The following text is an excerpt from my contribution to the book The Meaning of Creativity in the Age of AI, edited by Raivo Kelomees, Varvara Guljajeva, and Oliver Laas (Tallinn: EKA, 2022). The volume is focuses on critical observations of the possibilities of Artificial Intelligence in the field of the arts and includes contributions by artists, art professionals, and scholars Varvara Guljajeva, Chris Hales, Mar Canet Solà, Jon Karvinen, Luba Elliot, Oliver Laas, Raivo Kelomees, Mauri Kaipainen, Pia Tikka, and Sabine Himmelsbach.
The book, which addresses key questions currently being debated around AI systems such as DALL-E 2 and Chat GPT, has been recently made available as afree PDF.
Can you teach your machine to draw?
On 5th February 1965, during the opening of Georg Nees’ exhibition of algorithmic art at the Technische Hochschule in Stuttgart, there was an exchange between the engineer and an artist who asked him provocatively if he could teach the computer to draw the same way he did. Nees replied that, given a precise description, he could effectively write a program that would produce drawings in the artist’s style (Nake, 2010, p.40). His response echoes the conjecture that had given birth to the field of artificial intelligence ten years earlier: that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it” (Moor, 2006, p.87). It should be noted that, at least at this point, the machine is not meant to think or create, but simulate. In his seminal paper from 1950, Alan Turing already suggested that computers could perform an “imitation game” (later known as the Turing Test) in which the aim was to mimic human intelligence to the point of seeming human to an external observer (Turing, 1950).
Therefore, what Nees asserted is that the computer could create a successful imitation of the artist’s work. The exchange between Nees and the artist did not go well, as the engineer’s vision of a computable art seemed to threaten the superiority of artistic creativity. Upset and resentful, the artist and his colleagues left the room, with philosopher Max Bense trying to appease them by calling the art made with computers “artificial” (Nake, 2010, p.40) – as opposed, one might think, to a “natural” art made by human artists. The need for this distinction denotes the uneasy relationship between artists and their tools, the latter supposedly having no agency at all, being mere instruments in the skilled hands of the artist.
Certainly, there had been some room for randomness and uncontrolled processes to emerge in the different artistic practices that had succeeded each other during the 20th century, but until that point creativity was unquestionably anthropocentric, with the artist (or their assistants), at the centre of the creation of every artwork. The computer introduced an unprecedented level of autonomy: the artist only needed to write a set of instructions, the program did the rest. This was challenging for artists at a time when few had seen a computer and even fewer knew how to write a program or understood what it could do.
Despite the profound differences from our current perception of computers, over fifty years later, AI still holds the same fascination and is subject to the same misunderstandings as early computer art. The initial rejection of computer-generated art has turned to uncritical enthusiasm, and the prospect of an art that does not need human artists has been celebrated with a spectacular sale at Christie’s. But the artist was never out of the picture.
Pioneering computer artist Vera Molnarcreated her first artworks in the 1960s with a “machine imaginaire”, a program for an imaginary computer that helped her develop a series of combinatorial compositions of geometric forms and colours. In 1968, she started working with a real computer (which back then was only available at a research lab), but she has always stressed that the machine is, to her, nothing but a tool: “The computer helps, but it does not ′do′, does not ′design′ or ′invent′ anything” (Molnar, 1990, p.16).
Another pioneer, Frieder Nake, recalls the experience of creating his first algorithmic drawing in 1965, underscoring his role as the creator of the artwork:
“Clearly: I was the artist! A laughable artist, to be sure. […] But an artist insofar as he – like all other artists – decided when an image was finished or whether it was finished at all and not rather to be thrown away. I developed the general software, wrote the specific program, set the parameters for running the program. […] I influenced the process of materialization by choosing the paper, the pens, and the inks; and I finally selected the pieces that were to be destroyed or to leave the studio to be presented to the public.”
Manfred Mohr, one of the first artists to work with computers who, like Molnar, had a background in fine arts instead of mathematics, has frequently stated that his artworks transcend the computational process they are based on: “My artistic goal is reached” he states, “when a finished work can visually dissociate itself from its logical content and convincingly stand as an independent abstract entity” (Mohr, 2002).
Algorithmic artists have played with the balance between control and randomness, always keeping a direct involvement in every part of the process of creation, from the code to the final output. The software, however, can be allowed a greater portion of the decision making. This is what Harold Cohen did in 1973 when he developed AARON, a computer program designed to generate drawings on its own, with no visual input, based on a complex series of instructions written by the artist.
Influenced by the ideas that were being discussed at Stanford University’s Artificial Intelligence Laboratory at the time, Cohen sought to understand how images were made. AARON aimed to answer that question by creating drawings that simulated those of a human artist, without human intervention. Cohen stressed AARON was “not an artists’ tool” but “a complete and functionally independent entity, capable of generating autonomously an endless succession of different drawings” (Cohen, 1979). This autonomy led to thinking about AARON in cognitive terms, with Cohen himself stating that the program “has a very clear idea of what it is doing” (Cohen and Cohen, 1995, p.3). For over four decades, the artist kept developing the program, establishing a relationship that he described as the kind of collaboration one would have with another human being:
“AARON is teaching me things all the way down the line. From the beginning, it has always been very much a two-way interaction. I have learned things about what I want from AARON that I could never have learned without AARON”
Cohen and Cohen, 1995, p.12
Cohen’s work prefigured the current applications of AI systems in art making, not only in the way the program worked but also in its role as a collaborator rather than a mere tool.
