Introduction
With the growth of technology, art has taken to a new angle where artists now use computers as their main tool in the various art disciplines. There are various examples of digital art which include locative media, networking, generative drawing, information visualisation, wearable art, digital photography, digital performance, 3D animations, electro acoustic composition, as well as multimedia among others.
The earliest digital images to be created made a debut in the 1950s courtesy of Ben Laposky who created images known as ‘oscillons’. The late 1950s saw a new breed of other artists who created artworks digitally and since then, the world of art has been revolutionary (Smith, 2008, pp. 1-3). This paper looks at the impact digital technologies have had on ideas about and uses of drawing in contemporary art.
The impact digital technologies have had on ideas about and uses of drawing in contemporary art
The impact of digital technology on traditional disciplines that include drawing has been huge and artists have had to transform their old ways of printmaking, painting and sculpture. The whole prospect has been very interesting and its amazing how artists now use computers to draw, paint and print out their pieces of art. This has demanded artists to be more creative in the formation of their ideas and they have also had to learn how to do what they love using computers (Davison, 2006, pp. 1-2).
However, this has not failed to present some limitations since some tools like the ‘Scanner chrome’ are expensive and this has left out some artists from this technological process.
There has also been a disparity between the work the artists do on the computer and the final job they print out. Many a time, the final job has come short of their expectations, factor that has been taken back to a computer’s settings. The major problem here has been the use of colour whereby artists have printed out drawings or paintings which fall short of the screen display.
This has been attributed to the differences in colour value between the monitor and the printer. This is therefore leaving artists with no choice but to settle for projection of their artworks on big screens since they tend to loose their value upon printing. Digital technologies have also impacted negatively on the values of illusion and expression in the sense that drawings have lost their authenticity. Anything made on the computer is open to being copied and this has made many artists loose their originality.
To produce one artwork, artists find themselves collaborating with hardware and software technicians and this makes the reproduced work a “team effort” kind of thing. This has amounted to numerous frustrations among the artist fraternity where many of them are disgruntled with the results.
On the contrary, digital art has been applauded in that it is fast and flexible in the sense that artists get to finish their jobs fast. They also have the choice of editing their drawings on the computer without the risk of damaging them and finally print them out once they are satisfied (Davison, 2006, pp. 1-8).
Traditional art may involve discarding numerous materials just because they got messed up when the artist tried to make changes to the initial drawing. Traditional drawing is also time consuming and tedious. When it comes to painting, digital technology has scored highly and many artists have found computers very useful. Computers come with software such as Adobe, Live Picture and Photoshop which enable artists to come up with paintings which are more real in the shortest time.
The good thing about this software is that they are easy to run and thus open doors for many artists. However, the manipulations of images these programs offer tend to take away many artists from their primary ideas and this may lead to distorted artworks. On the other hand, digital technology has evolutionalized traditional art in that artists are able to come up with hybrid pieces of art courtesy of the various software (pp. 1-8).
Comparisons between generative drawing and information visualisation
Generative drawing is a kind of art that involves the use of computer software, a machine or a set of rules to compose, generate and construct artworks which could give multiple results in a complete artwork. The term generative refers to the multiplication of units to create an artwork. This procedure is not limited to a particular technology as it may take various forms.
The completed artwork is not necessarily “high-tech” but must have the potential to operate autonomously. Good examples of this kind of art include contemporary artists such as Brian Eno and John Cage. For instance, Eno’s contribution to Koan’s SSEYO music system birthed the Generative Music 1. Other examples of generative art include works done by Sol LeWitt who did an artwork, Anne Wilson who did sculptures and Celestino Soddu who did architectures (Christiane, 2003, pp. 13-145).
Generative art has also been used widely in the making of electronic music by several contemporary artists. In addition, computer animations and graphics also define what generative art is since it is used in their making as well. Some perfect examples of these animations and graphics work include the works done by Perlin Noise, L-Systems, as well as the use of physical modelling. These animations have become very popular and they are used in many applications today.
They have been used in the creation of movies, cartoons and television commercials and their purpose is to pass on unchoreographed details since they can speak for themselves without much of these. Good examples for these works include video game machines and the Pixar films which are artistry animated to provide viewers with the best results. Generative art is also broad in that it has been adopted by video jockey (VJ) culture as well as demo scenes which appeal most to the young generations.
They are used in recording studios, nightclubs, funded labs and animation companies. Generative art has also been used widely in architecture and industrial design in the creation and selection of samples. In addition, the use of robots and math art also inclines towards generative art. Several artists have adopted this systematic art and some of them include Kenneth Noland and Jackson Pollock (Galanter, 2006, pp. 2-10).
