Unveiling Generative Art: Where Machines Meet Creativity
Step into the fascinating world of generative art, where algorithms and human ingenuity collide! This guide, led by art historian Lev Shusharichev, explores its history and trends.
The art labeled as "generative" is also known as procedural, algorithmic, computer-generated, or digital art. All these descriptors add nuances to the central idea of this type of creativity: the artwork is not created by a human but rather generated (semi)automatically.
In this text, art historian and curator Lev Shusharichev provides a brief history of the field and discusses its trends and challenges. While today people generate music, design, and text, this overview focuses specifically on visual art.
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How It All Began
The roots of generative art can be traced deep in history—humans have always sought to simplify their work, even in the realm of creativity. If we understand the term broadly, we can find examples like linear perspective in painting or tonal systems in music. These are tools that partially automated the labor of artists and composers.
Methods of generative art can range from simple rules guiding the creation of artworks to complex computer systems. Generative art emerges as a result of a balance between two actors: the author, who selects the degree of control, makes decisions, or delegates them, and the system, which possesses relative autonomy and employs randomness as a method.
Artists of the early 20th century modernist movement approached our understanding of generative art. They employed randomness as a method, established playful procedures for creating artworks, and drew inspiration from machines. They sought new forms of expression and reinvented art.
For instance, the eccentric Salvador Dalí would shoot paint from a cannon onto canvases to create arbitrary color spots. Other surrealists used automatic drawing, attempting not to control what they were drawing. Dadaist Hans Arp composed word collages by randomly selecting fragments from newspapers. Constructivists, such as Alexander Archipenko, celebrated the aesthetics of mechanisms and believed that humans should collaborate with machines as co-authors. However, this was a period of utopian visions that had yet to materialize in reality.
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In the 1960s, the development of generative art was heavily influenced by technological progress. The early generative artists were also programmers, as they were mastering the newly emerged computers and software. The initial generative artworks were predominantly non-representational. Computers excelled at creating geometric and color combinations, from which artists selected the most expressive ones.
Some of the most famous and early representatives of generative art were Frieder Nake and Georg Nees. They utilized mathematical algorithms and programs to create abstract graphic compositions in the spirit of minimalism.
Frieder Nacke. Homage to Paul Klee. 1965
Another pioneer was John Whitney. In the 1960s and 1970s, he developed computer programs that allowed him to create abstract animated films. Whitney used mathematical formulas to create complex and colorful visual effects.
Another famous representative of generative art is the artist Harold Cohen. In the late 1960s, he developed a program called "Aaron" that could autonomously generate works of art. Cohen worked on developing "Aaron" for decades and equipped the program with a robot that allowed it to literally draw.
Harold Cohen. The first athletes. 1986
Artist Vera Molnar used existing programming languages, but also created her own programs. Her geometric compositions, abstract patterns and optical illusions are included in museum collections around the world.
Vera Molnar. 100 squares with 20 different yellow ones. 1977
Many works of that time are clearly inspired by cubism and abstract art of the first half of the 20th century. Artists primarily experimented with the possibilities of technology rather than with visual language. Generative works from the 1960s often reproduce effects that could be achieved by hand.
In the next decade there was a slight decline in interest in the direction. However, it continued to evolve and became even more widespread with the rise of personal computers in the 1980s and 1990s.
New generation
New multimedia capabilities and the development of artificial intelligence led to the explosive development of generative art in the third millennium. A distribution system for such art has emerged. Specialized platforms and markets for generative art have been created.
The spread of artificial intelligence has changed the situation. This became especially noticeable with the advent of publicly available services such as Midjorney and DALL-E. They have radically lowered the barrier to entry. Now a person only needs to formulate a request qualitatively and set the necessary settings.
Neural networks are also changing the image of generative art, which was rather abstract. With the help of the generative capabilities of AI, many figurative works are emerging. Since the neural network is trained on existing visual material, it can reproduce different styles. Works based on visualization and analysis of data sets also make generative art more diverse. This allows you to cover a wider range of topics.
An example is the work of Mario Klingeman. The researcher and artist insists that his works are about the code and the system itself, and not about the result it produces. He explores issues of human perception.
Mario Klingemann. Memories of Passersby I. 2018
Other trends are multidisciplinarity and interactivity. Artists combine computer graphics with physical objects, project algorithmically generated images onto architectural structures, or create multimedia installations. Generative art is getting closer to the viewer, with interfaces being created more and more often to allow the person to interact, modify the work, or control it.
