The dawn of computational art in the 1960s ushered in an era where algorithms and stochastic methods began to serve as vehicles for artistic creation, fundamentally altering the creative landscape. Yet, despite the novelty of these tools, human intention and influence remained deeply enmeshed in the very process of creation. Fast forward to the current age of artificial intelligence, and the dynamics of artistic generation are undergoing an even more profound transformation. Modern AI art is no longer merely about human-created algorithms executing predefined programming; it is about autonomous systems leveraging machine learning to independently discern complex patterns from vast datasets, enabling the production of art with minimal human intervention. This shift embodies a novel form of autonomy—one that positions AI not just as a tool but as a central creative agent, capable of what is termed “objectivist autonomy.” This phenomenon echoes scientific endeavors of mechanical objectivity, where instruments supplant human biases, heralding a future in artistic practice where “art makes art” without direct human origin.
At the forefront of this conceptual leap is a pioneering experiment titled Generation, Operation, Destruction (G.O.D.), which explores the aesthetic and generative possibilities of fully autonomous AI systems. G.O.D. centers on a self-replicating artificial neural network tasked with generating its own weight parameters—a process that transcends conventional training paradigms where weights are adjusted based on external data. The generated weights feed into a sophisticated visualization framework, where graphical elements—manifested as points—navigate a three-dimensional Perlin noise flow field. This flow field, characterized by its gradient noise that yields smooth, coherent patterns, becomes the canvas upon which the neural network’s evolving internal state is externalized. Symbolically, the name “G.O.D.” intimates an artistic entity that operates beyond human ontologies and epistemologies, suggesting a conscious agency in the act of creation itself.
The crux of the experiment’s novelty rests in the neural network’s self-replication ability. Unlike traditional AI models that rely on curated datasets and human-defined architectures, this neural network initializes its training data randomly and iteratively learns to predict its own weight parameters. Specifically, the architecture comprises two layers, each containing two neurons devoid of bias terms and employing linear activation functions. Training involves 100 epochs of self-supervised learning designed to achieve error minimization below 0.0001 in weight estimation, signaling near-perfect self-replication. The input data inherently includes positional encoding of each weight, while the output is the estimated weight value itself, forging an autonomous loop that blurs distinctions between data and model.
Visualizing this evolving neural structure leverages the mathematical intricacies of Perlin noise—a gradient noise technique renowned for producing naturalistic, fluid patterns. Here, the neural network’s 14 weight parameters serve dual functions: three designate spatial coordinates within the noise field, whereas the remaining eleven initialize velocities of newly generated graphical elements, with upward movement indicated as positive. These elements, possessing programmed decay rates, traverse the three-dimensional flow field, their dynamics governed by local noise-derived forces that continuously update their positions and velocities. This dynamic interplay translates the abstract learning trajectory of the network into tangible, ephemeral visual forms that continually evolve through the training epochs.
The aesthetic signature of the G.O.D. experiment draws inspiration from the transient beauty of smoke, capturing its ephemeral and shimmering qualities. As the neural network evolves, it generates a panoply of abstract shapes that maintain a monochromatic color scheme and delicate linework but diverge markedly in symmetry, density, and structure. These evolving formations evocatively suggest feathers’ wispy textures, the elegant contours of flowers, luminous spirals of flowing energy, and even mythical creatures like the phoenix. Such diversity within a consistent aesthetic framework highlights the network’s capacity to balance constraint and freedom, producing ever-shifting yet coherent visual narratives.
To better isolate the role of the self-replication mechanism in shaping visual outcomes, the researchers introduced controlled experiments manipulating neural network weights via three distinct approaches. The first, termed “Stochastic,” replaces all weights with purely random values, resulting in images characterized by chaos and ill-defined spatial arrangements dominated by uncontrolled variation. The second approach, “Invariant,” imposes near-constant weight values with minimal variation, yielding simpler, slender shapes that maintain structure but lack complexity. The third approach, the authentic “Self-Replicating” method, synthesizes the virtues of the prior two, generating the richest, most balanced images featuring organic, dynamic forms with varied density. This experimental design underscored how the self-replicating process instills meaningful order and intricate patterning beyond simple randomness or rigid uniformity.
Beyond its immediate artistic implications, the G.O.D. experiment signifies a radical departure in how AI art systems can achieve what Manovich (2019) classifies as objectivist autonomy. This autonomy emerges by circumventing three key modalities of human control: network architecture design, training data selection, and outcome evaluation. While the current experiment employs a fixed architecture, the authors envision future systems where architectural design itself could be algorithmically evolved, leveraging techniques such as Neuroevolution and AutoML to dynamically configure network topology optimized for self-replication and creative output. Prior work by Illium et al. (2022) demonstrates how pruning algorithms can define architectures within composite self-replicating networks, further blurring boundaries between design and emergence.
