Neurofilosofía y Pensamiento Actual Neurophilosophy and Contemporary Thought

jueves, 1 de octubre de 2026

The Algorithmic Eye: How Artificial Intelligence Learns to See Art

Bernabé Mallo

Doctor en Filosofía por la Universidad del País Vasco (UPV/EHU)
Investigador en neurofilosofía, evolución humana y origen del arte. / PhD in Philosophy – University of the Basque Country (UPV/EHU)
Researcher in neurophilosophy, human evolution, and the origins of art.

A review of the study by Yali Liu and Can Zhu (2025) in Scientific Reports: The use of deep learning and artificial intelligence-based creative teaching in art education

Introduction: when the machine looks at a painting

What does a machine see when it contemplates a painting? It experiences no aesthetic pleasure, is not moved by colours, evokes no memories. But can it learn to recognise what makes a work what it is? Can it decompose a brushstroke into its essential components —texture, colour, composition— and, from there, generate something new?

The study by Yali Liu and Can Zhu, published in 2025 in Scientific Reports (Nature Portfolio), addresses precisely this question. The authors propose and optimise an innovative artistic creation system called the Creative Intelligence Cloud (CIC), which combines generative adversarial networks (GANs) and convolutional neural networks (CNNs) to explore how deep learning can be applied to art education. The goal is not only to generate images, but to understand the process by which a machine acquires what we might call an "aesthetic knowledge".

This approach places us before a question that runs through our research on the origin of art: what does it mean to know a work of art? Is it enough to identify patterns, styles, and compositional structures, or does it require something more —a lived experience, a body that feels, a symbol that is inhabited— that the machine, for now, does not possess? The answer, as we shall see, forces us to distinguish between two forms of knowledge: the one based on statistical correlation and the one emerging from the single function of the nervous system.

The CIC system: anatomy of a visual learning process

The system proposed by Liu and Zhu is structured around two fundamental technical components: generative adversarial networks (GANs) and convolutional neural networks (CNNs). GANs, introduced by Ian Goodfellow in 2014, consist of two networks competing with each other: a generator, which produces images, and a discriminator, which tries to distinguish generated images from real ones. This adversarial competition forces the generator to progressively improve the quality of its creations.

CNNs, for their part, are the tool through which the system "reads" images. Unlike a traditional neural network, CNNs are designed to process data with a grid structure —such as the pixels of an image— and are capable of detecting local features (edges, textures, patterns) that are then combined into higher-level representations (forms, objects, compositions). It is, in a sense, an algorithmic imitation of how the human visual cortex processes information: from simple features to complex configurations.

The CIC system is trained on massive art repositories, such as the WikiArt dataset, which contains tens of thousands of paintings labelled by style, artist, and period. From this training, the model learns to decompose works into their constituent elements: brushstrokes, colour palettes, compositional structures. It does not "see" the painting as a meaningful whole, but as a matrix of features that it can analyse, compare, and recombine.

The "data lens": how the machine learns the visual language

The learning process of the CIC system illustrates what, in another review on this blog, we have called the "data lens" of artificial intelligence. Unlike a photographic camera, which captures the light of a real scene, AI does not see the world directly. It fragments billions of pre-existing images into minimal units of visual information —the so-called tokens— and associates them with linguistic labels (words such as "impressionist", "portrait", "blue"). In this way, the machine learns a "visual language of the world" that allows it, once trained, to move fluidly and autonomously across artistic movements, styles, and eras.

This process has profound implications. On the one hand, it allows unprecedented versatility: a model trained on WikiArt can generate an image in the style of Van Gogh, a hyperrealistic photograph, or a geometric abstraction with equal ease. On the other hand, it raises the question of what "originality" means when the system is trained on millions of existing works. The machine does not invent from scratch; it recombines what it has learned. Its "creativity" is, at bottom, a sophisticated form of statistical interpolation.

What the machine cannot learn: the symbolon gap

Here is where technical analysis meets philosophy. Liu and Zhu demonstrate that the CIC system is capable of analysing works of art, identifying styles, and generating new images. But does it understand what it does? The answer, from the perspective of our research, is negative. The machine can manipulate visual and linguistic tokens, but it does not inhabit the symbol. There is no one "inside" who recognises or is recognised in the act of creation.

This distinction is crucial. Symbolon, in its original etymological sense, is an act of recognition through shared codes. When a human artist creates, they do not merely combine forms: they project their interiority, communicate an experience, seek an encounter with the other. The work of art is a bridge between subjectivities. The machine, by contrast, produces objects that can be interpreted as art, but it does not generate the living process —the survival substrate, the symbolic dimension, the drive toward wholeness— that gives meaning to human creation.

The CIC system, by decomposing works into statistical features, loses precisely what makes a work a work: its character as an encounter. The machine can learn about art, but it cannot learn from art. It has no body that feels, no history that hurts, no need that drives it. Its knowledge is, at best, a shadow of human aesthetic knowledge.

A reading from the S/Y/C model

The study by Liu and Zhu offers a concrete case in which the functioning of the Law of Biological Coherence S/Y/C that forms the core of our research can be observed (Mallo, 2023, 2025, 2026a, 2026b). The three dimensions of our thesis unfold here with uncommon clarity, precisely because the contrast with the machine allows us to see what we take for granted in the human.

The S (Survival) dimension manifests in the usefulness of the CIC system. Its stated goal is to improve art education, provide automated feedback, and broaden access to creative tools. This is a legitimate adaptive function: technology, like Neolithic pottery, is adopted when it solves a practical problem. But the survival of the system is not that of the organism using it. The machine does not need to learn art in order to survive; the human who uses it does. AI is a tool for cultural survival, not a subject that survives through it.

The Y (Symbolon) dimension is the point of greatest contrast. The CIC system manipulates symbols —labels, styles, visual representations— with impressive efficiency. But it does not inhabit the symbol. There is no mutual recognition, no community of lived shared codes. The machine can classify a painting as "impressionist", but it does not know what it means to be impressionist, has not felt the light of southern France, has not argued with an academic about brushwork. The human symbolon is lived; the machine's is simulated.

The C (Wholeness) dimension manifests in the machine's search to close a form, to generate a coherent image. GANs, in particular, operate through a process of convergence: the generator and the discriminator compete until they reach an equilibrium, a point at which the generated images are indistinguishable from real ones. This convergence is a form of algorithmic wholeness. But it is a wholeness without yearning, without the biological drive that in humans seeks to integrate parts into a coherent whole. The machine "closes" a form because its loss function tells it to, not because it needs to make sense of its experience.

Surgical Philosophy invites us to make a precise analytical cut in the study by Liu and Zhu. It is not about rejecting technology nor idealising it, but about distinguishing levels. At the level of technical production, the CIC system is a powerful tool that can expand the capacities of the artist and the educator. At the level of aesthetic experience, AI cannot replace the embodied subjectivity that constitutes the heart of art. And at the level of cultural responsibility, an ethical framework is needed —such as the technological humanism we have discussed in other reviews— to ensure that these tools are used to enhance the human, not to replace it.

Final considerations: art as encounter, not as classification

The study by Yali Liu and Can Zhu has the merit of offering a rigorous and well-documented vision of how deep learning can be applied to art education. Their CIC system demonstrates that machines are capable of analysing, classifying, and generating images with a degree of sophistication that a decade ago would have seemed impossible. But, at the same time, their work reveals —perhaps unintentionally— the limits of that approach.

The machine can learn to recognise a style, but not to understand why that style matters. It can decompose a brushstroke, but not to feel what the brushstroke expresses. It can generate a new image, but not to inhabit the process of creation as an act of encounter with the other.

