--- schema_version: "library-reader-v1" source_type: "original_book" title: "AI 3.0（畅销书《复杂》作者、复杂系统前沿科学家梅拉妮·米歇尔，历经10年思考，厘清人工智能与人类智能的全新力作，全景式地展现了人工智能的..." author: null edition: null publisher: null year: null isbn: null library_document_id: "75afffda-b96b-4df5-89ff-2ec037cfed66" source_sha256: "21c9bed4763f96ba9dce4c4e24be07b2ddb63e08acce9107a510c1026714c784" pdf_physical_page_count: 491 pdf_physical_page_index_base: 0 range_start: 440 range_count: 10 html_url: "https://workshop.closeclaw.top/api/ai/read/75afffda-b96b-4df5-89ff-2ec037cfed66?start=440&limit=10" markdown_url: "https://workshop.closeclaw.top/api/ai/read/75afffda-b96b-4df5-89ff-2ec037cfed66.md?start=440&limit=10" previous_url: "https://workshop.closeclaw.top/api/ai/read/75afffda-b96b-4df5-89ff-2ec037cfed66.md?start=430&limit=10" next_url: "https://workshop.closeclaw.top/api/ai/read/75afffda-b96b-4df5-89ff-2ec037cfed66.md?start=450&limit=10" --- # AI 3.0（畅销书《复杂》作者、复杂系统前沿科学家梅拉妮·米歇尔，历经10年思考，厘清人工智能与人类智能的全新力作，全景式地展现了人工智能的... > 页码规则：PDF_PHYSICAL_PAGE_INDEX 是从 0 开始的权威文件物理页索引；PRINTED_PAGE 是页面中识别出的印刷页码，可能为空，二者不得混用。 ## PDF_PHYSICAL_PAGE_INDEX=440 | PRINTED_PAGE=NOT_DETECTED ## PDF_PHYSICAL_PAGE_INDEX=441 | PRINTED_PAGE=NOT_DETECTED 27 W. Knight,“How Long Before AI Systems Are Hacked in Creative New Ways?”, MIT Technology Review , Dec. 15, 2016. 28 J. Clune,“How Much Do Deep Neural Networks Understand About the Images They Recognize?”, lecture slides (2016), accessed Dec. 7, 2018. 07 确保价值观一致，构建值得信赖、有道德的人工智能 1 D. Palmer,“AI Could Help Solve Humanity's Biggest Issues by Taking Over from Scientists, Says DeepMind CEO”, Computing , May 26, 2015. 2 S. Lynch,“Andrew Ng: Why AI Is the New Electricity”, Insights by Stanford Business , March 11, 2017. 3 J. Anderson, L. Rainie, and A. Luchsinger, “Artificial Intelligence and the Future of Humans”, Pew Research Center, Dec. 10, 2018. 4 关于人工智能和大数据的道德问题的两种最新处理方法参见C. O'Neil, Weapons of Math Destruction: ## PDF_PHYSICAL_PAGE_INDEX=442 | PRINTED_PAGE=NOT_DETECTED 5 C. Domonoske,“Facebook Expands Use of Facial Recognition to ID Users in Photos”, National Public Radio, Dec. 19, 2017. 7 J. Snow,“Amazon's Face Recognition Falsely Matched 28 Members of Congress with Mugshots”, Free Future (blog), ACLU, July 26, 2018. 8 B. Brackeen,“Facial Recognition Software Is Not Ready for Use by Law Enforcement”, Tech Crunch , June 25, 2018. ## PDF_PHYSICAL_PAGE_INDEX=443 | PRINTED_PAGE=NOT_DETECTED 17 同上, 192。 ## PDF_PHYSICAL_PAGE_INDEX=444 | PRINTED_PAGE=NOT_DETECTED 08 强化学习，最重要的是学会给机器人奖励 ## PDF_PHYSICAL_PAGE_INDEX=445 | PRINTED_PAGE=NOT_DETECTED 2 更确切地说，这是一种被称为“值函数学习”（value learning）的强化学习方法，但并不是唯一可行的 方 法 。 