UK and EU

AI Efficiency in Government

We are for and against Europe, if you’ll forgive the expression
– Sir Humphrey

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

# Define the neural network structure
def define_layers():
    return {
        'Pre-Input': ['Life','Earth', 'Cosmos', 'Sound', 'Tactful', 'Firm', ],
        'Yellowstone': ['Europe'],
        'Input': ['Britain', 'Union'],
        'Hidden': [
            'Risk',
            'Oligarchy',
            'Peers',
        ],
        'Output': ['Existential', 'Deploy', 'Fees', 'Client', 'Confidentiality',    ]
    }

# Define weights for the connections
def define_weights():
    return {
        'Pre-Input-Yellowstone': np.array([
            [0.6],
            [0.5],
            [0.4],
            [0.3],
            [0.7],
            [0.8],
            [0.6]
        ]),
        'Yellowstone-Input': np.array([
            [0.7, 0.8]
        ]),
        'Input-Hidden': np.array([[0.8, 0.4, 0.1], [0.9, 0.7, 0.2]]),
        'Hidden-Output': np.array([
            [0.2, 0.8, 0.1, 0.05, 0.2],
            [0.1, 0.9, 0.05, 0.05, 0.1],
            [0.05, 0.6, 0.2, 0.1, 0.05]
        ])
    }

# Assign colors to nodes
def assign_colors(node, layer):
    if node == 'Europe':
        return 'yellow'
    if layer == 'Pre-Input' and node in ['Sound', 'Tactful', 'Firm']:
        return 'paleturquoise'
    elif layer == 'Input' and node == 'Union':
        return 'paleturquoise'
    elif layer == 'Hidden':
        if node == 'Peers':
            return 'paleturquoise'
        elif node == 'Oligarchy':
            return 'lightgreen'
        elif node == 'Risk':
            return 'lightsalmon'
    elif layer == 'Output':
        if node == 'Confidentiality':
            return 'paleturquoise'
        elif node in ['Client', 'Fees', 'Deploy']:
            return 'lightgreen'
        elif node == 'Existential':
            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()
    weights = define_weights()
    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 and weights
    for layer_pair, weight_matrix in zip(
        [('Pre-Input', 'Yellowstone'), ('Yellowstone', 'Input'), ('Input', 'Hidden'), ('Hidden', 'Output')],
        [weights['Pre-Input-Yellowstone'], weights['Yellowstone-Input'], weights['Input-Hidden'], weights['Hidden-Output']]
    ):
        source_layer, target_layer = layer_pair
        for i, source in enumerate(layers[source_layer]):
            for j, target in enumerate(layers[target_layer]):
                weight = weight_matrix[i, j]
                G.add_edge(source, target, weight=weight)

    # Customize edge thickness for specific relationships
    edge_widths = []
    for u, v in G.edges():
        if u in layers['Hidden'] and v == 'Kapital':
            edge_widths.append(6)  # Highlight key edges
        else:
            edge_widths.append(1)

    # Draw the graph
    plt.figure(figsize=(12, 16))
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=10, width=edge_widths
    )
    edge_labels = nx.get_edge_attributes(G, 'weight')
    nx.draw_networkx_edge_labels(G, pos, edge_labels={k: f'{v:.2f}' for k, v in edge_labels.items()})
    plt.title(" ")
    
    # Save the figure to a file
    # plt.savefig("figures/logo.png", format="png")

    plt.show()

# Run the visualization
visualize_nn()

Fig. 9 The Foreign Office. It’s quite ready to go along with the European ID as a quid pro quo for a deal over the Butter Mountain, The Wine Lake, and Milk Ocean … the Lamb War and the Cod Stick (life node). But in brief, the UK joined the EU with anarchic intent – easier to blow it up from the inside.

AI Efficiency in Football

What makes for a suitable problem for AI? Clear objective function to optimise against (ends), Massive combinatorial search space (means-resourcefulness), and Either lots of data and/or an accurate and efficient simulator (resources)
– Demis Hassabis

Optimizing Humanity: The Perils of Efficiency in Networks

In the age of artificial intelligence, humanity finds itself enthralled by the seductive logic of optimization. Neural networks, with their elegant architecture of inputs, compression, and outputs, have emerged as the preeminent metaphor for progress. The formula is simple yet profound: abundant data, a combinatorial explosion of possibilities, and a clear metric to optimize. But as we apply this paradigm to fields as varied as chemistry, literature, and sport, an uncomfortable question arises: are we optimizing ourselves out of the very things that make life meaningful?