Art, in its origin and in its function, is not a classification of visual features. It is an encounter between embodied subjectivities. And that encounter, for now, remains the exclusive patrimony of the living. AI can be an ally in art education, a tool that broadens access and enhances creativity. But it cannot occupy the place of the artist, nor of the spectator, nor of the educator. Because art, like survival, symbol, and wholeness, is something that is lived before it can be measured.

Now, what does this research contribute to the whole of our inquiry into the origin of art? We believe that its most notable contribution is not only what it reveals about AI's capacities, but the comparison it makes possible. By observing how an artificial system, organised by us, decomposes and recomposes humanity's visual legacy, we are offered an unexpected mirror for thinking about how the biological system itself, tens of thousands of years ago, might have fragmented, assimilated, and reformulated the world to create something that today encompasses us all and everything.

Comparing these two ways of constructing creation —the artificial, guided by algorithms and data; the biological, guided by needs, biases, and yearnings— can be useful for consolidating a possible theory about how biological systems in general came to produce art. It is not about equating both processes, but about using the contrast as a heuristic tool: the machine shows us, through absence, what life contributes. And in that absence —the lack of a body that feels, of a history that hurts, of a need that drives— we find precisely the keys to what art, in its origin, was and continues to be.

References

Liu, Y., & Zhu, C. (2025). The use of deep learning and artificial intelligence-based creative teaching in art education. Scientific Reports, *15*, Article 15763. https://doi.org/10.1038/s41598-025-00892-9

Mallo, B. (2023). La construcción neuro-simbólica. Una aproximación al funcionamiento del cerebro desde una perspectiva multidisciplinar [Doctoral thesis, University of the Basque Country - Euskal Herriko Unibertsitatea]. ADDI Repository. http://hdl.handle.net/10810/62701

Mallo, B. (2025). Arte y biología: Una aproximación neurofilosófica al origen de la experiencia estética. https://www.amazon.com/dp/B0E8Y5WZMK

Mallo, B. (2025). Art and biology: A neurophilosophical approach to the origin of aesthetic experience. https://www.amazon.com/dp/B0E8Y6C2XN

Mallo, B. (2026a). De la filosofía quirúrgica a la ley de coherencia biológica S/Y/C: Hacia una investigación sobre el origen del arte en la especie Homo. https://isbn.bibna.gub.uy/catalogo.php?mode=detalle&nt=57196

Mallo, B. (2026a). De la filosofía quirúrgica a la ley de coherencia biológica S/Y/C: Hacia una investigación sobre el origen del arte en la especie Homo [Kindle edition]. https://www.amazon.com/dp/B0GYGTJD5C

Mallo, B. (2026b). From surgical philosophy to the law of biological coherence S/Y/C: Toward a study of the origin of art in the Homo lineage. https://isbn.bibna.gub.uy/catalogo.php?mode=detalle&nt=57197

Yu, C., & Liu, Z. (2026). Multi-task convolutional network for artwork analysis and personalized guidance. Scientific Reports, *16*, Article 70478. https://doi.org/10.1038/s41598-026-70478-6

Autor / Author

Bernabé Mallo
Doctor en Filosofía – Universidad del País Vasco / Euskal Herriko Unibertsitatea (UPV/EHU)
Investigador independiente en neurofilosofía, evolución humana y origen del arte.

Bernabé Mallo
PhD in Philosophy – University of the Basque Country / Euskal Herriko Unibertsitatea (UPV/EHU)
Independent researcher in neurophilosophy, human evolution, and the origin of art.

Enlaces / Links

Página de autor Amazon / Amazon Author Page: https://www.amazon.com/author/bernabemallo
ORCID: https://orcid.org/0000-0001-9002-9728
Plataforma EHUenRed / Link EHUenRed: https://www.ehu.eus/es/web/masterrak-eta-graduondokoak/red-latinoamericana-de-posgrados
Canal YouTube / Channel YouTube: https://www.youtube.com/@neuroideas815
Canal YouTube / Channel YouTube: https://www.youtube.com/channel/UCBsf6OZ482NjST6QA-hvYtQ
Publicaciones y proyectos en desarrollo / Publications and projects:
https://www.amazon.com/author/bernabemallo
https://ehuenred.theglocal.network/ideas/el-origen-del-arte-en-el-cerebro-de-makapansgat-al-moma-del-primate-al-sapiens

El ojo algorítmico: cómo la inteligencia artificial aprende a ver el arte

Bernabé Mallo

Una reseña del estudio de Yali Liu y Can Zhu (2025) en Scientific Reports: The use of deep learning and artificial intelligence-based creative teaching in art education

Introducción: cuando la máquina mira un cuadro

¿Qué ve una máquina cuando contempla una pintura? No experimenta placer estético, no se conmueve con los colores, no evoca recuerdos. Pero, ¿puede aprender a reconocer lo que hace que una obra sea lo que es? ¿Puede descomponer una pincelada en sus componentes esenciales —textura, color, composición— y, a partir de ahí, generar algo nuevo?

El estudio de Yali Liu y Can Zhu, publicado en 2025 en Scientific Reports (Nature Portfolio), aborda precisamente esta cuestión. Los autores proponen y optimizan un sistema innovador de creación artística denominado Creative Intelligence Cloud (CIC), que combina redes generativas antagónicas (GAN) y redes neuronales convolucionales (CNN) para explorar cómo el aprendizaje profundo puede aplicarse a la educación artística. El objetivo no es solo generar imágenes, sino comprender el proceso mediante el cual una máquina adquiere lo que podríamos llamar un "conocimiento estético".

Este planteamiento nos sitúa ante una pregunta que atraviesa nuestra investigación sobre el origen del arte: ¿qué significa conocer una obra de arte? ¿Es suficiente con identificar patrones, estilos y estructuras compositivas, o se requiere algo más —una experiencia vivida, un cuerpo que sienta, un símbolo que se habite— que la máquina, por ahora, no posee? La respuesta, como veremos, nos obliga a distinguir entre dos formas de conocimiento: la que se basa en la correlación estadística y la que emerge de la función única del sistema nervioso.

El sistema CIC: anatomía de un aprendizaje visual

El sistema propuesto por Liu y Zhu se articula en torno a dos componentes técnicos fundamentales: las redes generativas antagónicas (GAN) y las redes neuronales convolucionales (CNN). Las GAN, introducidas por Ian Goodfellow en 2014, consisten en dos redes que compiten entre sí: una generadora, que produce imágenes, y una discriminadora, que intenta distinguir las imágenes generadas de las reales. Esta competencia adversarial obliga al generador a mejorar progresivamente la calidad de sus creaciones.

Las CNN, por su parte, son la herramienta mediante la cual el sistema "lee" las imágenes. A diferencia de una red neuronal tradicional, las CNN están diseñadas para procesar datos con una estructura de rejilla —como los píxeles de una imagen— y son capaces de detectar características locales (bordes, texturas, patrones) que luego se combinan en representaciones de mayor nivel (formas, objetos, composiciones). Es, en cierto modo, una imitación algorítmica de cómo la corteza visual humana procesa la información: desde rasgos simples hasta configuraciones complejas.

El sistema CIC se entrena con repositorios masivos de arte, como el dataset WikiArt, que contiene decenas de miles de pinturas etiquetadas por estilo, artista y época. A partir de este entrenamiento, el modelo aprende a descomponer las obras en sus elementos constituyentes: pinceladas, paletas de colores, estructuras compositivas. No "ve" el cuadro como un todo significativo, sino como una matriz de características que puede analizar, comparar y recombinar.

