Revolution

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• What makes for a suitable problem for AI (Demis Hassabis, Nobel Lecture)?
  • Space: Massive combinatorial search space
  • Function: Clear objective function (metric) to optimize against
  • Time: Either lots of data and/or an accurate and efficient simulator
• Guess what else fits the bill (Yours truly, amateur philosopher)?
  • Space
    1. Intestines/villi
    2. Lungs/bronchioles
    3. Capillary trees
    4. Network of lymphatics
    5. Dendrites in neurons
    6. Tree branches
  • Function
    1. Energy
    2. Aerobic respiration
    3. Delivery to "last mile" (minimize distance)
    4. Response time (minimize)
    5. Information
    6. Exposure to sunlight for photosynthesis
  • Time
    1. Nourishment
    2. Gaseous exchange
    3. Oxygen & Nutrients (Carbon dioxide & "Waste")
    4. Surveillance for antigens
    5. Coherence of functions
    6. Water and nutrients from soil

-- Nobel Prize in Chemistry, 2024

The process of relabeling the nodes in a neural network, such as the one provided, evokes a sense of transformation akin to reinterpreting the timeless works of William Shakespeare. Just as the original nodes—representing biological components like DNA, RNA, CD8+, and TNF-α—carry specific roles within an immunological framework, Shakespeare’s 38 plays each embody distinct narratives, emotions, and conflicts that can be mapped onto this structure. The task of selecting 17 of these plays to correspond with the 17 nodes requires a careful consideration of thematic resonance, much like how the formula w = 1/(1 + X/Y) balances noise and signal odds to determine edge weights. In this metaphor, X/Y could represent the chaos of human experience versus the clarity of purpose in each play, guiding the connections between layers—Suis, Voir, Choisis, Deviens, and M’èléve—and their newly assigned dramatic counterparts.

Fig. 24 The midcingulo-insular system stands as the oldest, the primal scaffold upon which all later networks were draped. Before there was fine motor control, before there was deliberative planning or executive function, there was the insula—an ancient sentinel of interoception, regulating the body’s hidden symphony of autonomic rhythms. It is here, in the depths of the salience network, that vertebrates first learned to detect the signals of survival: pain, hunger, temperature, and the visceral stirrings of self-preservation. The midcingulate cortex, though more evolved than its limbic predecessors, still bears the signature of this foundational system—tasked with evaluating effort, action, and adversity. This is the neural network that bridges raw sensation with decision, the fulcrum of attention-switching, ensuring that the organism responds to what matters in the moment. The other networks arrived later, layered refinements upon this ancient core. The pericentral system, with its precise somatomotor control, is an invention of mammals, honed in primates to craft tools and wield symbols. The dorsal stream, a navigator of goals, emerged with complex movement and spatial reasoning. The lateral system, seat of abstraction and executive function, belongs to the neocortical expansion of higher mammals, where foresight and flexibility reign. And the medial network, weaving self-regulation into the fabric of cognition, belongs to the default mode, where identity and reflection consolidate. But beneath them all, the midcingulo-insular remains—the oldest sentinel of salience, the network that does not think but knows, the one that ensures existence before action, before reason, before anything else.

Beginning with the Suis layer, which includes nodes like “DNA, RNA, 5%” and “Lipopolysaccharide,” the foundational elements of life and immunity suggest plays rooted in origins and primal struggles. “Macbeth,” with its dark ambition and inevitable downfall, aligns with “DNA, RNA, 5%,” symbolizing the genetic seeds of fate. “Othello,” a tale of jealousy and betrayal, fits “Peptidoglycans, Lipoteichoics,” reflecting the structural tensions of identity. “King Lear,” with its storm of familial discord, mirrors “Lipopolysaccharide,” a trigger of inflammation and chaos. “Richard III,” driven by cunning and deformity, suits “N-Formylmethionine,” a starting point of bacterial recognition. “The Tempest,” a play of natural forces and reconciliation, captures “Glucans, Chitin,” while “Romeo and Juliet,” with its specific tragic love, embodies “Specific Antigens,” the unique markers of recognition.

Moving to the Voir layer, “PRR & ILCs, 20%” stands alone as the pattern-recognizing sentinel, akin to “A Midsummer Night’s Dream,” where perception and illusion intertwine to reveal deeper truths. In the Choisis layer, “CD8+, 50%” and “CD4+” represent immune warriors, paralleled by “Henry V,” a king leading with decisive valor, and “Julius Caesar,” where loyalty and betrayal shape leadership’s course. The Deviens layer, with its effector and regulatory nodes, finds expression in “Hamlet” for “TNF-α, IL-6, IFN-γ,” a play of intense action and inner turmoil; “Measure for Measure” for “PD-1 & CTLA-4,” balancing justice and restraint; and “The Winter’s Tale” for “Tregs, IL-10, TGF-β, 20%,” a story of redemption and healing.

Finally, the M’èléve layer elevates the network to systemic harmony. “Antony and Cleopatra” reflects the “Complement System,” a grand interplay of passion and destruction. “Coriolanus,” with its martial pride, aligns with the “Platelet System,” clotting the wounds of war. “Titus Andronicus,” visceral and relentless, suits the “Granulocyte System.” “As You Like It,” with its pastoral unity, captures “Innate Lymphoid Cells, 5%,” while “Twelfth Night,” a comedy of mistaken identities, mirrors “Adaptive Lymphoid Cells,” adapting to the chaos of recognition. This relabeling transforms the network into a dramatic tapestry, where Shakespeare’s plays illuminate the interplay of noise and signal, chaos and order.

