essay on computer arts

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This blog post is adapted from an essay written by Michael Mansfield, curator of film and media arts, to accompany the exhibition, Watch This! Revelations in Media Art , opening April 24 and running through September 7.

In 1964, Marshall McLuhan wrote that artists were the only people able to "encounter technology with impunity," suggesting that they would be fearless in their approach, unafraid of the consequences, and could light the way for the rest of us. The stunning range of artistic engagements with technology in the last 70 years certainly supports this theory. The twentieth century introduced hi-fidelity stereo, broadcast television, videotape, orbital satellites and computer systems, each one touched by artists in one fashion or another and providing a pretty fantastic index to the changes in our cultural perceptions.

Perhaps one of the more dramatic introductions has been in computing. Today, we encounter computers on a daily basis without giving it much thought. For many of us it is on a minute-to-minute basis. But not so long ago, coming upon a computer was a rare event. They were novel machines. And for the average person, they were pretty foreign. Computers were primarily linked to the government and the military, making them even more mysterious. The ENIAC machine, the first electronic computer system, was created in the mid-1940s to calculate firing tables for artillery. Mainframe computers and the languages articulated for them were the invention of research centers largely funded by the United States Army and the Pentagon. Before long, the Department of Defense began developing ARPANET as a means to decentralize authority and safeguard information in response to geopolitical tensions with the Soviet Union during the cold war. But the computer's practical and creative potentials were immediately fragmented, co-opted by both play and politics. Computer code was written for videogames such as Alan Turing's computer chess programs in the late 1940s and 1950s, and employed to predict democratic elections, like Dwight D. Eisenhower's 1952 presidential victory. Commercial companies soon began funding research labs that paired engineers with artists to encourage experimentation and stretch these new inventions. So, even while military computers envisioned triggering a nuclear apocalypse, artists were envisioning an alternative, or in the least, a more human application.

Nam June Paik arrived to New York as an immigrant in 1964. He had recently completed his studies in music and had become an enthusiastic champion of technology and electronics for use in performances and art making. New York offered rich territory in the communications industries, ground ripe with new technologies. At the time, an experimental venture in New Jersey between the Western Electric Company and the American Telephone and Telegraph Company (AT&T) founded Bell Telephone Laboratories, Incorporated. More commonly referred to as "Bell Labs," the venture was pioneering research into human computer uses in art and devoting valuable time on their mammoth GE600 computer for artists to explore sound, animation and stereoscopic vision. Bell Labs had seated several artists like Michael Noll , Laurie Spiegel , Lillian Schwartz and Stan VanDerBeek , working both independently and collaboratively. It was a hotbed of experimental art.

Paik had previously studied western music in both Japan and Western Europe before moving to the United States. He had been formally trained in the arrangement of orchestral scores, actions in a script, and instrumentation. They were programs. The practical and conceptual relationships between compositions for music and code for computer programs are sound, so to speak. In 1966, Paik was invited to Bell Labs and introduced to FORTRAN computer programming by James Tenney and Michael Noll. He was well prepared and by 1967 Paik was a "Resident Visitor".

Nam June Paik's FORTRAN printouts from his piece  ETUDE 1  © Nam June Paik Estate

In his research at Bell Labs, Paik produced videos, graphics, computer punch cards, negatives and continuous feed printouts, all compiled from the FORTRAN programming language. Three distinct programs leading to computer-generated media reveal his work with both moving and static images. Digital Experiment at Bell Labs is a starkly minimal video recording of the computer screen, marking a gesture toward the origins of computer imaging. A second piece is Confused Rain (1967), a computer generated print that results from randomly placed letters spelling out C O N F U S E, suggesting a "mix of real rain and simulated rain in the computer." The third complete work is ETUDE , a previously unknown computer composition from 1968. In ETUDE , Paik wrote a computer program to create four concentric, intersecting circles displaying the somewhat irreverent text LOVE HATE GOD DOG, each repeating word composing its own diameter. These are sophisticated programs by Paik, and such prominent use of type, text and lettering in his explorations is significant. It relates not only to Paik's experience scripting Fluxus performances or the work of his contemporaries John Cage , Yoko Ono and Ray Johnson , it corresponds with his want of a poetic alternative to the ordered structure of the programming language itself. Such emotional binaries as "love" and "hate," or disparate phonetic games like "god" and "dog," while certainly Fluxus absurdities, also input a human-ness to the machine. It whimsically described Paik's relationship to the world around him in 1968 —in human readable terms— and invoked the symbolic codes invested in these novel, man made machines.