El "lente de datos": cómo la máquina aprende el lenguaje visual

El proceso de aprendizaje del sistema CIC ilustra lo que en otra reseña de este blog hemos denominado el "lente de datos" de la inteligencia artificial. A diferencia de una cámara fotográfica, que captura la luz de una escena real, la IA no ve el mundo directamente. Fragmenta miles de millones de imágenes preexistentes en unidades mínimas de información visual —los llamados tokens— y las asocia con etiquetas lingüísticas (palabras como "impresionista", "retrato", "azul"). De este modo, la máquina aprende un "lenguaje visual del mundo" que le permite, una vez entrenada, moverse de manera fluida y autónoma entre corrientes artísticas, estilos y épocas.

Este proceso tiene implicaciones profundas. Por un lado, permite una versatilidad sin precedentes: un modelo entrenado con WikiArt puede generar una imagen en el estilo de Van Gogh, una fotografía hiperrealista o una abstracción geométrica con la misma facilidad. Por otro lado, plantea la pregunta de qué significa "originalidad" cuando el sistema se entrena con millones de obras existentes. La máquina no inventa desde cero; recombina lo que ha aprendido. Su "creatividad" es, en el fondo, una forma sofisticada de interpolación estadística.

Lo que la máquina no puede aprender: la brecha del symbolon

Aquí es donde el análisis técnico se encuentra con la filosofía. Liu y Zhu demuestran que el sistema CIC es capaz de analizar obras de arte, identificar estilos y generar imágenes nuevas. Pero, ¿comprende lo que hace? La respuesta, desde la perspectiva de nuestra investigación, es negativa. La máquina puede manipular tokens visuales y lingüísticos, pero no habita el símbolo. No hay nadie "dentro" que reconozca o sea reconocido en el acto de creación.

Esta distinción es crucial. El symbolon, en su sentido etimológico originario, es un acto de reconocimiento mediante códigos compartidos. Cuando un artista humano crea, no solo combina formas: proyecta su interioridad, comunica una experiencia, busca un encuentro con el otro. La obra de arte es un puente entre subjetividades. La máquina, por el contrario, produce objetos que pueden ser interpretados como arte, pero no genera el proceso vivo —el sustrato de supervivencia, la dimensión simbólica, la pulsión de completitud— que da sentido a la creación humana.

El sistema CIC, al descomponer las obras en características estadísticas, pierde precisamente lo que hace que una obra sea una obra: su carácter de encuentro. La máquina puede aprender sobre el arte, pero no puede aprender desde el arte. No tiene un cuerpo que sienta, una historia que duela, una necesidad que impulse. Su conocimiento es, en el mejor de los casos, una sombra del conocimiento estético humano.

Una lectura desde el modelo S/Y/C

El estudio de Liu y Zhu ofrece un caso concreto donde se puede observar el funcionamiento de la Ley de coherencia biológica S/Y/C que constituye el núcleo de nuestra investigación (Mallo, 2023, 2025, 2026a, 2026b). Las tres dimensiones de nuestra tesis se despliegan aquí con una claridad poco común, precisamente porque el contraste con la máquina nos permite ver lo que en el humano damos por sentado.

La dimensión S (Supervivencia) se manifiesta en la utilidad del sistema CIC. Su objetivo declarado es mejorar la educación artística, proporcionar retroalimentación automatizada, ampliar el acceso a herramientas de creación. Esta es una función adaptativa legítima: la tecnología, como la cerámica neolítica, se adopta cuando resuelve un problema práctico. Pero la supervivencia del sistema no es la del organismo que lo utiliza. La máquina no necesita aprender arte para sobrevivir; el humano que la usa, sí. La IA es una herramienta de supervivencia cultural, no un sujeto que sobreviva a través de ella.

La dimensión Y (Symbolon) es el punto de mayor contraste. El sistema CIC manipula símbolos —etiquetas, estilos, representaciones visuales— con una eficacia impresionante. Pero no habita el símbolo. No hay un reconocimiento mutuo, una comunidad de códigos compartidos vividos. La máquina puede clasificar una pintura como "impresionista", pero no sabe lo que significa ser impresionista, no ha sentido la luz del sur de Francia, no ha discutido con un académico sobre la pincelada. El symbolon humano es vivido; el de la máquina, simulado.

La dimensión C (Completitud) se manifiesta en la búsqueda de la máquina por cerrar una forma, por generar una imagen coherente. Las GAN, en particular, operan mediante un proceso de convergencia: el generador y el discriminador compiten hasta alcanzar un equilibrio, un punto en el que las imágenes generadas son indistinguibles de las reales. Esta convergencia es una forma de completitud algorítmica. Pero es una completitud sin anhelo, sin la pulsión biológica que en el humano busca integrar las partes en un todo coherente. La máquina "cierra" una forma porque su función de pérdida se lo indica, no porque necesite dar sentido a su experiencia.

La Filosofía Quirúrgica nos invita a aplicar un corte analítico preciso al estudio de Liu y Zhu. No se trata de rechazar la tecnología ni de idealizarla, sino de distinguir niveles. En el nivel de la producción técnica, el sistema CIC es una herramienta poderosa que puede ampliar las capacidades del artista y del educador. En el nivel de la experiencia estética, la IA no puede reemplazar la subjetividad encarnada que constituye el corazón del arte. Y en el nivel de la responsabilidad cultural, es necesario un marco ético —como el humanismo tecnológico que hemos discutido en otras reseñas— que garantice que estas herramientas se utilizan para potenciar lo humano, no para sustituirlo.

Consideraciones finales: el arte como encuentro, no como clasificación

El estudio de Yali Liu y Can Zhu tiene el mérito de ofrecer una visión rigurosa y bien documentada de cómo el aprendizaje profundo puede aplicarse a la educación artística. Su sistema CIC demuestra que las máquinas son capaces de analizar, clasificar y generar imágenes con un grado de sofisticación que hace una década habría parecido imposible. Pero, al mismo tiempo, su trabajo revela —quizás sin proponérselo— los límites de ese enfoque.

La máquina puede aprender a reconocer un estilo, pero no a comprender por qué ese estilo importa. Puede descomponer una pincelada, pero no a sentir lo que la pincelada expresa. Puede generar una imagen nueva, pero no a habitar el proceso de creación como un acto de encuentro con el otro.

El arte, en su origen y en su función, no es una clasificación de características visuales. Es un encuentro entre subjetividades encarnadas. Y ese encuentro, por ahora, sigue siendo patrimonio exclusivo de lo vivo. La IA puede ser una aliada en la educación artística, una herramienta que amplíe el acceso y potencie la creatividad. Pero no puede ocupar el lugar del artista, ni del espectador, ni del educador. Porque el arte, como la supervivencia, el símbolo y la completitud, es algo que se vive antes de que se pueda medir.

Ahora bien, ¿qué aporta esta investigación al conjunto de nuestra indagación sobre el origen del arte? Creemos que lo más destacado no es solo lo que revela sobre las capacidades de la IA, sino la comparación que hace posible. Al observar cómo un sistema artificial, organizado por nosotros, descompone y recompone el legado visual de la humanidad, se nos ofrece un espejo inesperado para pensar cómo el propio sistema biológico, hace decenas de miles de años, pudo haber fragmentado, asimilado y reformulado el mundo para crear algo que hoy nos abarca a todos y a todo.

Comparar estas dos maneras de construir la creación —la artificial, guiada por algoritmos y datos; la biológica, guiada por necesidades, sesgos y anhelos— puede ser útil para afianzar alguna posible teoría sobre cómo los sistemas biológicos en general llegaron a producir arte. No se trata de equiparar ambos procesos, sino de utilizar el contraste como herramienta heurística: la máquina nos muestra, por ausencia, lo que la vida aporta. Y en esa ausencia —la falta de un cuerpo que sienta, de una historia que duela, de una necesidad que impulse— encontramos precisamente las claves de lo que el arte, en su origen, fue y sigue siendo.

