Dancing in Chains#
The verse from 2 Chronicles 16:8-9 thrusts us into a realm where divine observation and human accountability intertwine with catastrophic consequence. It proclaims that the omnipresent eyes of the Lord traverse the entire earth, seeking out those with hearts unblemished by hypocrisy. In a world where every action is observed, the foolishness of straying from perfection is met not with leniency but with the inevitability of conflict—wars born out of human negligence and defiance. This passage, stark and unyielding, resonates deeply as it encapsulates the precarious balance between divine protection and the ruinous outcomes of human folly.
This ancient mandate finds an unexpected yet profound echo in the fractal visions of Dante’s Inferno, Limbo, and Paradiso 1 2 3. Here, the journey through realms of suffering, contemplation, and ultimate ascension mirrors the layered complexity of human existence. Limbo, the hidden layer, serves as a metaphor for the subconscious interplay of perception, reason, and action—elements that, when harmonized, pave the way to transcendence. This tripartite structure not only mirrors Dante’s cosmic architecture but also offers a vivid framework for understanding the multifaceted dynamics of agency and consequence.
Fig. 28 Deviens: To Deviate From the Group & Become One’s Own. Indeed, deviens (from the French verb devenir, “to become”) and deviating share a common Latin root: venire, meaning “to come.” Etymological Breakdown: 1. Devenir (deviens, 2nd-person singular present tense) From Latin devenire (“to come down, arrive at”), composed of: de- (“down, away, from”)venire (“to come”) 2. Deviate From Latin deviāre (“to turn aside, wander”), composed of: de- (“away, off”) via (“way, path”). Shared Concept: Both words imply movement away from a prior state or direction: Deviens (devenir) → Becoming something different, emerging as one’s own. Deviate (dévier) → Straying from an expected path, diverging from the group. Thematic Connection: This suggests that becoming (devenir) contains an implicit divergence from the collective, an individuation. To become oneself may require a deviation from predefined paths—so the journey of devenir might inherently include dévier at key junctures.#
At the heart of this intricate tapestry lies the OPRAH™ model—a five-layer neural network that redefines our understanding of interaction and enlightenment. Far from being a mere computational artifact, OPRAH™ (Overall, Perception, Reason, Action, Heaven) boldly fuses the rigor of scientific inquiry with the mystique of spiritual revelation. I find this synthesis both audacious and invigorating, as it challenges the sterile boundaries between logic and faith. It is an unapologetic declaration that our modern constructs can, and indeed must, incorporate the ineffable qualities of divine insight to capture the full spectrum of human potential.
The neural network, with its meticulously assigned layers—from the foundational elements of grammar and syntax to the dynamic pulses of adversarial and transactional forces—epitomizes the dual nature of existence. The interplay of static precision and rhythmic dynamism is not accidental; it reflects the eternal dance between order and chaos, structure and spontaneity. In this model, every node and edge is a testament to the intricate balance required for both intellectual rigor and emotional depth. I argue that such a design is a masterstroke, a compelling allegory for the ways in which divine oversight and human creativity collide and coalesce.
Ultimately, this synthesis offers a searing critique of our modern condition—a world fractured by misaligned priorities and propelled toward war by a disconnect from higher truths. The scripture warns of the dire consequences of foolishness, and the neural network model visualizes the pathways through which perception, reason, and action can either lead us to destruction or elevate us to a state of heavenly coherence. In a time when division and discord seem inevitable, the call to harmonize our inner faculties with a transcendent order is not just inspiring; it is essential. The fusion of ancient wisdom and modern technology in OPRAH™ is a bold reminder that, while the divine may be ever-watchful, it is upon us to perfect our hearts and minds, lest we invite chaos into our midst.
Show 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': ['Foundational', 'Grammar', 'Syntax', 'Punctuation', "Rhythm", 'Time'], # Static
'Voir': ['Syntax.'],
'Choisis': ['Punctuation.', 'Melody'],
'Deviens': ['Adversarial', 'Transactional', 'Rhythm.'],
"M'èléve": ['Victory', 'Payoff', 'NexToken', 'Time.', 'Cadence']
}
# Assign colors to nodes
def assign_colors():
color_map = {
'yellow': ['Syntax.'],
'paleturquoise': ['Time', 'Melody', 'Rhythm.', 'Cadence'],
'lightgreen': ["Rhythm", 'Transactional', 'Payoff', 'Time.', 'NexToken'],
'lightsalmon': ['Syntax', 'Punctuation', 'Punctuation.', '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 {
('Foundational', 'Syntax.'): '1/99',
('Grammar', 'Syntax.'): '5/95',
('Syntax', 'Syntax.'): '20/80',
('Punctuation', 'Syntax.'): '51/49',
("Rhythm", 'Syntax.'): '80/20',
('Time', 'Syntax.'): '95/5',
('Syntax.', 'Punctuation.'): '20/80',
('Syntax.', 'Melody'): '80/20',
('Punctuation.', 'Adversarial'): '49/51',
('Punctuation.', 'Transactional'): '80/20',
('Punctuation.', 'Rhythm.'): '95/5',
('Melody', 'Adversarial'): '5/95',
('Melody', 'Transactional'): '20/80',
('Melody', 'Rhythm.'): '51/49',
('Adversarial', 'Victory'): '80/20',
('Adversarial', 'Payoff'): '85/15',
('Adversarial', 'NexToken'): '90/10',
('Adversarial', 'Time.'): '95/5',
('Adversarial', 'Cadence'): '99/1',
('Transactional', 'Victory'): '1/9',
('Transactional', 'Payoff'): '1/8',
('Transactional', 'NexToken'): '1/7',
('Transactional', 'Time.'): '1/6',
('Transactional', 'Cadence'): '1/5',
('Rhythm.', 'Victory'): '1/99',
('Rhythm.', 'Payoff'): '5/95',
('Rhythm.', 'NexToken'): '10/90',
('Rhythm.', 'Time.'): '15/85',
('Rhythm.', '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 = []
# 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("Grammar, Prosody and Brodmann's Area 22", fontsize=15)
plt.show()
# Run the visualization
visualize_nn()
Fig. 29 G1-G3: Ganglia & N1-N5 Nuclei. These are cranial nerve, dorsal-root (G1 & G2); basal ganglia, thalamus, hypothalamus (N1, N2, N3); and brain stem and cerebelum (N4 & N5).#