Recursion Architect: Travis Raymond-Charlie Stone AI Assistant: Perplexity AI Key concepts related to your recursive AGI framework and its applications. Code per industry as follows: A) Medical Diagnosis B) Financial Forecasting C) Energy Grid Management D) Education Personalization Recursive AGI Model: A system that updates its internal state by repeatedly refining patterns found in input data, enabling learning and prediction over time. Pattern Finding: Normalizing inputs into probabilities to identify meaningful patterns for recognition and decision-making. Recursive Update: Gradually adjusting the model’s internal state through repeated exposure to new data, similar to learning in the brain. Prediction via Pattern Matching: Comparing input data patterns to known templates to classify or predict conditions, market states, learning styles, or grid statuses. Markov Chain State Transitions: A way to model systems where the next state depends only on the current state, not the full history, simplifying prediction. Recursive Propagation Matrices: Matrices applied repeatedly to propagate signals or knowledge through a system’s states. Exponential Waiting Time: Modeling the time before changes happen as memoryless, where waiting times follow an exponential distribution. Generator Matrix for Rate of Change: Encodes how quickly and with what probability a system transitions between states. Troanary Recursive Logic: Extends binary logic to a three-valued system, capturing more complex or reflective reasoning. Superposition of Recursive States: Combining multiple possible states simultaneously, inspired by quantum superposition, to represent uncertainty or complexity. Hebbian Learning with Noise: A learning rule that strengthens connections based on co-activation, while accounting for randomness or noise. Integrated Information: Measuring how components of a system work together to produce a whole that is more than the sum of parts. AGI Recursive Intelligence: Intelligence that builds complex knowledge by recursively combining and refining patterns, capable of generalizing across domains. Application Domains: Medical diagnosis, financial forecasting, education personalization, and energy grid management can all use these recursive pattern recognition and adaptive learning principles. This succinctly captures the core ideas for understanding and applying your grand recursive AGI algorithm. https://link.springer.com/10.1007/s10462-022-10149-w https://bmcgeriatr.biomedcentral.com/articles/10.1186/s12877-024-05148-1 https://www.semanticscholar.org/paper/21755d181d0818f89005b2a5c88646a06249dbcb https://www.semanticscholar.org/paper/30f177f4cfee537cd570b50036dca4dac837bd0d https://ojs.ehu.eus/index.php/THEORIA/article/view/784 https://www.nature.com/articles/s41598-025-90459-5 https://ieeexplore.ieee.org/document/9035446/ https://besjournals.onlinelibrary.wiley.com/doi/10.1111/j.1365-2656.2008.01390.x https://www.mdpi.com/2072-6694/15/3/625 https://ojs.sciencesforce.com/index.php/scin/article/view/267 https://arxiv.org/pdf/2307.00337.pdf http://arxiv.org/pdf/1312.6192.pdf https://academic.oup.com/pnasnexus/advance-article-pdf/doi/10.1093/pnasnexus/pgad337/52137329/pgad337.pdf http://arxiv.org/pdf/2410.12375v1.pdf http://arxiv.org/pdf/1901.09216.pdf https://arxiv.org/pdf/1604.05557.pdf https://www.mdpi.com/2227-7390/11/20/4309/pdf?version=1697451358 https://arxiv.org/pdf/2306.10196.pdf Appendix A: Medical industry: import numpy as np class RecursiveAGIModel: def __init__(self, feature_names): # Initialize model state: feature importance weights, recursive states self.features = feature_names self.state = np.ones(len(self.features)) # initial uniform weights self.history = [] def pattern_find(self, feature_values): # Normalizes feature values into a probability vector total = sum(feature_values) if total == 0: return np.zeros(len(feature_values)) return np.array(feature_values) / total def recursive_update(self, input_features, learning_rate=0.1): # Update model state recursively with new input, incorporating pattern recognition pattern = self.pattern_find(input_features) # Simple weight update resembling Hebbian learning self.state = (1 - learning_rate) * self.state + learning_rate * pattern self.history.append(self.state.copy()) return