Referencias bibliográficas

Mallo, B. (2023). La construcción neuro-simbólica. Una aproximación al funcionamiento del cerebro desde una perspectiva multidisciplinar [Tesis doctoral, Universidad del País Vasco - Euskal Herriko Unibertsitatea]. Repositorio ADDI. http://hdl.handle.net/10810/62701

Mallo, B. (2025). Arte y biología: Una aproximación neurofilosófica al origen de la experiencia estética. https://www.amazon.com/dp/B0E8Y5WZMK

Mallo, B. (2025). Art and biology: A neurophilosophical approach to the origin of aesthetic experience. https://www.amazon.com/dp/B0E8Y6C2XN

Mallo, B. (2026a). De la filosofía quirúrgica a la ley de coherencia biológica S/Y/C: Hacia una investigación sobre el origen del arte en la especie Homo [Versión Kindle]. https://www.amazon.com/dp/B0GYGTJD5C

Yu, C., & Liu, Z. (2026). Multi-task convolutional network for artwork analysis and personalized guidance. Scientific Reports, *16*, Article 70478. https://doi.org/10.1038/s41598-026-70478-6

Autor / Author

Bernabé Mallo
Doctor en Filosofía – Universidad del País Vasco / Euskal Herriko Unibertsitatea (UPV/EHU)
Investigador independiente en neurofilosofía, evolución humana y origen del arte.

Bernabé Mallo
PhD in Philosophy – University of the Basque Country / Euskal Herriko Unibertsitatea (UPV/EHU)
Independent researcher in neurophilosophy, human evolution, and the origin of art.

Enlaces / Links

domingo, 20 de septiembre de 2026

Art as Agency: When Things Act Upon Us

Bernabé Mallo

A review of Art and Agency: An Anthropological Theory by Alfred Gell (1998)

Introduction: the art that makes us act

What makes an image, an object, or a building move us, compel us to stop, provoke an emotion, or incite us to act? The traditional answer of Western aesthetics has been: beauty, form, symbolic meaning. The British anthropologist Alfred Gell (1945–1997) proposed a radically different answer in his posthumous work Art and Agency: An Anthropological Theory, published by Clarendon Press in 1998. For Gell, art is not fundamentally a vehicle of meaning nor an object of aesthetic contemplation, but a form of instrumental action: a means of influencing the thoughts and actions of others.

Gell died in January 1997, just a few months before the book saw the light. The text, which remained in a state of "work in progress" according to his editor, nevertheless constitutes one of the most influential legacies of the anthropology of art of the last quarter century. His proposal challenges the most deeply rooted conceptions of the discipline and forces us to rethink what art is, who produces it, and what effects it has on the social world.

This review explores the central ideas of Gell —agency, index, abduction, and the art nexus— and connects them with our research on the origin of art, the S/Y/C model, and Surgical Philosophy.

The critique of passive aesthetics: art as action

Gell starts from an uncomfortable observation for the anthropological and aesthetic tradition: existing theories adopt an overwhelmingly passive attitude towards art. Art is contemplated, interpreted, deciphered, but its capacity to do things in the world is not recognised. Against this passivity, Gell proposes an inversion: art is, above all, action. "Instead of symbolic communication, I place all the emphasis on agency, intention, causation, result, and transformation. I view art as a system of action, intended to change the world rather than to encode symbolic propositions about it."

This reformulation has profound consequences. The anthropology of art should not concern itself with a special class of objects —those we call "beautiful" or "artistic"— but with a category of action: the making of things that influence others. Any object, in the appropriate context, can become an index of artistic agency. What defines art is not its form or its meaning, but its function within a system of social relations.

Agency and index: the fundamental concepts

Gell's theoretical edifice rests on two interconnected concepts: agency and index.

Agency is the capacity to initiate a series of events in the world, to cause effects, to influence the thought and action of others. In the context of art, agency does not reside solely in the human artist who creates the object. The objects themselves —paintings, sculptures, tattoos, idols— mediate and embody social agency. They are "things that act": not in a literal sense, as if they had intentions of their own, but in the sense that they participate in networks of social relations, acting as vehicles for the agency of their creators or users.

The index, in Gell's terminology (borrowed from the semiotics of Charles Sanders Peirce), is the object that mediates agency. An index is something that, in its mere existence and configuration, points towards something else: towards the artist who made it, towards the model it represents, towards the recipient who receives it. Gell argues that art objects are indexes of the agency of those who produced or used them. They are not symbolic signs that refer to conventional meanings; they are traces, vestiges, proofs of an action.

This distinction is crucial. Gell rejects the idea that art is fundamentally a symbol, in the Saussurean sense of an arbitrary sign that refers to a concept. Art does not "say" things; it "does" things. It does not communicate meanings; it produces effects. An idol is not a symbol of the deity; it is an instrument for acting upon the divine world. A tattoo is not an ornament; it is a mark that binds a person to a lineage, to a status, to a system of social obligations.

Abduction and the art nexus: how agency works

If art is action mediated by indexes, how exactly does this mediation work? Gell turns here to another Peircean concept: abduction. Abduction is the inferential process by which, from an index, we infer the agency that produced it. We see a footprint in the sand and deduce that someone walked there. We see a painting and deduce that someone painted it, with certain intentions, in certain circumstances.

For Gell, art operates precisely through this abduction of agency. The object leads us to infer an agency that transcends it: the artist, the model, the recipient. But abduction is not a mere logical deduction; it is a psychological process, often automatic and unconscious, that leads us to attribute agency to the object. Art "captures" our attention, "enchants" us (enchantment), makes us experience the object as if it had its own power.

Gell organises these relations into what he calls the art nexus: a network of relations between four terms: the artist (who produces the index), the index (the object), the prototype (what the index represents or evokes), and the recipient (who experiences the index). Combinations of these terms generate a series of "formulas" that describe the different types of artistic agency. The art nexus is thus an analytical tool for mapping the complex networks of agency that unfold around art objects.

The distributed person and the extended mind

Two final concepts complete Gell's theoretical edifice. The first is the distributed person: the idea that a person's agency is not limited to their body, but extends to the objects they produce, the marks they leave, the things that belong to them. An artist, in this sense, is distributed across their works; their agency continues to act through them long after their death.

The second is the extended mind: Gell suggests that art objects function as extensions of the mind, as external vehicles of cognition and agency. This idea, which anticipates contemporary debates in the philosophy of mind, connects directly with our research on the origin of art and the role of external symbols in cognitive evolution.

Connection with research on the origin of art (S/Y/C)

Gell's theory offers a conceptual framework that illuminates crucial aspects of the Law of Biological Coherence S/Y/C that forms the core of our research (Mallo, 2023, 2025, 2026a, 2026b). His concepts of agency, index, and abduction allow us to reread prehistoric art not as a system of symbolic meanings, but as a device of social action.

The S (Survival) dimension manifests in the instrumental function of art that Gell emphasises. Art objects are, in many ethnographic contexts, tools of social survival: amulets that protect, masks that mediate with spirits, marks that ensure group belonging. Art is not a luxury; it is a technology for acting upon the world.

The Y (Symbolon) dimension requires qualification in light of Gell. For our thesis, symbolon is the act of recognition through shared codes. Gell, by rejecting the symbolic dimension of art, emphasises instead indexicality: the object as a trace of an agency. But both aspects are not mutually exclusive. The Gellian index can be the materialisation of symbolon: the object is the physical support through which symbolic agency is exercised. An idol not only acts; it is also recognised as the presence of the deity, and that recognition is a symbolic act.