Previously, artists would create many images using an algorithm in order to select just a few successful ones. The perfection of modern algorithms allows each generation to be considered a complete work. The author of the code sets a limit on the number of images, and then the program works completely independently. The new trend is called long-form generative art. This takes the autonomy of art-producing systems to a new level.
Blockchain technology has provided a suitable way to verify such digital works. This preserves the originality of digital copies. Otherwise, they could be produced indefinitely, and thus lose their value. The viewer-buyer can order an image that will be generated and transferred to them as an NFT. The first example was the Autoglyphs algorithm. But then entire platforms began to emerge, such as ArtBlocks, which operate on this principle. On such platforms, artists exhibit not individual works, but their algorithms.
Autoglyph #6
The popularity of this direction does not allow us to review a significant number of artists. Let's briefly dwell on a few personalities, noting that most modern authors continue to work with various kinds of abstraction.
Tyler Hobbs is an artist who specializes in creating algorithms for generating abstract and complex compositions. He is one of those who promote long-form generative art. In his works, he explores the interaction between the digital and the natural world. Therefore, his works combine a strict structure that comes from the computer, and the organic chaos that can be observed around.
Tyler Hobbs. Fidenza#58
Dmitry Chernyak is a Canadian artist who strives for the works generated by his code to touch the viewers just as deeply as art created by a human. His works are distinguished by their originality and intriguing visual effects.
Dmitry Chernyak. Red October. 2020
Matt Kane is an artist and programmer. He also creates long-running algorithms that generate works in the NFT format. For example, he is inspired by the phases of the lunar calendar, so one of his series resembles observing celestial bodies.
Matt Kane. Gazers #170
The "Asemic" algorithm was created by a group of authors including Emily Edelman, Dima Ofman, and Andrew Badr. This program is distinguished by the fact that it generates meaningless pictographic signs. The algorithm creates non-existent letters and words, which the artists invite the viewer to look at as a graphic combination, stripped of linguistic content.
Emily Edelman,Dima Ofman and Andrew Badr. Asemica #836. 2021
The artist Emily C creates algorithms to generate realistic textures and forms. She is interested in the process of combining various materials and patterns into a single visual whole, as well as the stories that each of them can bring to the overall picture. She is inspired by physical objects: textiles, collages, and wallpapers. She recreates them in the digital space and assembles them into compositions.
Emily. Away from the keyboard
Fundamental issues of generative art
The generative art movement is evolving, acquiring new tools. The combination with blockchain technologies has created a new wave of popularity. It can be assumed that generative art will survive the NFT boom and take on new forms. And since this is the art that will accompany humanity, it is worth taking a closer look at the questions and problems that it raises.
Generative art involves the use of non-human agents such as artificial intelligence and computer algorithms. This raises the question: who is the real author of a generative work of art: the person who wrote the algorithm, or the machine that generated it? Should only the algorithm be considered the work of a person, and all generated content - the work of a machine?
And this is not only a theoretical issue - it lies in the practical plane of sales and transfer of copyrights. And one can imagine such a development of technology when programs become even more autonomous. Therefore, the balance between the independence of the system and the control of the author continues to be the main nerve of generative art.
This type of creativity has the advantage of being able to reproduce endlessly. However, this can also lead to problems with the uniqueness and value of such works. For now, this is solved either by control by a person who generates individual works, or by an artificial limitation for the algorithm.
The challenge for generative art is the search for new expressiveness. On the one hand, an attempt is needed to create a unique computer aesthetics that would not be simply a digital repetition of analog works. On the other hand, there is a need to overcome abstraction, which is gradually pushing this direction towards design.
Art of the future?
Generative art is a direction that continues to develop and attract the attention of artists and scientists, viewers and collectors. It represents a symbiosis of art and science, opening up new horizons of creativity. Being at the technological forefront, generative art has not yet become widely popular, but has formed its own circle of creators and admirers. This happened because the masters of contemporary art are not yet actively using these tools.
Nevertheless, algorithms and neural networks are increasingly penetrating biennales, museums and galleries, and are becoming part of the unified world of art. But it is important to note that generative forms do not (and are unlikely to displace) works that are created by people. As always happens in cultural history, a new revolutionary technology seems to threaten the classics, but then they continue to develop in parallel.
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