Resilience 🗡️❤️💰

Vision, in its primordial act, is a framing of the infinite. The poet’s eye, in a fine frenzy rolling, glances from heaven to earth and back again, registering forms not as they are but as they might be. In this glance lies a paradox: sight, though bound by the finite edges of the world, carries within it an excess, a remainder beyond what can be named. What the poet sees is never merely what is before them; it is the residue of probability, the spectral remains of what could have been, the shifting sum of all that was. Sight compresses this manifold into singularity, pressing the emergent chaos of possibility into the fixed shape of meaning.

Fig. 8 Let it be known that vision is Apollonian whereas hearing is Dionysian. The poets process is a sequence from one to the other, giving birth to tragedy out of the spirit of music!

Yet vision alone is not enough. To see is to begin the process, but to name is to complete it. Imagination, that alchemical force, bodies forth forms unknown and renders them intelligible. What begins as spectral—unmoored from certainty, flickering at the edge of consciousness—finds stability in language. Here, the great compression of human experience takes place: a vast field of stochastic potentials, each moment of improbability branching like a fractal, collapses into the crisp certainty of words. Probability is a game of shifting sums, but language asserts: here is this thing, not another. Naming is an act of dominion over the improbable.

In this sense, language is the most enduring wager against uncertainty. Every spoken word is a vestige of error survived; every linguistic form, a calibrated response to the stochastic landscape of history. To articulate is to impose order upon the unknowable, to give airy nothing a local habitation and a name. Yet, in this naming, something is also lost. The eye, in its frenzy, captures what language cannot: the raw simultaneity of being. Words segment, define, and crystallize. They establish limits where once there was fluidity, erect borders around the indeterminate. The poet’s pen transforms vision into language, but in so doing, it arrests the endless drift of meaning, making the mutable immutable, the formless fixed.

Fig. 9 Tragedy? Out of the spirit of music? Let’s take it back to first principles!

But the transition from vision to language is not merely a loss; it is also an evolution. The act of seeing is Apollonian: it carves lines, asserts clarity, transforms the emergent into the understood. It halts time, rendering the fleeting eternal. The act of hearing, however, is Dionysian: it immerses, enfolds, and dissolves the boundary between self and sound. Language, though birthed from the Apollonian impulse to fix meaning, retains a trace of its Dionysian origins. For language is not merely visual; it is also aural. To speak is to summon the past into the present, to make the absent momentarily real. Speech, unlike inscription, cannot remain inert. It must be performed, sounded, reanimated.

Thus, while vision seizes, language bridges. It carries forward the weight of sight but animates it through sound. It transforms vision’s static certainty into something that moves, breathes, and lives within time. Probability begins with uncertainty, but language does not erase that uncertainty—it encodes it. Every phrase is a relic of past permutations, every utterance a recalibration of what came before. To hear, to speak, to write is to enter into the great stochastic experiment of existence, to stand at the threshold between what is known and what remains beyond the reach of sight. The poet’s eye sees the unknown; the poet’s tongue dares to name it. But the greatest truths may always lie in the unspoken, in the silence between words, where the vast probability of all that might have been lingers, waiting for another glance, another hearing, another name.

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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': ['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. 10 Resources, Needs, Costs, Means, Ends. This is an updated version of the script with annotations tying the neural network layers, colors, and nodes to specific moments in Vita è Bella, enhancing the connection to the film’s narrative and themes: