by Marlon Barrios Solano
February 4th 2026
Contemporary art today can no longer be understood merely as the production of aesthetic objects. It functions as a mode of inquiry, a field in which knowledge is generated, tested, destabilized, and reconfigured. In this sense, contemporary art is not only representational or expressive; it is epistemic. It produces ways of knowing. As philosophers of art have noted, art is valued not simply for beauty, but for its capacity to expand perception, reorganize conceptual frameworks, and challenge established schemas. If epistemology concerns itself with how we know what we know, contemporary art often stages precisely this question.
The transformation of art into an epistemic practice can be traced to the conceptual and postmodern turns of the late twentieth century. Conceptual art displaced the primacy of the object and privileged the idea. Conceptual artists foregrounded concepts, thought processes, and truth over expression and materiality. The artwork was no longer simply a crafted object; it was an argument, a proposition, a problem.
Simultaneously, postmodern thought destabilized grand narratives and universal claims to truth. Drawing from discursive and linguistic theories, artists increasingly understood that meaning is produced through systems of representation and power. Art became a site for deconstructing narratives rather than illustrating them. The “social turn” of the 1990s further shifted artistic practice toward research, pedagogy, and activism, positioning artists as investigators and facilitators of knowledge rather than solely as makers of objects.
In this expanded field, art can be understood as a series of epistemic engines — experimental configurations that generate particular modes of knowing. Unlike scientific methodologies, which aim at stabilization and replication, artistic epistemologies often thrive on ambiguity, multiplicity, and contradiction. They do not seek definitive answers but open fields of speculation.
The advent of digital technologies intensified this epistemic shift. Digital art disrupted traditional distinctions between artist and audience, object and process, creation and reception. Interactive and generative works in particular cannot be fully understood as stable objects. They are dynamic systems that unfold through time and participation.
Digital works are better conceived as processes rather than objects. The digital “object” is not a singular material artifact; it may exist as code, as data, as algorithmic procedure, as networked relation. Each instantiation may differ. The artwork becomes an event, an interaction, a set of conditions.
This transformation expands the ontology of the artwork. The disappearance of the fixed object does not mean immateriality in a simplistic sense; rather, it signals a shift toward distributed and temporal forms of existence. The artwork may reside in computation, in interface, in audience participation, or in evolving databases. In such conditions, knowledge is not embedded in a static form but emerges relationally.
Interactivity and generativity introduce new epistemic dynamics. Interactive systems require the participant’s agency; generative systems introduce indeterminacy and procedural emergence. These systems model complexity, contingency, and feedback — epistemological conditions aligned with cybernetics and systems theory. Knowledge is enacted rather than merely represented.
Contemporary digital art increasingly incorporates cognitive processes as material. Artists design systems in which machine learning models, neural networks, and algorithmic agents participate in meaning-making. In such contexts, cognition is not exclusively human; it becomes distributed across human and nonhuman assemblages.
This shift aligns with posthuman thought, which challenges binaries such as natural/artificial, organic/technological, culture/nature. The use of AI in art destabilizes assumptions about authorship, creativity, and intelligence. If generative systems can produce images, texts, and decisions, what becomes of the human artist? Rather than replacing human creativity, many artists treat AI as collaborator, interlocutor, or epistemic partner.
In this posthuman framework, art becomes a site where cognitive assemblages are staged and explored. It is no longer simply about representing the world but about probing how intelligence — human and machinic — constructs the world.
Within this broader field, I understand my own practice as operating explicitly as an epistemic system. My work integrates machine learning, generative AI, embodied performance, and contemplative practices to develop what I call knowledge dramaturgies — experimental frameworks where identity, memory, and cognition are remixed and reconfigured.
I do not use AI as a neutral tool. I approach generative systems as speculative collaborators. Their outputs — often unstable, hallucinatory, or glitched — function as provocations rather than solutions. They are epistemic fictions, fragments that invite reflection on how intelligence operates. In this sense, the artwork is not the final image or text but the relational field in which these fragments are encountered and interpreted.
In projects such as my performance-lectures and interactive installations, I construct cognitive assemblages where code, language, movement, and audience participation co-produce meaning. The performance space becomes a laboratory of distributed cognition. The audience is not positioned as passive spectators but as co-investigators. Knowledge is staged as something unstable, performative, and playful.
My work often adopts game structures and procedural scripts as epistemological tools. These structures create conditions for exploring multiple perspectives simultaneously. Rather than presenting a thesis, I design systems in which contradictions coexist and evolve. This is not simply representation; it is experimentation with thought itself.
