. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 12.4. 采用测试驱动开发完善库的功能 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498 21. 最后的项目：构建多线程 web server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507 21.1. 建立单线程 web server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552 22.4. D - 实用开发工具 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

………………………… 7 4.2 针对人工智能应用安全风险 ………………………… 9 5. 综合治理措施 ……………………………………………… 10 6. 人工智能安全开发应用指引 ……………………………… 12 6.1 模型算法研发者安全开发指引 ……………………… 12 6.2 人工智能服务提供者安全指引 ……………………… 13 6.3 重点领域使用者安全应用指引 ……………………… 14 6 应用场景，提出通过安全软件开发、数据质量提升、安全建设运维、测评监测 加固等技术手段提升人工智能产品及应用的安全性、公平性、可靠性、鲁棒性- 3 - 人工智能安全治理框架 的措施。 2.3 综合治理措施方面。明确技术研发机构、服务提供者、用户、政府 部门、行业协会、社会组织等各方发现、防范、应对人工智能安全风险的措施 手段，推动各方协同共治。 2.4 安全开发应用指引方面。明确模型算法研发者、服务提供者、重点 安全开发应用指引方面。明确模型算法研发者、服务提供者、重点 领域用户和社会公众用户，开发应用人工智能技术的若干安全指导规范。 3. 人工智能安全风险分类 人工智能系统设计、研发、训练、测试、部署、使用、维护等生命周期 各环节都面临安全风险，既面临自身技术缺陷、不足带来的风险，也面临不当 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

慢思考能力 传统软件是辅助人的工具，Agent是能够自主工作的数字员工，是新的生产力政企、创业者必读 22 DeepSeek出现之前的十大预判 之九 开源效果追赶上闭源  技术开放，吸引广大开发人员和用户使用  很多公司参与开源，帮助改进产品，众人拾柴火焰高， 反哺开源产品，形成正循环政企、创业者必读 DeepSeek出现之前的十大预判 之十 中美差距快速缩小  美国预训练堆算 DeepSeek颠覆式创新——用户体验 32 ——掀起新一轮AI科普教育 认知决定行动，这场全民AI科普对推动中国AI发展功不可没政企、创业者必读 开源改变行业格局，建立强大生态  开源战胜闭源，促使全球公司、开发者等转到开源  建立强大生态，成为全球人工智能根技术，无推广情况下各国 政府、企业、云厂商纷纷接入，获得全球最大影响力  改变中美竞争格局：美国是闭源封闭垄断思路，中国领导开源 文化，加速中国领先地位 因为开源透明可信任，企业和 政府可做大量私有化部署  一个开源产品获得突破之后，全世界都能分享成果，结束中国百模大 战，节省大量成本  很多公司参与开源，帮助改进产品，很多人基于DS生态开发应用产 品，增加影响力，人人为我，我为人人  技术开放，对人工智能行业形成蓬勃发展，寒武纪大爆炸，推动AGI 行业发展 DeepSeek颠覆式创新——开源 34政企、创业者必读 成本的急剧降低

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 6.2 Web framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Python Module Index 257 Index 259 iiiTornado Documentation, Release 6.5.1 Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed. By using non-blocking world” example web app for Tornado: import asyncio import tornado class MainHandler(tornado.web.RequestHandler): def get(self): self.write("Hello, world") def make_app(): return tornado.web.Application([

Tornado [https://www.tornadoweb.org] is a Python web framework and asynchronous networking library, originally developed at FriendFeed [https://en.wikipedia.org/wiki/FriendFeed]. By using non-blocking s] Hello, world Here is a simple “Hello, world” example web app for Tornado: import asyncio import tornado class MainHandler(tornado.web.RequestHandler): def get(self): self.write("Hello write("Hello, world") def make_app(): return tornado.web.Application([ (r"/", MainHandler), ]) async def main(): app = make_app() app.listen(8888) await asyncio.Event().wait()if

Amazon Web Services, Inc. or its Affiliates. All rights reserved. Presenter: Haichen Shen, Yao Wang Amazon SageMaker Neo, Deep Engine Science Dynamic Model in TVM AWS AI© 2019, Amazon Web Services while loop Limitation of TVM/graph runtime ● Cannot compile and run dynamic models© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Support dynamic model in TVM ● Support Any-dim Graph dispatch for a (sub-)graph In collaboration with Jared Roesch, Zhi Chen, Wei Chen© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. “Any” in Relay typing Any: represent an unknown

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon/Intel Confidentia Presenter: Zhi Chen, Cody Yu Amazon SageMaker Neo, Deep Engine Science Bring Your Own Codegen to TVM TVM AWS AI© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Considering You... Design and manufacture a deep learning chip which achieves amazing performance on widely-used operators Suppression (NMS) is too new to be supported by your chip But NMS is supported by TVM!© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Let TVM Be the Compiler of Your Chip Your

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Animesh Jain Amazon SageMaker Neo Compilation of Quantized Models in TVM AWS AI© 2019, Amazon Web Services, Inc. or its Affiliates 𝑟𝑒𝑎𝑙_𝑣𝑎𝑙𝑢𝑒 = 𝑠𝑐𝑎𝑙𝑒 ∗ (𝑞𝑢𝑎𝑛𝑡𝑖𝑧𝑒𝑑_𝑣𝑎𝑙𝑢𝑒 − 𝑧𝑒𝑟𝑜_𝑝𝑜𝑖𝑛𝑡)© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Quantization in TVM • Quantization within pre-quantized graph in TFLite or MxNet • Use high-level wrapper ops of QNN dialect© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. TVM Overview Framework Graph Mxnet TF ….

one can make. The magic of watching AI do your work for you feels like the early days of email and web search – technologies that fundamentally changed our world. The better / faster / cheaper impacts 1993 with release of the World Wide Web (WWW) into the public domain, which allowed users to create websites; however, Tim Berners-Lee invented the World Wide Web in 1989, per CERN. Source: Google, USA Morgan Stanley, ‘Google and Meta: AI vs. Fundamental 2H Debates’ (7/23), Our World in Data, other web sources per MS Years to 50% Adoption of Household Technologies in USA, per Morgan Stanley Consumer

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Deploying GluonCV models using TVM© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon with TVM© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Deploy GluonCV Models https://arxiv.org/pdf/1907.02154.pdf© 2019, Amazon Web Services, Inc. or its Affiliates Amazon Trademark Overall Performance AWS DeepLens Acer aiSage NVIDIA Jetson Nano© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Effects of Vision-specific