While in residence at Bell Labs, Paik wrote in a letter, "it is my ambition to create the first computer-opera in music history." Though Paik would abandon his work in FORTRAN shortly after these works were realized, that ambition remains an eloquent reminder of his interests in the human nature of technology that he strived so hard to reveal. And elements of this inspiration —the coexistence of humankind with the ingenious things humans make— would remain key threads throughout his artistic practice.

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Essay on Computer and its Uses for School Students and Children

500+ words essay on computer.

In this essay on computer, we are going to discuss some useful things about computers. The modern-day computer has become an important part of our daily life. Also, their usage has increased much fold during the last decade. Nowadays, they use the computer in every office whether private or government. Mankind is using computers for over many decades now. Also, they are used in many fields like agriculture, designing, machinery making, defense and many more. Above all, they have revolutionized the whole world.

History of Computers

It is very difficult to find the exact origin of computers. But according to some experts computer exists at the time of world war-II. Also, at that time they were used for keeping data. But, it was for only government use and not for public use. Above all, in the beginning, the computer was a very large and heavy machine.

Working of a Computer 

The computer runs on a three-step cycle namely input, process, and output. Also, the computer follows this cycle in every process it was asked to do. In simple words, the process can be explained in this way. The data which we feed into the computer is input, the work CPU do is process and the result which the computer give is output.

Components and Types of Computer

The simple computer basically consists of CPU, monitor, mouse, and keyboard . Also, there are hundreds of other computer parts that can be attached to it. These other parts include a printer, laser pen, scanner , etc.

The computer is categorized into many different types like supercomputers, mainframes, personal computers (desktop), PDAs, laptop, etc. The mobile phone is also a type of computer because it fulfills all the criteria of being a computer.

Get the huge list of more than 500 Essay Topics and Ideas

Uses of Computer in Various Fields

As the usage of computer increased it became a necessity for almost every field to use computers for their operations. Also, they have made working and sorting things easier. Below we are mentioning some of the important fields that use a computer in their daily operation.

Medical Field

They use computers to diagnose diseases, run tests and for finding the cure for deadly diseases . Also, they are able to find a cure for many diseases because of computers.

Whether it’s scientific research, space research or any social research computers help in all of them. Also, due to them, we are able to keep a check on the environment , space, and society. Space research helped us to explore the galaxies. While scientific research has helped us to locate resources and various other useful resources from the earth.

For any country, his defence is most important for the safety and security of its people. Also, computer in this field helps the country’s security agencies to detect a threat which can be harmful in the future. Above all the defense industry use them to keep surveillance on our enemy.

Threats from a Computer

Computers have become a necessity also, they have become a threat too. This is due to hackers who steal your private data and leak them on internet. Also, anyone can access this data. Apart from that, there are other threats like viruses, spams, bug and many other problems.

The computer is a very important machine that has become a useful part of our life. Also, the computers have twin-faces on one side it’s a boon and on the other side, it’s a bane. Its uses completely depend upon you. Apart from that, a day in the future will come when human civilization won’t be able to survive without computers as we depend on them too much. Till now it is a great discovery of mankind that has helped in saving thousands and millions of lives.

Frequently Asked Questions on Computer

Q.1  What is a computer?

A.1 A computer is an electronic device or machine that makes our work easier. Also, they help us in many ways.

Q.2 Mention various fields where computers are used?

A.2  Computers are majorly used in defense, medicine, and for research purposes.

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• The Rise of Computers in Art

In Beverly Jones’s essay, “Computer Graphics: Effects of Origins,” she tracks the history of the computer’s influence on art and argues that as it grows, it will require the unification of multiple fields to reach its potential as an art form. She uses examples of how the separation of fields has limited art and then lists other types of innovations that found ways to combine multiple sciences. One early example of computer animation was a piece called Stained Glass Windows by a team from the Army Ballistics Research Laboratory. This was a team of researchers, not trained with any artistic background. In fact, at the time, the divide between aesthetics and technological research even made it so that the use of color was only an aesthetic choice and never used for practical purposes (1990, 23).

She later describes the issues with separated fields once computer graphics became a more complex art form, finding a place in feature length films. For example, in early films, artists were not able to recreate the variation in human movement, instead creating movement that “was smooth and lacked variety” (1990, 26). Again, this was because artists, computer scientists, and other scientists had not yet figured out how to combine their skills to put together the best possible product. Jones also cites how the worlds created by computer artists were designed to “look real,” but sometimes lacked scientific reality concerning laws of physics and optics (1990, 28) In order for this creation to be the most realistic, all these sciences should come together and harness the technology.

Towards the end, Jones makes sure to give examples of how past advances in science were able to unite different factions. She talks about how Gödel, Einstein, and Heisenberg all combined to bring “relativity and contextuality to the physical sciences” (1990, 29). Their advances, when put together, allowed for all of the physical sciences to move forward, and the computer sciences can be equally benefitted by advances by artists and researches of all varieties working together.