Efficiency in the Age of Networks

Pep Guardiola’s Manchester City offers a striking case study. The team has become the pinnacle of footballing efficiency, a machine-like embodiment of the networked approach to sport. By assembling a billion-dollar squad and applying an AI-like tactical framework, Guardiola has engineered an unprecedented run of dominance: six Premier League titles in seven years. On the surface, this is success beyond measure, a triumph of systemic intelligence over the chaos of competition.

This tiki-taka only Guardiola can do it. Why everyone is copying Guardiola. We have no creativity; we have no genius anymore. You will never see a player like Ronaldinho anymore, or Hazard or whatever. Because when he’s going to be young, you know those coaches what they’re going to tell him, if you don’t pass the ball, I’ll put you on the bench. All football comes from the street.
– Patrice Evra

Yet, a growing chorus of voices laments that something essential has been lost. Critics accuse Guardiola of “destroying football,” stripping the game of its unpredictability and individuality. His approach optimizes for one output node—victory—at the expense of others: spontaneity, artistry, and the human drama that has long defined the sport. Ronaldinho, perhaps the most luminous node of footballing creativity in recent memory, exemplifies this tension. Guardiola’s decision to exclude him from Barcelona’s plans was a statement: the era of the solo genius was over, supplanted by the primacy of the system.

The Tyranny of Optimization

This is not just a story about football. It is a parable about the broader challenge of living in a world governed by networks. In a neural network, the hidden layer compresses a vast combinatorial space into actionable insights. Efficiency is the goal, and it is achieved by relentlessly optimizing the metric at the output layer. In Guardiola’s football, the output is victories. In capitalism, it is capital gains. Both are systems that ruthlessly prune the branches of possibility to maximize returns on investment.

But what happens when the optimization itself becomes pathological? Humanity, unlike AI, does not have a single, unified objective function. We are a species of multitudes, driven by competing desires for meaning, beauty, connection, and transcendence. The richness of our lives emerges from the interplay of these goals, much like the improvisational brilliance of Ronaldinho in his prime. Yet capitalism, with its relentless focus on profit, flattens this complexity. It treats the multidimensional space of human aspirations as if it could be reduced to a single node in the output layer.

The Machine and the Muse

The result is a profound dissonance. Systems built to optimize for narrow objectives—whether they are football teams or multinational corporations—begin to suffocate the very ecosystems they inhabit. Football becomes machine-like, dominated by tactics and metrics. Art loses its soul as algorithms chase virality. Even human relationships are commodified, reduced to “engagement” metrics on social media platforms.

We see the consequences everywhere. Guardiola’s City, for all its brilliance, struggles to evoke the passion once inspired by flawed but transcendent teams like Brazil in 2002 or Manchester United under Sir Alex Ferguson. These were teams where individuality thrived within a collective, where moments of ineffable genius emerged from the unpredictable interplay of human beings. In contrast, today’s optimized football feels clinical, devoid of the improvisational spark that once made the sport a global obsession.

The Cost of Ignoring Complexity

Capitalism is the ultimate neural network, with its relentless demand for efficiency and its ruthless pruning of what does not generate returns. But its focus on singular optimization comes at a cost. Humanity is not a monolithic entity with a singular goal; it is a complex, emergent system, a tapestry woven from countless threads. By prioritizing one thread—profit, victory, or efficiency—we risk unraveling the whole.

The irony is that the very structure of a neural network acknowledges the richness of possibility. Inputs, hidden layers, and outputs are all interconnected, each influencing the others. A well-trained network balances competing signals, finding harmony in complexity. But when one node dominates—when the metric becomes the master—we lose the ability to adapt, to innovate, to celebrate the unexpected.

Toward a New Metric

The challenge, then, is not to reject optimization but to broaden our definition of success. In football, this might mean valuing artistry and creativity as much as trophies. In capitalism, it could mean redefining returns to include social and environmental well-being. And in AI, it might mean designing systems that enhance human flourishing rather than displacing it.

Ultimately, the tension between efficiency and humanity is not new. It is the central drama of the modern age, the Faustian bargain we have made with technology and capital. The question is whether we can renegotiate the terms of that bargain before we optimize ourselves into irrelevance. Football without its Ronaldinhos is like life without its poets: efficient, perhaps, but unbearably hollow. Let us not forget that the beauty of a network lies not in its output alone, but in the richness of its connections.