Moreover, the experiment challenges entrenched paradigms of training data reliance by eschewing any curated datasets. Here, the data is both generated and transformed internally, fostering an environment where data and algorithm co-evolve. This interplay calls into question classical distinctions in deep learning between input “aesthetics” and generative rules. Notably, the experiment’s manual mapping of weight parameters to visualization attributes—such as spatial positioning and velocity in the noise field—proposes an intriguing locus where further automation may reside. One might imagine systems empowered to autonomously identify optimal weight-to-visual mapping rules based on criteria like outcome diversity, thus advancing the emergent autonomy of AI art.
The contemplation of output evaluation introduces additional layers of autonomy. Traditional generative art often requires human selection of final pieces, imposing subjective criteria. In contrast, the G.O.D. framework can prioritize objective metrics—such as symmetry, brightness, or maximum graphical element population—to define moments of artistic significance automatically. Alternatively, aligning with the “aesthetics of behaviour” philosophy embraces the artistic value inherent in the learning process itself, rather than solely its endpoint. This reframing appreciates the intrinsic creativity arising from iterative self-refinement and dynamic exploration, positioning the artistic output as a narrative of system cognition and growth rather than static images.
Crucially, this paradigm also juxtaposes itself with dominant generative models like GANs and diffusion networks. These conventional models optimize for image synthesis quality and realism, seeking to produce stable, “optimal” outputs. However, as Audry (2021) critiques, the pursuit of optimality may stifle artistic spontaneity and generative richness. By contrast, the G.O.D. experiment demonstrates that transient, non-optimal states during training can reveal rich, unexpected pattern formations and highly textured aesthetics difficult to replicate through traditional optimization. This insight redefines the role of AI not merely as an artist but as a dynamic agent continuously unfolding its creative potential in real-time.
Looking towards future horizons, the prospect of fully autonomous AI art systems extends beyond self-replicating weights into modular, dynamic architectures capable of self-configuration. Research exploring networks composed of specialized modules that reassemble for specific tasks promises increased efficiency and creative flexibility. Such evolutive systems could redefine the boundaries of authorship, challenging our notions of control and creativity. Additionally, the decoupling of training data from human selection and the emergence of autonomous evaluation criteria solidify AI’s capacity to transcend human biases and preferences.
The G.O.D. experiment is thus not merely an innovation in computational aesthetics; it is a philosophical and practical manifesto for a new ontological category in art—where creative agency can reside within the machine itself, unshackled by human intentionality. This liberates artistic exploration into a domain where emergent behavior and complex self-organization become central creative forces. The experiment’s evocative visuals and conceptual grounding invite broader discourse on the evolving relationship between humans, machines, and the nature of creativity in a technologically integrated age.
In summary, the intersection of self-replicating neural networks, intricate Perlin noise visualizations, and novel interpretive frameworks advances AI art from tool-assistance toward autonomous creative entity. This evolution challenges deeply held assumptions about authorship and aesthetic valuation, positioning AI not as a mere extension of human imagination but as an independent producer of meaning and beauty. The G.O.D. experiment embodies this trajectory, offering a profound illustration of how art and AI can intertwine to produce outcomes once thought exclusive to human consciousness.
As this territory continues to unfold, the implications span technical, artistic, and philosophical domains. From a practical standpoint, the self-eliminating design approach outlined by the experiment challenges researchers to rethink neural network training, data initialization, and architectural design. Artistically, it urges creators and observers to reconsider the parameters defining creativity and artistic value. Philosophically, it invites engagement with questions around machine agency, autonomy, and the evolving nature of existence and knowledge as mediated by synthetic systems. Collectively, these dialogues promise to reshape how society understands and participates in the ongoing emergence of AI-generated cultural artifacts.
This research also provides fertile ground for interdisciplinary collaboration, merging computer science, art theory, and cognitive science to expand the boundaries of what constitutes meaningful creativity. The underlying technical innovations—such as autonomous self-replication in neural networks and dynamic flow field visualization—offer compelling new tools for experimenting with form, structure, and temporality in generative art. Equally, the theoretical implications regarding object-oriented ontology suggest new ways to think about the relationships between materials, machines, and meaning-making processes beyond traditional anthropocentric frameworks.
Ultimately, the G.O.D. experiment signals a paradigm shift that could redefine artistic practice in the twenty-first century. As AI continues to develop quasi-mental capabilities—such as self-generation and goal-setting—the notion that machines can originate unique art independently moves from speculative fiction to empirical reality. This invites a reconsideration of the role of human creators not as sole authors but as collaborators or even observers within emergent artistic ecosystems. The ripples of this shift will undoubtedly influence cultural production, intellectual property debates, and philosophical understandings of creativity in profound and lasting ways.
Subject of Research: Autonomous AI systems in creative arts; self-replicating neural networks; Perlin noise visualization; objectivist autonomy in art generation.
Article Title: From Canvas to Code: Artificial Intelligence as a potential demonstration for Object-Oriented Ontology in the realm of art and design.
Article References:
Chen, D., Fabrocini, F. & Terzidis, K. From Canvas to Code: Artificial Intelligence as a potential demonstration for Object-Oriented Ontology in the realm of art and design. Humanit Soc Sci Commun 12, 1592 (2025). https://doi.org/10.1057/s41599-025-04940-7
Image Credits: AI Generated