The C (Wholeness) dimension manifests in the abduction of agency that the viewer performs. The art nexus, with its four terms, is a system that seeks coherence: the viewer integrates the index into a network of relations that give it meaning. The artist's agency, that of the prototype, that of the recipient, are articulated in a whole that transcends the mere materiality of the object. This integration is a form of wholeness: the object is understood as part of a broader system of action.

Surgical Philosophy invites us to make a precise analytical cut in Gell's work. It is not about accepting his rejection of aesthetics without more, but about recognising that his approach opens a field of analysis that traditional aesthetics had neglected: art as action, as agency, as power. At the same time, we must resist the temptation to reduce art to agency. The symbolic dimension, which Gell excludes, is also constitutive of artistic experience. Surgical Philosophy allows us to distinguish levels: the level of agency (what art does), the level of the symbol (what art means), and the level of experience (what art makes us feel). The three are irreducible to one another.

Critique and reception

Gell's work has generated intense debate. His critics, such as Howard Morphy, argue that his approach diverts attention from human agency by attributing agency to the objects themselves. The aesthetic and semantic properties that Gell excludes from his analysis are, for Morphy, integral to the understanding of art as a mode of action. Robert Layton maintains that Gell fails to explain the distinctive ways in which art objects extend the agency of their creators, and that his rejection of Saussurean semiotics is untenable.

Other critiques point to Gell's ethnographic bias, with his emphasis on Asia and the Pacific, and his scant attention to Western art. The internal coherence of his theory has also been questioned, especially regarding the relationship between agency and indexicality.

Despite these critiques, the impact of Art and Agency has been profound. The book has reformulated the anthropology of art, inspired new research on materiality, agency, and power, and built bridges between anthropology, archaeology, art history, and philosophy.

Final considerations: art as action and as encounter

Alfred Gell's posthumous work is one of those rare contributions that transform a field by changing the questions we ask. His proposal to study art as agency, as index, as action, forces us to look at objects not only for what they represent or signify, but for what they do in the social world.

For our research on the origin of art, Gell's theory offers a valuable complement to the neuroscientific and philosophical perspectives we have explored. Art, in its origin, was not only a symbolic expression or an aesthetic response; it was also —and perhaps above all— a tool of social action. Cave paintings, body ornaments, ivory figurines: all of them were indexes of an agency that sought to influence the world, the spirits, the others.

But art is not only agency. It is also encounter. The abduction that Gell describes, that process by which we attribute agency to an object, is an act of recognition. And recognition is the essence of symbolon. Art objects speak to us because we recognise in them the presence of a subjectivity, an intention, a history. That presence is not only an effect of power; it is also an invitation to encounter. And in that encounter, art fulfils its deepest function: that of connecting the living with the living, the living with the dead, humans with the sacred.

Gell taught us to see art as action. We add: art is action because it is encounter. And encounter is, at its root, the fullest form of agency and of symbol.

References

Gell, A. (1998). Art and agency: An anthropological theory. Clarendon Press. https://philpapers.org/rec/GELAAA

Layton, R. (2003). Art and agency: A reassessment. Journal of the Royal Anthropological Institute, *9*(3), 447–464. https://doi.org/10.1111/1467-9655.00158

Mallo, B. (2025). Arte y biología: Una aproximación neurofilosófica al origen de la experiencia estética. https://www.amazon.com/dp/B0E8Y5WZMK

Mallo, B. (2025). Art and biology: A neurophilosophical approach to the origin of aesthetic experience. https://www.amazon.com/dp/B0E8Y6C2XN

Morphy, H. (2009). Art as a mode of action: Some problems with Gell's Art and Agency. Journal of Material Culture, *14*(1), 5–27. https://doi.org/10.1177/1359183508100006

Thomas, N. (1998). Foreword. In A. Gell, Art and agency: An anthropological theory (pp. vii–xiii). Clarendon Press.

Autor / Author

Bernabé Mallo
Doctor en Filosofía – Universidad del País Vasco / Euskal Herriko Unibertsitatea (UPV/EHU)
Investigador independiente en neurofilosofía, evolución humana y origen del arte.

Bernabé Mallo
PhD in Philosophy – University of the Basque Country / Euskal Herriko Unibertsitatea (UPV/EHU)
Independent researcher in neurophilosophy, human evolution, and the origin of art.

Enlaces / Links

El arte como agencia: cuando las cosas actúan sobre nosotros

Bernabé Mallo

Una reseña de Art and Agency: An Anthropological Theory de Alfred Gell (1998)

Introducción: el arte que nos hace actuar

¿Qué hace que una imagen, un objeto o un edificio nos conmueva, nos obligue a detenernos, nos provoque una emoción o nos incite a actuar? La respuesta tradicional de la estética occidental ha sido: la belleza, la forma, el significado simbólico. El antropólogo británico Alfred Gell (1945–1997) propuso una respuesta radicalmente distinta en su obra póstuma Art and Agency: An Anthropological Theory, publicada por Clarendon Press en 1998. Para Gell, el arte no es fundamentalmente un vehículo de significado ni un objeto de contemplación estética, sino una forma de acción instrumental: un medio para influir en los pensamientos y acciones de los demás.

Gell falleció en enero de 1997, apenas unos meses antes de que el libro viera la luz. El texto, que quedó en un estado de "obra en progreso" según su editor, constituye no obstante uno de los legados más influyentes de la antropología del arte del último cuarto de siglo. Su propuesta desafía las concepciones más arraigadas de la disciplina y nos obliga a repensar qué es el arte, quién lo produce y qué efectos tiene sobre el mundo social.

Esta reseña explora las ideas centrales de Gell —la agencia, el índice, la abducción y el nexo del arte— y las conecta con nuestra investigación sobre el origen del arte, el modelo S/Y/C y la Filosofía Quirúrgica.

La crítica a la estética pasiva: el arte como acción

Gell parte de una constatación incómoda para la tradición antropológica y estética: las teorías existentes adoptan una actitud abrumadoramente pasiva frente al arte. El arte se contempla, se interpreta, se descifra, pero no se reconoce su capacidad de hacer cosas en el mundo. Frente a esta pasividad, Gell propone una inversión: el arte es, ante todo, acción. "En lugar de comunicación simbólica, pongo todo el énfasis en la agencia, la intención, la causación, el resultado y la transformación. Veo el arte como un sistema de acción, destinado a cambiar el mundo en lugar de codificar proposiciones simbólicas sobre él".

Esta reformulación tiene consecuencias profundas. La antropología del arte no debe ocuparse de una clase especial de objetos —aquellos que llamamos "bellos" o "artísticos"— sino de una categoría de acción: el hacer cosas que influyen en otros. Cualquier objeto, en el contexto adecuado, puede convertirse en un índice de agencia artística. Lo que define al arte no es su forma ni su significado, sino su función en un sistema de relaciones sociales.

Agencia e índice: los conceptos fundamentales

El edificio teórico de Gell se sostiene sobre dos conceptos interconectados: agencia e índice.

La agencia es la capacidad de iniciar una serie de eventos en el mundo, de causar efectos, de influir en el pensamiento y la acción de otros. En el contexto del arte, la agencia no reside únicamente en el artista humano que crea el objeto. Los objetos mismos —las pinturas, las esculturas, los tatuajes, los ídolos— median y encarnan la agencia social. Son "cosas que actúan": no en un sentido literal, como si tuvieran intenciones propias, sino en el sentido de que participan en redes de relaciones sociales, actuando como vehículos de la agencia de sus creadores o usuarios.

El índice, en la terminología de Gell (tomada de la semiótica de Charles Sanders Peirce), es el objeto que media la agencia. Un índice es algo que, en su mera existencia y configuración, señala hacia algo más: hacia el artista que lo hizo, hacia el modelo que representa, hacia el destinatario que lo recibe. Gell argumenta que los objetos de arte son índices de la agencia de quien los produjo o de quien los utiliza. No son signos simbólicos que remiten a significados convencionales; son huellas, vestigios, pruebas de una acción.