Information visualisation is a kind of art whose aim is to amplify cognition. It thus exploits graphics with the aim of helping people interpret and understand the artworks more. This art has been favoured greatly by technology since now artists are able to access affordable and high performance graphics (ISR, 2001, pp. 2).
Information visualisation has been adopted by many professionals today to interpret data of different magnitude. For instance, this kind of art is used in providing context in weather reports as well as financial reports. Images that maybe used to pass on this kind of information include bar charts, pie charts and line graphs.
The artists ensure that they portray the dynamics and stabilities which add strength to the work in an effort to stimulate people to interpret the data as intended. Information visualisation is regarded as sublime in the sense that the artworks produced are absolutely great. The artworks are also regarded as uncanny in that their mystery provokes the minds of people leading them to formulate concepts and relate what they see with what is being talked about.
Information visualisation can also be referred to as conceptual art since it seeks to inform the masses through the generation of thought provoking artworks. They can therefore not be described as aesthetic but as works that are informative with the aim of educating people on various issues and can therefore be described as an “aesthetic of administration”.
Some of the contemporary information visualisation artists include Lisa Jevbratt who worked on the rhizome, Collection Daros art works from Switzerland, the works of Julie Mehretu, Marina Zurkow, Julian Bleecker and Scott Paterson among others (Sack, 2002, 1-22).
Generative art on the other hand is credited for giving its fans the ‘wow’ factor due to the intensity of creativity in them. The artworks appear clever though people get bored watching and analysing them some minutes into the excitement due to their limited information. This is a setback brought about by computer programmers who indulge in generative art without a background in fine arts. They therefore cannot meet the required standards that an artist should when laying out these artworks.
In addition, the programmer’s goal is to create a visually appalling piece of work and his dream dies right there thus denying the artwork of substance. Generative art is supposed to generate paintings that have both an aesthetic value and a degree of complexity that keeps people’s attention on them.
It is therefore right to say that when compared to information visualisation, generative art is more aesthetic than informative. Information visualisation is more information focused and the artworks used represent data which is of importance to the viewers whereas generative art is more bent on the creation of visually attractive algorithms which do not have to necessary carry an informative message (Christiane, 2003, pp. 13-145).
A strong feature that rules in generative art is that it does not apply to the use of complexity science as in information visualisation in the production of artworks. Complexity science is reductive and this is a strong opposite in generative art which is bent towards multiplication of units.
Another difference between generative art and information visualisation is that generative art is random whereas information visualisation can be described as chaotic. The reason why information visualisation arts are refered to as chaotic is because they deal with figures that are unpredictable. For instance, weather is unpredictable and so are the operations in the stock market.
An example of randomised generative art is the works of Wolfgang Amadeus Mozart who did music. His composition was based on the blending of random dices which made his type of music interesting to listen to.
He managed to do so by ordering and disordering the mix to create an interesting piece which would have been otherwise plain. Other examples include Elsworth Kelly, William Burroughs and Carl Andre. Generative art is thus more self-opiniated as compared to information visualisation which is dictated upon by the figures in the market (Galanter, 2006, pp. 2-10).
Information visualisation on the other hand is not random and a set of rules must be followed in the creation of the artworks. The artwork must tell an informative story and that is the main reason why it cannot afford to be random. However, telling a great story that will capture the attention of the viewer requires breaking some of these rules.
Conclusion
Digital technologies have with no doubt had a major impact on the ideas and uses of drawing in contemporary art as evidenced by this paper. The birth of computers has evolutionalised the world of art and it has come with its benefits and limitations.
From this study, drawing in contemporary art has become faster and more creative courtesy of the many features that come with computer software such as Adobe and Photoshop among others. These software have enabled artists to become more creative and complete their artworks in the shortest time possible.
The limitations include lack of authenticity since data compiled on computers can be accessed by many people. This has led to reproduction of similar artworks and this discredits the original creator. Some of these software are too expensive and this discriminates on the junior artists who may be struggling to get to the top.
The comparisons between generative art and information visualisation have shed light on the differences and similarities between these two areas of digital practice. Information visualisation is more descriptive and thus richer in passing information to the viewers whereas generative art has more aesthetic than educative value.
References
Christiane, P., 2003. Digital Art. Thames and Hudson Limited, 8(3), pp. 15-145.
Davison, M., 2006. Impact of digital imaging on fine art teaching and practice. Staffordshire University, 6(1), pp. 1-8.
Galanter, P., 2006. What is generative art? Complexity theory as a context for art theory. Interactive Telecommunications Program, 4(2), pp. 2-10.
ISR., 2002. Information visualisation research. University of California, 2(1), pp. 2.
Sack, W., 2002. Aesthetics of information visualisation. UCSC Education Journal, 1(1), pp. 1-22.
Smith, D., 2008. Around and about digital new media art. World through digital art, 4(2), pp. 1-3.