还 有 一 种 被 称 为 “ 策 略 学 习 ” （ policylearning）的强化学习方法，其目标是直接学习一个给定状态下要执行的动作，而非首先学习如何获得更优动作的值。 3 C. J. Watkins & P. Dayan,“Q-Learning”, Machine Learning 8, nos. 3-4 (1992): 279-292. 与强化学习有关的详细技术性介绍，参阅R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction , 2nd ed. (Cambridge, Mass.: MIT Press, 2017)。 5 参考示例，请查阅如下文章：P. Christiano et al.,“Transfer from Simulation to Real World Through Learning Deep Inverse Dynamics Model”, arXiv: 1610.03518(2016); J. P. Hanna and P. Stone, “Grounded Action Transformation for Robot Learning in Simulation”, in Proceedings of the Conference of the American Association for Artificial Intelligence (2017), 3834-3840; A. A. Rusu et al., “Sim-to-Real Robot Learning from Pixels with Progressive Nets”, in Proceedings of the First Annual Conference on Robot Learning , CoRL (2017); S. James, A. J. Davison, and E. Johns, “Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-stage Task”, in Proceedings of the First Annual Conference on Robot Learning , CoRL (2017); M. Cutler, T. J. Walsh, and J. P. How,“Real-World Reinforcement Learning via Multifidelity Simulators”, IEEE Transactions on Robotics 31, no. 3 (2015): 655- 671。 ## PDF_PHYSICAL_PAGE_INDEX=446 | PRINTED_PAGE=NOT_DETECTED 09 学会玩游戏，智能究竟从何而来 1 P. Iwaniuk,“A Conversation with Demis Hassabis, the Bullfrog AI Prodigy Now Finding Solutions to the World's Big Problems”, PCGamesN , accessed Dec. 7, 2018. 2 “From Not Working to Neural Networking”, Economist , June 25, 2016. 3 M. G. Bellemare et al.,“The Arcade Learning Environment: An Evaluation Platform for General Agents”, Journal of Artificial Intelligence Research 47(2013): 253-279. 这里更深层的技术性原理是：DeepMind的程序使用了一种被称为“epsilon-greedy”的方法来选择每个时步的动作。程序以epsilon 的概率随机选择一个动作；以1-epsilon 的概率选择具有最高值的动作。epsilon 是一个介于0和1之间的数值，其初始值被设置为接近1，经多个片段训练后逐渐减小。 ## PDF_PHYSICAL_PAGE_INDEX=447 | PRINTED_PAGE=NOT_DETECTED 5 R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction , 2nd ed. (Cambridge, Mass.: MIT Press, 2017), 124. 更 多 细 节 参 见 V. Mnihet al.,“Human-Level Control Through Deep Reinforcement Learning”, Nature 518, no. 7540 (2015): 529。 7 V. Mnih et al.,“Playing Atari with Deep Reinforcement Learning”, Proceedings of the Neural Information Processing Systems (NIPS) Conference, Deep Learning Workshop (2013). 8 “Arthur Samuel”, History of Computer website. 9 塞缪尔的程序使用了可变数量的层，具体选择哪种，取决于行棋动作。 10 塞缪尔的程序在每一轮还使用了一种被称为“alpha-beta剪枝”的方法来确定博弈树中的哪些节点不需要被评估。alpha-beta剪枝也是IBM的国际象棋程序深蓝的重要组成部分。 ## PDF_PHYSICAL_PAGE_INDEX=448 | PRINTED_PAGE=NOT_DETECTED 12 同上。 ## PDF_PHYSICAL_PAGE_INDEX=449 | PRINTED_PAGE=NOT_DETECTED 下一段：[继续读取](https://workshop.closeclaw.top/api/ai/read/75afffda-b96b-4df5-89ff-2ec037cfed66.md?start=450&limit=10)