At the time of writing this article, I doubt that Beverly Jones could ever foresee the computer advances that society has made today. When Jane McGonigal talks about the way that video games can help change the world in her presentation for TED.com, she is referring to a complex computerized world where the world is aesthetically realistic, but also able to adapt and react to real world human decisions (2010). The reasons she thinks they can be useful is because they so closely reflect the real world. These advancements are due to the cohesive effort of all kinds of scientists to replicate the “scientific reality” of the world.

Because everyone has worked together to the point that this replication is possible, computer animation has inarguable become an art. Jones questions where this art comes from, if it is original or if it all a copy, but I think that it is apparent that the combination of all the forces of scientists and artists produces a thrilling and beautifully constructed aesthetic experience.

Jones, B. J. (1990). Computer Graphics: Effects of Origins. LEONARDO: Digital Image – Digital Cinema Supplemental Issue, pp. 21-30.

McGonigal, J. (2010): Gaming can make a better world. TED: Ideas worth spreading. Retrieved November 24, 2013, from http://www.ted.com/talks/jane_mcgonigal_gaming_can_make_a_better_world.html

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There should be no Computer Art

Frieder Nake Bulletin of the Computer Arts Society, October 1971, p. 18-19.

Soon after the advent of computers it became clear that there was a great potential application for them in the area of artistic creation. Before 1960, digital computers helped to produce poetic texts and music; analog computers (or only oscilloscopes) generated drawings of sets of mathematical curves and representations of oscillations (see e.g. [3], [5] and [7] ). But it was not before the first exhibitions of computer produced pictures were held (1965) that a greater public took notice of this threat, as some said, –progress, as others thought. The threat and progress being the use of an extremely complicated, sophisticated, expensive and rational machine in the arts, i.e. in one of the last refuges of the irrational, as some believe. And it took another three years before there was a tremendous breakthrough caused by two big international exhibitions of “computer art” (“Cybernetic Serendipity”, London 1968, “Computers and Visual Research”, Zagreb 1969).

Since then, a serious discussion has been going on in the art world about the consequences and implications of the use of computers. Art magazines are full of articles, exhibitions are held everywhere, seminars are offered by art schools, books are published, portfolios are sold. Computer conferences have their computer art sections, computer journals publish technical papers. Computer scientists are flattered by the Iittle public success they make and amused by the interest artists develop. Artists surrender to the pressures of the new technique or laugh at the results, and get humiliated by the attitudes that scientists assume when they try to communicate with each other.

The discussion centers around the question “is it or is it not art?”, and is heated, often extremely ignorant and prejudiced, showing virtually no progress, highly repetitive, although the few interesting new methods and the little knowledge of computers that one needs have been published several years ago. (See e.g. [4]. For a recent survey including a bibliography, see [8]).

I was involved in this development from its beginning onward (1964). I found the way the art scene reacted to the new creations interesting, pleasing and stupid. I stated in 1970 [6] that I was no longer going to take part in exhibitions.

I find it easy to admit that computer art did not contribute to the advancement of art if we judge “advancement” by comparing the computer products to all existing works of art. In other words, the repertoire of results of aesthetic behaviour has not been changed by the use of computers. (This point of view, namely that of art history, is shared and held against “computer art” by many art critics, compare e.g. [2] .)

There is no doubt in my mind, on the other hand, that interesting new methods have been found, which can be of some significance for the creative artist. And beyond methodology, but certainly influenced by it, we find that a thorough understanding of “computer art” includes an entirely new relationship between the creator(s) and the creation: BENSE uses the term “art as a model for art” in this context [1].

The dominating and most important person in the art world today is the art dealer. He determines what is to be sold and what is not. lt is the art dealer who actually created a new style, not the artist. Progress in the world of pictures today is the same as that in the world of fashionable clothes and cars: each fall, the public is presented with a new fashion, artificially (sic!) created almost a year before in the centers (Paris, London for clothes, Detroit for cars, New York for pictures).

Differences from one year to the next are rarely ever substantial, in the majority of cases they are superficial and geared according to the salesmen’s requests and analysis of the market.

lt seems to me that “computer art” is nothing but one of the latest of these fashions, emerging from some accident, blossoming for a while, subject matter for shallow “philosophical” reasoning based on prejudice and misunderstanding as well as euphoric over-estimation, vanishing into nowhere giving room to the next fashion. The big machinery, still surrounded by mystic clouds, is used to frighten artists and to convince the public that its products are good and beautiful. Quite frankly, I find this use of the computer ridiculous.

ln many publications on “computer art” we read complaints that “real” artists do not have access to computers because of the forbidding expense of the machine, and because of the artists’ lack of knowledge in programming. We also read that we could obtain really interesting and new results if artists had the opportunity (money) to realize their ideas using a computer, perhaps being helped by programmers and mathematicians.*

At the same time, artists become aware of the role they play in providing an aesthetic justification of and for bourgeois society. Some reject the system of prizes and awards, disrupt big international exhibitions, organize themselves in cooperatives in order to be independent of the galleries, contribute to the building of an environment that people can Iive in.