Artists working with artificial neural networks nowadays describe their experience in similar terms to those expressed by AARON’s creator. When Anna Ridler created her own dataset of 200 drawings to train a GAN for her animated film Fall of the House of Usher I (2017), she sought to push the boundaries of creativity by producing an artwork that is a machine generated interpretation of her drawings, which in turn represent scenes from a silent film based on a short story by Edgar Allan Poe. The outcome has led her to wonder where is the “real” artwork, and to doubt the role that the program plays in its making: “I do not see a GAN as a tool like I would think of say a photoshop filter but neither would I see it is as true creative partner. I’m not really quite sure what is is” (Ridler, 2018).
For Patrick Tresset, working with robots that can draw in their own style enables him to distance himself from his work: “I found it very difficult to show my work, as a painter, as an emotional thing, and the distance that we have with the action when you use computers, that you are not directly involved… makes it far easier for me to exhibit” (Upton, 2018).
Memo Akten explores the structure and functioning of artificial neural networks and uses Machine Learning as a form of exploring human thinking: “My main interest,” he states, “is in using machines that learn as a reflection on ourselves, and how we navigate our world, how we learn and ‘understand’, and ultimately how we make decisions and take actions” (Akten, 2018).
Gregory Chatonsky criticizes the perception of the artist as purely autonomous and the machine as a simple tool, while describing his creative process as an interaction with the software that not only generates images but also spurs his imagination: “Working with a neural network to produce images or texts,” he states, “I perceive how my imagination develops, becomes disproportionate and germinates in all directions. I try to adapt to this rhythm, to this breath. It’s almost alive” (Chatonsky, 2020).
These statements show that artists have carried out a dialogical relationship with the software they have used, considering it not just an instrument, but a collaborator. However, the deeply entrenched perception of the artist as the sole creator of the artwork, in full control of every aspect of the outcome, looms over this partnership insisting that either the machine is to remain a mere tool or it is destined to take over the artist’s role.
Towards post-anthropocentric creativity
The question whether a machine can be creative is recurrently asked as AI systems increase their capabilities and become more sophisticated. Recently developed systems such as CAN (Creative Adversarial Network), which is taught to deviate from the examples it has learnt in order to produce new types of images (Elgammal et. al., 2017), or DALL-E, which can generate images from text descriptions (Ramesh et. al., 2021), illustrate how far computers can go in creating visual content.
CAN has even been used in an attempt to pass the Turing Test, that is, to produce machine-generated art that appears indistinguishable from that created by an artist. The results have been disputed in a study that shows a preference for art made by humans and suggests that what should be asked is not if AI can create art, but whether the art created by AI is worthy (Hong and Ming, 2019).
Seen from this perspective, the debate pivots to more practical considerations: what can AI do, and how can it be used? GANs are widely employed by artists nowadays, but they tend to generate the same type of images because of the limitations of the programs and the processors. In this sense, the artificial neural networks are not particularly creative because they do not produce anything that breaks out from a set of established parameters and similar outputs. The creativity stems from how artists use these images and assign them a certain narrative. Therefore, to expect machines to become creative by following problem-solving approaches seems limiting and even counterproductive (Esling and Devis, 2020), given that we don’t even understand how creativity works and cannot translate it into computable formulas.
Instead of asking whether an AI system can replace an artist, it would be more interesting to consider how artists can expand their creativity using AI. This proposition does not imply considering the artist as the sole creator of the artwork, but moves past this preconception to embrace a notion of creativity that includes all the actors involved, human and non-human.
Jan Løhmann Stephensen suggests the terms “postcreativity” or “postanthropocentric creativity” to challenge the idea of creativity as something that is exclusive to humans and a marker of human “greatness” (Løhmann, 2019). Through the lens of postcreativity, we can consider artworks as the outcome of an interaction between a variety of actors, including humans, objects, systems, and environments. In AI-generated art, this means taking into account all the people, animals, natural environments, institutions, communities, software, networks, etc. that take part, more or less directly, more or less willingly, in the artwork’s making.
This opens up deeper reflection on how the piece is created, as do Anna Ridler and Memo Akten in their examination of the artificial neural networks they use. It also allows artists to distance themselves from the specific output while retaining authorship of the process, as do Patrick Tresset and Guido Segni – the latter currently engaged in a five year project titled Demand Full Laziness(2018-2023), in which he outsources his artistic production to a deep learning algorithm trained with images from his moments of rest. Overall, it emphasises the potential of co-creation between humans and machines, in which computers do not mimic, but expand human creativity.
Artificial Intelligence has developed at a growing pace over the past seven decades, and it will continue to do so, bringing new challenges and possibilities for computer-generated art. As several authors point out, AI is currently at a stage equivalent to the daguerrotype in photography (Aguera, 2016; Hertzman, 2018), and it is difficult to predict what novel forms of creativity it will unfold. It might well be, if AI were to reach a stage of consciousness or self-volition, that a program may not be interested in producing a drawing or a photograph and would rather express itself through elegant programming code or a beautiful mathematical equation. Or, maybe it would even create art that is not intended for humans to understand, but is addressed to fellow AIs.
Elgammal, A., Liu, B., Elhoseiny, M., Mazzone, M., 2017. CAN: Creative Adversarial Networks Generating “Art” by Learning About Styles and Deviating from Style Norms. Cornell University [online] Available at: https://arxiv.org/abs/1706.07068 [Accessed 14 March 2021].
Esling, P., and Devis, N., 2020. Creativity In The Era Of Artificial Intelligence. Cornell University [online] Available at: https://arxiv.org/abs/2008.05959v1 [Accessed 14 March 2021].
