. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.5. 控制流 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 6.2. match 控制流结构 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 6.3. if let 和 let else 简洁控制流 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 7.2. 定义模块来控制作用域与私有性 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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生产关系的大幅改变，加速重构传统行业模式，颠覆传统的就业观、生育观、 教育观，对传统社会秩序的稳定运行带来挑战。 （c）未来脱离控制的风险。随着人工智能技术的快速发展，不排除人工 智能自主获取外部资源、自我复制，产生自我意识，寻求外部权力，带来谋求 与人类争夺控制权的风险。 4. 技术应对措施 针对上述安全风险，模型算法研发者、服务提供者、系统使用者等需从 训练数据、算力设施、模型算法 性。 4.1.2 数据安全风险应对 （a） 在训练数据和用户交互数据的收集、存储、使用、加工、传输、提 供、公开、删除等各环节，应遵循数据收集使用、个人信息处理的安全规则， 严格落实关于用户控制权、知情权、选择权等法律法规明确的合法权益。 （b） 加强知识产权保护，在训练数据选择、结果输出等环节防止侵犯知 识产权。 （c） 对训练数据进行严格筛选，确保不包含核生化导武器等高危领域敏 检测，设计有效、可靠的对齐算法，确保价值观风险、伦理风险等可控。 （e）研发者应结合目标市场适用法律要求和风险管理要求，评估人工智 能产品和服务能力成熟度。 （f）研发者应做好人工智能产品及所用数据集的版本管理，商用版本应 可以回退到以前的商用版本。 （g）研发者应定期开展安全评估测试，测试前明确测试目标、范围和安 全维度，构建多样化的测试数据集，涵盖各种应用场景。 （h）研发者应制定明确的测试规则和方法，包括人工测试、自动测试、

Agile approach, program managers need to work with stakeholders representing the requirements, systems engineering, contracting, cost estimating, and testing communities to design processes around short processes. It presents options for structuring a program, developing a contract strategy, shaping systems engineering processes, managing requirements, and developing cost estimates for programs with a ....................................................................................... 20 9 Systems Engineering ....................................................................................

next layers of AI infrastructure: agentic interfaces, enterprise copilots, real-world autonomous systems, and sovereign models. Rapid advances in artificial intelligence, compute infrastructure, and global implications are just starting to emerge. AI agents could reshape how users interact with digital systems – from customer support and onboarding to research, scheduling, and internal operations. Enterprises For AI = Artificial General Intelligence93 Artificial General Intelligence, or AGI, refers to systems capable of performing the full range of human intellectual tasks – reasoning, planning, learning

across a growing number of sources and formats, data engineering and architecture teams must design systems that not only scale but also deliver real-time access and insights. However, the complexity isn’t of structured, semi-structured, and unstructured data with high-quality indexing. Yet, fragmented systems and inconsistent formats create silos, limiting accessibility and reducing effectiveness. Scalability Data Layers—platforms that seamlessly support analytics, search, and AI workloads at scale. These systems break down silos, reduce data sprawl, and deliver timely, actionable insights to power next-generation

throughout the organization 2. Ch. 19 – Enable and Inject Learning into Daily Work a. Complex systems are impossible to predict for all outcomes i. Dr. Steven Spear - resilient organizations are “skilled the tools that we gave them.” 2. Accidents are due to the inevitable design problems in complex systems that we build; they are system problems – not individual problems iii. Effective practices 1. Business Review): organizations are typically structured as:1. Standardized Model – where routine and systems govern everything; including strict compliance with budget and schedule 2. Experimental Model –

self-evident to a reasonable person • Sense - Categorize - Respond • Apply “Best Practices” Ordered Systems Obvious Sense Categorize Respond Best Practice Rigid ConstraintsComplicated • Cause & Effect Practices” Ordered Systems Obvious Sense Categorize Respond Best Practice Complicated Sense Analyze Respond Good Practice Rigid Constraints Governing ConstraintsComplex • Systems are without agents also modify the system • Probe - Sense - Respond • Apply “Emergent Practices” Unordered Systems Obvious Sense Categorize Respond Best Practice Complicated Sense Analyze Respond Good

Way: The Principles of Feedback 27 i. KEEP PUSHING QUALITY CLOSER TO THE SOURCE 1. In complex systems, adding more inspection steps and approval processes actually increases the likelihood of future organization c. Most admired DevOps organizations and successful 2015 IPO. b. CONSIDER BOTH SYSTEMS OF RECORD AND SYSTEMS OF ENGAGEMENT i. Gartner Bi-modal IT 1. Type 1 – System of Record – “Doing it right” production environment and ensuring service levels are met v. Infosec – team responsible for securing systems and data vi. Release Managers – the people responsible for coordinating the production deployment

exactly what IT leaders must avoid; continuously transforming and modernizing the company’s IT systems makes Fowler’s strangler pattern into an IT strategy rather than just a coding tactic. If you missed planning, and cost reduction. It documented as-is and to-be architectures, demonstrated alignment of systems with business needs, and did the “rigorous” up-front analysis and centralized planning that could Changed in a way that now favors “building” over “buying.” There are now ways of custom-developing systems that preserve many of the advantages of buying off the shelf.  The risk of developing a system