. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6. 枚举和模式匹配 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 15.5. RefCell 与内部可变性模式 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429 18.3. 面向对象设计模式的实现 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 19. 模式与模式匹配 . . . . . . . . . . . . . . .

人类训练数据接近枯竭  合成数据无法创造新知识  推理能力难以泛化，成本高昂 全面超越人类的人工智能在逻辑上不成立政企、创业者必读 15 DeepSeek出现之前的十大预判 之二 慢思考成为新的发展模式  大模型发展范式正在从「预训练」转向「后训练」和「推理时计算」  大模型厂商都在探索慢思考、思维链技术政企、创业者必读 DeepSeek出现之前的十大预判 之三 模型越做越专  除了 技术创新——让过去做不到的事情可以做到  体验创新——让使用起来很难很复杂的东西变得很简单易用  市场推广创新——让过去很难得到的东西可以得到  商业模式创新——让过去很昂贵的东西变得很便宜甚至免费 DeepSeek正是符合这四种创新模式的完美例子 25 颠覆式创新的四种方式政企、创业者必读 DeepSeek-R1突破了大模型Scaling Law瓶颈 导致大模型悲观论 认为大模型的能力无法进一步得到质的提升 DeepSeek颠覆式创新——开源 34政企、创业者必读 成本的急剧降低  DeepSeek可适配国产硬件，促进国产硬件发展  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

使用、滥用甚至恶意利用带来的安全风险。 3.1 人工智能内生安全风险 3.1.1 模型算法安全风险 （a）可解释性差的风险。以深度学习为代表的人工智能算法内部运行逻 辑复杂，推理过程属黑灰盒模式，可能导致输出结果难以预测和确切归因，如 有异常难以快速修正和溯源追责。 （b）偏见、歧视风险。算法设计及训练过程中，个人偏见被有意、无意引入， 或者因训练数据集质量问题，导致算法设计目的、输出结果存在偏见或歧视， 提高攻击效率，包括挖掘利用漏洞、破解密码、生成恶意代码、发送钓鱼邮件、 网络扫描、社会工程学攻击等，降低网络攻击门槛，增大安全防护难度。 （e）模型复用的缺陷传导风险。依托基础模型进行二次开发或微调，是 常见的人工智能应用模式，如果基础模型存在安全缺陷，将导致风险传导至下 游模型。 3.2.2 现实域安全风险 （a）诱发传统经济社会安全风险。人工智能应用于金融、能源、电信、交通、 民生等传统行业领域，如自动驾驶、智能诊疗等，模型算法存在的幻觉输出、 区 别对待，带来系统性、结构性的社会歧视与偏见。同时，拉大不同地区人工智 能鸿沟。 （b）挑战传统社会秩序的风险。人工智能发展及应用，可能带来生产工具、 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

flag; if both are specified the individual flag takes precedence): • autoreload=True: The app will watch for changes to its source files and reload itself when anything changes. This reduces the need to changes (but see also the command-line interface in main) tornado.autoreload.watch(filename: str) → None Add a file to the watch list. All imported modules are watched by default. tornado.autoreload.add_reload_hook(fn: ExpectLog context manager. Parameters • logger – Logger object (or name of logger) to watch. Pass an empty string to watch the root logger. • regex – Regular expression to match. Any log entries on the specified

flag; if both are specified the individual flag takes precedence): autoreload=True: The app will watch for changes to its source files and reload itself when anything changes. This reduces the need to Utilities tornado.autoreload — Automatically detect code changes in development start() wait() watch() add_reload_hook() main() tornado.concurrent — Work with Future objects Future run_on_executor() main)tornado.autoreload.watch(filename: str [https://docs.python.org/3/library/stdtypes.html#str]) → None [https://docs.python.org/3/library/constants.html#None] Add a file to the watch list. All imported

happen within hours. Kaitlin Elliott Head of Firmwide Generative AI Solutions To find out more, watch Morgan Stanley: Shaping the Future of Financial Services and ask us about our Eval Frameworks. 7 experts across domains, deep research saved an average of 4 hours per complex task. For more detail, watch BBVA puts AI into the hands of every team. 17 AI in the EnterpriseLesson 6 Unblock your developers

on how to use this tool, please see the following talk from JuliaCon 2022: https://www.youtube.com/watch?v=BFvpw Allocation Profiler ExampleCHAPTER 33. PROFILING 444 In this simple example, we use PProf REPL, ; can be used to suppress output. ; also has a different meaning within [ ], something to watch out for. ; can be used to separate expressions on a single line, but are not strictly necessary in StatStruct && prev == current) to detect notification of changes to the mtime or inode. However, using watch_file for this operation is preferred, since it is more reliable and efficient, although in some situations

on how to use this tool, please see the following talk from JuliaCon 2022: https://www.youtube.com/watch?v=BFvpw Allocation Profiler ExampleCHAPTER 33. PROFILING 445 In this simple example, we use PProf REPL, ; can be used to suppress output. ; also has a different meaning within [ ], something to watch out for. ; can be used to separate expressions on a single line, but are not strictly necessary in StatStruct && prev == current) to detect notification of changes to the mtime or inode. However, using watch_file for this operation is preferred, since it is more reliable and efficient, although in some situations

on how to use this tool, please see the following talk from JuliaCon 2022: https://www.youtube.com/watch?v=BFvpw Allocation Profiler ExampleCHAPTER 33. PROFILING 444 In this simple example, we use PProf REPL, ; can be used to suppress output. ; also has a different meaning within [ ], something to watch out for. ; can be used to separate expressions on a single line, but are not strictly necessary in StatStruct && prev == current) to detect notification of changes to the mtime or inode. However, using watch_file for this operation is preferred, since it is more reliable and efficient, although in some situations

on how to use this tool, please see the following talk from JuliaCon 2022: https://www.youtube.com/watch?v=BFvpw Allocation Profiler ExampleCHAPTER 33. PROFILING 444 In this simple example, we use PProf REPL, ; can be used to suppress output. ; also has a different meaning within [ ], something to watch out for. ; can be used to separate expressions on a single line, but are not strictly necessary in StatStruct && prev == current) to detect notification of changes to the mtime or inode. However, using watch_file for this operation is preferred, since it is more reliable and efficient, although in some situations