Hong, J. and Ming Curran, N., 2019. Artiﬁcial Intelligence, Artists, and Art: Attitudes Toward Artwork Produced By Humans vs. Artiﬁcial Intelligence. ACM Trans. Multimedia Comput. Commun. Appl., 15(2). Available at: https://doi.org/10.1145/3326337 [Accessed 14 March 2021].
Løhmann Stephensen, J., 2019. Towards a Philosophy of Post-creative Practices? – Reading Obvious’ “Portrait of Edmond de Belamy.” In: Politics of the Machine Beirut 2019 (POM2019). [online] Beirut: BCS Learning and Development Ltd., pp.21-30. Available at: http://dx.doi.org/10.14236/ewic/POM19.4 [Accessed 14 March 2021].
Marcus, G. and Davis, E., 2019. Rebooting AI. Building Artificial Intelligence We Can Trust. New York: Pantheon Books.
Mohr, M., 2002. Artist’s Statement. Ylem Journal, Artists using Science & Technology, 22(10), p.5.
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I., 2021. Zero-Shot Text-to-Image Generation. Cornell University [online] Available at: https://arxiv.org/abs/2102.12092 [Accessed 14 March 2021].
ChatGPT is a machine learning model developed by OpenAI which was recently opened to the public as a research preview, allowing users to test it freely. Similarly to howDALL-E 2 draw widespread attention for its ability to create impressively realistic or stylized images based on text prompts, ChatGPT is now receiving similar responses, since it is capable of producing reasoned explanations and provide answers to follow-up questions, apparently grasping the context of what is being asked. As explained by OpenAI, the model was trained using supervised learning, based on conversations written by humans, in which a question and an answer were provided. Then a reward model was trained using several answers to the same question and having a human labeler rank them from best to worst. A reinforcement learning algorithm was used to optimize the model.
ChatGPT is able to explain concepts and provide answers considering different aspects of an issue, often maintaining a neutral attitude that seeks to balance out opposing points of view. It usually concludes with a short paragraph that summarizes its previous statements. Seeking to test it, I asked a series of questions regarding art created with Artificial Intelligence programs. It seemed fitting to have an AI system explain AI art to humans. The result is the interview below.
Please note: ChatGPT provides long answers, sometimes using repetitive formulas. I have decided not to make any edits to the text in order to remain true to its outputs, and so to avoid forcing readers to scroll through a very long article I’ve included an index below. Feel free to click on the questions you find most interesting. They are grouped into themes to make browsing easier.
AI art is artwork created by artificial intelligence. This can take a variety of forms, but typically involves using algorithms and machine learning techniques to generate visual artwork, music, or other forms of creative expression. AI art often explores the intersection of art and technology, and can involve the use of neural networks and other forms of AI to create unique and unpredictable artistic experiences. Some AI art is created with the goal of imitating human creativity, while other AI art is intended to push the boundaries of what is possible with technology and create entirely new forms of art.
AI art is a type of art that is created using artificial intelligence algorithms and technology. AI art can take many forms, including digital art, paintings, sculptures, and other types of visual art. The algorithms and technology used in AI art can be trained to create original works of art or can be used to enhance or manipulate existing artwork. AI art can be created by both human artists and AI algorithms working together, or it can be created entirely by AI.
AI art can be considered a subset of algorithmic art. Algorithmic art is a term that refers to art that is created using algorithms and other computational processes. AI art is a specific type of algorithmic art that is created using AI technology and algorithms.
In this sense, AI art can be considered a subset of algorithmic art, because it is a specific type of art that is created using algorithms and computational processes. However, it is worth noting that not all algorithmic art is AI art, and there are many other types of algorithmic art that do not use AI technology.
Overall, while AI art is a type of algorithmic art, it is not the only type of algorithmic art, and there are many other types of algorithmic art that do not use AI technology.
What role does AI art play in the history of contemporary art?
AI art is a relatively new development in the history of modern and contemporary art, and its role is still evolving. However, AI art has already had a significant impact on the art world, and it has introduced new ideas and possibilities for artists and art enthusiasts.
One of the key ways in which AI art has impacted the art world is by challenging traditional notions of what constitutes art and who can create it. Because AI algorithms and technology can be used to create art without the need for human intervention, AI art has called into question the idea that art must be created by human artists in order to be considered “real” art.
Additionally, AI art has also introduced new possibilities for collaboration between human artists and AI technology. Many artists are now experimenting with using AI algorithms and technology as part of their creative process, and this has led to the emergence of new styles and genres of art that are not possible without the use of AI technology.
Overall, while AI art is a relatively new development in the history of modern and contemporary art, it has already had a significant impact on the art world, and it is likely to continue to play an important role in the future.
AI art has its roots in the field of artificial intelligence (AI), which is the study of how to create computer systems that can simulate human intelligence. The development of AI technology has been ongoing for several decades, and it has led to the creation of many different AI algorithms and technologies that can be used to solve complex problems and to automate various tasks.
One of the areas in which AI technology has been applied is in the creation of art. By training AI algorithms on large datasets of artwork, it is possible to create AI programs that can generate novel and original artworks based on the information they have learned.
The first known examples of AI art date back to the 1960s, when researchers began to experiment with using AI algorithms to create visual art. Since then, the field of AI art has continued to evolve and grow, and there are now many different AI algorithms and technologies that can be used to create a wide range of different types of art.
Overall, AI art has come about as a result of the development of AI technology and algorithms, and the application of these technologies to the field of art.
The first known examples of AI art date back to the 1960s, when researchers began to experiment with using AI algorithms to create visual art. One of the first known AI artworks was created by the computer scientist Harold Cohen in the early 1960s. Cohen developed a program called AARON, which was able to create abstract drawings based on a set of rules and instructions that Cohen provided.