Esta distinción es crucial. Gell rechaza la idea de que el arte sea fundamentalmente un símbolo, en el sentido saussuriano de un signo arbitrario que remite a un concepto. El arte no "dice" cosas; "hace" cosas. No comunica significados; produce efectos. Un ídolo no es un símbolo de la deidad; es un instrumento para actuar sobre el mundo divino. Un tatuaje no es un adorno; es una marca que vincula a una persona con un linaje, con un estatus, con un sistema de obligaciones sociales.

La abducción y el nexo del arte: cómo funciona la agencia

Si el arte es acción mediada por índices, ¿cómo funciona exactamente esta mediación? Gell recurre aquí a otro concepto de Peirce: la abducción. La abducción es el proceso inferencial mediante el cual, a partir de un índice, inferimos la agencia que lo produjo. Vemos una huella en la arena y deducimos que alguien caminó por allí. Vemos una pintura y deducimos que alguien la pintó, con ciertas intenciones, en ciertas circunstancias.

Para Gell, el arte opera precisamente mediante esta abducción de la agencia. El objeto nos lleva a inferir una agencia que lo trasciende: el artista, el modelo, el destinatario. Pero la abducción no es una mera deducción lógica; es un proceso psicológico, a menudo automático e inconsciente, que nos lleva a atribuir agencia al objeto. El arte "captura" nuestra atención, nos "encanta" (enchantment), nos hace experimentar el objeto como si tuviera poder propio.

Gell organiza estas relaciones en lo que denomina el nexo del arte (art nexus): un entramado de relaciones entre cuatro términos: el artista (quien produce el índice), el índice (el objeto), el prototipo (lo que el índice representa o evoca) y el receptor (quien experimenta el índice). Las combinaciones de estos términos generan una serie de "fórmulas" que describen los distintos tipos de agencia artística. El nexo del arte es, así, una herramienta analítica para mapear las complejas redes de agencia que se despliegan en torno a los objetos de arte.

La persona distribuida y la mente extendida

Dos conceptos finales completan el edificio teórico de Gell. El primero es la persona distribuida: la idea de que la agencia de una persona no se limita a su cuerpo, sino que se extiende a los objetos que produce, a las marcas que deja, a las cosas que le pertenecen. Un artista, en este sentido, se distribuye en sus obras; su agencia continúa actuando a través de ellas mucho después de su muerte.

El segundo es la mente extendida: Gell sugiere que los objetos de arte funcionan como extensiones de la mente, como vehículos externos de la cognición y la agencia. Esta idea, que anticipa debates contemporáneos en filosofía de la mente, conecta directamente con nuestra investigación sobre el origen del arte y el papel de los símbolos externos en la evolución cognitiva.

Conexión con la investigación sobre el origen del arte (S/Y/C)

La teoría de Gell ofrece un marco conceptual que ilumina aspectos cruciales de la Ley de coherencia biológica S/Y/C que constituye el núcleo de nuestra investigación (Mallo, 2023, 2025, 2026a, 2026b). Sus conceptos de agencia, índice y abducción permiten releer el arte prehistórico no como un sistema de significados simbólicos, sino como un dispositivo de acción social.

La dimensión S (Supervivencia) se manifiesta en la función instrumental del arte que Gell subraya. Los objetos de arte son, en muchos contextos etnográficos, herramientas de supervivencia social: amuletos que protegen, máscaras que median con los espíritus, marcas que aseguran la pertenencia al grupo. El arte no es un lujo; es una tecnología de acción sobre el mundo.

La dimensión Y (Symbolon) requiere una matización a la luz de Gell. Para nuestra tesis, el symbolon es el acto de reconocimiento mediante códigos compartidos. Gell, al rechazar la dimensión simbólica del arte, enfatiza en cambio la indexicalidad: el objeto como huella de una agencia. Pero ambos aspectos no son excluyentes. El índice gelliano puede ser la materialización del symbolon: el objeto es el soporte físico a través del cual se ejerce la agencia simbólica. Un ídolo no solo actúa; también es reconocido como la presencia de la deidad, y ese reconocimiento es un acto simbólico.

La dimensión C (Completitud) se manifiesta en la abducción de la agencia que el espectador realiza. El nexo del arte, con sus cuatro términos, es un sistema que busca la coherencia: el espectador integra el índice en una red de relaciones que le dan sentido. La agencia del artista, la del prototipo, la del receptor, se articulan en un todo que trasciende la mera materialidad del objeto. Esta integración es una forma de completitud: el objeto se comprende como parte de un sistema de acción más amplio.

La Filosofía Quirúrgica nos invita a aplicar un corte analítico preciso a la obra de Gell. No se trata de aceptar sin más su rechazo de la estética, sino de reconocer que su enfoque abre un campo de análisis que la estética tradicional había descuidado: el arte como acción, como agencia, como poder. Al mismo tiempo, debemos resistir la tentación de reducir el arte a la agencia. La dimensión simbólica, que Gell excluye, es también constitutiva de la experiencia artística. La Filosofía Quirúrgica nos permite distinguir niveles: el nivel de la agencia (lo que el arte hace), el nivel del símbolo (lo que el arte significa) y el nivel de la experiencia (lo que el arte nos hace sentir). Los tres son irreductibles entre sí.

Crítica y recepción

La obra de Gell ha generado un intenso debate. Sus críticos, como Howard Morphy, argumentan que su enfoque desvía la atención de la agencia humana al atribuir agencia a los objetos mismos. Las propiedades estéticas y semánticas que Gell excluye de su análisis son, para Morphy, integrales a la comprensión del arte como modo de acción. Robert Layton sostiene que Gell no logra explicar las formas distintivas en que los objetos artísticos extienden la agencia de sus creadores, y que su rechazo de la semiótica saussuriana es insostenible.

Otras críticas señalan el sesgo etnográfico de Gell, con su énfasis en Asia y el Pacífico, y su escasa atención al arte occidental. También se ha cuestionado la coherencia interna de su teoría, especialmente en lo que respecta a la relación entre agencia e indexicalidad.

A pesar de estas críticas, el impacto de Art and Agency ha sido profundo. El libro ha reformulado la antropología del arte, ha inspirado nuevas investigaciones sobre materialidad, agencia y poder, y ha tendido puentes entre la antropología, la arqueología, la historia del arte y la filosofía.

Consideraciones finales: el arte como acción y como encuentro

La obra póstuma de Alfred Gell es una de esas raras contribuciones que transforman un campo al cambiar las preguntas que nos hacemos. Su propuesta de estudiar el arte como agencia, como índice, como acción, nos obliga a mirar los objetos no solo por lo que representan o significan, sino por lo que hacen en el mundo social.

Para nuestra investigación sobre el origen del arte, la teoría de Gell ofrece un complemento valioso a las perspectivas neurocientíficas y filosóficas que hemos explorado. El arte, en su origen, no fue solo una expresión simbólica o una respuesta estética; fue también —y quizás sobre todo— una herramienta de acción social. Las pinturas rupestres, los adornos corporales, las figurillas de marfil: todos ellos fueron índices de una agencia que buscaba influir en el mundo, en los espíritus, en los otros.

Pero el arte no es solo agencia. Es también encuentro. La abducción que Gell describe, ese proceso por el cual atribuimos agencia a un objeto, es un acto de reconocimiento. Y el reconocimiento es la esencia del symbolon. Los objetos de arte nos hablan porque reconocemos en ellos la presencia de una subjetividad, de una intención, de una historia. Esa presencia no es solo un efecto de poder; es también una invitación al encuentro. Y en ese encuentro, el arte cumple su función más profunda: la de conectar a los vivos con los vivos, a los vivos con los muertos, a los humanos con lo sagrado.

