Engineering

Sympathetic Engineering

The critical reception of The Passion of the Christ reflects an enduring tension between emotional resonance and theological or narrative interpretation. Critics, whose scores reflect a split reaction, seem to grapple with the film’s brutal intensity, which prioritizes physical suffering (Physics: Sacrifice) over metaphysical exploration (Instinct) or moral resolution (Decisions: Good vs. Bad). Audiences, in contrast, overwhelmingly praised the film, suggesting its visceral and symbolic layers resonate on a deeply personal, instinctual level.

In this context, the neural network model visualized above offers a framework to analyze how The Passion operates across multiple layers of human experience:

1. Physics Layer (Cosmos, Earth, Life, Sacrifice, Means, Ends):

   • The film’s portrayal of Christ’s suffering aligns strongly with “Sacrifice” and “Means,” driving an emotional response that bypasses traditional narrative arcs to focus on physicality and immediacy.

2. Metaphysics Layer (Instinct):

   • Critics’ mixed reviews may stem from a disconnect between the raw instinctual response evoked by the film and its lack of broader metaphysical exploration. The instinctual connection is underscored by the visual and auditory brutality, which appeals to the “yellow” node in our neural framework.

3. Decisions Layer (Bad, Good):

   • The film’s binary moral framing resonates more with audiences who intuitively align with the “Good” node (Paleturquoise). However, critics may feel the film doesn’t sufficiently interrogate the complexities of “Bad” or the nuances of morality.

4. Games Layer (David, Old & New Morality, Solomon):

   • The absence of Solomon’s wisdom (Paleturquoise) and a heavy reliance on Old Testament morality (Lightgreen) may contribute to a lack of narrative depth, leaving critics dissatisfied while reinforcing the film’s emotional impact for viewers steeped in traditional narratives.

5. Outcomes Layer (Levant, Imposthume, Priestly, Wisdom, Temple):

   • The outcomes hinge on the “Temple” (Paleturquoise) as a symbolic structure that many audiences find uplifting. Critics, however, may feel the focus on physicality (Sacrifice) detracts from the promise of Wisdom and Priestly reconciliation.

The audience’s “A+” CinemaScore reflects how The Passion successfully activates primal, embodied reactions tied to the “Instinct” node while offering a resolution in the “Temple” node. Critics, however, operate from a higher-level processing framework, assessing the film’s failure to bridge the combinatorial space between “Means” and “Ends” (highlighted in the visualized network as the turquoise pathway).

In sum, The Passion of the Christ functions as a cinematic “Means” node emphasizing physical sacrifice and instinctual responses but struggles to reconcile with broader metaphysical or moral questions. The framework helps illuminate why the film polarizes critics and captivates general audiences, offering insight into its enduring cultural and spiritual impact.

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

# Define the neural network structure; modified to align with "Aprés Moi, Le Déluge" (i.e. Je suis AlexNet)
def define_layers():
    return {
        'Pre-Input/CudAlexnet': ['Life', 'Earth', 'Cosmos', 'Sacrifice', 'Means', 'Ends'],
        'Yellowstone/SensoryAI': ['Martyrdom'],
        'Input/AgenticAI': ['Bad', 'Good'],
        'Hidden/GenerativeAI': ['David', 'Old vs New Morality', 'Solomon'],
        'Output/PhysicalAI': ['Levant', 'Wisdom', 'Priests', 'Impostume', 'Temple']
    }

# Assign colors to nodes
def assign_colors(node, layer):
    if node == 'Martyrdom':
        return 'yellow'
    if layer == 'Pre-Input/CudAlexnet' and node in [ 'Ends']:
        return 'paleturquoise'
    if layer == 'Pre-Input/CudAlexnet' and node in [ 'Means']:
        return 'lightgreen'
    elif layer == 'Input/AgenticAI' and node == 'Good':
        return 'paleturquoise'
    elif layer == 'Hidden/GenerativeAI':
        if node == 'Solomon':
            return 'paleturquoise'
        elif node == 'Old vs New Morality':
            return 'lightgreen'
        elif node == 'David':
            return 'lightsalmon'
    elif layer == 'Output/PhysicalAI':
        if node == 'Temple':
            return 'paleturquoise'
        elif node in ['Impostume', 'Priests', 'Wisdom']:
            return 'lightgreen'
        elif node == 'Levant':
            return 'lightsalmon'
    return 'lightsalmon'  # Default color

# Calculate positions for nodes
def calculate_positions(layer, center_x, offset):
    layer_size = len(layer)
    start_y = -(layer_size - 1) / 2  # Center the layer vertically
    return [(center_x + offset, start_y + i) for i in range(layer_size)]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    center_x = 0  # Align nodes horizontally

    # Add nodes and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        y_positions = calculate_positions(nodes, center_x, offset=-len(layers) + i + 1)
        for node, position in zip(nodes, y_positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(assign_colors(node, layer_name))

    # Add edges (without weights)
    for layer_pair in [
        ('Pre-Input/CudAlexnet', 'Yellowstone/SensoryAI'), ('Yellowstone/SensoryAI', 'Input/AgenticAI'), ('Input/AgenticAI', 'Hidden/GenerativeAI'), ('Hidden/GenerativeAI', 'Output/PhysicalAI')
    ]:
        source_layer, target_layer = layer_pair
        for source in layers[source_layer]:
            for target in layers[target_layer]:
                G.add_edge(source, target)

    # Draw the graph
    plt.figure(figsize=(12, 8))
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=10, connectionstyle="arc3,rad=0.1"
    )
    plt.title("Old vs. New Morality", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 10 Agent AI Nvidia Vision & Test-Time Scaling. Here’s a scheme: user or machine (yellow node) -> prompt/response -> agent AI -> [plan/monumental, tool use/antiquarian, critique/critical] -> [Calculator, Web Search, Semantic Search, SQL Search, Generate Podcast]