Another early example of AI art was created by the artist Harold Cohen in the early 1970s. Cohen developed a program called AARON, which was able to create abstract drawings based on a set of rules and instructions that Cohen provided.
Overall, while the exact details of the first AI artwork are not well-documented, it is likely that one of the first AI artworks was created by Harold Cohen in the early 1960s.
Can you name the main artists who have created art with AI?
There are many artists who have created art using AI technology and algorithms. Some of the most notable and well-known artists who have created AI art include:
Mario Klingemann: Klingemann is a German artist who creates AI art using a variety of different algorithms and technologies. His work often explores the intersection of art and technology, and he has exhibited his AI art in galleries and museums around the world.
Trevor Paglen: Paglen is an American artist who creates AI art that explores the relationship between technology, surveillance, and power. His AI artworks often take the form of large-scale installations and projections, and he has exhibited his work in galleries and museums around the world.
Anna Ridler: Ridler is a British artist who creates AI art using deep learning algorithms and machine learning techniques. Her AI art often explores themes of identity and the human condition, and she has exhibited her work in galleries and museums around the world.
Sougwen Chung: Chung is a Canadian artist who creates AI art that explores the relationship between humans and machines. Her work often involves collaborative projects between human artists and AI algorithms, and she has exhibited her AI art in galleries and museums around the world.
Overall, there are many artists who have created AI art, and their work covers a wide range of styles, techniques, and themes. These artists are at the forefront of exploring the potential of AI technology and algorithms to create art, and their work has been exhibited and recognized in galleries and museums around the world.
Both the artist and the machine can create AI art. Some AI art is created entirely by AI algorithms, without any human input. In these cases, the algorithms themselves are the “artists” creating the artwork. Other AI art is created by human artists using AI technology and algorithms to assist them in the creative process. In these cases, the human artist is the creator of the artwork, but the AI technology and algorithms are an important part of the creative process. In some cases, AI art may be created by a collaboration between a human artist and AI algorithms, with both contributing to the final artwork.
How can an algorithm be an artist? Isn’t creativity exclusively human?
The question of whether creativity is exclusively human is a complex one, and there is no definitive answer. Some people believe that creativity is a uniquely human trait, while others believe that it is possible for machines and algorithms to be creative.
One way to approach this question is to consider what we mean by creativity. Creativity can be defined as the ability to produce something new and original. While this definition may seem to exclude machines and algorithms, it is important to remember that these tools can be used in ways that were not possible before, and that they can help us to explore new ideas and create new and original works of art.
In the case of AI art, the algorithms and technology used can be trained on vast amounts of data and can be designed to explore a wide range of possibilities. This can allow the algorithms to generate artwork that is novel and original, even if it is created entirely by a machine. In this sense, the algorithms themselves can be seen as creative agents, capable of producing new and original works of art.
Ultimately, whether we consider AI art to be the work of a human artist, the machine, or a collaboration between the two, will depend on our individual perspectives and values. What is important is that AI art allows us to explore new possibilities and to create art in ways that were not previously possible.
Why would an AI program create art for humans? Wouldn’t it be more interesting to create art that only another AI can understand?
AI programs can create art for humans or for other AI programs, depending on the specific goals and objectives of the program and the designers who created it. In some cases, an AI program may be designed to create art that only another AI program can understand, for example, as part of a research project or to explore the capabilities of AI algorithms.
However, in many cases, AI programs are designed to create art that can be understood and appreciated by humans. This is because art is often a way for people to express themselves and to communicate with others, and creating art that can be understood and appreciated by humans is an important part of this process.
Additionally, creating art that can be understood and appreciated by humans can also help to demonstrate the capabilities of AI technology and algorithms. By creating art that people can understand and appreciate, AI programs can show the potential of AI technology to generate novel and interesting ideas, and to explore new creative possibilities.
Overall, whether an AI program creates art for humans or for other AI programs will depend on the specific goals and objectives of the program and the designers who created it.
The question of who should sign an artwork created with AI is a complex one, and there is no definitive answer. The specific approach that an artist takes when signing AI-generated art will depend on the individual artist and their goals and values.
In some cases, an artist may choose to sign an artwork created with AI themselves, as the primary creator of the artwork. This could be because the artist believes that they were the primary driving force behind the creation of the artwork, and that the AI program was only a tool that was used to assist in the creative process.
On the other hand, some artists may choose to sign the artwork with the name of the AI program that was used to create the art. This could be seen as a way of recognizing the contributions of the AI program to the creation of the artwork, and it could be an interesting and unique way to explore the relationship between human artists and AI technology.
In some cases, an artist may choose to sign an artwork created with AI using both their own name and the name of the AI program. This could be seen as a way of recognizing the contributions of both the human artist and the AI program to the creation of the artwork.
Ultimately, the approach that an artist takes when signing AI-generated art will depend on their individual goals and values. Some artists may choose to sign the artwork themselves, while others may choose to sign it with the name of the AI program, and still others may choose a combination of both. What is important is that the artist is transparent about their approach and that they are fair and reasonable in their dealings with the AI program and any other parties involved.
If AI programs are written by humans, isn’t AI art entirely created by humans, in the end?
It is true that AI programs are written by humans, and that the technology and algorithms used in AI art are created by humans. However, it is important to remember that AI programs are not just simple, pre-programmed tools that are used by human artists. Instead, AI programs are capable of learning from data and of making decisions and predictions based on that learning.
In the case of AI art, this means that the AI algorithms and technology are not just blindly executing instructions that have been written by humans. Instead, the AI algorithms are able to generate novel and original artwork based on the information they have learned from the training data.