Gell nos enseñó a ver el arte como acción. Nosotros añadimos: el arte es acción porque es encuentro. Y el encuentro es, en su raíz, la forma más plena de la agencia y del símbolo.

Referencias bibliográficas

Gell, A. (1998). Art and agency: An anthropological theory. Clarendon Press. https://philpapers.org/rec/GELAAA

Layton, R. (2003). Art and agency: A reassessment. Journal of the Royal Anthropological Institute, *9*(3), 447–464. https://doi.org/10.1111/1467-9655.00158

Mallo, B. (2025). Arte y biología: Una aproximación neurofilosófica al origen de la experiencia estética. https://www.amazon.com/dp/B0E8Y5WZMK

Mallo, B. (2025). Art and biology: A neurophilosophical approach to the origin of aesthetic experience. https://www.amazon.com/dp/B0E8Y6C2XN

Morphy, H. (2009). Art as a mode of action: Some problems with Gell's Art and Agency. Journal of Material Culture, *14*(1), 5–27. https://doi.org/10.1177/1359183508100006

Thomas, N. (1998). Foreword. En A. Gell, Art and agency: An anthropological theory (pp. vii–xiii). Clarendon Press.

Autor / Author

Bernabé Mallo
Doctor en Filosofía – Universidad del País Vasco / Euskal Herriko Unibertsitatea (UPV/EHU)
Investigador independiente en neurofilosofía, evolución humana y origen del arte.

Bernabé Mallo
PhD in Philosophy – University of the Basque Country / Euskal Herriko Unibertsitatea (UPV/EHU)
Independent researcher in neurophilosophy, human evolution, and the origin of art.

Enlaces / Links

viernes, 18 de septiembre de 2026

The Data Lens: How Artificial Intelligence Decomposes and Reassembles Art

Bernabé Mallo

A review of the article by Hu Yang, Yunxian Zou, and Bo Gao (2026): From Digitization to the AI Era: Digital Technology Trajectories in Fine-Art Creation

Introduction: when the machine learns to look

What happens when a machine tries to "see" the world? Not with eyes, but with an algorithm that fragments millions of images into tiny visual fragments, associates them with words, and learns to recombine them according to statistical patterns? The answer, which may sound like science fiction, is the basis of artistic creation with artificial intelligence.

The article by Hu Yang, Yunxian Zou, and Bo Gao, published in 2026 in the Asia Pacific Economic and Management Review, analyses precisely this transformation. The authors propose a historical journey through the three phases of digital artistic creation —from early digitisation to the emergence of generative AI— and offer a conceptual framework for understanding how technology is reconfiguring authorship, originality, and the distribution of art .

The central concept of their analysis is the "data lens" of AI. Unlike a camera, which captures the light of a real scene, AI fragments billions of pre-existing images into "visual tokens" and associates them with linguistic tokens (words). In this way, the machine "learns the visual language of the world" and, once trained, can move fluidly and autonomously across artistic movements, styles, and eras.

This process, as we shall see, not only redefines what it means to "create" art, but also raises profound questions about artistic agency, intellectual property, and the role of the artist in the era of algorithmic automation.

Three phases of digital artistic creation

Yang, Zou, and Gao propose a practice-based periodisation that distinguishes three phases in the evolution of digital technology applied to fine art . Each phase not only introduces new tools, but transforms the artist's relationship with their work and with the public.

Phase I (1980–2000): medium translation

The first phase, which the authors call "medium translation" , is characterised by the digitisation of traditional media. Images become files, and logical processes —programming— enter the artistic craft . During these two decades, digital tools functioned primarily as mechanisms for extending analogue practices.

However, even in this early phase, later debates about agency and automation were prefigured. As the authors note, "even when digital tools largely functioned as translation and extension mechanisms, they already prefigured later debates about agency and automation by embedding creativity in operational sequences and delegating parts of form-making to computational processes" . The artist was beginning to be, not only a creator of objects, but a designer of procedures.

Phase II (2000–2010): platformised networking

The second phase, "platformised networking" , is marked by the emergence of Web 2.0 and social media . The publication and circulation of works become continuous, low-threshold, and socially integrated. Art no longer circulates as an isolated object, but as a node within communicative circuits.

This phase, however, has a dark side. The platformisation of artistic creation subjects artists to regimes of visibility governed by algorithms, metrics, and interfaces that determine what is seen and what is ignored. The authors describe this as a "distributed curation regime" in which circulation is determined by compatibility with interfaces and performance in terms of engagement, rather than by artistic merit . The political economy of creative labour becomes explicit: platforms extract value from the unpaid work of creators, who provide content, attention, and behavioural data without equivalent control or compensation .

Phase III (2010–present): algorithmic autonomy

The third and current phase is that of "algorithmic autonomy" . It is defined by the rise of machine learning systems that can synthesise images and styles through statistical inference, shifting artistic creation from direct pixel manipulation toward model-mediated generation.

The authors analyse two key technical milestones: Generative Adversarial Networks (GANs), which demonstrated that plausible synthetic images could be produced through adversarial learning, and diffusion models, which have since dominated the field for their ability to generate high-fidelity images and allow more flexible and controllable editing .

The crucial change, they note, is not only the improvement in image quality, but the relocation of creative labour. A visible portion of the artist's skill shifts toward specifying constraints, iterating instructions, testing variations, and curating results—activities that resemble direction, selection, and orchestration more than manual construction . The work of art emerges as a negotiated outcome of interaction with a probabilistic system.

The "data lens": how AI learns the visual language

The most suggestive concept in the article is the "data lens" of AI. Unlike the photographic camera, which captures the light of a real scene, AI does not see the world directly. Instead, it fragments billions of pre-existing images into "visual tokens" (minimal units of visual information) and associates them with "linguistic tokens" (words). In this way, the machine learns a "visual language of the world" that allows it, once trained, to move fluidly and autonomously across artistic movements, styles, and eras.

This process has profound implications. On the one hand, it allows unprecedented versatility: an AI model can generate an image in the style of Van Gogh, a hyperrealistic photograph, or a geometric abstraction with equal ease. On the other hand, it raises the question of what "originality" means when the system is trained on millions of existing works. As the authors note, the AI phase intensifies long-standing debates by redistributing artistic agency across instructions, model priorities, datasets, and post-selection .

Artistic agency thus becomes a distributed concept. It no longer resides solely in the artist, but is shared among:

Instructions (how the request is formulated).
Model priorities (what the algorithm is predisposed to produce).
Training data (which visual cultures are represented and how).
Post-selection (what is accepted, refined, and contextualised).

Technological humanism as a response

Faced with this scenario, Yang, Zou, and Gao propose a normative-analytical framework they call "technological humanism" . This approach sets out three conditions for responsible AI-mediated artistic creation:

Traceable delegation: the ability to follow and understand the decisions made by the system.
Situated cultural responsibility: the recognition that AI systems are not neutral, but reflect biases and cultural values.
Infrastructural transparency: the need for artists, educators, and museums to understand the technical and economic conditions under which generative systems operate.

Technological humanism is not a rejection of AI, but an invitation to use it with judgement, not to delegate to it decisions that require human discernment, and to maintain cultural integrity in the context of algorithmic production . The authors conclude their article with operational principles so that artists, educators, and museums can evaluate and sustain agency and cultural integrity under algorithmic production .

Connection with research on the origin of art (S/Y/C)

The analysis by Yang, Zou, and Gao resonates with the research we have been developing on the S/Y/C model of neuronal functioning and the Law of Biological Coherence (Mallo, 2023, 2025, 2026a, 2026b). The "data lens" of AI and its implications for artistic creation can be reinterpreted in light of our three dimensions.