To reflect this vision, the updated code redefines the layers with these 17 plays, preserving the structure while infusing it with literary depth. The edge weights, calculated via w = 1/(1 + X/Y), remain intact, symbolizing the tension between each play’s turmoil and resolution. Below is the revised code, integrating this Shakespearean reinterpretation into the neural network’s framework.

This reimagined network, now a stage for Shakespeare’s dramas, retains its mathematical integrity while inviting a new narrative lens, where the interplay of plays echoes the original biological symphony.

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

# Define the neural network layers with Shakespeare's plays
def define_layers():
    return {
        'Suis': ['Macbeth', 'Othello', 'King Lear', 'Richard III', 'The Tempest', 'Romeo and Juliet'],
        'Voir': ['A Midsummer Night’s Dream'],  
        'Choisis': ['Henry V', 'Julius Caesar'],  
        'Deviens': ['Hamlet', 'Measure for Measure', 'The Winter’s Tale'],  
        "M'èléve": ['Antony and Cleopatra', 'Coriolanus', 'Titus Andronicus', 'As You Like It', 'Twelfth Night']  
    }

# Assign colors to nodes (retained from original for consistency)
def assign_colors():
    color_map = {
        'yellow': ['A Midsummer Night’s Dream'],  
        'paleturquoise': ['Romeo and Juliet', 'Julius Caesar', 'The Winter’s Tale', 'Twelfth Night'],  
        'lightgreen': ['The Tempest', 'Measure for Measure', 'Coriolanus', 'As You Like It', 'Titus Andronicus'],  
        'lightsalmon': ['King Lear', 'Richard III', 'Henry V', 'Hamlet', 'Antony and Cleopatra'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights (unchanged from original)
def define_edges():
    return {
        ('Macbeth', 'A Midsummer Night’s Dream'): '1/99',
        ('Othello', 'A Midsummer Night’s Dream'): '5/95',
        ('King Lear', 'A Midsummer Night’s Dream'): '20/80',
        ('Richard III', 'A Midsummer Night’s Dream'): '51/49',
        ('The Tempest', 'A Midsummer Night’s Dream'): '80/20',
        ('Romeo and Juliet', 'A Midsummer Night’s Dream'): '95/5',
        ('A Midsummer Night’s Dream', 'Henry V'): '20/80',
        ('A Midsummer Night’s Dream', 'Julius Caesar'): '80/20',
        ('Henry V', 'Hamlet'): '49/51',
        ('Henry V', 'Measure for Measure'): '80/20',
        ('Henry V', 'The Winter’s Tale'): '95/5',
        ('Julius Caesar', 'Hamlet'): '5/95',
        ('Julius Caesar', 'Measure for Measure'): '20/80',
        ('Julius Caesar', 'The Winter’s Tale'): '51/49',
        ('Hamlet', 'Antony and Cleopatra'): '80/20',
        ('Hamlet', 'Coriolanus'): '85/15',
        ('Hamlet', 'Titus Andronicus'): '90/10',
        ('Hamlet', 'As You Like It'): '95/5',
        ('Hamlet', 'Twelfth Night'): '99/1',
        ('Measure for Measure', 'Antony and Cleopatra'): '1/9',
        ('Measure for Measure', 'Coriolanus'): '1/8',
        ('Measure for Measure', 'Titus Andronicus'): '1/7',
        ('Measure for Measure', 'As You Like It'): '1/6',
        ('Measure for Measure', 'Twelfth Night'): '1/5',
        ('The Winter’s Tale', 'Antony and Cleopatra'): '1/99',
        ('The Winter’s Tale', 'Coriolanus'): '5/95',
        ('The Winter’s Tale', 'Titus Andronicus'): '10/90',
        ('The Winter’s Tale', 'As You Like It'): '15/85',
        ('The Winter’s Tale', 'Twelfth Night'): '20/80'
    }

# Define edges to be highlighted in black (unchanged logic)
def define_black_edges():
    return {
        ('Macbeth', 'A Midsummer Night’s Dream'): '1/99',
        ('Othello', 'A Midsummer Night’s Dream'): '5/95',
    }

# Calculate node positions (unchanged)
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 (unchanged logic)
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    edges = define_edges()
    black_edges = define_black_edges()
    
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    
    mapping = {}
    counter = 1
    for layer in layers.values():
        for node in layer:
            mapping[node] = f"{counter}. {node}"
            counter += 1
            
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            new_node = mapping[node]
            G.add_node(new_node, layer=layer_name)
            pos[new_node] = position
            node_colors.append(colors.get(node, 'lightgray'))
    
    edge_colors = []
    for (source, target), weight in edges.items():
        if source in mapping and target in mapping:
            new_source = mapping[source]
            new_target = mapping[target]
            G.add_edge(new_source, new_target, weight=weight)
            edge_colors.append('black' if (source, target) in black_edges else 'lightgrey')
    
    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=edge_colors,
        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("OPRAH™: Pericentral - Shakespeare Edition. Grok-3", fontsize=18)
    plt.show()

visualize_nn()

Fig. 25 The Human Connectome. Beyond the cellular intelligence of genome, exposome, transcriptome, proteome, metabolome. We grapple with servers, client, agent, decentralization, mesh. Metaphors from the nervous system, immune system, artificial intelligence, and C-suit principal-agent affairs find convergence in this space. Herein we interrogate the current landscape to identify five macro-scale brain network naming schemes and conventions utilized in the literature, ignoring inconsistencies while pointing out convergence across disparate human endeavors to delineate the noise/signal ratio as guide, avoiding confusion as a matter of design.