The Future of LLM Technology: The Road to AGI

Next-Generation Architecture Design

# Future architecture concept example
class NextGenArchitecture:
    """Next-generation model architecture concept"""
    
    def __init__(self):
        # 1. Dynamic architecture
        self.dynamic_layers = DynamicTransformer(
            min_layers=12,
            max_layers=96,
            adaptive=True
        )
        
        # 2. Memory system
        self.memory_system = HierarchicalMemory(
            working_memory_size=10000,
            long_term_memory_size=1e9,
            retrieval_mechanism='neural'
        )
        
        # 3. Reasoning engine
        self.reasoning_engine = SymbolicReasoner(
            logic_rules=self.load_logic_rules(),
            neural_interface=True
        )
        
        # 4. Adaptive learning
        self.meta_learner = MetaLearningModule(
            learning_rate_adaptation=True,
            architecture_search=True
        )
    
    def forward(self, inputs, task_type):
        # Adjust architecture dynamically
        architecture = self.adapt_architecture(task_type)
        
        # Retrieve relevant memories
        relevant_memory = self.memory_system.retrieve(inputs)
        
        # Augment inputs
        enhanced_input = self.combine_with_memory(inputs, relevant_memory)
        
        # Dynamic reasoning
        if self.requires_reasoning(task_type):
            output = self.reasoning_forward(enhanced_input)
        else:
            output = self.neural_forward(enhanced_input)
        
        # Update memory
        self.memory_system.update(inputs, output)
        
        return output
    
    def self_improve(self, feedback):
        """Self-improvement mechanism"""
        # Analyze error patterns
        error_patterns = self.analyze_errors(feedback)
        
        # Adjust architecture
        if error_patterns.architectural_issue:
            self.meta_learner.modify_architecture()
        
        # Update knowledge
        if error_patterns.knowledge_gap:
            self.active_learning(error_patterns.gap_area)
        
        # Improve reasoning
        if error_patterns.reasoning_flaw:
            self.reasoning_engine.update_rules()