My primer on Generative Art.
Generative Art: The Evolution of Art Created by Machines
What is Generative Art?
Generative art is a form of art created using an autonomous system that operates according to a set of rules or algorithms defined by the artist. Unlike traditional art, the artist does not have complete control over the final visual art output, but rather sets up a process that collaboratively generates the art with the machine.
Brief History
The history of generative art can be traced back centuries to early examples like the musical dice games created in 18th century Europe. However, the field start creating generative art really took off in the 1960s with the advent of computer technology which allowed artists like Frieder Nake, Georg Nees and A. Michael Noll to start experimenting with computer-generated generative art.
Generative Art Today
In recent years, generative art has seen a resurgence in popularity and new recognition through the rise of digital art, NFTs, and increased access to software tools that allow endless unique creations to be instantiated algorithmically. Contemporary generative artists have pushed the boundaries of creative AI and computer vision to further explore the evolving relationship between generative computer art and humans and machines in artistic collaboration.
Overview of This Article
This article will provide an overview of the evolution of generative art, from early pioneers to current leading practitioners. We’ll examine some of the key techniques and technologies that power generative art, and also discuss important theories around concepts like randomness, computational creativity, and machine intelligence. The story of generative art gives us insight into foundational questions about the nature of art itself, and the increasing role technology plays in its creation and perception. By understanding the past, present and future possibilities of generative art, we can develop a deeper appreciation for it as an important artistic movement.
The History and Origins of Generative Art
Early Examples
Some of the earliest examples of art and music created using autonomous systems date back centuries, though the term “generative art” was not used at the time.
In the 18th century, mathematical games like the Musikalisches Würfelspiel or “musical dice game” were popular in Europe. These involved using dice rolls to randomly generate music from precomposed options according to a set system.
Birth of Computer-Based Generative Art
It wasn’t until the 1960s that generative art as we know it today really emerged, with the advent of computer technology providing new capabilities for algorithmic creation.
Pioneers like Frieder Nake, Georg Nees, and A. Michael Noll started experimenting with the art institute using computers to automatically generate artistic compositions according to rule-based systems designed by the artist. Some key early exhibitions displaying this pioneering computer art include Generative Computergrafik in 1965.
Establishment of Key Concepts
In the following decades, academics like Max Bense and Abraham Moles began theorizing about generative art through concepts like algorithmic information theory. Philosopher Philip Galanter later connected generative art to complexity theory in his influential 2003 paper.
Artists like John Cage and Karl Sims further explored artificial life simulation, randomness, and evolutionary systems throughout the late 20th century as generative art was established as a movement.
Recent Resurgence
From the 2000s onwards, generative art has seen a major resurgence in interest and popularity, helped by the rise of digital art, creative coding, AI art and NFTs. Contemporary artists have built upon the foundations to push generative art in new experimental directions integrating cutting-edge technology.
Defining Characteristics of Generative Art
Generative art is a unique form of art with some core characteristics that distinguish it from traditional art forms. Some of the key qualities of generative artwork include:
Algorithmic Systems
At its core, generative art relies on an algorithmic system — a set of rules and procedures — designed by the artist to create the work either partially or in whole. This can be a mathematical formula, computer program, mechanical process, biological process, or other autonomous generative design technique.
Machine Collaboration
The algorithmic system generates the artwork with some degree of randomness and autonomy, requiring the artist to give up complete control. The artist guides the overarching creation process, but the machine collaborates in the creation. The output is neither fully human-made nor machine-made but a hybrid.
Unpredictable Results
The autonomous and random aspects of the algorithmic system lend an unpredictability and uniqueness to the output. The same program can produce substantially different results when run multiple times due to the variability.
Focus on Process
Generative art often focuses more on the system and process of creation designed by the artist rather than the specific end product. The artwork exists in the algorithm rather than any individual output.
Complexity
The algorithms often generate complexity displaying order and chaos — similar to patterns found in nature. They are guided by the artist’s aesthetic intentions to produce results that resonate emotionally and intellectually.
Abstraction
The works avoid figurative representation and often employ abstract visuals as a result of the unpredictable generative systems. Forms, shapes, colors, motion are articulated through computer code rather than attempting to represent real world subjects.
Techniques and Technologies Powering Generative Art
Artists working in generative art employ a diverse range of techniques and technologies to bring their algorithmic visions to life. Some of the most common include:
Programming Languages
Languages like Processing, OpenGL, and C++ are commonly used to code the generative systems and render the visuals. Languages provide the artist control over creating customized algorithms.
Machine Learning
Machine learning algorithms like neural networks can be leveraged to develop AI systems that create generative art, like DeepDream which mimics human perception.
Computer Simulation
Advanced physics and biology simulations can model complex phenomena which are visually brought to life using computer graphics. Flocking simulations are a popular example.
Automated Machines
Robotic systems can execute physical generative art processes in the real world in tandem with digital algorithms by drawing, painting, or manipulating materials.
Augmented Reality
AR overlays virtual generative art and reactive displays onto the physical environment through devices like Microsoft HoloLens, bridging the real and digital.
Interactive Web Tools
Websites like CanvasCycle and ShapeFactory allow users to instantly create generative artworks online by providing pre-made algorithms that can be tweaked and customized.
Genetic Algorithms
Algorithms inspired by Darwinian evolution generate art through principles like random mutation and selection — allowing computer “breeding” of art based on traits deemed aesthetically pleasing.
Pioneers and Seminal Works of Generative Art
While generative art has seen a recent surge, many pioneers established the field in prior decades through their pioneering artworks. Some key figures and pieces include:
Frieder Nake
- One of the earliest generative artists, Nake created distinctive computer-generated algorithmic artworks like Walk-Through-Raster in 1965 using a plotter machine. His random geometric forms displayed entirely new artistic possibilities opened up by computation.
