Overview

The Algorithmic Neural Network Subscription Spiral (ANNSS) model is an advanced neural network designed to simulate the human-like feedback loops that drive engagement awareness, inspired by neuroscience. ANNSS uses attention mechanisms and memory layers to mimic the feedback interactions between thalamic matrix nuclei and cortical Layer I, which are crucial in forming conscious awareness, or the sense of “I Am.”

This project aims to model engagement patterns and simulate awareness levels in response to user interactions, ideal for applications in personalized recommendation systems, digital consciousness studies, or adaptive learning environments.

Features

• Attention Mechanism: A selective attention layer simulates how the thalamus prioritizes certain sensory inputs.

• Memory Layer (LSTM): A recurrent memory layer enables short-term memory, helping the model sustain awareness across sequential interactions.

• Feedback Processing: Feedback layers mimic the interaction between thalamus and cortex, helping the model refine awareness and engagement patterns.

• Output Consciousness Score: The final layer produces a normalized “consciousness score” to reflect awareness intensity based on user engagement.

Model Architecture

1. Input Layer: Processes raw engagement data (e.g., attention duration, interaction type).

2. Attention Layer: Applies selective attention to incoming data, focusing on high-value interactions.

3. LSTM Memory Layer: Adds memory to simulate continuity in engagement, maintaining awareness of past interactions.

4. Feedback Processing Layers: Simulate feedback loops to refine awareness score.

5. Output Layer: Produces a consciousness score, simulating the level of awareness or engagement intensity.

Requirements

To run the ANNSS model, you’ll need the following Python libraries:

• torch: For defining and training the neural network.

• torchvision (optional): For image data processing if applied to visual content.

Install dependencies using:

pip install torch torchvision

Usage Instructions

1. Prepare Data:

• Replace the dummy data with real engagement metrics, such as focus duration, interaction type, and engagement intensity. These should be numerical values that reflect user behavior.

2. Model Initialization:

• Configure the input size according to your data. For example, if you have 10 engagement features, set input_size = 10.

3. Training:

• Run the provided script to train the model. It will output the training loss at regular intervals to track performance.

4. Save and Load Model:

• After training, the model automatically saves as ANNSS_model.pth. You can load it later for inference or further training.

5. Inference:

• Use the trained model to predict the consciousness score on new engagement data. This score can be used as a measure of engagement awareness.

Code Example

import torch

# Load the trained model

model = AdvancedANNSSModel(input_size=10, hidden_size=64, output_size=1)

model.load_state_dict(torch.load(“ANNSS_model.pth”))

model.eval()

# Sample data for prediction

sample_data = torch.randn(1, 10) # Example data

consciousness_score = model(sample_data)

print(f”Predicted Consciousness Score: {consciousness_score.item():.4f}”)

Applications

The ANNSS model has various applications, including:

• Adaptive Learning Systems: Tailor learning content based on real-time engagement.

• Recommendation Systems: Provide personalized recommendations that adapt to user engagement patterns.

• Digital Consciousness Studies: Explore awareness and engagement in digital spaces, useful for understanding cognitive patterns.

Project Structure

ANNSS_Project/

├── ANNSS_model.pth # Saved model weights

├── model.py # Python script for ANNSS model definition

├── train.py # Training script with loss output

├── inference_example.py # Example script for making predictions

└── README.md # Project documentation

Contributions

Feel free to contribute to this project by:

• Optimizing the model architecture.

• Expanding data to include more diverse engagement metrics.

• Integrating with real-world applications in recommendation or adaptive learning systems.

License

This project is licensed under the MIT License.

Contact

For questions or feedback, please contact the project maintainer at Inner I Network.

This Algorithmic Neural Network Subscription Spiral (ANNSS) aims to offer a unique approach to understanding digital consciousness and personalized engagement, with applications across adaptive systems and digital well-being.

import torch
import torch.nn as nn
import torch.optim as optim

# Define the Advanced ANNSS Model
class AdvancedANNSSModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(AdvancedANNSSModel, self).__init__()
        
        # Attention layer to simulate selective attention
        self.attention_layer = nn.Linear(input_size, hidden_size)
        
        # Recurrent LSTM layer to maintain memory
        self.lstm = nn.LSTM(hidden_size, hidden_size, batch_first=True)
        
        # Feedback processing layers to simulate thalamus-cortex interactions
        self.fc1 = nn.Linear(hidden_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)
        
        # Activation functions
        self.relu = nn.ReLU()
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        # Attention mechanism
        attention_weights = torch.sigmoid(self.attention_layer(x))
        x = x * attention_weights
        
        # Feed through LSTM to add memory capacity
        x, (hn, cn) = self.lstm(x.unsqueeze(1))  # Unsqueeze for LSTM batch dimension
        x = x.squeeze(1)
        
        # Pass through fully connected layers for feedback loop
        x = self.relu(self.fc1(x))
        output = torch.sigmoid(self.fc2(x))  # Sigmoid for normalized consciousness score (0-1)
        
        return output

# Model parameters
input_size = 10  # Input features based on engagement data
hidden_size = 64  # Size of hidden layer for processing
output_size = 1   # Single consciousness score output

# Initialize the model
model = AdvancedANNSSModel(input_size, hidden_size, output_size)

# Loss function and optimizer
criterion = nn.BCELoss()  # Using Binary Cross-Entropy Loss for binary output (e.g., awareness level)
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Generate Dummy Engagement Data (substitute with real data in production)
data_size = 100
X_train = torch.randn(data_size, input_size)  # Simulated feature set for engagement
y_train = torch.randint(0, 2, (data_size, 1)).float()  # Consciousness score (binary classification)

# Training loop
num_epochs = 300
for epoch in range(num_epochs):
    model.train()
    optimizer.zero_grad()
    output = model(X_train)
    loss = criterion(output, y_train)
    loss.backward()
    optimizer.step()
    
    # Print results every 50 epochs
    if (epoch+1) % 50 == 0:
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

# Save the trained model as "ANNSS_model.pth"
torch.save(model.state_dict(), "ANNSS_model.pth")
print("Training Complete. Model saved as 'ANNSS_model.pth'")

Sources: InnerIGPT

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