The S (Survival) dimension manifests in the dependence of AI systems on large datasets that reflect existing visual cultures. This dependence is not neutral: data incorporate biases, exclusions, and power relations. AI, by learning from this data, can perpetuate and amplify those inequalities. Technological humanism, by demanding transparency and cultural responsibility, is a way of attending to the survival of diverse artistic traditions in the face of algorithmic homogenisation.

The Y (Symbolon) dimension is central to the tokenisation process described by the authors. AI fragments images into visual tokens and associates them with linguistic tokens, creating a bridge between the visual and the verbal. But this symbolon —this act of recognition through shared codes— is profoundly different from the one performed by a human artist. The artist does not merely combine tokens, but inhabits symbols, charging them with personal and cultural meaning. AI, by contrast, manipulates symbols from the outside, without a lived experience to sustain them.

The C (Wholeness) dimension manifests in the process of selection and curation that the authors identify as a central part of the artist's work in the AI era. The artist working with generative models does not merely generate images, but selects, refines, and contextualises, seeking a coherent totality. But this wholeness is different from the one sought by the traditional artist: the human artist seeks to close a form from inner experience; the artist with AI seeks to close a process from interaction with a probabilistic system.

Surgical Philosophy invites us to make a precise analytical cut in the analysis by Yang, Zou, and Gao. It is not about rejecting AI or accepting it uncritically, but about distinguishing levels: the level of technical production (where AI is a powerful tool), the level of aesthetic experience (where AI cannot replace embodied subjectivity), and the level of cultural responsibility (where technological humanism offers an ethical framework). AI can be an ally in artistic creation, but it cannot occupy the place of the artist as a source of intentionality and meaning.

Final considerations: art as an encounter between the human and the algorithmic

The article by Yang, Zou, and Gao has the merit of offering a lucid and well-informed vision of the digital transformation of art. Its periodisation into three phases helps us understand that the emergence of AI is not an absolute rupture, but the continuation of a process that began with digitisation and intensified with platformisation. And its proposal for a technological humanism offers an ethical framework for navigating this new territory.

AI, like the "data lens" the authors describe, does not capture the light of a real scene, but fragments and recombines humanity's visual legacy. This allows it impressive versatility, but also strips it of connection with lived experience. Art, in its origin and in its function, is not merely a combination of forms, but an encounter between embodied subjectivities. AI can facilitate this encounter, but it cannot occupy the place of either pole.

Now, what would happen if we transferred this same scheme —digitisation, assimilation, reformulation— to the biological system that gave rise to art? If AI begins with the digitisation of pre-existing images, the original nervous system began with the digitisation of knowledge of its lived niche: the information necessary for the most fundamental thing, survival. That niche was then assimilated according to its own biases, shaped by the relationship with the natural environment and by its own biological functions, whose first imperative was survival and, later, other equally fundamental needs.

Finally, there would remain what we might call the "prehistoric AI" : that primordial neuronal function capable of reformulating and conceptualising, from the possibilities of the biological system itself, the interest of survival and the search for wholeness, in order to apply them to practice. Was that the artificial intelligence of the past? Was that the first embodied algorithm that allowed our species to create symbols, tools, and art? The question remains open.

And it is precisely this openness that defines our research. Contemporary AI offers us an unexpected mirror: by observing how it fragments, assimilates, and recombines humanity's visual legacy, we can better understand how the nervous system itself, tens of thousands of years ago, fragmented, assimilated, and recombined the world to create something radically new. Art, from Makapansgat to generative algorithms, remains the testimony of a single function: that of a system that needs to survive, to symbolise, and to achieve wholeness.

References

Mallo, B. (2025). Arte y biología: Una aproximación neurofilosófica al origen de la experiencia estética. https://www.amazon.com/dp/B0E8Y5WZMK

Mallo, B. (2025). Art and biology: A neurophilosophical approach to the origin of aesthetic experience. https://www.amazon.com/dp/B0E8Y6C2XN

Yang, H., Zou, Y., & Gao, B. (2026). From digitization to the AI era: Digital technology trajectories in fine-art creation. Asia Pacific Economic and Management Review, 3(1). https://doi.org/10.62177/apemr.v3i1.1086

Autor / Author

Bernabé Mallo
Doctor en Filosofía – Universidad del País Vasco / Euskal Herriko Unibertsitatea (UPV/EHU)
Investigador independiente en neurofilosofía, evolución humana y origen del arte.

Bernabé Mallo
PhD in Philosophy – University of the Basque Country / Euskal Herriko Unibertsitatea (UPV/EHU)
Independent researcher in neurophilosophy, human evolution, and the origin of art.

jueves, 1 de octubre de 2026

The Algorithmic Eye: How Artificial Intelligence Learns to See Art

Bernabé Mallo

A review of the study by Yali Liu and Can Zhu (2025) in Scientific Reports: The use of deep learning and artificial intelligence-based creative teaching in art education

Introduction: when the machine looks at a painting

The CIC system: anatomy of a visual learning process

The "data lens": how the machine learns the visual language

What the machine cannot learn: the symbolon gap

A reading from the S/Y/C model

Final considerations: art as encounter, not as classification

References

Enlaces / Links

El ojo algorítmico: cómo la inteligencia artificial aprende a ver el arte

Bernabé Mallo

Una reseña del estudio de Yali Liu y Can Zhu (2025) en Scientific Reports: The use of deep learning and artificial intelligence-based creative teaching in art education

Introducción: cuando la máquina mira un cuadro

El sistema CIC: anatomía de un aprendizaje visual

El "lente de datos": cómo la máquina aprende el lenguaje visual

Lo que la máquina no puede aprender: la brecha del symbolon

Una lectura desde el modelo S/Y/C

Consideraciones finales: el arte como encuentro, no como clasificación

Referencias bibliográficas

Enlaces / Links

domingo, 20 de septiembre de 2026

Art as Agency: When Things Act Upon Us

Bernabé Mallo

A review of Art and Agency: An Anthropological Theory by Alfred Gell (1998)

Introduction: the art that makes us act

The critique of passive aesthetics: art as action

Agency and index: the fundamental concepts

Abduction and the art nexus: how agency works

The distributed person and the extended mind

Connection with research on the origin of art (S/Y/C)

Critique and reception

Final considerations: art as action and as encounter

References

Enlaces / Links

El arte como agencia: cuando las cosas actúan sobre nosotros

Bernabé Mallo

Una reseña de Art and Agency: An Anthropological Theory de Alfred Gell (1998)

Introducción: el arte que nos hace actuar

La crítica a la estética pasiva: el arte como acción

Agencia e índice: los conceptos fundamentales

La abducción y el nexo del arte: cómo funciona la agencia

La persona distribuida y la mente extendida

Conexión con la investigación sobre el origen del arte (S/Y/C)

Crítica y recepción

Consideraciones finales: el arte como acción y como encuentro

Referencias bibliográficas

Enlaces / Links

viernes, 18 de septiembre de 2026

The Data Lens: How Artificial Intelligence Decomposes and Reassembles Art

Bernabé Mallo

A review of the article by Hu Yang, Yunxian Zou, and Bo Gao (2026): From Digitization to the AI Era: Digital Technology Trajectories in Fine-Art Creation

Introduction: when the machine learns to look

Three phases of digital artistic creation

Phase I (1980–2000): medium translation

Phase II (2000–2010): platformised networking

Phase III (2010–present): algorithmic autonomy

The "data lens": how AI learns the visual language

Technological humanism as a response

Connection with research on the origin of art (S/Y/C)

Final considerations: art as an encounter between the human and the algorithmic

References

Enlaces / Links

Vistas de página la semana pasada