A. Michael Noll
- In 1962, Noll created some of the very first computer generated patterns by using mathematical functions. His innovative approach used the computer as an artistic collaborator rather than just a labor saving device.
Harold Cohen
- Cohen’s AARON drawing system developed in the 1970s was an early pioneers of AI art. The system could generate original images of stylized human figures and landscapes evolving in complexity over time through machine learning.
Karl Sims
- Sims’ 1990s artworks like Galápagos dramatically demonstrated the potential of artificial evolution. Stunning biomechanical 3D forms were “bred” by the computer over generations through simulated mutation and selection.
Casey Reas
- Reas is a leading contemporary generative artist associated with Processing. Works like Process 15 employ algorithmic systems exploring emergent behavior, chaos, and complexity theory visualized through constantly shifting geometric drawings.
Refik Anadol
- A current practitioner at the cutting edge, Anadol creates immersive multimedia sculptures and installations driven by generative data painting, machine learning, and parametric design, forging new territory in the confluence of art and AI.
Curating and Collecting Generative Art
As a relatively new form of art heavily tied to technology, generative art poses unique challenges for curators, collectors, and art institutions. Some key considerations include:
Process vs Product
A key question is whether to curate the output or the underlying process — should the code or just its results be exhibited? Displaying the algorithms helps convey the rules-based system central to generative art.
Randomness and Variability
The unpredictability and lack of a definitive final piece means curators have to decide whether to showcase a single output or arrange displays triggering real-time own generative art output.
Technology Conservation
The close linkage to computers and custom software means properly archiving and preserving the technical element is crucial for conservation, an issue with many digital art forms.
Interactivity
Generative art often has an interactive component inviting audience inputs that impact the creation code art itself. Curators have to decide how much freedom vs constraint to enable within exhibits.
Connections with NFTs
Many newer generative artists issue their algorithmic artworks as NFTs with blockchain-verified ownership and provenance. However, institutions are still wrestling with collecting and exhibiting NFT art.
Educational Context
Providing viewers proper background on the algorithms powering the art and the artistic intent behind them remains important for contextualizing generative art, which may seem esoteric at first glance.
There remain open questions around how to best exhibit, conserve, and promote understanding of generative art. But institutions like the Whitney Museum and New York’s Bitforms gallery provide some early models.
Theories and Questions Surrounding Generative Art
In addition to the practical creation of generative art, many theorists have explored profound conceptual questions raised by this emerging art form.
What is creativity?
A core question is whether the ability to autonomously generate novel and valuable artworks implies that the generative system is “creative” in some capacity. This ties into theories of computational creativity and artificial intelligence.
How much control should the artist relinquish?
There is debate around how much unpredictability versus human artistic control is ideal for creating resonating generative art. Allowing total randomness risks losing meaning, but too much control discounts the collaborative aspect.
What is the role of randomness?
Introducing randomness into art challenges notions of creative intentionality. But proponents argue randomness mimics real-world complexity and allows serendipitous emergence of engaging forms fine art.
Does complexity improve art?
Information and complexity theory argue art benefits from a balance of order and disorder. But this view is contested by those who believe complexity alone does not dictate aesthetic merit.
How do algorithms shape perception?
The algorithms underlying generative art provide new ways of filtering and representing sensory data. This opens discussions around the inner workings of human versus machine perception.
What does it mean to be an artist?
By expanding art’s creative process beyond physical media, generative art forces a re-examination of assumptions around artistic skill, authorship, and even the concept of an artwork itself.
There remain vigorous open debates around these philosophical uncertainties introduced by generative art. But they force a re-assessment of modern art itself’s deepest meanings and purposes.
The Future and Significance of Generative Art
As we have explored, generative art has progressed rapidly from its origins many centuries ago to a recent resurgence as a highly relevant and impactful art movement. Where might it go next and why does it matter?
Pushing Creative Boundaries
By collaborating with machines in the creative process, generative art allows both artists and audiences to engage with art in completely novel ways not possible previously. It opens new creative frontiers.
Integrating with Emerging Technologies
As technologies like AI, AR/VR, robotics, and the blockchain continue evolving rapidly, integrating them can propel generative art in exponentially more advanced and interactive directions.
Revealing Inner Workings of Art
By exposing the algorithms underneath, generative art pulls back the curtain on some of the patterns and causal forces that ultimately give rise to the subjective experience of art itself, whether created by humans or machines.
Philosophical Implications
Generative art acts as a testing ground for philosophical questions about cognition, aesthetics, complex systems theories, and what it means to create art be creative. The knowledge gained influences our worldviews.
Accelerating Technological Impact on Art
As algorithms and computers take on increasingly prominent roles in culture, generative art provides an early glimpse of how technology may transform the arts and force us to rethink longstanding assumptions about art.
The generative art movement sits at the bleeding edge exploring new artistic possibilities opened up by technology. As digitization accelerates, its forward-thinking ideas and experiments will only increase in relevance. Generative art is poised to lead the vanguard into art’s technological future.
Exploring Generative Art Yourself
Now that we’ve covered the evolution, techniques, theories and questions surrounding creating generative of art, you may be feeling inspired to try creating your own generative artworks. Here are some suggested starting points if you want to dive deeper into this fascinating artistic domain:
- Find generative art tutorials and tools to learn the basics of coding and generating algorithmic artworks from scratch
- Browse galleries of generative art examples to draw inspiration from different styles and approaches
- Access libraries like p5.js that provide pre-built functionality to quickly create interactive generative designs online
- Try using AI systems like DALL-E3to generate conceptual generative art directions and prompts
The possibilities for participatory engagement with generative art are expanding rapidly. Dive in to start figuring out which creative algorithms speak to you — have fun watching your art generate itself!
