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

Shakespeare’s Human-Centric Vision: The Tempest as a Political History of Statecraft

Shakespeare’s body of work is, without question, fundamentally human-centric. While his plays explore themes that touch on the cosmic, the tragic, and the metaphysical, they always return to the actions, choices, and consequences of human beings within their own constructed worlds. His histories, tragedies, and comedies alike function as deeply political texts, engaging with the struggles of governance, succession, law, and power. If one were to properly situate The Tempest within Shakespeare’s overarching concerns, it would be far more fitting to place it within the realm of history and statecraft rather than a purely philosophical or fantastical category. The play is not simply a meditation on magic and exile—it is a meditation on rule, justice, and the transition of power.

..evoking parallels with neural networks, tree branches, and Visualization of dendritic structures, evoking parallels with neural networks, tree branches, and respiratory bronchioles.

The central figure of The Tempest, Prospero, is, first and foremost, a ruler—a deposed Duke of Milan who must reclaim a form of sovereignty, even if only on an island governed by his will. His story is not just one of revenge or reconciliation, but of governance: how power is wielded, how it is lost, and how it might be restored. This is the same thematic territory as Shakespeare’s histories, from Richard II to Henry IV to Hamlet, all of which revolve around the fragility of political order and the legitimacy of rulers. What sets The Tempest apart is that it is, in a sense, a final reflection on these concerns—one that acknowledges the artifice of rulership itself. Prospero, in abandoning his magical powers, enacts the ultimate gesture of a ruler who steps away from dominion, echoing the abdications, depositions, and reluctant successions that define Shakespeare’s history plays.

The economic intelligence of nature: a visualization of dendritic structures, evoking parallels with neural networks, tree branches, and respiratory bronchioles.

If The Tempest were viewed through the lens of history and statecraft, it would also fit neatly into the lineage of political drama that Shakespeare crafted over his career. Julius Caesar, Coriolanus, Macbeth, and Measure for Measure all interrogate the nature of governance, the burden of leadership, and the moral weight of political decisions. The Tempest is no different—it is about the structuring of order from chaos, the imposition of governance upon lawlessness, and, ultimately, the relinquishing of control when the time for rule has passed. In this sense, it is as much about statecraft as any of the explicitly historical plays.

What makes Shakespeare so distinctly human-centric is precisely this obsession with power and governance. Unlike other literary traditions that might place gods, fate, or abstract cosmic forces at the center of narrative development, Shakespeare’s universe is one where humans bear responsibility for their own actions. Even when fate seems to intervene—be it through witches in Macbeth or storms in The Tempest—it is ultimately the decisions of rulers, nobles, and commoners alike that determine the outcomes of history. The world of Shakespeare is never dictated solely by divine will but by the machinations, ambitions, and follies of men and women. It is for this reason that The Tempest, rather than being relegated to a vague metaphysical or allegorical space, should instead be understood as a late meditation on political rule, governance, and the dynamics of statecraft.

The Tempest and the Shakespeare Model: A Political Play Among Many. When viewed in context with the Shakespeare model we developed, The Tempest takes on an even clearer role within Shakespeare’s broader interrogation of power and political order. Within our framework, The Tempest originally occupied a space of Voir—early pattern recognition, liminality, and transition—but this assignment, while useful in a structural sense, does not fully capture the play’s deeper political significance. Instead, if we return to the broader patterns of Shakespeare’s work, The Tempest fits far more naturally alongside the great history plays that dominate his canon.

Consider the thematic connections between The Tempest and Henry IV, for instance. Both plays deal with the responsibilities of rule and the question of legitimacy—Prospero, like Prince Hal, must prove himself as a leader, albeit in very different ways. Similarly, The Tempest shares DNA with Measure for Measure, in which a ruler (Duke Vincentio) experiments with governance through indirect means, withdrawing and then returning to impose order. This theme of rule as something that can be tested, reconsidered, and ultimately restored runs throughout Shakespeare’s corpus, linking plays that are ostensibly tragedies, comedies, or histories.

Across diverse domains of biology and computation, branching structures appear as a universal design principle. From the fractal-like bifurcations of trees to the dendritic networks of neurons, and even the bronchioles within the lungs, nature employs efficient, scalable, and self-similar architectures to optimize function. These structures facilitate rapid information transfer, efficient oxygen distribution, and recursive connectivity—echoing similar principles in artificial neural networks and computational models. This shared geometry underscores the fundamental economy of nature, where optimal design principles are preserved across scales, from micro to macro systems. So, too, with CG-BEST and the Bard's oeuvre!
— Yours Truly

Even Hamlet, which is primarily a revenge tragedy, engages deeply with the responsibilities of statecraft. Hamlet’s hesitation in avenging his father is not just a personal failing but a political dilemma—how does one balance private vengeance with the public duty of a prince? The Tempest, too, interrogates this balance, with Prospero ultimately choosing not to use his powers for revenge but for reconciliation, thus asserting a philosophy of rule that is more in line with justice than sheer dominion. If Hamlet’s delay is the tragic failure of a ruler who cannot act, Prospero’s decision to relinquish power is the philosophical resolution of a ruler who understands when to step away.

When mapped against the structure of our Shakespeare model, then, The Tempest does not sit comfortably as a play of transition alone. Instead, it demands recognition as a play about power—one that reflects on governance, legitimacy, and the ethics of rulership in a way that echoes Shakespeare’s history plays. If anything, it is a final meditation on statecraft, encapsulating lessons learned from the entire corpus that came before it.

Thus, the realignment of The Tempest under history and statecraft is not just a matter of taxonomic correctness but a necessary shift that recognizes Shakespeare’s deeply political nature. The Shakespeare model, as it currently stands, organizes his plays into thematic structures that reflect broad categories of human experience, but it must also allow for movement, adaptation, and refinement as insights develop. If Shakespeare’s work is ultimately about the intricacies of power, then The Tempest belongs firmly within that lineage—a final reflection on statecraft, rule, and the relinquishing of control.

Show code cell source Hide code cell source

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. 13 Fractal Scaling. So we have CG-BEST cosmogeology, biology, ecology, symbiotology, teleology. This maps onto tragedy, history, epic, drama, and comedy. It works because of thermodynamics, nature, battle, identity, and errors.