Prometheus

The distinction between music and the other arts rests not only in its non-representational nature but in its immunity to tokenization. Unlike painting, literature, or film, where representation is an intrinsic part of the craft—whether in mimetic depiction or symbolic abstraction—music engages directly with human perception at a level that bypasses external objects entirely. It does not show a sunset, nor does it tell a story in a literal sense. Instead, it operates in the domain of pure form, structuring emotion and cognition without requiring an intermediary symbol. This makes it not only the most metaphysical of the arts but also the most resistant to commodification in the form of tokens.

Fig. 13 Our protagonist’s odyssey unfolds across landscapes of five flats, six flats, and three sharps. With remarkable delight and sure-footedness, the journey explores the full range of extensions and alterations of basic triads and seventh chords across all seven modes.

Consider the visual arts. A painting, no matter how abstract, inevitably retains some degree of reference to the physical world. Even the most radical non-representational movements—Malevich’s Black Square, Kandinsky’s color harmonies—derive their impact from their negation of representation, from the viewer’s expectation of seeing something tangible. And yet, the very existence of these works as singular objects, their fetishization in museums, and their stratospheric valuations in the art market reveal how deeply they are ensnared in tokenization. A Rembrandt or a Rothko is not merely a conduit of aesthetic experience but a symbol of wealth, status, or historical prestige. The artwork is not merely seen; it is owned.

Literature and film are even more susceptible. A novel’s first edition, a film’s original reel, a signed manuscript—each of these becomes a commodity beyond the work’s content. Worse still, literature and film, while capable of transcendent moments, frequently fall into the trap of representation. They depict characters, scenes, and events in a way that makes their reality-dependent. A novel might be deeply psychological, but it remains tied to language, to the necessity of description, to the burden of narration. Film, being visual, cannot escape the world’s materiality at all. It may stylize or distort reality, but it is always bound to its representation. Its power rests in illusion—its greatest achievements come when it tricks the mind into believing the impossible. But this reliance on illusion means it is never fully free.

Music, by contrast, is absolute. It exists only in time, not space. One does not own a symphony in the way one owns a painting. A score is merely a set of instructions; the true work is ephemeral, residing only in performance. There is no “original” version of Beethoven’s Eroica, no first print that is worth millions. Even the fetishization of vinyl records, rare pressings, or historical instruments cannot fundamentally alter the fact that music’s essence is in its sound, which is infinitely reproducible and yet never the same twice. One does not possess music; one experiences it.

This untethered nature also immunizes music against the absurdities of tokenization that infect other arts. The soccer ball supposedly used by Pelé in his first World Cup goal—what is that but a meaningless artifact, its value entirely fictitious? It is not the goal itself, not the event, not the feeling. It is a dead object, imbued with significance only by human superstition. The same applies to a first edition of Moby-Dick or Van Gogh’s Sunflowers—the difference between these and an ordinary copy or reproduction is purely an artifice of scarcity. But music? A bootleg recording of Glenn Gould playing Bach is still Glenn Gould playing Bach. The Moonlight Sonata played on a battered piano is still the Moonlight Sonata. The experience is unmediated by provenance, by history, by the aura of an object.

Of course, one might argue that music has its own equivalents of fetishization. Certain recordings, particular performances, or even the aura surrounding a specific conductor or performer can elevate a musical experience beyond its mere sound. But the fundamental difference is that music’s essence remains untainted by these artifacts. The magic of Beethoven’s Ninth does not reside in a particular manuscript, nor in the instruments used at its premiere, nor in the historical moment in which it was played. It is an ever-renewable, ever-present force, available to any listener at any time.

If we return to the neural network structure you provided, it is clear how these ideas map onto the architecture of cognition and representation. The Voir layer—pure information—acts as the undistorted sensory input of music. It is not yet symbolic, not yet burdened by representation. It is processed through the Choisis layer—our decision-making, our weighing of its meaning—but even then, music does not land in the realm of tokenized outcomes. It does not become a static signifier, a relic of possession. Instead, it moves fluidly through the Deviens and M’èléve layers, shaping and reshaping itself with every performance, every new listener, every moment of engagement. Music exists in this constant state of becoming, untethered to the past, immune to the greed of collectors, untainted by the fetishization of objects.