I find it very strange that, in this situation, outsiders from technology should begin to move into the world of art and try to save it with new methods of creation, old results, and by surrendering to the given “laws of the market” in a naive and ignorant manner. The fact that they use new methods makes them blind to notice that they actually perpetuate a situation which has become unbearable for many artists.

Computers ought not to be used for the creation of another art fashion.

Questions like “is a computer creative” or “is a computer an artist” or the Iike should not be considered serious questions, period. ln the light of the problems we are facing at the end of the 20th century, those are irrelevant questions.

Computers can and should be used in art in order to draw attention to new circumstances and connections and to forget “art”.

There is no need for the production of more works of art, particularly no need for “computer art”. Art (better: the aesthetic object) comes afterwards (but it does come). Aesthetic information as such is interesting only for the rich and the ruling. For the others (and they are in the majority) it comes “with”. Namely with other information. Thus, the interest in computers and art should be the investigation of aesthetic information as part of the investigation of communication. This investigation should be directed by the needs of the people.

We should not be interested in producing some more nice and beautifuI objects by computers. We shouId be interested in producing a film on, say, the distribution of wealth. Such a film is interesting because of its content; the interest in the content is enhanced by an aesthetically satisfying presentation. That is, the role of the computer in the production and presentation of semantic information which is accompanied by enough aesthetic information is meaningful; the role of the computer in the production of aesthetic information per se and for the making of profit is dangerous and senseless. (lt is interesting to notice in this context that HELMAR FRANK, after a successful beginning in information aesthetics, gave it up and concentrated more and more on problems of education and psychology.)

Reiterating the argument: I don’t see a task for the computer as a source of pictures for the galleries. I do see a task for the computer as a convenient and important tool in the investigation of visual (and other) aesthetic phenomena as part of our daily experience.

As concrete projects to be investigated I propose:

1. The study of the alienation of the artist from his product which is caused by technology in general and by computers in particular (the distance between the artist and his work increases [1] ). What are the good, what are the bad effects of the division of labor taking place in art?

2. Investigation of the repertoires of signs used by individual artists and styles in the past and present. Such repertoires have been described occasionally, but not rigorously enough. The emphasis of such a project should be to describe those repertoires (and their various levels) in a way suitable for an application of information aesthetics.

3. Design and performance of experiments to test the significance of aesthetic measures defined so far; perhaps new definition of such measures.

4. Investigation of the importance of aesthetic information in various areas (education, propaganda, environments of work and living). This work would have to be based on a rigorous numerical definition of “aesthetic information”.

* ln some places, this is being tried by universities and companies; e.g. University of Madrid, Mathematisch Centrum (Amsterdam), Ohio State University, University of Toronto, IBM (the Whitneys) and others. Companies, of course, see the potential advertising power.

[1] M. BENSE: Cartesianische Aufklarung uber Kunst. Mitteilungen des Instituts fur moderne Kunst Nurnberg. Nr. 2/3. Mai 1971.

[2] J. BENTHALL: Technology and Art 15, Computer Graphics at Brunel. Studio International June 1970, 247-248.

[3] H.W.F RAN KE: Computergraphik-Computerkunst. Bruckmann Verlag Munchen. 1971

[4] M. KRAMPEN, P. SEITZ (eds.): Design and Planning 2. Hastings House, New York. 1967.

(5) F. NAKE: Erzeugung aesthetischer Objekte mit Rechenanlagen. ln: R. GUNZENHAUSER (Hrsg.), Nicht-numerische Informationsverarbeitung. Springer-Verlag Wien. 1968.

[6] F. NAKE: Statement, PAGE 8 1970.

[7] G. PFEIFFER: Kunst 10(1970). Kunst and Computer. Magazin 1883-1901.

[8] S.Y. SEDELOW: The Computer in the Humanities and Fine Arts. Computing Surveys 2(1970), 89-11 O.

Imprint & Data Protection

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 ).

Computer science has drawn from and contributed to many disciplines and practices since it emerged as a field in the middle of the 20th century. Those interactions, in turn, have contributed to the evolution of information technology – new forms of computing and communications, and new applications – that continue to develop from the creative interactions between computer science and other fields.