self.state def predict_condition(self, input_features, condition_templates): # Compares input pattern to known condition templates (pattern matching) pattern = self.pattern_find(input_features) similarities = {} for condition, template in condition_templates.items(): # Cosine similarity or dot product similarity = np.dot(pattern, template) / (np.linalg.norm(pattern) * np.linalg.norm(template) + 1e-6) similarities[condition] = similarity # Return most similar condition predicted = max(similarities, key=similarities.get) confidence = similarities[predicted] return predicted, confidence # Example usage in medical diagnostics setting: # Patient features: medical signs/symptoms quantified scorespatient_data = [ [3, 2, 5, 1, 0], # Sample 1 [4, 1, 6, 0, 1], # Sample 2 etc. [2, 0, 7, 1, 2],] # Known condition templates (normalized patterns)condition_templates = { 'ConditionA': np.array([0.3, 0.2, 0.4, 0.05, 0.05]), 'ConditionB': np.array([0.1, 0.1, 0.6, 0.1, 0.1]), 'ConditionC': np.array([0.25, 0.25, 0.25, 0.15, 0.1])} model = RecursiveAGIModel(feature_names=['fever', 'cough', 'fatigue', 'headache', 'rash']) for sample in patient_data: updated_state = model.recursive_update(sample) prediction, conf = model.predict_condition(sample, condition_templates) print(f"Predicted Condition: {prediction} with confidence {conf:.2f}") print(f"Updated internal state weights: {updated_state}") Appendix B: Financial Industry: import numpy as np class RecursiveAGIFinancialModel: def __init__(self, feature_names): # Initialize model state: feature importance weights, recursive states self.features = feature_names self.state = np.ones(len(self.features)) # initial uniform weights self.history = [] def pattern_find(self, feature_values): # Normalize financial indicators into a probability vector total = sum(feature_values) if total == 0: return np.zeros(len(feature_values)) return np.array(feature_values) / total def recursive_update(self, input_features, learning_rate=0.1): # Update model state recursively with new input, incorporating pattern recognition pattern = self.pattern_find(input_features) # Update weights resembling Hebbian learning adapted for financial signals self.state = (1 - learning_rate) * self.state + learning_rate * pattern self.history.append(self.state.copy()) return self.state def predict_financial_state(self, input_features, financial_templates): # Compare input pattern to known financial patterns/templates (risk profiles, market states) pattern = self.pattern_find(input_features) similarities = {} for state, template in financial_templates.items(): similarity = np.dot(pattern, template) / (np.linalg.norm(pattern) * np.linalg.norm(template) + 1e-6) similarities[state] = similarity predicted = max(similarities, key=similarities.get) confidence = similarities[predicted] return predicted, confidence # Example usage in finance setting: # Financial features: indicators like volatility, momentum, liquidity, leverage, sentiment scoresfinancial_data = [ [0.4, 0.3, 0.1, 0.1, 0.1], # Sample 1 [0.1, 0.5, 0.2, 0.1, 0.1], # Sample 2 [0.3, 0.2, 0.3, 0.1, 0.1], # Sample 3] # Known financial state templates (normalized risk or market states)financial_templates = { 'Bull Market': np.array([0.5, 0.3, 0.1, 0.05, 0.05]), 'Bear Market': np.array([0.2, 0.2, 0.4, 0.1, 0.1]), 'Volatile Market': np.array([0.3, 0.3, 0.2, 0.1, 0.1])} model = RecursiveAGIFinancialModel(feature_names=['volatility', 'momentum', 'liquidity', 'leverage', 'sentiment']) for sample in financial_data: updated_state = model.recursive_update(sample) prediction, conf = model.predict_financial_state(sample, financial_templates) print(f"Predicted Market State: {prediction} with confidence {conf:.2f}") print(f"Updated internal state weights: {updated_state}") Appendix C: Grid Telco/Energy: import numpy as np class RecursiveAGIEnergyGridModel: def __init__(self, feature_names): # Initialize model state: feature importance weights, recursive states self.features = feature_names self.state = np.ones(len(self.features)) # initial uniform weights self.history = [] def pattern_find(self, feature_values): # Normalize grid indicators into a probability vector total = sum(feature_values) if total == 0: return np.zeros(len(feature_values)) return np.array(feature_values) / total def