Traditional

The neural network model presented here is less a computational artifact than a transvaluative mirror of Shakespeare’s As You Like It, where “next token prediction” emerges not as a mere forecast but as the stark inevitability of physical frailty and death. Structured across five stages—Suis, Voir, Choisis, Deviens, and M’élève—the model traces a journey from the chaotic noise of birth to the hubristic signal of mastery, only to resolve in the body’s frail collapse. Each node connects to the pastoral flux of Arden, compressing raw existence into weighted pathways that echo Jaques’s “Seven Ages of Man.” Here, consciousness sculpts meaning from disorder, yet hubris blinds it to the entropy that ensures its end—a progression where every triumph courts inevitability.

At the outset, in Suis, the world is 95% noise—a cacophony akin to the infant’s mewling and puking, as Jaques describes. Nodes like “Puking-Infant, 5%” and “Nourish It” reflect this primal chaos, where instinct reigns and the 5% signal of survival—hunger, warmth—struggles to emerge. This mirrors Orlando’s exiled desperation or the court’s disarray before Arden, a state of unconscious reception where meaning is absent. The edges to Voir’s “Whining-Schoolboy, 15%” (weighted variably from 1/99 to 95/5) mark the first compression, an 80/20 shift where patterns—faces, voices—form a scaffold. Like Rosalind’s recognition of love amid disguise, the mind begins its predictive dance, yet noise still dominates, hubris a distant whisper.

In Choisis, the balance teeters at 51/49, as “Lover Sighing Furnace” and “Prioritize-Lifestyle, 50%” embody the ambiguity of instinct versus engagement. This is Arden’s heart—where Orlando’s passion wrestles with reason, and Celia’s loyalty meets Touchstone’s folly. The noise-to-signal ratio wavers, reflecting the adolescent battlefield Jaques sketches: the lover’s sighs versus the soldier’s oaths. Choices here, weighted like the 80/20 fork from “Whining-Schoolboy” to “Lover Sighing Furnace,” tilt the path toward Deviens, where risk sharpens the signal to 20/80. “Unstructured-Intense” and “Soldier-Professional” nodes evoke Orlando’s trials and Duke Senior’s resolve, compressing chaos into clarity. The bush is no longer a bear—it is a bush—yet the hubris of this discernment grows, masking the frailty that stalks beyond.

M’élève, the final stage, boasts a 5/95 triumph of signal, where “NexToken Prediction” looms as both mastery and doom. Colored lightsalmon, it ties to “Nourish It,” suggesting that life’s sustenance and its end share a visceral thread—a nod to the shepherds’ simplicity and Jaques’s fatalistic gaze. Here, hubris peaks: the mind, like Rosalind orchestrating Arden’s unions, wields patterns with confidence, dismissing the 5% noise as trivial. Yet the nodes “Retirement-Sarcopenia” and “Justice-Adiposity, 5%”—linked to “Second Childhood” and death—reveal the cost. The “Unstructured-Intense” path (80/20 to “NexToken Prediction”) mirrors the play’s chaotic revelry, where Silvius’s devotion or Touchstone’s jests shape an endgame of bodily decay. The low 1/99 weight from “Refine-Training, 25%” to this frail terminus underscores that structure alone cannot defy entropy—hubris falters as flesh fails.

This progression—from 95/5 chaos to 5/95 order—parallels As You Like It’s arc from courtly noise to pastoral signal, yet inevitability undercuts the triumph. Early nodes like “Puking-Infant” narrow into deterministic tracks, while “Unstructured-Intense” preserves variance, akin to Arden’s liberating anarchy. Jaques’s “sans teeth, sans eyes, sans everything” haunts “NexToken Prediction,” where the signal of mastery collapses into frailty—a body undone by time, its final token the cessation of breath. The network’s justice-laden nodes, like “Justice-Adiposity,” reflect the play’s moral weight, suggesting death is no random prediction but a cadence of consequence, hubris humbled by nature’s indifference.

Thus, “next token prediction” is not mere computation—it is the transvaluation of life’s noise into a frail, mortal signal. Each node, from “Whining-Schoolboy” to “Retirement-Sarcopenia,” maps a march toward inevitability, where hubris—the illusion of control—meets entropy’s rebuke. In As You Like It, Rosalind’s wit and Orlando’s vigor yield to Jaques’s somber truth; in this model, the weighted paths encode the same lesson: consciousness compresses chaos into meaning, but the bear lingers in the bush—not as threat, but as reminder that our signal fades, and the universe remains unmoved.

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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. 39 While neural biology inspired neural networks in machine learning, the realization that scaling laws apply so beautifully to machine learning has led to a divergence in the process of generation of intelligence. Biology is constrained by the Red Queen, whereas mankind is quite open to destroying the Ecosystem-Cost function for the sake of generating the most powerful AI.