So, is music the greatest art? Perhaps the more precise question is: is it the purest? In a world that drowns itself in tokens, in representation, in the obsessive categorization of artifacts and symbols, music stands alone as an ungraspable, untouchable force. It is, in the truest sense, spiritual—because it can never be owned.

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import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network layers
def define_layers():
    return {
        'Suis': ['Fewer Men', 'Greater Honor', 'And If To Live', 'Country Loss', "If We Are Mark’d", 'To Die, We Enough'],  # Static
        'Voir': ['Information'],  
        'Choisis': ['Baseline', 'Decision'],  
        'Deviens': ['Adverse Event Markers', 'Comorbidity/ICD Code', 'Temporal Changes'],  
        "M'èléve": ['Mortality Rate', 'Organ Failure', 'Hospitalization', 'Dependency', 'Physical Frailty']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Information'],  
        'paleturquoise': ['To Die, We Enough', 'Decision', 'Temporal Changes', 'Physical Frailty'],  
        'lightgreen': ["If We Are Mark’d", 'Comorbidity/ICD Code', 'Organ Failure', 'Dependency', 'Hospitalization'],  
        'lightsalmon': ['And If To Live', 'Country Loss', 'Baseline', 'Adverse Event Markers', 'Mortality Rate'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights (hardcoded for editing)
def define_edges():
    return {
        ('Fewer Men', 'Information'): '1/99',
        ('Greater Honor', 'Information'): '5/95',
        ('And If To Live', 'Information'): '20/80',
        ('Country Loss', 'Information'): '51/49',
        ("If We Are Mark’d", 'Information'): '80/20',
        ('To Die, We Enough', 'Information'): '95/5',
        ('Information', 'Baseline'): '20/80',
        ('Information', 'Decision'): '80/20',
        ('Baseline', 'Adverse Event Markers'): '49/51',
        ('Baseline', 'Comorbidity/ICD Code'): '80/20',
        ('Baseline', 'Temporal Changes'): '95/5',
        ('Decision', 'Adverse Event Markers'): '5/95',
        ('Decision', 'Comorbidity/ICD Code'): '20/80',
        ('Decision', 'Temporal Changes'): '51/49',
        ('Adverse Event Markers', 'Mortality Rate'): '80/20',
        ('Adverse Event Markers', 'Organ Failure'): '85/15',
        ('Adverse Event Markers', 'Hospitalization'): '90/10',
        ('Adverse Event Markers', 'Dependency'): '95/5',
        ('Adverse Event Markers', 'Physical Frailty'): '99/1',
        ('Comorbidity/ICD Code', 'Mortality Rate'): '1/9',
        ('Comorbidity/ICD Code', 'Organ Failure'): '1/8',
        ('Comorbidity/ICD Code', 'Hospitalization'): '1/7',
        ('Comorbidity/ICD Code', 'Dependency'): '1/6',
        ('Comorbidity/ICD Code', 'Physical Frailty'): '1/5',
        ('Temporal Changes', 'Mortality Rate'): '1/99',
        ('Temporal Changes', 'Organ Failure'): '5/95',
        ('Temporal Changes', 'Hospitalization'): '10/90',
        ('Temporal Changes', 'Dependency'): '15/85',
        ('Temporal Changes', 'Physical Frailty'): '20/80'
    }

# Calculate positions for nodes
def calculate_positions(layer, x_offset):
    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
    return [(x_offset, y) for y in y_positions]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    edges = define_edges()
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    
    # Add nodes and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(colors.get(node, 'lightgray'))   
    
    # Add edges with weights
    for (source, target), weight in edges.items():
        if source in G.nodes and target in G.nodes:
            G.add_edge(source, target, weight=weight)
    
    # Draw the graph
    plt.figure(figsize=(12, 8))
    edges_labels = {(u, v): d["weight"] for u, v, d in G.edges(data=True)}
    
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=15, connectionstyle="arc3,rad=0.2"
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
    plt.title("Indicator, Influencer, Impacted", fontsize=23)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 14 Nostalgia & Romanticism. When monumental ends (victory), antiquarian means (war), and critical justification (bloodshed) were all compressed into one figure-head: hero