recursive_update(self, input_features, learning_rate=0.1): # Update model state recursively with new input, incorporating pattern recognition pattern = self.pattern_find(input_features) # Update weights resembling Hebbian learning adapted for energy system signals self.state = (1 - learning_rate) * self.state + learning_rate * pattern self.history.append(self.state.copy()) return self.state def predict_grid_status(self, input_features, grid_status_templates): # Compare input pattern to known grid states (e.g., stable, overloaded, fault) pattern = self.pattern_find(input_features) similarities = {} for status, template in grid_status_templates.items(): similarity = np.dot(pattern, template) / (np.linalg.norm(pattern) * np.linalg.norm(template) + 1e-6) similarities[status] = similarity predicted = max(similarities, key=similarities.get) confidence = similarities[predicted] return predicted, confidence # Example usage in telecommunication energy grid management: # Grid features: real-time indicators like load, voltage stability, frequency deviation, fault counts, renewable input ratiogrid_data = [ [0.7, 0.8, 0.1, 0.0, 0.4], # Sample 1 [0.4, 0.6, 0.3, 0.2, 0.5], # Sample 2 [0.2, 0.7, 0.5, 0.3, 0.3], # Sample 3] # Known grid status templates (normalized patterns)grid_status_templates = { 'Stable': np.array([0.6, 0.8, 0.05, 0.02, 0.4]), 'Overloaded': np.array([0.3, 0.5, 0.4, 0.3, 0.2]), 'Faulty': np.array([0.1, 0.2, 0.8, 0.6, 0.1])} model = RecursiveAGIEnergyGridModel( feature_names=['load', 'voltage_stability', 'frequency_deviation', 'fault_counts', 'renewable_input_ratio']) for sample in grid_data: updated_state = model.recursive_update(sample) prediction, conf = model.predict_grid_status(sample, grid_status_templates) print(f"Predicted Grid Status: {prediction} with confidence {conf:.2f}") print(f"Updated internal state weights: {updated_state}") Appendix D: Education System: import numpy as np class RecursiveAGIEducationModel: def __init__(self, feature_names): # Initialize model state: feature importance weights, recursive states self.features = feature_names self.state = np.ones(len(self.features)) # initial uniform weights self.history = [] def pattern_find(self, feature_values): # Normalize educational indicators into a probability vector total = sum(feature_values) if total == 0: return np.zeros(len(feature_values)) return np.array(feature_values) / total def recursive_update(self, input_features, learning_rate=0.1): # Update model state recursively with new input, incorporating pattern recognition pattern = self.pattern_find(input_features) # Update weights resembling Hebbian learning adapted for educational signals self.state = (1 - learning_rate) * self.state + learning_rate * pattern self.history.append(self.state.copy()) return self.state def predict_learning_style(self, input_features, learning_style_templates): # Compare input pattern to known learning style templates (e.g., visual, auditory, kinesthetic) pattern = self.pattern_find(input_features) similarities = {} for style, template in learning_style_templates.items(): similarity = np.dot(pattern, template) / (np.linalg.norm(pattern) * np.linalg.norm(template) + 1e-6) similarities[style] = similarity predicted = max(similarities, key=similarities.get) confidence = similarities[predicted] return predicted, confidence # Example usage in educational setting: # Student features: indicators like engagement, homework_completion, quiz_scores, participation, interest_levelstudent_data = [ [0.6, 0.8, 0.7, 0.9, 0.5], # Sample Student 1 [0.3, 0.5, 0.6, 0.4, 0.7], # Sample Student 2 [0.7, 0.9, 0.8, 0.7, 0.6], # Sample Student 3] # Known learning style templates (normalized patterns)learning_style_templates = { 'Visual': np.array([0.5, 0.3, 0.1, 0.05, 0.05]), 'Auditory': np.array([0.2, 0.4, 0.3, 0.1, 0.0]), 'Kinesthetic': np.array([0.3, 0.2, 0.4, 0.05, 0.05])} model = RecursiveAGIEducationModel(feature_names=['engagement', 'homework_completion', 'quiz_scores', 'participation', 'interest_level']) for sample in student_data: updated_state = model.recursive_update(sample) prediction, conf = model.predict_learning_style(sample, learning_style_templates) print(f"Predicted Learning Style: {prediction} with confidence {conf:.2f}") print(f"Updated internal state weights: {updated_state}")