大模型技术的未来：通向AGI之路

下一代架构设计

# 未来架构概念示例
class NextGenArchitecture:
    """下一代模型架构概念"""
    
    def __init__(self):
        # 1. 动态架构
        self.dynamic_layers = DynamicTransformer(
            min_layers=12,
            max_layers=96,
            adaptive=True
        )
        
        # 2. 记忆系统
        self.memory_system = HierarchicalMemory(
            working_memory_size=10000,
            long_term_memory_size=1e9,
            retrieval_mechanism='neural'
        )
        
        # 3. 推理引擎
        self.reasoning_engine = SymbolicReasoner(
            logic_rules=self.load_logic_rules(),
            neural_interface=True
        )
        
        # 4. 自适应学习
        self.meta_learner = MetaLearningModule(
            learning_rate_adaptation=True,
            architecture_search=True
        )
    
    def forward(self, inputs, task_type):
        # 动态调整架构
        architecture = self.adapt_architecture(task_type)
        
        # 检索相关记忆
        relevant_memory = self.memory_system.retrieve(inputs)
        
        # 增强输入
        enhanced_input = self.combine_with_memory(inputs, relevant_memory)
        
        # 动态推理
        if self.requires_reasoning(task_type):
            output = self.reasoning_forward(enhanced_input)
        else:
            output = self.neural_forward(enhanced_input)
        
        # 更新记忆
        self.memory_system.update(inputs, output)
        
        return output
    
    def self_improve(self, feedback):
        """自我改进机制"""
        # 分析错误模式
        error_patterns = self.analyze_errors(feedback)
        
        # 调整架构
        if error_patterns.architectural_issue:
            self.meta_learner.modify_architecture()
        
        # 更新知识
        if error_patterns.knowledge_gap:
            self.active_learning(error_patterns.gap_area)
        
        # 优化推理
        if error_patterns.reasoning_flaw:
            self.reasoning_engine.update_rules()