Normative

Rupert Murdoch’s “Project Harmony,” a final, desperate bid to cement Lachlan’s dominion over his sprawling media empire, unfolds as a transvaluative saga—from the noise of familial discord to the illusory signal of control, only to falter on the precipice of inevitability. Within the neural network of his life’s work, mirrored in the five stages—Suis, Voir, Choisis, Deviens, and M’élève—Murdoch’s journey reflects the relentless compression of chaos into order, a cadence that crescendos in hubris yet collapses into frailty. As in As You Like It, where Jaques’s “Seven Ages” chart a path from infant mewling to “sans everything,” Murdoch’s empire-building compresses the noise of ambition into a signal of power, only for “next token prediction” to emerge as the frail, mortal limit—his body’s betrayal and the empire’s unraveling.

In the beginning, Suis, Murdoch’s world was 95% noise—a cacophony of Australian ink and ambition, akin to the “Puking-Infant” node. Born into a modest media lineage, he inherited chaos: a single newspaper, a fractious industry, and a hunger for more. The 5% signal—instinct, survival—propelled him, much like Orlando’s raw vigor in Arden. Nodes like “Nourish It” and “Know It” mark this primal stage, where the empire’s roots took hold amid disorder. The edges to Voir’s “Whining-Schoolboy, 15%” (weighted from 1/99 to 95/5) signal the first leap—an 80/20 shift—as he mastered patterns: acquisitions, influence, the Sun’s brash voice. Like Rosalind donning Ganymede’s guise, Murdoch began sculpting meaning from entropy, his signal rising amid the din.

Choisis, at 51/49, mirrors the ambiguity of Arden’s crossroads. “Lover Sighing Furnace” and “Prioritize-Lifestyle, 50%” nodes capture Murdoch’s midlife pivot—courting power through Fox News, News Corp, and a global stage. Here, instinct battled engagement: expand or consolidate? Trust Lachlan or pit him against James? The noise-to-signal teetered, echoing Jaques’s lover and soldier, as choices weighted paths (e.g., 80/20 from “Whining-Schoolboy” to “Lover Sighing Furnace”) toward Deviens. At 20/80, “Unstructured-Intense” and “Soldier-Professional” reflect the risk-laden empire peak—Fox’s rightward surge, tabloid scandals—where chaos sharpened into clarity. The bush was no bear; it was a platform, and Murdoch’s hubris swelled, believing he could wield the signal indefinitely.

M’élève, the 5/95 apex, is where “Project Harmony” takes root—a hubristic gambit to rewrite the trust, silencing James, Elisabeth, and Prudence to crown Lachlan. “NexToken Prediction,” in lightsalmon, ties back to “Nourish It,” a poetic arc from sustenance to foresight, yet it heralds frailty—Murdoch’s collapsing at breakfast with Rebekah Brooks, his 93-year-old frame buckling under stress. The “Unstructured-Intense” node (80/20 to “Retirement-Sarcopenia” or “Existential Cadence”) mirrors Arden’s revelry gone awry—siblings at war, a court rejecting his “top hat” trust in December 2024. “Justice-Adiposity, 5%” nods to Jaques’s justice fading into pantaloon, a moral weight undone by physical decline. The 1/99 path from “Refine-Training” to this end reveals the folly: no structure could outpace entropy’s pull.

“Project Harmony” thus emerges as Murdoch’s final compression—a bid to force order on a family and empire fracturing into noise. Early nodes like “Puking-Infant” locked into deterministic tracks, while “Unstructured-Intense” fueled variance, much like Arden’s pastoral anarchy. Yet hubris blinded him to inevitability: the 5% noise—the bear in the bush—persisted as his children’s defiance, his body’s frailty, and time’s indifference. In As You Like It, Rosalind’s wit bends chaos to harmony, but Murdoch’s signal falters—Lachlan’s reign contested, the trust unyielding. “Next token prediction” is no triumph; it is death’s quiet token, the empire’s signal fading to 95/5 chaos anew.

This arc—from 95/5 noise to 5/95 signal and back—lays bare Murdoch’s transvaluation: a titan who sculpted meaning from disorder, only to see hubris invite entropy’s revenge. The network’s weighted paths chart his rise and fall, each node a step through life’s tangled stage toward a frail, inevitable curtain. Like Jaques’s “sans everything,” “Project Harmony” sought mastery but found mortality—a reminder that even the loudest signal fades, and the universe shrugs.