Beyond Productivity argues that, at the beginning of the 21st century, information technology (IT) is forming a powerful alliance with creative practices in the arts and design to establish the exciting new, domain of information technology and creative practices—ITCP. There are major benefits to be gained from encouraging, supporting, and strategically investing in this domain.

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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

About this sample

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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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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Research on Fine Arts Practice and Innovative Talent Training in Universities, JFYB2557.

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Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Fukushima, Japan

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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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Essay on Importance of Computer

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500 Words Essay on Importance of Computer

Introduction.

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Despite the numerous benefits, the use of computers also brings challenges such as cybersecurity threats and digital divide. Addressing these issues is crucial for a safe and inclusive digital future. On the brighter side, the future of computers is promising with advancements like quantum computing, artificial intelligence, and virtual reality. These technologies are expected to further enhance our lives, solve complex problems, and open new avenues of exploration.

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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

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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:

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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.

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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
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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.

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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.

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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.

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

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Art, creativity, and the potential of artificial intelligence.

1. AI-Art: GAN, a New Wave of Generative Art

2. pushing the creativity of the machine: creative, not just generative, 3. ai in art and art history, 4. ai art: blurring the lines between the artist and the tool, author contributions, conflicts of interest.

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Mazzone, M.; Elgammal, A. Art, Creativity, and the Potential of Artificial Intelligence. Arts 2019 , 8 , 26. https://doi.org/10.3390/arts8010026

Mazzone M, Elgammal A. Art, Creativity, and the Potential of Artificial Intelligence. Arts . 2019; 8(1):26. https://doi.org/10.3390/arts8010026

Mazzone, Marian, and Ahmed Elgammal. 2019. "Art, Creativity, and the Potential of Artificial Intelligence" Arts 8, no. 1: 26. https://doi.org/10.3390/arts8010026

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Computer Vision, ML, and AI in the Study of Fine Art

Ongoing research in the analysis of art is building upon the vast store of algorithms and knowledge from mainstream computer vision, deep learning, and artificial intelligence.

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Computer Vision in the Study of Fine-Art Paintings and Drawings

Computer-based tools for image analysis of art, computer methods in resolving debates in art analysis, problems in art analysis that resist techniques from mainstream ai research, conclusions, opportunities, and future directions, acknowledgments.

In the past decade, computer vision (CV), machine learning (ML), and artificial intelligence (AI) have been applied to problems in the history and interpretation of fine-art paintings and drawings. Such automated methods provide new tools for connoisseurs and art scholars and have resolved art-historical debates that proved resistant to traditional art-historical methods. Nevertheless, immense challenges and opportunities remain for the application of AI in the study of art, specifically on problems that are inadequately addressed by mainstream AI research. For this reason, art analysis serves as a grand challenge to image-based AI.

Key Insights

Fine-art images are some of the most sophisticated, complex and valuable images ever created.

Such images present entire classes of problems not well addressed by mainstream AI research.

Fine-art paintings and drawings thus serve as a grand challenge to AI.

The computational tools, used with deep understanding of the relevant art-historical context, empower art scholars to answer outstanding questions, pose new classes of questions, and develop richer interpretive strategies.

Advances in imaging technology and especially CV and AI have, for decades, benefited nearly every scientific and engineering discipline, including medicine, geology, biology, chemistry, and psychology. Consider that works of art bear the most memorable and important images ever created by humans, and many works themselves are exceedingly valuable—not just financially but culturally. It is natural, then, that computer methods, properly guided by scholars’ knowledge of history and context, should be of service in the humanistic studies of art as well. In fact, in the past few years, rigorous automated image analysis has assisted some art historians, critics, and connoisseurs in their scholarly studies of fine-art paintings and drawings.

Such rigorous computer image analysis of fine art is rather different from traditional “digital humanities,” which has generally concentrated on digital methods of capture and display but where the fundamental analyses and interpretations are still performed by human scholars and connoisseurs. 38 As we shall see, however, artworks pose several profound problems that require sophisticated methods beyond those in traditional digital humanities. These represent a grand challenge to AI, well beyond what is generally addressed in research in digital humanities programs and even mainstream artificial intelligence.

This article explores three manifestations of research into computer image-based analysis of fine art. First, the article offers an example of how computer image analysis can help art scholars by expanding traditional non-automatic approaches. Then, we refer to some debates and issues in art scholarship that have been aided significantly—and in some cases solved—thanks in large part to computer methods. The article then turns to broad problems in image-based intelligence that arise in art analysis that are inadequately addressed by mainstream AI research and hence present great opportunities for research. The article concludes with thoughts about future directions.

