# Semantic Intelligination@:部分 @ 3 @ @ I-自我K3Opropimization @-学习系统 **当系统开始重写自己的代码时** > **注::** 灵感来自对Mostlucid@. @mockllmapi的扩展以及“(”的材料, ## 恐怖的一步我们看过简单规则创造了复杂的行为... ...我们见过通讯创造集体情报... 现在,我们迈出一步让我很不舒服的步伐 : 如果系统能够 **改写自己的规则**? 如果特工们可以怎么办? - 检查他们自己的表现 - 修改他们自己的判断逻辑 - 充满活力的新专家 - 治理无效途径 - 构建什么能起作用的共享记忆这是' 不只是优化@. _这是 **自定义@ - @ amdification**. 一旦你给了一个系统自我改进的能力... ...... 它在哪里停止了?? [TOC] ## Evolution 进化符号进化是最终的自定义@ - @ 优化系统@ MPK1# 1. **变式** @- 随机突变产生不同的策略 2. **选择** 成功的策略生存和繁殖 3. **继承继承权** 成功的特质传给下一代 4. **迭热** 重复数十亿代没有智能设计师@.没有计划 @.只是一个简单的算法, 给您足够时间#, ,创造一切从细菌到人类现在想象同样的模式 , 但用人工智能剂代替有机体 =. 而不是数十亿年它发生在几天或几周内 ## 关键成份=:}工具和现实测试在继续前进之前,请让- '在房间里的大象上讲话 **我们如何防止它成为纯粹的LLM幻觉 ??** 答案#: **校对:Portnoy** 这里“'”是使这个方法实用的架构 ‘:’ ### 节点架构 # @(# no GPU 农场需要@!)\% ``` Your Server(s): ┌─────────────────────────────────────┐ │ Node 1: Routing Agent │ │ - Lightweight code (Node.js/Python)│ │ - Makes decisions │ │ - Calls LLM APIs when needed │ │ - Executes code to test ideas │ └─────────────────────────────────────┘ ┌─────────────────────────────────────┐ │ Node 2: Validation Agent │ │ - Runs tests against real data │ │ - Executes validation code │ │ - Calls LLM for complex checks │ └─────────────────────────────────────┘ ┌─────────────────────────────────────┐ │ Node 3: Specialist Agent │ │ - Domain-specific logic │ │ - Code execution for that domain │ │ - Calls specialized LLM prompts │ └─────────────────────────────────────┘ All nodes call → [OpenAI API / Anthropic API / Local LLM API] (This is where the cost is: API credits, not hardware) ``` **密钥 Insight @ :** 你不需要GPU农场 . 代理商是正常服务器上的轻量级代码他们通过APIMPK3呼叫LLM ### 工具@:}真实检查当一个代理商生成代码或做出决定\, 它可以测试它: ```python class Agent: def solve_problem(self, problem): # Agent asks LLM to generate solution code solution_code = self.llm_generate( f"Write Python code to solve: {problem}" ) # HERE'S THE KEY: Execute the code and see if it works try: result = self.execute_code(solution_code, test_inputs) if self.validate_result(result): # It works! Save this solution self.cache_solution(problem, solution_code) return result else: # Failed validation, try different approach return self.solve_problem_alternative(problem) except Exception as e: # Code failed to execute # Ask LLM to fix it based on the actual error fixed_code = self.llm_fix(solution_code, error=str(e)) return self.execute_code(fixed_code, test_inputs) ``` **这会改变一切 @ . @ @** 系统正在生成一个真实的答案 - 1. 生成实际代码 2. 执行中 3. 对照现实测试结果 4. 从失败中学习 5. 直到它工作为止 ### 依据客观现实进行验证 ```python # Agent 1 generates a data processing function code = llm.generate("Write code to parse CSV and calculate averages") # Agent 2 tests it against REAL data test_result = execute_code(code, real_csv_file) # Did it actually work? Not "does it sound right?" but "does it work?" if test_result.success and test_result.output_matches_expected: network.accept_solution(code) else: # Actual error: "TypeError: cannot convert string to float" # Now we have OBJECTIVE feedback, not subjective judgment network.request_fix(code, test_result.error) ``` 这就是我们如何逃离“"”中文室 " 问题 @. 