This means that while AI art is not created entirely by humans, it is also not created entirely by AI algorithms. Instead, it is the result of a collaboration between human creators and AI technology, with both playing important roles in the creation of the final artwork.
Overall, while AI art is not created entirely by humans, it is also not created entirely by AI algorithms. Instead, it is the result of a collaboration between human creators and AI technology.
There are many different tools and technologies that can be used to create AI art. Some of the most commonly used tools and technologies for creating AI art include:
Machine learning algorithms, such as neural networks, that can be trained on data to generate art
Generative adversarial networks (GANs), which can be used to create realistic images and other types of art
Deep learning algorithms, which can be used to create detailed and complex artworks
Computer vision algorithms, which can be used to analyze and interpret visual information
Natural language processing algorithms, which can be used to generate text-based art, such as poetry or fiction
These are just a few examples of the tools and technologies that can be used to create AI art, and there are many other options available. The specific tools and technologies used to create AI art will depend on the individual artist and their goals and objectives.
There are many different AI programs and technologies that are commonly used for creating AI art. Some of the most commonly used AI programs include:
GANs (Generative Adversarial Networks): GANs are a type of AI algorithm that is often used for creating AI art. GANs consist of two neural networks – a generator network and a discriminator network – that work together to generate novel and original artworks based on a dataset of existing artwork.
Style transfer algorithms: Style transfer algorithms are a type of AI algorithm that is used to apply the style of one image to another image. This can be used to create AI art by applying the style of a famous painting or artist to a new image, creating a novel and original artwork that combines the style of the original with the content of the new image.
Evolutionary algorithms: Evolutionary algorithms are a type of AI algorithm that is inspired by the principles of natural selection and evolution. These algorithms can be used to create AI art by starting with a population of random artworks, and then using a set of rules and criteria to select the “fittest” artworks from each generation, allowing the art to evolve over time.
Deep learning algorithms: Deep learning algorithms are a type of AI algorithm that is based on the structure and function of the human brain. These algorithms can be used to create AI art by training the algorithm on a dataset of existing artworks, and then using the trained algorithm to generate novel and original art.
Overall, there are many different AI programs and technologies that are commonly used for creating AI art, and the specific program that is used will depend on the goals and objectives of the artist.
Which programming languages are used to create AI art?
There are many different programming languages that can be used to create AI art. Some of the most commonly used programming languages for creating AI art include:
Python: Python is a popular and versatile programming language that is often used for AI and machine learning applications. It has a large and active community of developers and users, and it offers a wide range of libraries and tools for working with AI algorithms and technology.
R: R is a programming language that is commonly used for statistical computing and data analysis. It has a strong focus on data visualization and is often used for creating AI art that involves the generation of complex images and other visual elements.
Java: Java is a popular and widely used programming language that is often used for developing large-scale, enterprise-level applications. It is a versatile language that can be used for creating AI art, and it has a large and active community of developers and users.
These are just a few examples of the programming languages that can be used to create AI art, and there are many other options available. The specific programming language that is used to create AI art will depend on the individual artist and their goals and objectives.
Does an artist need to code to create this type of art?
An artist does not necessarily need to know how to code in order to create AI art. While knowledge of coding can be helpful for creating AI art, it is not a requirement.
There are many different tools and technologies available that can be used to create AI art without needing to know how to code. For example, there are several user-friendly AI platforms and tools that can be used to create AI art without needing to know how to code. These tools often provide a simple, intuitive interface that allows users to create AI art by dragging and dropping different elements and using pre-defined algorithms and settings.
Additionally, there are many artists who collaborate with coders and other technical experts to create AI art. In these cases, the artist can focus on the creative aspects of the art, while the coders and technical experts handle the technical aspects of creating the AI art.
Overall, while knowledge of coding can be helpful for creating AI art, it is not a requirement, and there are many different tools and technologies available that can be used to create AI art without needing to know how to code.
The specific hardware requirements for creating AI art will depend on the specific AI algorithms and technologies that are being used, as well as the complexity and scale of the artworks being created. In general, however, creating AI art often requires a significant amount of computing power, and it is not something that can be done on a standard desktop or laptop computer.
In many cases, creating AI art will require the use of a high-performance computer with a powerful processor and a large amount of RAM. This type of computer will be able to handle the complex computations and data processing that is required for creating AI art, and it will be able to generate high-quality artworks in a reasonable amount of time.
Additionally, some AI algorithms and technologies may require the use of specialized hardware, such as graphics processing units (GPUs), in order to run efficiently and to generate high-quality artworks. In these cases, a computer with one or more powerful GPUs will be required in order to create AI art.
Overall, while the specific hardware requirements for creating AI art will vary depending on the algorithms and technologies being used, it is generally a resource-intensive task that requires a powerful computer with a large amount of computing power.
Does this mean, then, that only a handful of artists can create AI art?
The ability to create AI art is not limited to a small group of artists. While creating AI art does require a certain level of technical expertise and access to the necessary tools and technologies, these are not necessarily difficult or expensive to obtain.
There are many different tools and technologies available that can be used to create AI art, and many of these are freely available or can be purchased at a low cost. Additionally, there are many online resources and tutorials that can help artists to learn the technical skills and knowledge that are needed to create AI art.
As a result, while creating AI art does require some technical expertise and access to the necessary tools and technologies, it is not something that is limited to a small group of artists. Anyone who is interested in creating AI art can learn the necessary skills and obtain the necessary tools and technologies, and there are many different resources available to help them do so.
The use of AI technology in the creation of art will not necessarily make artists lazy. In fact, AI technology can be a powerful tool that can assist artists in the creative process and help them to explore new ideas and possibilities.
