. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.3. 方法语法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 的所有权系统。如果你是一个特别细致的学习者，喜欢在进入下一环节之前学习每一 个细节，你可能会想要跳过第二章，直接阅读第三章，等到你想要通过项目应用所学到的细节 时再回到第二章。 第五章讨论结构体（struct）和方法，第六章介绍枚举（enum）、match 表达式和 if let 控 制流结构。在 Rust 中，创建自定义类型需要用到结构体和枚举。 第七章介绍 Rust 的模块（module）系统，其中的私有性规则用来组织代码和公开的 Rust。我们会通过 rustup 下载 Rust，这是一个管理 Rust 版本和相关工具的命 令行工具。下载时需要联网。 注意：如果你出于某些理由倾向于不使用 rustup，请到 Rust 的其他安装方法页面 查 看其它安装选项。 接下来的步骤会安装最新的稳定版 Rust 编译器。Rust 的稳定性确保本书所有示例在最新版本 的 Rust 中能够继续编译。不同版本的输出可能略有不同，因为 Rust

，开 源人工智能技术，共同研发人工智能芯片、框架、软件，引导产业界建立开放 生态，增强供应链来源多样性，保障人工智能供应链安全性稳定性。 5.6 推进人工智能可解释性研究。从机器学习理论、训练方法、人机 交互等方面组织研究人工智能决策透明度、可信度、纠错机制等问题，不断提 高人工智能可解释性和可预测性，避免人工智能系统意外决策产生恶意行为。 5.7 人工智能安全风险威胁信息共享和应急处置机制。持续跟踪分析 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、 混合测试等，利用沙箱仿真等技术对模型进行充分测试和验证。 （i） 研发者应评估人工智能模型算法对外界干扰的容忍程度，以适用范 围、注意事项或使用禁忌的形式告知服务提供者和使用者。

DeepSeek-R1 探索出RL方 法，且公开技术 • 诞生推理型Scaling Law DeepSeek颠覆式创新——技术创新 29政企、创业者必读 技术上实现对美国的赶超 掌握通向AI下一阶段的方法论 DeepSeek颠覆式创新——技术创新  美国人仍在遵循预训练Scaling law范式，走堆显卡路线，如 “星际之门”计划  中国DeepSeek-R1的创新突破，诞生推理型Scaling DeepSeek的优化降低对推理硬件的要求，减少推理成本  训练成本降低，堆显卡模式受质疑，探索新思路，算法优化空间大  无需训练自己的基座模型，直接部署在DeepSeek上，不用重复发明轮子  公开蒸馏方法，帮助其他模型提升能力，实现了模型制造模型，犹如工业母机  小模型可部署在企业内电脑或一体机上，使用成本降低，形成分布式推理网络  技术门槛降低， 可标准化、SaaS化部署，下载就能用 DeepSeek颠覆式创新——成本暴跌

app = make_app() app.listen(8888) await asyncio.Event().wait() if __name__ == "__main__": asyncio.run(main()) This example does not use any of Tornado’s asynchronous features; for that see this simple Tornado is different from most Python web frameworks. It is not based on WSGI, and it is typically run with only one thread per process. See the User’s guide for more on Tornado’s approach to asynchronous use IOLoop.run_in_executor to asynchronously run a blocking function on another thread, but note that the function passed to run_in_executor should avoid referencing any Tornado objects. run_in_executor

make_app() app.listen(8888) await asyncio.Event().wait()if __name__ == "__main__": asyncio.run(main()) This example does not use any of Tornado’s asynchronous features; for that see this simple frameworks. It is not based on WSGI [https://wsgi.readthedocs.io/en/latest/], and it is typically run with only one thread per process. See the User’s guide for more on Tornado’s approach to asynchronous use IOLoop.run_in_executor to asynchronously run a blocking function on another thread, but note that the function passed to run_in_executor should avoid referencing any Tornado objects. run_in_executor

compiler, types exist only at compile time and cannot be manipulated or expressed at run time. In Julia, types are themselves run-time objects, and can also be used to convey information to the compiler. What for the + function. Existing code then seamlessly applies to the new data types. Partly because of run-time type inference (augmented by optional type annotations), and partly because of a strong focus interactive session, type CTRL-D (press the Control/^ key together with the d key), or type exit(). When run in interactive mode, julia displays a banner and prompts the user for input. Once the user has entered

compiler, types exist only at compile time and cannot be manipulated or expressed at run time. In Julia, types are themselves run-time objects, and can also be used to convey information to the compiler. What for the + function. Existing code then seamlessly applies to the new data types. Partly because of run-time type inference (augmented by optional type annotations), and partly because of a strong focus interactive session, type CTRL-D (press the Control/^ key together with the d key), or type exit(). When run in interactive mode, julia displays a banner and prompts the user for input. Once the user has entered

compiler, types exist only at compile time and cannot be manipulated or expressed at run time. In Julia, types are themselves run-time objects, and can also be used to convey information to the compiler. What for the + function. Existing code then seamlessly applies to the new data types. Partly because of run-time type inference (augmented by optional type annotations), and partly because of a strong focus interactive session, type CTRL-D (press the Control/^ key together with the d key), or type exit(). When run in interactive mode, julia displays a banner and prompts the user for input. Once the user has entered

compiler, types exist only at compile time and cannot be manipulated or expressed at run time. In Julia, types are themselves run-time objects, and can also be used to convey information to the compiler. What for the + function. Existing code then seamlessly applies to the new data types. Partly because of run-time type inference (augmented by optional type annotations), and partly because of a strong focus interactive session, type CTRL-D (press the Control/^ key together with the d key), or type exit(). When run in interactive mode, julia displays a banner and prompts the user for input. Once the user has entered

compiler, types exist only at compile time and cannot be manipulated or expressed at run time. In Julia, types are themselves run-time objects, and can also be used to convey information to the compiler. What for the + function. Existing code then seamlessly applies to the new data types. Partly because of run-time type inference (augmented by optional type annotations), and partly because of a strong focus interactive session, type CTRL-D (press the Control/^ key together with the d key), or type exit(). When run in interactive mode, julia displays a banner and prompts the user for input. Once the user has entered