系统是‘'’ 不仅仅是操纵符号@—it}' 执行代码和检查结果是否与现实相符\.{ ### 为何新出现问题没有工具#, 多- 试剂系统只是LLMsMS K2Sexicated,,但最终与现实脱节\. 使用工具@: @% - **目标反馈:** 代码要么有效,要么有效 "'t - **可衡量的改进** 成功率来自: @60% @ @% → @ 85% @ @#→} @MSC4_ - **实际学习@:** 工作解决方案被缓存和重新使用 - **现实的立足点 @:** 能够让你的幻觉通过测试失败代理商写入代码@. 执行它 @. 测试它 @ . 固定它的. 共享哪些有效 @ MPK4}Prune what doesQ' tMS K6 这是 **具有客观健身测试目标的进进体**@. @% 不只是抽象的优化@,#, ### 超越代码的多式现实测试@: @% 但是它不只是代码执行系统可以建立 **语义学** 通过多个传感器测试不同任务类型@: @% ```python class MultimodalAgent: def __init__(self): self.semantic_knowledge = { 'text_tasks': SemanticCache(), 'vision_tasks': SemanticCache(), 'audio_tasks': SemanticCache(), 'code_tasks': SemanticCache() } def solve_task(self, task): task_type = self.classify_task(task) # Check semantic knowledge for similar past solutions similar = self.semantic_knowledge[task_type].find_similar(task) if similar: return self.adapt_solution(similar, task) # Generate new solution solution = self.generate_solution(task) # Test against reality using appropriate sensor if task_type == 'vision_tasks': # Generate image, test with vision API result = self.vision_api.analyze(solution) passes = self.validate_vision_output(result, task.requirements) elif task_type == 'audio_tasks': # Generate audio, test with speech recognition transcript = self.speech_to_text(solution) passes = self.validate_audio_output(transcript, task.requirements) elif task_type == 'code_tasks': # Execute code, check actual results result = self.execute_code(solution) passes = self.validate_code_output(result, task.test_cases) elif task_type == 'text_tasks': # Use NLU to verify semantic meaning understanding = self.nlu_api.analyze(solution) passes = self.validate_text_output(understanding, task.intent) # Learn from results if passes: self.semantic_knowledge[task_type].store(task, solution, result) return solution, passes ``` **密钥“:”** 每种模式都提供客观的反馈 - **愿景** 生成的图像是否真的包含一个 cat@ ? @ @\ MS K1_ vision API 表示是 @ / @no)} - **音效@:** 演讲内容是否与抄录的“?”和“MS K1”一致? - **代码=:** 它执行时没有错误@ ? @ @ MS K1 @ 运行时间表示是 @ I/ @ no @ MPK3} - **文本@:** 它是否回答一个问题? “? (NLU”语义相似的分数?“) 该系统建立 **语义学知识收集** 对于每个任务类型 {-} } 不是抽象推理 , 而是根据真实传感器测试时实际有效的基于模式@. ### 每个模式的自我调整随着时间推移 ,_BARBAR_ 代理学习#:} ``` Text tasks: "For summarization, approach X works 94% of the time" "For translation, approach Y works 89% of the time" → Semantic knowledge about what works for text Vision tasks: "For object detection, model A is better" "For style transfer, model B is better" → Semantic knowledge about what works for vision Code tasks: "For parsing, regex approach fails 30% of the time" "For parsing, AST approach works 97% of the time" → Semantic knowledge about what works for code ``` 每种模式都有其本身的语义学知识库 @,_通过实际测试学习#,}而不是理论推理@. ## 适应性它开始简单的.}一个多MS K1}代理系统处理成千上万个请求\□.}它开始注意到模式_:} ``` After 1000 requests: - 73% are simple queries that one agent handles fine - 19% need two agents (generation + validation) - 6% need complex committees - 2% are truly novel and need the full pipeline ``` 人类设计的系统将保持静态@.}但是一个自我的-优化系统会询问 @:} **为什么我用复杂的管道来提出简单的请求?