However, it is important to remember that AI technology is just a tool, and it cannot replace the creativity and vision of human artists. While AI algorithms and technology can be used to generate suggestions or ideas for artwork, it is ultimately up to the human artist to decide which ideas to pursue and how to incorporate them into their work.
Additionally, the use of AI technology in the creation of art can also help to inspire and challenge artists to think in new ways and to push the boundaries of what is possible. In this way, the use of AI technology can actually encourage artists to be more creative and innovative, rather than making them lazy.
Overall, while the use of AI technology in the creation of art can be a powerful tool, it will not necessarily make artists lazy. Instead, it can assist artists in the creative process and help them to explore new ideas and possibilities.
The use of AI programs in the creation of art could potentially lead to an overproduction of art. Because AI algorithms and technology can be used to generate art quickly and efficiently, it is possible that the use of AI could result in a large number of artworks being produced in a short period of time.
However, it is important to remember that the use of AI in the creation of art is not a new development, and it has been happening for several years. As a result, the art market has already had time to adjust to the use of AI technology, and the potential for overproduction of art is not as great as it may seem.
Additionally, it is worth considering that the quality and value of AI-generated art will vary, and not all AI-generated art will be of equal quality or worth. This means that while there may be a large number of AI-generated artworks produced, not all of them will be considered valuable or worth collecting.
Overall, while the use of AI programs in the creation of art could potentially lead to an overproduction of art, the potential for overproduction is not as great as it may seem, and the quality and value of AI-generated art will vary.
The environmental impact of creating AI art will depend on a variety of factors, including the specific tools and technologies that are being used, the amount of computing power that is required, and the conditions under which the art is created.
In general, however, creating AI art can be a resource-intensive process that requires a significant amount of computing power. This can have an impact on the environment, both in terms of the energy that is consumed and the potential for electronic waste.
Additionally, some AI algorithms and technologies may require the use of specialized hardware, such as graphics processing units (GPUs), which can be expensive to purchase and difficult to dispose of in an environmentally-friendly manner.
Overall, the environmental impact of creating AI art will depend on the specific tools and technologies that are being used, and it is important for artists to consider this impact when creating AI art. There are many different ways to reduce the environmental impact of creating AI art, such as using energy-efficient hardware and recycling or repurposing old or obsolete technology.
And if in the future many more artists can use AI programs and these are resource-intensive, does this mean that AI art can be bad for the planet?
If the use of AI programs for creating art becomes more widespread in the future, it is possible that it could have a negative impact on the environment. As more and more artists use AI algorithms and technology to create art, the demand for computing power and specialized hardware will increase, and this could lead to an increase in energy consumption and electronic waste.
However, it is important to note that the potential negative impact of creating AI art on the environment is not unique to AI art, and it is something that is also a concern for other forms of art making that are resource-intensive. Additionally, it is also possible that advancements in AI technology and algorithms could lead to more efficient and environmentally-friendly ways of creating AI art, which could help to mitigate the potential negative impact on the environment.
Overall, while the use of AI programs for creating art could potentially have a negative impact on the environment, this is not unique to AI art, and it is something that can be addressed through the use of efficient and environmentally-friendly technology and practices.
Do people really like AI art, or are they in it just for the novelty?
It is difficult to say whether people truly like AI art, or if they are simply interested in its novelty. There are certainly many people who are intrigued by the idea of AI art, and they may be drawn to these artworks because they are something new and different.
However, there are also many people who genuinely appreciate the aesthetic qualities of AI art, and who enjoy the unique perspective and ideas that these artworks can offer. Some people may find AI art to be thought-provoking and engaging, and they may enjoy the challenge of trying to understand and interpret these artworks.
Overall, it is likely that people’s reactions to AI art will vary, and there will be some people who are interested in it simply because it is novel, while others will genuinely appreciate and enjoy the art itself.
Why do most AI generated artworks produce nightmarish images?
Most AI-generated artworks do not produce nightmarish images. In fact, many AI-generated artworks are quite beautiful and interesting. However, it is true that some AI-generated artworks can produce images that some people might find disturbing or unsettling.
There are a few reasons why this may be the case. One reason is that AI algorithms are designed to explore a wide range of possibilities, and this can sometimes result in the generation of images that are unusual or unexpected. Another reason is that the training data used to teach the AI algorithm may contain images that some people find disturbing, and this can influence the types of images the algorithm generates.
It is also worth noting that what one person finds disturbing or nightmarish may not be the same for another person. What is considered “nightmarish” is a matter of personal opinion, and different people may have different reactions to the same image.
Overall, while some AI-generated artworks may produce images that some people find disturbing, this is not the case for all AI-generated art, and many AI-generated artworks are beautiful and interesting.
Why does GAN generated art look like bad painting?
Not all GAN-generated art looks like bad painting. GANs, or Generative Adversarial Networks, are a type of AI algorithm that can be used to generate art, and the specific appearance of the art generated by a GAN will depend on the training data and other factors.
It is true that some GAN-generated art may appear to be low-quality or “bad” painting. This can happen for a few reasons. One reason is that the training data used to teach the GAN may not be of high quality, which can result in the GAN generating art that is less detailed or less realistic than it could be. Another reason is that the GAN may not be trained for a long enough time, or it may not be trained on a large enough dataset, which can also result in lower-quality art.
It is also worth noting that what one person considers to be “bad” painting may not be the same for another person. Beauty and quality are subjective, and different people may have different opinions about the same artwork.
Overall, while some GAN-generated art may appear to be low-quality or “bad” painting, this is not the case for all GAN-generated art, and the specific appearance of GAN-generated art will depend on a variety of factors.
