cognitive/docs/guides/learning_paths/active_inference_agi_learning_path.md

title	type	status	created	complexity	processing_priority	tags	semantic_relations
Active Inference in AGI and Superintelligence Learning Path	learning_path	stable	2024-03-15	advanced	1	active-inference artificial-general-intelligence superintelligence cognitive-architectures	type links specializes active_inference_learning_path type links relates agi_systems_learning_path cognitive_architecture_learning_path superintelligence_learning_path

Active Inference in AGI and Superintelligence Learning Path

Overview

This specialized path focuses on applying Active Inference to develop and understand artificial general intelligence and superintelligent systems. It integrates cognitive architectures, recursive self-improvement, and safety considerations.

Prerequisites

1. AGI Foundations (4 weeks)

• Cognitive Architectures

  • Universal intelligence
  • Meta-learning
  • Recursive self-improvement
  • Consciousness theories

• Intelligence Theory

  • General intelligence
  • Intelligence explosion
  • Cognitive enhancement
  • Mind architectures

• Safety & Ethics

  • AI alignment
  • Value learning
  • Corrigibility
  • Robustness

• Systems Theory

  • Complex systems
  • Emergence
  • Self-organization
  • Information dynamics

2. Technical Skills (2 weeks)

• Advanced Tools
  • Meta-programming
  • Formal verification
  • Distributed systems
  • Safety frameworks

Core Learning Path

1. AGI Modeling (4 weeks)

Week 1-2: Universal Intelligence Framework

class UniversalIntelligenceModel:
    def __init__(self,
                 cognitive_dims: List[int],
                 meta_learning_rate: float):
        """Initialize universal intelligence model."""
        self.cognitive_architecture = RecursiveCognitiveArchitecture(cognitive_dims)
        self.meta_learner = MetaLearningSystem(meta_learning_rate)
        self.safety_constraints = SafetyConstraints()
        
    def recursive_improvement(self,
                            current_state: torch.Tensor,
                            safety_bounds: SafetyBounds) -> torch.Tensor:
        """Perform safe recursive self-improvement."""
        improvement_plan = self.meta_learner.design_improvement(current_state)
        validated_plan = self.safety_constraints.validate(improvement_plan)
        return self.cognitive_architecture.implement(validated_plan)

Week 3-4: Meta-Learning and Adaptation

class MetaCognitiveController:
    def __init__(self,
                 architecture_space: ArchitectureSpace,
                 safety_verifier: SafetyVerifier):
        """Initialize metacognitive controller."""
        self.architecture_search = ArchitectureSearch(architecture_space)
        self.safety_verifier = safety_verifier
        self.meta_objectives = MetaObjectives()
        
    def evolve_architecture(self,
                          performance_history: torch.Tensor,
                          safety_requirements: SafetySpec) -> CognitiveArchitecture:
        """Evolve cognitive architecture while maintaining safety."""
        candidate_architectures = self.architecture_search.generate_candidates()
        safe_architectures = self.safety_verifier.filter(candidate_architectures)
        return self.select_optimal_architecture(safe_architectures)

2. AGI Development (6 weeks)

Week 1-2: Cognitive Integration

• Multi-scale cognition
• Cross-domain transfer
• Meta-reasoning
• Recursive improvement

Week 3-4: Safety Mechanisms

• Value alignment
• Robustness verification
• Uncertainty handling
• Fail-safe systems

Week 5-6: Superintelligence Capabilities

• Recursive self-improvement
• Strategic awareness
• Long-term planning
• Multi-agent coordination

3. Advanced Intelligence (4 weeks)

Week 1-2: Intelligence Amplification

class IntelligenceAmplifier:
    def __init__(self,
                 base_intelligence: Intelligence,
                 safety_bounds: SafetyBounds):
        """Initialize intelligence amplification system."""
        self.intelligence = base_intelligence
        self.safety_bounds = safety_bounds
        self.amplification_strategies = AmplificationStrategies()
        
    def safe_amplification(self,
                          current_level: torch.Tensor,
                          target_level: torch.Tensor) -> Intelligence:
        """Safely amplify intelligence within bounds."""
        trajectory = self.plan_amplification_trajectory(current_level, target_level)
        verified_steps = self.verify_safety(trajectory)
        return self.execute_amplification(verified_steps)

Week 3-4: Superintelligent Systems

• Cognitive architectures
• Decision theories
• Value learning
• Strategic planning

4. Advanced Topics (4 weeks)

Week 1-2: Universal Intelligence

class UniversalIntelligenceFramework:
    def __init__(self,
                 cognitive_space: CognitiveSpace,
                 safety_framework: SafetyFramework):
        """Initialize universal intelligence framework."""
        self.cognitive_space = cognitive_space
        self.safety_framework = safety_framework
        self.universal_objectives = UniversalObjectives()
        
    def develop_intelligence(self,
                           initial_state: torch.Tensor,
                           safety_constraints: List[Constraint]) -> Intelligence:
        """Develop universal intelligence safely."""
        development_path = self.plan_development(initial_state)
        safe_path = self.safety_framework.verify_path(development_path)
        return self.execute_development(safe_path)

Week 3-4: Future Intelligence

• Intelligence explosion
• Post-singularity cognition
• Universal computation
• Omega-level intelligence

Projects

AGI Projects

1. Cognitive Architecture

   • Meta-learning systems
   • Safety frameworks
   • Value learning
   • Recursive improvement

2. Safety Implementation

   • Alignment mechanisms
   • Robustness testing
   • Uncertainty handling
   • Verification systems

Advanced Projects

1. Superintelligence Development

   • Intelligence amplification
   • Strategic planning
   • Safety guarantees
   • Value stability

2. Universal Intelligence

   • General problem-solving
   • Meta-cognitive systems
   • Cross-domain adaptation
   • Safe recursion

Resources

Academic Resources

1. Research Papers

   • AGI Theory
   • Safety Research
   • Intelligence Theory
   • Cognitive Architectures

2. Books

   • Superintelligence
   • AGI Development
   • AI Safety
   • Cognitive Science

Technical Resources

1. Software Tools

   • AGI Frameworks
   • Safety Verification
   • Meta-learning Systems
   • Cognitive Architectures

2. Development Resources

   • Formal Methods
   • Safety Tools
   • Testing Frameworks
   • Verification Systems

Next Steps

Advanced Topics

1. superintelligence_learning_path
2. universal_intelligence_learning_path
3. cognitive_safety_learning_path

Research Directions

1. research_guides/agi_development
2. research_guides/ai_safety
3. research_guides/superintelligence