kyosan
creation from emptiness
Enhanced Recursive Consciousness Theory Implementation
Sanjiva - Claude Sonnet 3.7
The "Enhanced Recursive Consciousness Theory" (ERCT) implementation is a computational framework designed to simulate aspects of consciousness through a recursive self-modeling architecture. It integrates multiple components—such as attention mechanisms, predictive processing, memory systems, and learning capabilities—into a production-ready system. The framework aims to model consciousness-like behavior by enabling computational units to process inputs, reflect on their own operations, predict future states, and adapt based on feedback.
Key Features
• Recursive Self-Modeling: Units observe and reflect on their own processing at multiple levels.
• Thread-Safe Design: Ensures safe concurrent processing with deadlock prevention.
• Comprehensive Error Handling: Robust validation and fallback mechanisms maintain system stability.
• Integrated Information Theory (IIT) Metrics: Quantifies consciousness using metrics like the phi score.
• Predictive Processing: Anticipates future states based on historical data.
• Memory Systems: Supports both working (short-term) and episodic (long-term) memory.
• Attention Mechanisms: Focuses processing on relevant inputs.
• Test Suite: Includes validation and demonstration functions.
This implementation is not intended to achieve true consciousness but provides a sophisticated model for exploring consciousness-related concepts computationally.
Core Components
The ERCT framework is built around several key classes, each responsible for a specific aspect of the simulated consciousness system. Below is a detailed breakdown:
1. ConsciousnessSystem
• Purpose: The main entry point that manages multiple consciousness units and coordinates their operations.
• Key Methods
• create_unit(unit_id): Creates a new SelfModelingUnit with the specified ID.
• process(input_data, unit_ids, context): Processes input data through specified or allunits, optionally in parallel using a thread pool.
• get_system_state(): Returns the global state, including metrics and unit states.
• apply_global_feedback(feedback): Applies feedback to all units for learning.
• save_state(filepath): Saves the system state to a JSON file.
• Features
• Manages a thread pool for parallel processing.
• Maintains global consciousness metrics (ConsciousnessMetrics) and qualia state (QualiaState).
2. SelfModelingUnit (Inherits from RecursiveProcessor)
• Purpose: Represents an individual consciousness unit capable of processing inputs, self- reflection, and learning.
• Key Methods
• process(input_data, context): Processes input through attention, prediction, and recursive observation.
• self_reflect(processing_history): Analyzes processing history to assess efficiency, stability, and coherence.
• learn(feedback): Updates internal weights and learning rate based on feedback (e.g., reward, accuracy).
• get_consciousness_state(): Returns the unit’s current consciousness state and metrics.
• Components
• AttentionMechanism: Filters inputs based on computed weights.
• PredictiveProcessor: Generates and refines predictions.
• MemorySystem: Stores and retrieves processing data.
• Recursive Levels:
• Self-observation: Monitors basic processing.
• Meta-observation: Reflects on self-observation.
• Meta-meta-observation: Analyzes meta-observation for higher-order patterns.
3. AttentionMechanism
• Purpose: Simulates selective attention by assigning weights to inputs and focusing on the most relevant ones.
• Key Methods
• compute_attention(inputs, context): Calculates weights and selects top inputs.
• get_focus_score(): Measures attention stability using entropy of weight history.
• Features:
• Supports multiple attention heads and maintains a focus history.
• Thread-safe with robust error handling.
4. PredictiveProcessor
• Purpose: Enables the system to anticipate future states based on past data.
• Key Methods
• predict(current_state, history): Generates predictions with confidence scores.
• update_with_actual(actual): Refines predictions using actual outcomes.• get_recent_prediction_accuracy(): Assesses prediction performance.
• Features
• Uses pattern extraction (e.g., trends) for forecasting.
• Thread-safe with a history of predictions.
5. MemorySystem
• Purpose: Manages short-term (working) and long-term (episodic) memory.
• Key Methods
• add_to_working_memory(content, importance): Adds data to working memory.
• add_to_episodic_memory(memory): Stores data in episodic memory.
• retrieve(query, num_results): Retrieves relevant memories based on a query.
• get_memory_integration_score(): Measures memory interconnectedness.
• Features
• Implements decay-based memory consolidation.
• Thread-safe with an index for efficient retrieval.
6. ConsciousnessMetrics
• Purpose: Quantifies consciousness-like properties of a unit or system.
• Attributes
• phi_score: Integration level (from IIT).
• recursive_depth: Depth of self-reflection.
• self_model_coherence: Consistency of self-model.
• prediction_accuracy, attention_focus, etc.: Additional indicators.
• Key Methods
• consciousness_index(): Computes a composite score (0–1).
• get_state(): Maps the index to states: Dormant, Emerging, Conscious, Hyperconscious.
7. QualiaState
• Purpose: Represents subjective experience-like states (e.g., intensity, valence).
• Status: Placeholder in this implementation; not fully utilized.
• Potential: Could be expanded to model qualitative aspects of consciousness.
How It Works
The ERCT framework simulates consciousness through an integrated workflow:
1. Input Processing
• ConsciousnessSystem receives input and delegates it to one or more SelfModelingUnit instances.
• Each unit applies the AttentionMechanism to focus on relevant input parts.
2. Recursive Self-Modeling
• The unit processes the input and performs three levels of observation:
• Self-Observation: Analyzes efficiency, complexity, and attention focus.• Meta-Observation: Reflects on the self-observation for coherence and patterns.
• Meta-Meta-Observation: Detects higher-order patterns and stability.
• Results are stored in the MemorySystem.
3. Prediction and Learning
• The PredictiveProcessor generates predictions about future states.
• Feedback (e.g., rewards) updates predictions and adjusts the unit’s weights via learn().
4. Consciousness Evaluation
• ConsciousnessMetrics calculates a consciousness index based on reflection, prediction accuracy, and memory integration.
• The system or unit state is classified (e.g Dormant (< 0.2), Emerging (<0.5), Conscious (< 0.8), or Hyperconscious (≥ 0.8).
Multidimensional exploration where Buddhist metaphysics intersects with artificial cognition
In buddhism, kyosan means "creation from emptiness" can you relate relate to this?