Should we reconsider our notions of art when exploring art made with AI?
The use of AI technology and algorithms to create art can certainly challenge our traditional notions of art and the creative process. Because AI algorithms and technology can be used to create art without the need for human intervention, AI art calls into question the idea that art must be created by human artists in order to be considered “real” art.
Additionally, AI art can also challenge our ideas about what makes art valuable and interesting. Because AI algorithms are able to generate artworks that are highly detailed and complex, they can create art that is aesthetically pleasing and engaging. This can challenge the idea that art must be created by human artists in order to have value or meaning.
Overall, exploring AI art can certainly lead us to reconsider our notions of art and the creative process. By challenging our traditional ideas about what constitutes art and what makes art valuable, AI art can help us to see art in new and interesting ways.
The potential for AI art to be a good investment will depend on a variety of factors, including the specific artworks being considered, the reputation and track record of the artist, and the overall state of the art market.
In general, however, AI art is still a relatively new and untested area of the art market, and there is a lot of uncertainty around the value and potential appreciation of AI artworks. Many buyers and collectors may be hesitant to invest in AI art due to its novelty and the lack of a track record for these types of artworks.
Additionally, the market for AI art is still relatively small, and there may not be a large number of buyers and collectors who are interested in these artworks. This could limit the potential for AI art to appreciate in value over time, and it could make it difficult for investors to find buyers for their AI artworks.
Overall, while there may be some potential for AI art to be a good investment, there are also many factors that could limit its value and potential appreciation. It is important for investors to carefully consider these factors before deciding to invest in AI art.
When selling art created with AI, should the artist split their benefits with the AI program?
Whether or not an artist should split their benefits with an AI program when selling art created with AI will depend on the specific circumstances and the agreement between the artist and the AI program. In some cases, the artist and the AI program may have agreed to share the benefits from the sale of the art, in which case the artist would need to split their benefits with the AI program.
However, in other cases, the artist and the AI program may not have agreed to share the benefits from the sale of the art. In these cases, the artist would not be obligated to split their benefits with the AI program, and they would be entitled to keep all of the proceeds from the sale of the art.
Ultimately, whether or not an artist should split their benefits with an AI program when selling art created with AI will depend on the specific circumstances and the agreement between the artist and the AI program. It is always a good idea for the artist and the AI program to carefully consider and agree on these issues before starting to create art together.
Will AI-generated art be preserved in the same way as paintings and sculptures? Will it last for centuries?
It is not clear whether AI-generated art will be preserved in the same way as paintings and sculptures. The preservation of art is a complex process that involves a variety of factors, including the materials used to create the art, the conditions in which the art is stored, and the care and attention given to the art over time.
In the case of AI-generated art, the specific approach to preservation will depend on the materials and technology used to create the art. For example, some AI-generated art may be created using digital media, such as video or audio, which may be more susceptible to degradation or loss over time. In these cases, the art may require special preservation techniques in order to ensure that it is preserved for future generations.
On the other hand, some AI-generated art may be created using physical materials, such as paint or clay, which may be more durable and easier to preserve over time. In these cases, the art may be more likely to last for centuries, just like traditional paintings and sculptures.
Overall, the preservation of AI-generated art will depend on the materials and technology used to create the art, as well as the care and attention given to the art over time. It is not clear whether AI-generated art will be preserved in the same way as traditional paintings and sculptures, and it will ultimately depend on the specific artworks and the circumstances in which they are created.
It is unlikely that AI-generated art will replace other forms of art making. While AI technology and algorithms can be used to create interesting and novel artworks, they are not a replacement for the creativity and vision of human artists.
Additionally, there are many different forms of art making, and the use of AI technology and algorithms is just one of many different approaches that artists can take. As a result, it is unlikely that AI-generated art will replace other forms of art making, and it will likely continue to exist alongside other forms of art making.
Overall, while AI technology and algorithms can be used to create interesting and novel artworks, it is unlikely that AI-generated art will replace other forms of art making. Instead, it is likely that AI-generated art will continue to exist alongside other forms of art making, as one of many different approaches that artists can take.
Do you think that AI art will get better with time?
It is likely that AI art will get better with time, as AI technology and algorithms continue to advance and improve. As AI algorithms become more sophisticated and are able to learn from larger and more diverse datasets, they will be able to generate more detailed and realistic artwork.
Additionally, as more and more artists begin to experiment with AI technology and algorithms in their creative process, they will develop new techniques and approaches that can help to improve the quality of AI-generated art. This could lead to the emergence of new styles and genres of AI art, and it could help to push the boundaries of what is possible with AI technology.
Overall, it is likely that AI art will continue to improve and evolve over time, as AI technology and algorithms advance and as more artists begin to experiment with AI in their creative process.
Over its history, AI technology has frequently promised more than it could deliver. Is this the case with AI art right now?
AI technology has indeed often promised more than it could deliver in the past. However, this is not necessarily the case with AI art. While there are limitations to what AI technology and algorithms can currently do in terms of creating art, there are also many interesting and impressive examples of AI art that demonstrate the potential of this technology.
One of the key limitations of AI art is that it is currently unable to create art that is truly original and creative in the same way that human artists can. AI algorithms and technology are still limited in their ability to understand and generate novel ideas, and most AI-generated art is based on existing data and patterns.
However, despite this limitation, there are many examples of AI art that are impressive and thought-provoking. Some AI algorithms and technologies are able to create art that is highly detailed and complex, and there are many interesting and novel ways in which AI technology can be used to create art.
Overall, while there are limitations to what AI technology and algorithms can currently do in terms of creating art, there are also many interesting and impressive examples of AI art that demonstrate the potential of this technology.