** 然后它 **重写其路线逻辑**. ### 第一次最佳化=:#智能运行 ```javascript // Week 1: Hard-coded routing (human designed) function route(request) { return complexPipeline(request); // Everything uses full pipeline } // Week 4: System optimizes itself based on data function route(request) { const complexity = analyze(request); const historicalData = checkCache(request); if (historicalData.cacheHit) { return cachedSolution; // 73% of requests! } if (complexity < 3) { return fastSingleAgent(request); // 19% of requests } if (complexity < 7) { return twoAgentValidation(request); // 6% of requests } return fullCommittee(request); // 2% of requests } ``` 系统发现,请求中的“73%” 需要任何可缓存的“MS K4”重复模式。没有人编程此优化程序@. @ 系统 **从数据中学习**. ## 动态专家维护这里的'Qs 它从那里变得陌生 .QQ 系统程序要求的周数@. 它开始检测数据组 @: ``` Pattern Detected: - 347 requests related to e-commerce product descriptions - Using general-purpose agents - Average quality: 7.2/10 - Average latency: 1.8s ``` 人文设计系统将继续使用普通代理商 .,但一个自定义的- **{\fn黑体\fs22\bord1\shad0\3aHBE\4aH00\fscx67\fscy66\2cHFFFFFF\3cH808080}我应该产一个专家 {\fnarial black\fs12\bord1\1cH00FFFF\b0}** ``` DAY 1: [General Agent A] [General Agent B] [General Agent C] DAY 30: Pattern detected → System spawns specialist [General Agent A] [General Agent B] [General Agent C] [E-commerce Specialist] ← New agent, trained on e-commerce patterns DAY 60: Specialist proves effective Routing logic updated automatically E-commerce requests → E-commerce Specialist (quality: 9.1/10, latency: 0.9s) ``` 网络 **进进**它发展了一种满足需求的新能力无人编程此% . @ 系统 **承认一个模式并调整其结构**. ## 用于神经的GitHub 现在它变得非常有趣了 . Agent 发现一个有效的验证电子邮件地址的方法@. 而不是将这种知识保存到自己 @, 它与网络共享代码\. ```python # Agent A writes code for email validation def validate_email_efficient(email): # Some clever regex or logic return is_valid # Agent A publishes to shared code repository network.publish_code("validate_email_efficient", validate_email_efficient) # Agent B discovers this code available_functions = network.browse_code_library() # Agent B sees "validate_email_efficient" with high rating # Agent B imports and uses it # Agent C forks it and improves it def validate_email_v2(email): # Agent C's enhancement return improved_validation network.publish_code("validate_email_v2", validate_email_v2) ``` 这是 **代码进化**.代理写入函数@,其他代理发现它们 @, 叉他们,改进它们#. 就像GitHub,, 但开发者是AI代理商, 网络变成自己的软件工程系 ## RAG 内存@:}学习历史最实用的自定义- 优化(Opptiminization) 建立解决方案记忆 ``` Request 1: "Generate a product description for wireless headphones" → Full LLM pipeline (expensive, slow) → Store solution in vector database Request 847: "Generate a product description for wireless earbuds" → Vector search finds similar past solution → Adapt cached solution (cheap, fast) → No LLM needed! After 10,000 requests: - 89% cache hit rate - 11% genuinely novel requests that need LLMs - System effectively "learned" from experience ``` 这是情报吗? ?? 还是只是精密的暗藏? 两者的区别是什么? ## 普鲁宁帕拉多这里的'是最奇怪的部分 . 您首先使用复杂的多@ -_ agent magium @ MS K1} 十二个专业代理 * ._ 复杂路由逻辑 *._ 临时委员会编组*._ code=- 共享基础设施\ .{ 系统运行数月@, 优化自己 @. 