Risk

Absolutely. Borrowing from Jensen Huang’s NVIDIA framework and adapting it to your app’s architecture, here’s how we can reconfigure the points into a five-layer system that aligns with your vision of scalability and flow:

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1. CUDA → Source of Data (IRBs, Data Navigation)

This layer corresponds to the foundation: the acquisition and structuring of data. Just as CUDA powers computations for NVIDIA, this layer powers your app by enabling:

• Seamless integration with IRBs, electronic health records, and other data repositories.

• Navigation of the labyrinthine regulatory and ethical landscape surrounding data access.

• Ensuring that data inputs are clean, representative, and relevant to the use case (patients or students).

This is the backbone, the infrastructural powerhouse enabling everything else.

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2. Perceptual AI → Yellow Node (Instinctive Engagement)

Here, the Yellow Node acts as your app’s Perceptual AI:

• For Patients: This layer provides the visceral, instinctive interface—where risks and outcomes are intuitively grasped through visualizations, interactive journeys, and simulations.

• For Students: It offers a gamified, hands-on experience of exploring input variables and seeing their immediate effects on outputs like Kaplan-Meier curves or survival probabilities.

This layer engages the sympathetic nervous system, creating an immediate and instinctive understanding of what the app is doing. It’s the first touchpoint for users, ensuring they feel empowered, not overwhelmed.

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3. Agentic AI → Exploration and Labyrinth

The Agentic AI layer corresponds to your app’s role in guiding users through the “labyrinth”:

• Patients become active participants in their own health decisions, testing “what if” scenarios and exploring possible outcomes.

• Students transition from passively observing to actively experimenting, reverse-engineering outcomes by manipulating variables.

This is the exploratory, agentic phase, where users feel like the protagonist of their journey. It unlocks curiosity and agency, drawing users into the deeper combinatorial space of possibilities.

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4. Generative AI → Digestive and Reflective Layer

In this layer, the app becomes a reflective, contemplative tool akin to Generative AI:

• It synthesizes the user’s journey and explains the abstract underpinnings of what they’ve explored.

• For Patients: It reveals how the app calculated their outcomes—introducing concepts like base case risks, hazard ratios, and confidence intervals in a digestible way.

• For Students: It explains how their inputs influenced the outputs, helping them understand nuances like data representativeness, the impact of missing variables, and confidence intervals.

Here, users experience the “aha!” moment of digesting abstract concepts. This layer transitions users into the parasympathetic mode, fostering deep learning and contemplation.

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5. Physical AI → Real-World Impact

Finally, Physical AI corresponds to the tangible, actionable outcomes:

• For Patients: The app empowers them to make real-world health decisions based on informed consent. They leave the app with a clear understanding of their options and risks, ready to take the next step in their care.

• For Students: This layer translates theoretical learning into practical skills. They can apply their newfound understanding to real-world challenges in epidemiology, biostatistics, and data science.

This layer bridges the abstract with the physical, ensuring the app has a direct and measurable impact on users’ lives or learning journeys.

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Scalability and Alignment

This framework isn’t just metaphorically powerful; it sets the stage for modular scalability:

1. CUDA ensures robust data integration and regulatory compliance, making the app scalable across institutions and regions.

2. Perceptual AI provides an instinctive, universal interface, making it appealing to a wide audience.

3. Agentic AI allows customization and exploration, enhancing user retention and engagement.

4. Generative AI fosters deep learning, making the app indispensable for both patients and students.

5. Physical AI ensures real-world impact, creating measurable outcomes that validate the app’s value.

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This configuration aligns your app with a layered, modular structure that mirrors NVIDIA’s innovative product hierarchy, ensuring both intellectual clarity and practical scalability. It positions your app as a transformative tool capable of reshaping both informed consent and education.

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 structure; modified to align with "Aprés Moi, Le Déluge" (i.e. Je suis AlexNet)
def define_layers():
    return {
        'Pre-Input/CudAlexnet': ['Life', 'Earth', 'Cosmos', 'Sacrifice', 'Means', 'Ends'],
        'Yellowstone/SensoryAI': ['Martyrdom'],
        'Input/AgenticAI': ['Bad', 'Good'],
        'Hidden/GenerativeAI': ['David', 'Old vs New Morality', 'Solomon'],
        'Output/PhysicalAI': ['Levant', 'Wisdom', 'Priests', 'Impostume', 'Temple']
    }

# Assign colors to nodes
def assign_colors(node, layer):
    if node == 'Martyrdom':
        return 'yellow'
    if layer == 'Pre-Input/CudAlexnet' and node in [ 'Ends']:
        return 'paleturquoise'
    if layer == 'Pre-Input/CudAlexnet' and node in [ 'Means']:
        return 'lightgreen'
    elif layer == 'Input/AgenticAI' and node == 'Good':
        return 'paleturquoise'
    elif layer == 'Hidden/GenerativeAI':
        if node == 'Solomon':
            return 'paleturquoise'
        elif node == 'Old vs New Morality':
            return 'lightgreen'
        elif node == 'David':
            return 'lightsalmon'
    elif layer == 'Output/PhysicalAI':
        if node == 'Temple':
            return 'paleturquoise'
        elif node in ['Impostume', 'Priests', 'Wisdom']:
            return 'lightgreen'
        elif node == 'Levant':
            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()
    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 (without weights)
    for layer_pair in [
        ('Pre-Input/CudAlexnet', 'Yellowstone/SensoryAI'), ('Yellowstone/SensoryAI', 'Input/AgenticAI'), ('Input/AgenticAI', 'Hidden/GenerativeAI'), ('Hidden/GenerativeAI', 'Output/PhysicalAI')
    ]:
        source_layer, target_layer = layer_pair
        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=10, connectionstyle="arc3,rad=0.1"
    )
    plt.title("Old vs. New Morality", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 18 Maintaining cheerfulness in the midst of a gloomy (adversarial/sympathetic/data-simulation-input) task, fraught with immeasurable responsibility (cooperative/parasympathetic/function-to-optimize), is no small feat; and yet what is needed more than cheerfulness? Nothing succeeds if prankishness has no part in it. Excess (iterative/acetylcholine/vast-combinatorial-space) strength alone is the proof of strength. RICHER layers: rules (natural & social), instinct (rules based), call (pleasure principle), hidden (combinator), emergent (outcomes), red queen hypothesis (dynamism)