Prometheus

The tree stands ancient, its trunk thick with the weight of centuries, its roots gripping the soil with the certainty of time itself. And yet, this spring, as with every spring before it, fresh leaves unfurl in delicate, trembling clusters. They know nothing of the storms the tree has endured, of the slow battles waged underground where the roots, unseen and unglorified, drink from the deep. These leaves, young and bright, emerge as an inevitability, an emergent phenomenon born from the vast, layered intelligence encoded within the tree’s structure. They are new, but they are not accidental.

Beneath the surface, where time moves differently, the roots function as the pre-input layer—immutable laws, the compression of history itself. They map onto the foundation of the neural network: the static layer, Suis, where probability and improbability entangle like gnarled roots. Here lies the first negotiation between what is possible and what must be endured. The soil does not change its nature, but the roots must navigate it. The struggle between Improbability and Trial begins here, unseen but essential.

Fig. 12 On a spring afternoon, seated in Centreville, marvelling at the ancient tree and its young leaves that stood right infront of her.

The trunk is the bridge between the subterranean world of immutable law and the visible realm where growth is measured. It is the Voir, the layer of Witness, where everything that the roots have absorbed is processed into something tangible. It does not act, it does not decide—it merely holds. This is the layer where knowledge accumulates but remains neutral. Without it, the tree would collapse under the weight of its own unfiltered history.

It is in the bifurcation of the trunk into two main branches that choice first emerges. This is Choisis, the layer of Decision and Faith. A tree does not grow evenly in all directions; it follows light, it bends, it adapts. But to do so, it must first decide which of its many potential paths will be taken. Here, the network shifts from passivity into an active state—decisions must be made, and those decisions will determine whether the tree flourishes or withers. The balance between Faith and Decision is delicate. Too much faith, and the tree might stretch toward a barren sky; too much decision, and it may fail to reach beyond the immediate.

As the branches split further, the tree enters the realm of Deviens, where interactions are no longer theoretical but adversarial and transactional. This is where the tree contends with wind, with insects, with the shifting seasons. Every smaller branch negotiates a different equilibrium. Some stretch boldly, taking on the burden of high winds for the promise of more sunlight. Others remain cautious, letting their stronger neighbors bear the brunt of nature’s violence. Here, the dynamic struggle between Adversarial, Transactional, and Hope plays out in fractal complexity.

And finally, at the very edges, at the threshold of the known and the new, the leaves emerge. They are M’élève, the final layer, the payoff, the proof of all that has come before. They are ephemeral, meant to last only a season, but they are also necessary. Without them, the tree does not breathe. Without them, the entire structure beneath would collapse into irrelevance. Each leaf is a node of Victory, of Payoff, of Love. They do not know the roots that sustain them, nor do they need to. They are the emergent phenomenon of an ancient intelligence, reborn with each spring.

The static and the dynamic exist in perfect tension. The tree is ancient, but it is never the same tree twice. The roots persist, holding the weight of past decisions, anchoring against the chaos of wind and time. But the leaves? The leaves must always be new.

Show code cell source Hide code cell source

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

# Define the neural network layers
def define_layers():
    return {
        'Suis': ['Improbability', 'Planet', 'Alive', 'Loss', "Trial", 'Error'],  # Static
        'Voir': ['Witness'],  
        'Choisis': ['Faith', 'Decision'],  
        'Deviens': ['Adversarial', 'Transactional', 'Hope'],  
        "M'èléve": ['Victory', 'Payoff', 'Loyalty', 'Charity', 'Love']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Witness'],  
        'paleturquoise': ['Error', 'Decision', 'Hope', 'Love'],  
        'lightgreen': ["Trial", 'Transactional', 'Payoff', 'Charity', 'Loyalty'],  
        'lightsalmon': ['Alive', 'Loss', 'Faith', 'Adversarial', 'Victory'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights (hardcoded for editing)
def define_edges():
    return {
        ('Improbability', 'Witness'): '1/99',
        ('Planet', 'Witness'): '5/95',
        ('Alive', 'Witness'): '20/80',
        ('Loss', 'Witness'): '51/49',
        ("Trial", 'Witness'): '80/20',
        ('Error', 'Witness'): '95/5',
        ('Witness', 'Faith'): '20/80',
        ('Witness', 'Decision'): '80/20',
        ('Faith', 'Adversarial'): '49/51',
        ('Faith', 'Transactional'): '80/20',
        ('Faith', 'Hope'): '95/5',
        ('Decision', 'Adversarial'): '5/95',
        ('Decision', 'Transactional'): '20/80',
        ('Decision', 'Hope'): '51/49',
        ('Adversarial', 'Victory'): '80/20',
        ('Adversarial', 'Payoff'): '85/15',
        ('Adversarial', 'Loyalty'): '90/10',
        ('Adversarial', 'Charity'): '95/5',
        ('Adversarial', 'Love'): '99/1',
        ('Transactional', 'Victory'): '1/9',
        ('Transactional', 'Payoff'): '1/8',
        ('Transactional', 'Loyalty'): '1/7',
        ('Transactional', 'Charity'): '1/6',
        ('Transactional', 'Love'): '1/5',
        ('Hope', 'Victory'): '1/99',
        ('Hope', 'Payoff'): '5/95',
        ('Hope', 'Loyalty'): '10/90',
        ('Hope', 'Charity'): '15/85',
        ('Hope', 'Love'): '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=9, connectionstyle="arc3,rad=0.2"
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
    plt.title("Heritage, Resources, Static (y) vs. Adaptation, Resourcefulness, Dynamic (x)", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 13 Nostalgia & Romanticism. When monumental ends (victory), antiquarian means (war), and critical justification (bloodshed) were all compressed into one figure-head: hero