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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': ['Puking-Infant, 5%', 'Grammar', 'Nourish It', 'Know It', "Move It", 'Injure It'],  # Static
        'Voir': ['Whinning-Schoolboy, 15%'],  
        'Choisis': ['Prioritize-Lifestyle, 50%', 'Lover Sighing Furnace'],  
        'Deviens': ['Unstructured-Intense', 'Soldier-Professional', 'Refine-Training, 25%'],  
        "M'èléve": ['NexToken Prediction', 'Second-Childhood', 'Retirement-Sarcopenia', 'Justice-Adiposity, 5%', 'Existential Cadence']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Whinning-Schoolboy, 15%'],  
        'paleturquoise': ['Injure It', 'Lover Sighing Furnace', 'Refine-Training, 25%', 'Existential Cadence'],  
        'lightgreen': ["Move It", 'Soldier-Professional', 'Second-Childhood', 'Justice-Adiposity, 5%', 'Retirement-Sarcopenia'],  
        'lightsalmon': ['Nourish It', 'Know It', 'Prioritize-Lifestyle, 50%', 'Unstructured-Intense', 'NexToken Prediction'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights (hardcoded for editing)
def define_edges():
    return {
        ('Puking-Infant, 5%', 'Whinning-Schoolboy, 15%'): '1/99',
        ('Grammar', 'Whinning-Schoolboy, 15%'): '5/95',
        ('Nourish It', 'Whinning-Schoolboy, 15%'): '20/80',
        ('Know It', 'Whinning-Schoolboy, 15%'): '51/49',
        ("Move It", 'Whinning-Schoolboy, 15%'): '80/20',
        ('Injure It', 'Whinning-Schoolboy, 15%'): '95/5',
        ('Whinning-Schoolboy, 15%', 'Prioritize-Lifestyle, 50%'): '20/80',
        ('Whinning-Schoolboy, 15%', 'Lover Sighing Furnace'): '80/20',
        ('Prioritize-Lifestyle, 50%', 'Unstructured-Intense'): '49/51',
        ('Prioritize-Lifestyle, 50%', 'Soldier-Professional'): '80/20',
        ('Prioritize-Lifestyle, 50%', 'Refine-Training, 25%'): '95/5',
        ('Lover Sighing Furnace', 'Unstructured-Intense'): '5/95',
        ('Lover Sighing Furnace', 'Soldier-Professional'): '20/80',
        ('Lover Sighing Furnace', 'Refine-Training, 25%'): '51/49',
        ('Unstructured-Intense', 'NexToken Prediction'): '80/20',
        ('Unstructured-Intense', 'Second-Childhood'): '85/15',
        ('Unstructured-Intense', 'Retirement-Sarcopenia'): '90/10',
        ('Unstructured-Intense', 'Justice-Adiposity, 5%'): '95/5',
        ('Unstructured-Intense', 'Existential Cadence'): '99/1',
        ('Soldier-Professional', 'NexToken Prediction'): '1/9',
        ('Soldier-Professional', 'Second-Childhood'): '1/8',
        ('Soldier-Professional', 'Retirement-Sarcopenia'): '1/7',
        ('Soldier-Professional', 'Justice-Adiposity, 5%'): '1/6',
        ('Soldier-Professional', 'Existential Cadence'): '1/5',
        ('Refine-Training, 25%', 'NexToken Prediction'): '1/99',
        ('Refine-Training, 25%', 'Second-Childhood'): '5/95',
        ('Refine-Training, 25%', 'Retirement-Sarcopenia'): '10/90',
        ('Refine-Training, 25%', 'Justice-Adiposity, 5%'): '15/85',
        ('Refine-Training, 25%', 'Existential Cadence'): '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 = []
    
    # Create mapping from original node names to numbered labels
    mapping = {}
    counter = 1
    for layer in layers.values():
        for node in layer:
            mapping[node] = f"{counter}. {node}"
            counter += 1
            
    # Add nodes with new numbered labels 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):
            new_node = mapping[node]
            G.add_node(new_node, layer=layer_name)
            pos[new_node] = position
            node_colors.append(colors.get(node, 'lightgray'))
    
    # Add edges with updated node labels
    for (source, target), weight in edges.items():
        if source in mapping and target in mapping:
            new_source = mapping[source]
            new_target = mapping[target]
            G.add_edge(new_source, new_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=9, connectionstyle="arc3,rad=0.2"
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
    plt.title("OPRAH™: NexToken Prediction", fontsize=25)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 40 Space is Apollonian and Time Dionysian. They are the static representation and the dynamic emergent. Ain’t that somethin?