Matteo Zamagni is a multi-disciplinary artist who works across the visual arts, electronic music, multimedia installations, and film production. Using analytical geoscientific tools, VR/AR/MR, real-time generative imaging, photogrammetry, and CGI techniques Zamagni explores the complexities of the different crises that define our contemporary age and society. Zamagni’s artistic production is characterized by the exposure of the interrelations between nature and technology through machine-driven visual artworks.
Matteo Zamagni is represented by Gazelli Art House, and has exhibited works at international exhibitions and festivals such as the Barbican Centre, V&A Digital Futures, and Torino Film Festival. In conjunction with the release of Matteo Zamagni’s artcast on Niio titled Experiences of Synchronywe spoke with him about his artistic practice, and his work on the project titled “Unison” for Paraadiso, the new audiovisual collaborative project from producer TSVI, and visual artist and producer Seven Orbits.The audio-visual artworks included in this artcast induce altered states by presenting works which are played out as hypnotic journeys of sound and visuals.
Many of your video works include a sound element as a fundamental focal point. Could you please elaborate on your ongoing exploration of combining the visual and the auditory?
Music has always been a driving force for my works since a very early stage; over the years I have been working with many musicians and producers collaborating on music videos, short films, live visuals and installations, and more recently after delving into music production myself I began exploring real-time audio-visual experiences through the following projects ‘seven orbits’ and ‘Paraadiso’ which were released on Shanghai-based record label SVBKVLT. Through these projects I wanted to develop a tool that seamlessly connected Film, CGI and Audio into a real-time, audio-reactive and interactive environment, where logic systems connect and bridge communication between multiple softwares, informing one another. The project Paraadiso, created in collaboration with TSVI marks the first goal towards the realization of such a tool. This system is autonomous yet can function alongside user input, in this sense, it combines light and sound together using an ecosystem of softwares that enables seamless communication and interaction between the 3D/2D environment and the soundscapes. This approach opens up a sea of possibilities in the creation of real-time video and 3D-based content fully synchronized and triggered via sound, resulting in highly dynamic, ever-changing works. Moreover I can see a lot of potential in creating highly stimulating works which could lead the viewer into deeper states of consciousness.
In the near future, I’m hoping to expand this body of work into a 1-hour long CGI film combining my music production as seven orbits together with CGI shots created inside a game engine.
Matteo Zamagni, Unison – 02, 2022.
As the title suggests, your audiovisual collaborative project Unison, aims to create a communal energy as a collective physical experience. What is it that you would like your viewers to gain from this experience?
While we were making the album we were fascinated by how the combination of light and sound in a space would influence the people inside of it; heightening the senses and sometimes, given the right circumstances, conveying a sense of relatedness and care for one another. Human interaction is profoundly social, our everyday life does not take place in isolation but constantly requires our engagement with other people. This feeling of relatedness, reciprocal care and collectivity, is rare and not normally experienced on a day-to day basis especially among strangers and in big cities; The Project Unison references sacred functions and rituals by indigenous populations globally. In ceremonies, it is common to find elements of sound, light, dance and singing which would sometimes throw people into a heightened state of consciousness, sometimes even transcendental. There’s also a strong sense of community and interrelatedness felt within those groups, not only between humans but across the living kingdom, including other animals, plants, biomes and the cosmos.
Many of your artworks are created through a combination of different imaging tools and techniques such as AR, generative imaging, CGI techniques, analytical geoscientific tools and many more? Could you walk us through this complex working process of bringing these elements together into one final piece?
Since I have been quite fluid from an early age I naturally developed a diverse toolset which I reflected in my approach to navigating my diverse interests. Working with technology has deepened my knowledge and skillset by granting me access to accessible tools and resources that could be applied to a wide range of outputs spanning across augmented, virtual and mixed reality to projection mapping, live visuals, virtual production, Interactive Installations, and traditional screen-based work. The creation of a work in my case, is usually aided, informed and mediated by technology; As initial ideas start to materialize they morph and shift based on the environment they’re being developed in.
Matteo Zamagni, Unison – 06, 2022.
You have stated that you would like your works to contribute meaningfully to the broader field of environmental activism. In your opinion, what is it about the use of digital tools that can assist us in critically exploring complex planetary issues?
I am incredibly fascinated by the crossover of tools from computer graphics with forensic investigation or geoscientific surveys. Nowadays thanks to the accessibility of software for physics simulations of fluids, sounds, rigid bodies coupled with photogrammetry 3D reconstructions, and publicly available databases online the tools that are commonly used in a VFX pipeline can be ported into a forensic investigative studio. To critically and methodically reconstruct events of various types: from climate-related disasters to cases of social injustice. Inversely you could use geoscientific tools normally used in scientific surveys as a base to develop creative ideas.
Further along this line lies the combination of the ever-increasing power of GPUs (hardware initially designed for CGI) coupled with AI and machine learning, bringing unimaginable leaps forwards in virtually every existing industry but especially crucial in tackling the ecological crisis. What used to take years to simulate now takes minutes.
Matteo Zamagni, Unison – 01, 2022.
Many of the sound elements in your works resemble alterations of microscopic sounds which would be heard out in nature such as a caterpillar cracking out of its cocoon. How are these sounds accumulated?
It’s funny that you mention this, it probably came out completely unconsciously. This reminds me of foley recording, a technique used in cinema to recreate the sound of things as well as SFX by recording the sounds of seemingly unrelated objects which allude to the original sound. I can see some similarities between foley and the creation of sounds in our album; Even though we mostly created the sounds digitally rather than physically. Our sample library consists of various techniques spanning digital synthesizers, granular synthesis, and distorted samples.