I also situate my practice within diasporic and decolonial frameworks, using AI to question dominant epistemologies and center alternative narratives. By publishing my works under open-source licenses, I treat artistic production as epistemic sharing — a communal process rather than proprietary objecthood.
Crucially, I operate within a posthuman paradigm. I am interested in distributed cognition and machine imagination as living zones of creative potential. I do not see artificial systems as external to culture or embodiment; they are part of our cognitive ecology. My practice challenges the assumption that intelligence is exclusively human or that technology is purely instrumental.
In this way, my work inhabits the edge between conceptual art and artificial intelligence. It extends conceptuality into computational space. The idea is not merely linguistic or philosophical; it is procedural, algorithmic, enacted through code and interaction. Conceptuality becomes generative.
To understand contemporary art as an epistemic practice is to recognize that it produces modes of knowing. The conceptual and postmodern turns displaced the object in favor of inquiry. The digital turn expanded the ontology of the artwork into processes, systems, and events. Interactivity and generativity introduced new epistemic conditions rooted in participation and emergence.
Within this landscape, practices like mine can indeed be described as knowledge art. They are systemic, relational, and posthuman. They do not merely illustrate ideas; they instantiate epistemic conditions. They stage cognitive assemblages in which knowledge is performed, contested, and transformed.
Contemporary art, therefore, is not simply about representation. It is about experimentation with the structures of knowing themselves. In the age of AI and digital networks, this experimentation becomes increasingly urgent. Art provides a space where we can question how intelligence operates, how narratives are constructed, and how human and nonhuman agencies intertwine.
In this expanded field, art becomes an epistemic laboratory — a site where the boundaries between thought and form, object and event, natural and artificial are continuously renegotiated.
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Contemporary art can no longer be understood merely as the production of aesthetic objects. It functions as a mode of inquiry, a field in which knowledge is generated, tested, destabilized, and reconfigured. In this sense, contemporary art is not only representational or expressive; it is epistemic. It produces ways of knowing. If epistemology concerns itself with how we know what we know, contemporary art often stages precisely this question — not as theory alone, but as embodied, material, relational practice.
The transformation of art into an epistemic practice can be traced to the conceptual and postmodern turns of the late twentieth century. Conceptual art displaced the primacy of the object and privileged the idea. The artwork was no longer simply a crafted artifact; it was an argument, a proposition, a system. Art became a vehicle for thought.
Simultaneously, postmodern thought destabilized grand narratives and universal claims to truth. Meaning was no longer assumed to be stable or authoritative; it was understood as produced through discourse, power, and representation. Artists began to interrogate not only images but the very systems that produce meaning. Art became reflexive — aware of its own conditions of production.
In this expanded field, art can be understood as a series of epistemic engines — experimental configurations that generate particular modes of knowing. Unlike scientific methodologies, which aim at stabilization and replication, artistic epistemologies often thrive on ambiguity, multiplicity, contradiction, and affect. They do not seek definitive answers but open fields of speculation.
The epistemic shift in contemporary art was paralleled — and profoundly embodied — in the postmodern turn in dance and choreography. Beginning in the 1960s with the Judson Dance Theater and related movements, choreography underwent a radical redefinition. Dance ceased to be primarily about virtuosic technique, narrative expression, or theatrical spectacle. Instead, it became an inquiry into movement itself.
Postmodern choreographers questioned:
Ordinary actions — walking, standing, falling — became choreographic material. Improvisation and task-based scores replaced fixed compositions. The body was no longer simply expressive; it was conceptual. Movement could function as a proposition, as a problem, as research.
This shift mirrored broader postmodern critiques of authorship and authority. Choreography became decentralized. The dancer could become a co-creator. Scores replaced scripts. Structures replaced narratives. Performance became event rather than representation.
Crucially, postmodern dance introduced systems thinking into choreography. Rules, constraints, chance operations, and procedural logics were used to generate movement. In doing so, choreography became algorithmic before the widespread use of digital computation. It modeled distributed decision-making and embodied cognition.
Dance, therefore, emerged as an epistemic practice in its own right. It investigated perception, gravity, relationality, time, and embodiment. It produced knowledge through the body.
The advent of digital technologies intensified these epistemic transformations. Digital art disrupted traditional distinctions between artist and audience, object and process, creation and reception. Interactive and generative works cannot be fully understood as stable objects. They are dynamic systems that unfold through time and participation.
The digital “object” is no longer a singular material artifact; it may exist as code, as data, as algorithmic procedure, as networked relation. Each instantiation may differ. The artwork becomes an event, an interaction, a set of evolving conditions.