Computer image analysis has automated several tasks traditionally performed by connoisseurs and art historians, such as the analysis of composition in landscapes, 22 of brushstrokes and other marks, 23 of canvas weave, 24 lighting, 21 and general properties of style. 9 Such tools do not replace the connoisseur but instead empower art scholars and extend the analyses they can perform, including at scales impractical for efforts solely “by eye.” These digital tools, when used with an awareness of the art historical context, can provide rigor and consistency to analyses and enable the analyses of large-scale trends.

Consider just one example, the analysis of pose in portraiture—a formal property that artists set for numerous compositional and expressive ends. 39 Art scholars have traditionally used rather subjective, informal, and coarse descriptions of portrait poses—such as frontal, profile, or three-quarters view—which they determine entirely by eye. Such traditional methods scale poorly and preclude detailed analyses of thousands of portraits, as might reveal trends in portraiture over centuries or even over a single prolific portraitist’s career.

Computer image analysis offers a powerful tool for portrait pose analysis. Figure 1 shows how a pose can be described by three rotation angles as well as a “production model” of the projection of a generic head onto the artist’s picture plane. Two deep neural network (DNN) methods—Perspective-n-point 17 and Fine-grained Structured Aggregation network (FSA-net) 40 —can estimate these pose angles automatically based on the locations of extracted visual keypoints, such as the corners of the eyes, mouth, tip of the nose, and so on. These algorithms prove remarkably robust to variations in artistic style.

Such automated tools permit the rapid and accurate analysis of large corpora of portraits. Figure 2 shows computed distributions of the absolute value of the roll angle of 11,000 portraits from a half millennium of Western canon and Japanese art, here grouped by art movement. Yaw, tilt, and measures such as the location of the head within the frame of the artwork can be similarly estimated, all within four minutes on a desktop computer. 4

There is rich information to be interpreted in this, and related plots, but consider just the differences between Japanese Ukiyo-e (“Floating world”) art and so-called Naïve or Primitivist art. The box-whisker plots in Figure 2 confirm that Ukiyo-e portraits often exhibit large rotation angles associated with the mie poses of kabuki actors in dramatic moments in plays, or of geishas in seductive poses. By contrast, in Naïve or Primitivist portraits, poses are rather “simple,” favoring direct forward gaze. Such pronounced differences are evident in the representative works in Figure 3 .

Artists often take characteristic bodily stances in the studio when executing self-portraits. For instance, a right-handed artist will place the easel to the right of the plane mirror so as to better reach it with the dominant hand, and the artist’s head is often rotated somewhat toward the canvas. The head of a left-handed artist will be rotated in the opposite direction. The distribution of (signed) yaw angles will differ between right- and left-handed self-portraitists; indeed, such differences are evident in the computational data. That is, the computational results reveal which artists were likely left-handed. 4

The automated pose-estimation algorithms extend previous work, providing a foundation for a range of future studies in art analysis. 18 For instance, one can incorporate face-based gender recognition as a pre-processing step to explore gender-specific trends in poses. 25 Likewise, with possible style-based tailoring of face-recognition algorithms, one might explore trends in non-Western art, particularly Asian art, such as portraits from the Song Dynasty 41 or from the Renaissance. 13

Deep neural networks for art analysis.   Deep networks developed for natural photographs have been modified through their architectures and transfer training to perform well on segmentation of art images throughout a wealth of styles and media—a task vastly more difficult than in natural images. 14 Deep networks trained with large corpora of art images and contributed textual summaries of human responses can accurately predict human emotional responses to novel artworks. 1 Deep networks have been applied to the extremely challenging and important task of art authentication. 8 , 28 Most of these methods address just image information and thus have not been accepted by the art community because authentication also rests on studies of materials (pigment, canvas, and so on), provenance (documentary record of ownership and display), iconography, and more. As such, there are deep challenges to AI for incorporating such diverse forms of information for authentication, which will be discussed again later in this article.

Rigorous computer image analysis has not merely served as a tool easing traditional interpretive tasks but has also resolved art historical debates for which methods from traditional art scholarship proved incomplete or inadequate. In 2000, the celebrated British and American artist David Hockney, later aided by thin-film physicist Charles Falco, claimed to have “proven” that some artists as early as 1420 secretly used optical devices during the execution of some of their works, and more broadly that the use of optics led to an enhanced realism or “optical look” of the “ars nova” or new art of that time. 15 , 16

Rigorous computer image analysis has not merely served as a tool easing traditional interpretive tasks but has also resolved art historical debates.

