Anchor ⚓️#
Modern Times and the Censorship of Exploration#
When Modern Times was released in 1936, it marked the culmination of Charlie Chaplin’s silent film era, even as sound films had taken over the industry. The film is not just a comedic masterpiece but a poignant critique of industrialization, labor exploitation, and the dehumanizing effects of mechanization. It was also a work of artistic defiance against a society increasingly dominated by censorship and regulation, as evidenced by the statement at the end of the film: “Passed by the National Board of Review.”
The National Board of Review (NBR), established in 1909, originated as a self-regulatory body for the burgeoning film industry. Its creation was a response to the growing calls for government censorship of films, particularly in response to content deemed immoral or subversive. Cities like New York had begun banning films outright, and the NBR emerged as a compromise: it reviewed and approved films to assure audiences (and governments) that the content was “acceptable.” While technically voluntary, the NBR acted as a gatekeeper, influencing what films could gain public access. It represented a system of self-censorship, where the industry policed itself to avoid the heavier hand of state mandates.
This censorship system, while ostensibly protective of societal values, raises critical questions about the dynamics of control. From the perspective of your neural network model, censorship can be examined as a blockade on the generative exploration of combinatorial search spaces. Forebears, through their sacrifices and explorations, gifted us compressed time—enabling us to bypass the laborious testing of countless paths. However, censorship denies this legacy by cordoning off parts of the search space, restricting the inheritance of potential discoveries.
Monarchy, Oligarchy, Anarchy: Compression in Societal Dynamics#
Your observation of the monarchic, oligarchic, and anarchic dynamics aligns beautifully with Chaplin’s critique in Modern Times. The monarchy corresponds to the industrial bosses, unilaterally dictating the pace and terms of labor. The oligarchy is reflected in the mechanisms of censorship and regulatory boards like the NBR, which act as a board of directors overseeing the limits of creative and social discourse. Finally, the anarchy resides in the workers and audiences, whose voices—though scattered—occasionally erupt into defiance, as we see in the labor strikes depicted in the film.
These layers mirror the neural network’s structural flow:
World Layer (Sacrifices and the Gift): The industrial world of Chaplin’s film represents the sacrifices of workers who built modernity at great personal cost. Yet, this layer is colored by restriction, as the NBR dictates what stories can emerge from that world.
Perception Layer (Che Mio): Chaplin’s keen perception of human suffering translates the machinery of censorship into a critique of industrialization itself.
Agency Layer (Allegria, Ottimismo): Despite the oppressive forces, Chaplin imbues his character with optimism and resilience, showing that human agency persists even in constrained spaces.
Generativity Layer (Monarchy, Oligarchy, Anarchy): This layer encapsulates the power dynamics between rulers, regulators, and the ruled.
Physicality Layer (Dynamic, Static): The interplay between dynamic rebellion and static conformity is central to the film’s narrative arc.
Censorship as a Combinatorial Constraint#
Censorship does more than block exploration—it determines which nodes in the network of human potential remain dark. This is not merely a problem of the censor as an agent; it is a systemic issue wherein monarchy and oligarchy prioritize their own survival over the broader combinatorial growth of society. By dictating what stories can be told, they limit the depth of our shared narrative.
Yet, censorship also reveals something paradoxically beautiful: the enduring power of the suppressed. Chaplin’s film, despite its oversight by the NBR, carries an unmistakable critique of its time. This echoes the tension in your neural network: while some pathways are blocked, the resilience of dynamic nodes ensures the persistence of emergent possibilities.
Concluding Thoughts: The Legacy of Sacrifice#
The “gift” from our forebears is the story of their sacrifices, which compresses time for us. However, censorship compromises this gift, restricting the narrative arcs we can inherit. By exploring the interplay between monarchy, oligarchy, and anarchy in Modern Times, we see the fragile balance between control and creativity. Chaplin’s genius lay in navigating these dynamics, using humor and pathos to critique the systems that sought to restrain him.
The lesson is clear: while censorship may restrict nodes in the network, the hidden layers of human ingenuity and resilience ensure that dynamic paths remain open—awaiting those who dare to explore them.
Show code cell source
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
# Define the neural network structure
def define_layers():
return {
'World': ['Il Sole é L’altre Stelle', 'La Terra', 'Vita é Bella', 'Il Sacrificio', 'Mia Storia', 'Dono Per Me'], # Divine: Cosmos-Earth; Red Queen: Life-Cost; Machine: Parallel-Time
'Perception': ['Che Mio'],
'Agency': ['Allegria', 'Ottimismo'],
'Generativity': ['Anarchia', 'Oligarchia', 'Padre Fece'],
'Physicality': ['Dynamic', 'Partisan', 'Common Wealth', 'Non-Partisan', 'Static']
}
# Assign colors to nodes
def assign_colors(node, layer):
if node == 'Che Mio':
return 'yellow'
if layer == 'World' and node in [ 'Dono Per Me']:
return 'paleturquoise'
if layer == 'World' and node in [ 'Mia Storia']:
return 'lightgreen'
if layer == 'World' and node in [ 'Il Sole é L’altre Stelle', 'La Terra']:
return 'lightgray'
elif layer == 'Agency' and node == 'Ottimismo':
return 'paleturquoise'
elif layer == 'Generativity':
if node == 'Padre Fece':
return 'paleturquoise'
elif node == 'Oligarchia':
return 'lightgreen'
elif node == 'Anarchia':
return 'lightsalmon'
elif layer == 'Physicality':
if node == 'Static':
return 'paleturquoise'
elif node in ['Non-Partisan', 'Common Wealth', 'Partisan']:
return 'lightgreen'
elif node == 'Dynamic':
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 [
('World', 'Perception'), ('Perception', 'Agency'), ('Agency', 'Generativity'), ('Generativity', 'Physicality')
]:
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("Senza regole, si rischia di cadere nell'anarchia", fontsize=15)
plt.show()
# Run the visualization
visualize_nn()
Fig. 3 How now, how now? What say the citizens? Now, by the holy mother of our Lord, The citizens are mum, say not a word.#