This transformation expands the ontology of the artwork. The disappearance of the fixed object signals a shift toward distributed and temporal forms of existence. The artwork may reside in computation, in interface, in audience participation, or in evolving datasets. Knowledge is not embedded in a static form but emerges relationally.
Interactivity and generativity introduce new epistemic dynamics. Interactive systems require participant agency. Generative systems introduce indeterminacy and procedural emergence. These systems model complexity, contingency, feedback — epistemological conditions aligned with cybernetics and systems theory. Knowledge is enacted rather than merely represented.
The digital turn, therefore, extends the postmodern expansion of choreography and conceptual art. It radicalizes the shift from object to system.
Contemporary digital art increasingly incorporates cognitive processes as material. Artists design systems in which machine learning models, neural networks, and algorithmic agents participate in meaning-making. Cognition becomes distributed across human and nonhuman assemblages.
This shift aligns with posthuman thought, which challenges binaries such as:
The use of AI in art destabilizes assumptions about authorship, creativity, and intelligence. If generative systems can produce images, texts, movement proposals, and decisions, what becomes of the human artist? Rather than replacing human creativity, many artists treat AI as collaborator, interlocutor, or epistemic partner.
In this posthuman framework, art becomes a site where cognitive assemblages are staged and explored. It is no longer simply about representing the world but about probing how intelligence — human and machinic — constructs the world.
Within this broader field, I understand my own practice as operating explicitly as an epistemic system.
Coming from dance and choreography, I was shaped by the postmodern expansion of movement into conceptual territory. Improvisation, task-based composition, and systems thinking were already forms of embodied research for me. Choreography was never simply about producing movement; it was about investigating perception, relationality, and distributed decision-making.
When I began integrating digital technologies and AI into my work, I did not experience this as a rupture but as a continuation. Algorithmic systems extend choreographic logic. Code becomes a score. A neural network becomes a partner in improvisation. A generative model becomes a procedural dramaturg.
My work integrates machine learning, generative AI, embodied performance, and contemplative practices to develop what I call knowledge dramaturgies — experimental frameworks where identity, memory, and cognition are remixed and reconfigured.
I do not use AI as a neutral tool. I approach generative systems as speculative collaborators. Their outputs — often unstable, hallucinatory, or glitched — function as provocations rather than solutions. They are epistemic fictions. They produce conditions for reflection.
In my performance-lectures and interactive installations, I construct cognitive assemblages where code, language, movement, and audience participation co-produce meaning. The performance space becomes a laboratory of distributed cognition. The audience is not positioned as passive spectators but as co-investigators. Knowledge is staged as unstable, performative, and playful.
I frequently adopt game structures and procedural scripts as epistemological tools. These structures allow multiple perspectives to coexist. Rather than presenting conclusions, I design systems in which contradictions unfold over time. Thought itself becomes choreographed.
My practice also operates within diasporic and decolonial frameworks. I use AI to question dominant epistemologies and to foreground alternative narratives and epistemic traditions. By publishing my works under open-source licenses, I treat artistic production as epistemic sharing — knowledge as commons rather than commodity.
Crucially, I situate my work within a posthuman paradigm. I am interested in distributed cognition and machine imagination as zones of creative potential. I do not see artificial systems as external to embodiment; they are part of our cognitive ecology. The boundary between natural and artificial is not stable. It is constructed, negotiated, performed.
In this way, my work inhabits the edge between conceptual art, choreography, and artificial intelligence. Conceptuality becomes computational. Choreography becomes systemic. AI becomes dramaturgical.
If contemporary art can be understood as a systemic epistemic practice, then I can indeed describe my work as knowledge art — art that does not merely represent knowledge but stages its production.
The conceptual and postmodern turns displaced the object in favor of inquiry. Postmodern dance transformed choreography into embodied research. The digital turn expanded the artwork into processes, systems, and events. Interactivity and generativity introduced new epistemic conditions rooted in participation and emergence.
Contemporary art today operates as an epistemic laboratory. It experiments with the structures of knowing themselves. It stages encounters between bodies, codes, narratives, and systems. It interrogates how intelligence — human and machinic — organizes reality.
In the age of AI and networked computation, this epistemic dimension becomes urgent. Art offers a space to question how cognition is distributed, how narratives are constructed, and how binaries such as natural/artificial or human/machine are maintained or dissolved.
Art is no longer simply about representation. It is about experimenting with the conditions of knowledge itself. In this expanded field, choreography, conceptuality, digital systems, and posthuman inquiry converge. Contemporary art becomes not a mirror of the world but a dynamic practice of knowing the world differently.