Part of the evidence they adduced rested on Hockney’s claim that the complex chandelier in Jan van Eyck’s Arnolfini portrait was “in perfect perspective,” which (Hockney claimed) implied van Eyck used optics to draw it. Sophisticated homographic analysis of the image of the chandelier, based on an ACM Dissertation Award-winning thesis, 6 showed that in fact the chandelier deviated significantly from “perfect perspective,” thereby rebutting the optical proposal, at least for that painting. 5 , 6 , 29

Hockney also adduced Georges de la Tour’s Christ in the Carpenter’s Shop as evidence that this artist used optical projections. In brief, his claim rests on his reading of the work that the light source was “outside the [frame of the] picture.” Application of sophisticated and rigorous occluding-contour algorithms and maximum-likelihood estimation of the location of the source of illumination based on five classes of image information showed definitively that Hockney’s claim was false and that instead the source of the illumination was at the depicted candle. Such rigorous methods thus refuted Hockney’s argument. 36

Hockney and Falco claimed their most definitive evidence for Lorenzo Lotto’s putative use of optics arose in Husband and Wife , where they claimed geometric and other “anomalies” in the depicted carpet “proved” that Lotto used a small, concave mirror to project an image during the execution of this work. 16 Rigorous analysis using computer ray-tracing software showed instead that the proposed setup simply could not work as claimed. Moreover, when the required corrections to their setup were made, the computer ray-tracing evidence contradicted the optical claim. 27

Broadly speaking, computer image analysis supported and confirmed the unanimous independent scholarly rejection of Hockney’s theory. 35 Indeed, partially in response to the rigorous computer-assisted analyses, Hockney himself has retreated from his projection claim. Similarly, engineer and self-described non-artist Tim Jenison argued that Johannes Vermeer secretly used a catadioptric telescope during the creation of his sublime interior genre paintings, 20 but computer-enhanced analyses (and facts of historical and material culture) refuted Jenison’s claim. 31 , 32 , 37

Most traditional AI algorithms have been developed for analyzing natural photographs, videos, and specialized images such as medical x-radiographs. These images have properties that, at base, derive from the laws governing the natural world, be they everyday physics of objects and light, medical or remote images governed by electromagnetic radiation, and so on. Art images differ in numerous ways from those just listed and present several deep challenges that are inadequately addressed by current mainstream research, including the following:

Style.   There is no unanimous agreement concerning the definition of style in fine-art paintings. Nevertheless, style certainly refers to formal properties of color, composition, marks, brush strokes, and so on considered as distinct from the nominal subject matter. Portraits, to take just one genre, can be rendered in styles that are highly realistic, expressive, or abstract, in unnatural colors executed in a wide variety of marks and brush strokes and innumerable personal styles. The variety of styles found in paintings is vastly greater than that found in the natural photographs that have commanded the attention of the AI community.

Style presents several challenges to computer analysis, for instance the recognition of style and identifying an artist (“author”) by the style of a work: image segmentation, object recognition, and scene analysis. Works such as shown in Figure 4 present great challenges in these regards—challenges to human viewers and algorithms alike.

Small data sets.   As mentioned earlier in this article, an important recent development in image analysis is the use of DNNs trained with hundreds of millions or more natural photographs. Such approaches have provided human-level or superhuman-level performance on several image-analysis tasks. Unfortunately, such systems generally perform quite poorly on stylized paintings and drawings, for instance the work in Figure 4 .

The rather obvious approach would be to train deep networks with large corpora of representative art images. Alas, this approach generally cannot be followed directly because there simply are not enough representative art images. After all, the Spanish master Pablo Picasso—one of the most prolific artists of all time—leaves us merely 13,500 paintings. Johannes Vermeer leaves us just 33, each considered a distinctive masterpiece, such as shown in Figure 5 .

One way to overcome the relative paucity of art images is to transfer-train a DNN, that is, present art images as additional training patterns to a network previously trained with a very large number of photographs. This approach has proven of only modest value on problems such as image segmentation and is unlikely to provide significant benefit on more challenging problems, such as scene analysis. An alternate approach has shown success for the problem of image segmentation in artworks. We used deep networks to map artistic style onto photographs to thereby produce a large corpus of images of modern subjects rendered in artistic styles of artists. Training with this large corpus of “surrogate artworks” yields highly accurate performance on tasks such as segmentation. 14 Nevertheless, much research remains to be done.

Imaginary objects.   Non-realist artists frequently depict imaginary objects or creatures, such as angels, dragons, and so on. Artists from the Dada Movement of the early 20 th century, such as Salvador Dalí, Marcel Duchamp, Man Ray, and René Magritte, frequently altered the properties of objects for surprise. The fact that these objects are unusual or even unique is often central to the artist’s expressive goals, of course. The fact that such objects are rare or unique presents a great challenge to AI systems for recognizing such objects and interpreting their associated artworks. These objects do not appear in large corpora of photographs typically used for training networks for image analysis. Perhaps one approach would be to develop modular deep networks that can flexibly decompose an image passage into components and styles that are rarely found in images.

