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The “top five movies of all time” can vary depending on the criteria used, such as box office revenue, critical acclaim, cultural impact, or audience popularity. Here are five films that are frequently cited as some of the greatest ever made, based on their influence, critical reception, and legacy:

Fig. 1 Red Queen in the House - Prior Authorization and Trust in US Health Care. SEC tokenizing stocks & bonds is like IRB tokenizing access to EHR. Let’s explore this! Let’s segue to a related matter. The public response to the killing of United Healthcare’s CEO made it hard to ignore the erosion of trust in U.S. health care. Even before that, more questions were being raised about prior authorization – insurers’ tools for approving or denying a proposed treatment or drug. Patients need appropriate care in a timely manner. Providers need to spend less time dealing with barriers and bureaucracy to get patients that care. And, insurers need to be sure they are paying for the right care – not for overtreatment, unnecessary treatment, or overly aggressive care. Join us for a candid discussion of the friction points, the frustrations, and some emerging potential solutions as U.S. healthcare seeks that delicate balance

1. The Godfather (1972)
   Directed by Francis Ford Coppola, this epic crime drama is often regarded as one of the greatest films in cinematic history. It explores themes of power, family, and morality through the story of the Corleone mafia family (Layer 5).

2. Citizen Kane (1941)
   Directed by and starring Orson Welles, this groundbreaking film is celebrated for its innovative narrative structure, cinematography, and deep exploration of its protagonist, Charles Foster Kane. It’s a staple in discussions of the greatest films ever made (Layer 4).

3. Casablanca (1942)
   Directed by Michael Curtiz, this romantic drama set during World War II is renowned for its memorable performances, iconic lines, and timeless themes of love and sacrifice (Layer 1).

4. Schindler’s List (1993)
   Directed by Steven Spielberg, this powerful historical drama tells the true story of Oskar Schindler, who saved over a thousand Jewish refugees during the Holocaust. It’s praised for its emotional depth and historical significance (Layer 3).

5. The Shawshank Redemption (1994)
   Directed by Frank Darabont, this film adaptation of Stephen King’s novella is beloved for its themes of hope, friendship, and resilience. Despite a modest box office performance, it has become a classic through word of mouth and critical acclaim (Layer 2).

These films are often included in “greatest of all time” lists, but opinions can vary widely depending on personal taste and cultural context. – DeepSeek

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

# Define the neural network fractal
def define_layers():
    return {
        'World': ['Cosmos-Entropy', 'Planet-Tempered', 'Life-Needs', 'Ecosystem-Costs', 'Generative-Means', 'Cartel-Ends', ], ## Cosmos, Planet
        'Perception': ['Perception-Ledger'], # Life
        'Agency': ['Open-Nomiddleman', 'Closed-Trusted'], # Ecosystem (Beyond Principal-Agent-Other)
        'Generative': ['Ratio-Weaponized', 'Competition-Tokenized', 'Odds-Monopolized'], # Generative
        'Physical': ['Volatile-Distributed', 'Unknown-Players',  'Freedom-Crypto', 'Known-Transactions', 'Stable-Central'] # Physical
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Perception-Ledger'],
        'paleturquoise': ['Cartel-Ends', 'Closed-Trusted', 'Odds-Monopolized', 'Stable-Central'],
        'lightgreen': ['Generative-Means', 'Competition-Tokenized', 'Known-Transactions', 'Freedom-Crypto', 'Unknown-Players'],
        'lightsalmon': [
            'Life-Needs', 'Ecosystem-Costs', 'Open-Nomiddleman', # Ecosystem = Red Queen = Prometheus = Sacrifice
            'Ratio-Weaponized', 'Volatile-Distributed'
        ],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# 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()
    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'))  # Default color fallback

    # Add edges (automated for consecutive layers)
    layer_names = list(layers.keys())
    for i in range(len(layer_names) - 1):
        source_layer, target_layer = layer_names[i], layer_names[i + 1]
        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=9, connectionstyle="arc3,rad=0.2"
    )
    plt.title("Introducing DeepSeek: 54132", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 2 Align Elon Musks Reaction to Stargate with Emily’s “Mutiny & Obedience”. But before you do that, 54132 merely matched these movie ranks to the network layers. Reveals something that I’ll leave unstated. Tokens are digital assets (keys) to open up a ledger (yellow). What exactly the ledger is about is another topic altogether. But it might be API, block chain, or access to Microsofts Azure. In essence, the way forward for government-supported AI efforts must navigate this complex landscape of technological promise versus human complexity. By learning from the decentralized and transparent nature of cryptocurrency, there’s potential to create an AI ecosystem that not only advances our capabilities but also aligns with democratic ideals of openness and participation. The journey of AI from perception to physical reality, as depicted in the neural network, should ideally reflect a path where technology serves humanity’s collective needs, not just the interests of its creators or controllers. The thing to me that’s always been weird about this is that no one’s articulated a vision for what the world will look like. They’re always willing to say it comes down to supercharged scientific research, which may very well be true, but also requires rethinking how science is organized to take advantage of these models. What does that look like? There are a lot of other social components that have to fit into it