial to embrace its benefits while also addressing the challenges it presents. Computers have undoubtedly become an indispensable part of our lives, shaping the way we live and interact with the world.

Essay on Computer in 500-1000 words

Title: The Computer Revolution – Transforming Lives, Empowering Innovation

Introduction :

The computer has undoubtedly become an integral part of our modern world, revolutionizing the way we live, work, and communicate. This essay explores the profound impact of computers on society, delving into their history, evolution, and the transformative role they play in various aspects of our lives. From personal computers to smartphones and cloud computing, the computer has become an indispensable tool in education, business, healthcare, entertainment, and research. However, as computers continue to advance, challenges such as cybersecurity threats and privacy concerns arise, necessitating responsible use and the development of ethical frameworks.

The Evolution of Computers

The computer, as we know it today, has a rich history that dates back several decades. From the early mechanical devices to modern digital computers, the evolution of computers has been driven by advancements in technology and the quest for increased computational power and efficiency. Pioneers such as Charles Babbage, Alan Turing, and Grace Hopper laid the foundation for modern computing, introducing concepts like programmability and binary code.

Computing in Education

Computers have transformed the landscape of education. They have become essential tools for students, educators, and researchers. Computers facilitate online learning, providing access to vast amounts of educational resources, interactive tutorials, and collaborative platforms. They enable personalized learning experiences, adaptive assessments, and distance education, making education accessible to a wider audience. Additionally, computers enhance productivity, allowing students to complete assignments, conduct research, and communicate with peers and teachers more efficiently.

Computers in Business

The business world has been revolutionized by computers. From small startups to multinational corporations, computers have become indispensable for efficient operations, data management, and communication. They enable streamlined processes, data analysis, and decision-making. Computers have transformed various industries, including finance, marketing, supply chain management, and customer service. With the advent of e-commerce, computers have opened up new avenues for online businesses and global trade. The digitalization of business processes has increased efficiency, reduced costs, and facilitated global collaborations.

Computers in Healthcare

Computers have significantly impacted the healthcare industry, improving patient care, diagnostics, and research. Electronic health records (EHRs) enable secure storage and efficient retrieval of patient information, reducing errors and improving healthcare delivery. Computer-aided diagnostics and medical imaging technologies have enhanced accuracy and speed in detecting diseases. Telemedicine and telehealth have extended healthcare access to remote areas, allowing patients to consult with healthcare professionals virtually. Additionally, computers play a vital role in medical research, enabling data analysis, simulations, and drug discovery.

The Role of Computers in Entertainment and Media

Computers have transformed the entertainment and media industry. From digital streaming platforms to online gaming, computers have revolutionized the way we consume and create content. They enable immersive virtual reality experiences, computer-generated imagery (CGI) in movies, and interactive storytelling. Social media platforms provide avenues for self-expression, communication, and content sharing. Computers have democratized content creation, allowing individuals to create and distribute their work on platforms like YouTube, blogs, and podcasts.

Challenges and Concerns

While computers offer immense benefits, they also present challenges and concerns. Cybersecurity threats, such as hacking and identity theft, pose risks to individuals and organizations. Privacy concerns arise as personal data becomes more accessible and vulnerable to misuse. Additionally, the digital divide creates disparities in access to technology, limiting opportunities for certain populations. It is crucial to address these challenges through robust cybersecurity measures, privacy regulations, and efforts to bridge the digital divide.

Conclusion :

The computer revolution has transformed our lives, empowering innovation, enhancing productivity, and connecting people across the globe. Computers have revolutionized education, business, healthcare, and entertainment, enabling advancements and opening up new possibilities. However, as technology continues to evolve, it is important to address challenges such as cybersecurity threats, privacy concerns, and the digital divide. Responsible use, ethical frameworks, and continuous efforts to enhance cybersecurity and privacy safeguards are necessary to harness the full potential of computers. With responsible usage and thoughtful integration into various sectors, computers will continue to shape our world, fostering progress, innovation, and connectivity.

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