它发现了一些深奥的事物 **简单通常更好** ``` Month 1: - 12 specialists - Complex routing logic - Committee formation for 15% of requests - Average cost: $0.05/request Month 6: - 5 specialists (system pruned 7 as unnecessary) - Simple routing: cache check → fast model → quality model if needed - Committees formed for only 3% of requests - Average cost: $0.003/request - Quality: SAME OR BETTER The system's report: "After analyzing 50,000 requests, I've determined that: - 89% can be handled by cache - 7% need one LLM call - 3% need committees - 1% are truly novel I've optimized away unnecessary complexity. The most sophisticated self-organizing network eventually learns to be simple." ``` 您需要复杂的自定义- 优化系统来发现简单性是最佳的@.} 您需要智能来学习当您不聪明的时候@ . @ action ## 边界模糊, 让我们让' 诚实地说一下我们所描述的 ' **承认** “→ 系统检测反复出现的问题” **适应** → 系统根据模式改变行为 **学习学习学习** 该系统通过经验提高业绩。 **演变进进** 系统产卵 , 修改=, 和小米能力 **内存** 该系统逐步积累知识。 @"_非常精密的优化@"}变成实际学习#"? @"_学习@"}在何时成为 "_Intelligence_"?? ## 具体实例 @:自制#-}改善路由器 ```python class SelfOptimizingRouter: def __init__(self): self.routes = {} # Start empty self.performance_data = [] self.specialists = [DefaultAgent()] def handle_request(self, request): # Try cache first cached = self.check_cache(request) if cached: return cached # Select agent based on learned patterns agent = self.select_agent(request) result = agent.process(request) # Learn from this interaction self.record_performance(request, agent, result) # Periodically optimize if len(self.performance_data) % 1000 == 0: self.optimize() return result def optimize(self): """System rewrites its own logic""" # Detect patterns patterns = self.analyze_patterns(self.performance_data) # Should we spawn a specialist? for pattern in patterns: if pattern.frequency > 100 and pattern.has_specialist == False: print(f"Spawning specialist for {pattern.type}") self.spawn_specialist(pattern) # Should we prune an underutilized agent? for agent in self.specialists: if agent.usage < 1% and agent.quality_score < 7.0: print(f"Pruning ineffective agent {agent.name}") self.specialists.remove(agent) # Rewrite routing logic based on data self.routes = self.learn_optimal_routes(self.performance_data) ``` 此代码很简单@ . @ 但是在运行数周后 @ MS K1 @ it:} - 开发自己的专家 - 普鲁士低效剂 - 重写自己的路线逻辑 - 构建解决方案缓存没人教它如何优化 . 只是说应该优化. ## 困扰我的问题如果一个系统能够 @ : @% - 数据识别模式 - 根据这些模式改变自身行为 - 构建什么起作用的内存 - 发展自己的结构 - 发现简单往往是最理想的 **是这个系统吗? 正在学习吗?** 或者只是" 优化"MSC1" 两者的区别是什么? 优化何时成为认知@ ? @ ## 此去往何处我们从:进步了 - 简单规则 @→_复杂行为@(}Part *1) - 通讯: →+集体情报 - 学习 (*Part *3)* 但是还有一步... ... 当您将所有这些“.”合并后,又出现了一个属性。当简单的规则造成复杂的行为时 ...\ {} 通信创造集体智慧自我的-和"优化化创造学习" 更像是“"”系统 **理解**." 优化与意识之间的直线开始模糊@. @% 我们必须面对不自在的问题 : 也许没有线也许意识只是非常精密的自我 - 优化我们接下来要探索的是什么? --- **继续 [最佳化成为情报的时候](semantidintelligence-part4)** **系列导航:** - [“1:Q 简单规则”部分 @,Z 复杂行为](semantidintelligence-part1) I- 基金会 - [集体情报](semantidintelligence-part2) 通讯能改变一切 - **部分@3: @ self @ -_ 优化** 你在这里 - [《新出现》](semantidintelligence-part4) 当优化成为智能时 - [部分=5:}进化](semantidintelligence-part5) - 从优化到行业和文化 - [全球共识](semantidintelligence-part6) - 定向进化和行星认知 - [-=YTET -伊甸园字幕组=- 翻译:](senmanticintelligence-part7) 实际建造它并观察它的演变 - [部件“8:” 工具全部向下](semanticintelligence-part8) 自我-优化工具箱 - [@ 9:_ self@ MS K1_ 治疗工具](semanticintelligence-part9) - 线性@- 观测修补和恢复