Nonphysical conventions.   Because artists are not constrained to slavishly depict the physical world, they can employ nonphysical conventions in service of their artistic goals. Thus, they can depict figures floating into the heavens, bulls that swim, infants that glow, and so on, as exemplified in Figure 6. The computational problem here is to recognize the non-physical convention given that training sets of natural photographs do not depict scenes in such conventions.

The development of a work, as revealed by its multiple layers.   Many works of art, in particular Old Master easel paintings, were developed through underdrawings, revisions, and so on. These hidden layers are revealed through X-ray, infrared, and other imaging methods. Art scholars seek to understand an artist’s praxis and artistic intent by studying the changes in composition and other formal properties. For example, Figure 7 shows an X-ray and the visible image in the central passage in Rembrandt’s The Night Watch . Careful examination reveals numerous differences—large and small—between these two versions, and these are studied closely by art scholars crafting interpretations.

The computational task is to detect, represent, analyze, and ultimately interpret such changes, which have no counterpart in the vast number of natural photographs that dominate traditional AI research.

Abstraction.   Abstraction is an extremely important genre of art, spanning a great variety of forms and styles (See Figure 8), and one for which natural photographs have little or no relevance. The challenges to AI research include recognizing artists (“authors”) by their abstract works, 19 , 30 tracing the development of abstract styles throughout an artist’s career (and thus helping to establish the execution date of works), and related problems.

Recovering lost works.   A great deal of fine art, including some of the most important and influential artworks ever created, has been lost to war, fire, flood, iconoclasm, theft, and other reasons. 3 For example, Diego Velázquez’s Las Meninas is often considered one of the most important paintings of the Western canon; it barely escaped a fire in the Alcazar Palace in 1743. Alas, this artist’s Expulsion of the Moriscos , which in his day was even more highly praised than Las Meninas , was completely burned in that same fire. Recovering the image of such a work would be of immense value to art history and to our cultural heritage more generally. It would provide deeper insight into Velázquez and his oeuvre, the cultural environment of the Spanish Golden Age, provide insights into artists who were influenced by the lost work, and much more. 7

Computational methods based on DNNs have shown promise in recovering properties or portions of lost artwork, and thus show such as the colors in paintings by the Austrian artist Gustav Klimt, 26 and of ghost paintings 2 and missing passages of Rembrandt’s The Night Watch . 10 Computational reconstruction of a full image of a lost artwork would require a sophisticated integration of information in diverse forms: preparatory sketches and other works by the artist, copies by other artists, textual descriptions of the work, knowledge of the author’s working methods and media (available pigments and drawing tools), the likely date of execution, and more, as shown in the proof-of-concept computational reconstruction in Figure 9. 11

Semantics.   Much of human communications is indirect or non-explicit, and so only relatively narrow forms of intelligence can be captured in datasets of real images, for instance. While one of the core purposes of artwork was for the artist to convey a complex and indirect meaning, verbose descriptions for which accompany virtually every masterpiece.

As a result, perhaps the deepest and most challenging class of problems posed by art that is not adequately addressed by current traditional AI research concerns semantics, that is, deriving coherent “meanings” associated with works.

Mainstream AI approaches to semantic image analysis seek to form text summaries, such as captions, of a photographic image. The semantic problems arising in art are rather different. Here the goal is to infer the artist’s meaning or intention, for example what message, moral, story, or abstract idea the artist seeks to convey. Consider, for instance, René Magritte’s celebrated T he Treachery of Images in Figure 10. Mainstream semantic analysis would recognize the pipe, and the text, and presumably detect the contradiction, but not infer why the artist created this work and how this relates to the fact that this is a painting of a pipe, a painting of text, and much more. Preliminary results using deep networks extract components of meaning in religious art. 34

Fine art paintings and drawings represent some of the most carefully constructed, memorable, complex, and important images of any form, and they present deep challenges to computer vision and artificial intelligence that in many cases are not addressed by mainstream research focused on natural photographs, medical and remote sensed images, and others.

Ongoing research in the analysis of art is building upon the vast store of algorithms and knowledge from mainstream computer vision, deep learning, and artificial intelligence. Continued progress demands an integration of humanists’ knowledge of art historical facts and contexts as well as on computer scientists’ knowledge of algorithms, and creativity in tailoring them to problems in art. Such a research program promises to empower humanist scholars and shed light upon some of the most sophisticated and fascinating aspects of intelligence—human and machine. 12 , 33

The author wrote much of this article while an External Reader at the Library at the Getty Research Institute, Los Angeles and as a Leonardo@Djerassi Fellow in Residency at the Djerassi Foundation, Woodside, CA, and would like to thank these institutions for their support.

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May 2024 Issue

Vol. 67 No. 5

Pages: 68-75

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