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17．一个更丰富的理论解释了人类的努力与计算过程的混合，参阅H. R. Ekbia and Bonnie A. Nardi，Heteromation，and Other Stories of Computing and Capitalism（Cambridge，MA：MIT Press，2017）。
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18．Bureau of Labor Statistics, “Contingent and Alternative Employment Arrangements, May 2017,”Economic News Release, U.S. Department of Labor, June 7, 2018.
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19．U.S. Government Accountability Office, Contingent Workforce: Size, Characteristics, Earnings, and Benefits, GAO-15-168R (Washington, DC: Government Accountability Office, 2015).
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20．Lawrence F. Katz and Alan B. Krueger, “The Rise and Nature of Alternative Work Arrangements in the United States, 1995–2015”(NBER Working Paper Series, no. 22667, National Bureau of Economic Research, Cambridge, MA, September 2016).
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21．Diana Farrell and Fiona Greig, The Online Platform Economy: Has Growth Peaked? (JPMorgan Chase Institute, 2017).
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22．David Weil, The Fissured Workplace: Why Work Became So Bad for So Many and What Can Be Done to Improve It (Cambridge, MA: Harvard University Press, 2014).
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23．Wei-Chu Chen，Sara Kingsley，Greg Minton和Ming Yin贡献了原始定量数据分析，Syed Shoaib Ali，Deepti Kulkarni，Jason Qualls和Kathryn Zyskowski贡献了本书的定性数据分析。如果没有他们，这项研究就不可能完成。玛丽主导了在美国和印度进行的所有实地调查和工人访谈，完成了调查设计、第二章中的历史分析以及贯穿全书的定性分析。西达尔特主导了本书中所有的线上实验和定量分析，以及第三章中对请求者访谈的定性分析。在整个过程中，Andrea Alarcón，Sarah Hamid，Rebecca Hoffman，Kate Miltner，Christopher Persaud和Steven Schirra也对研究提供了宝贵的科研帮助。
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24．Winter Mason and Siddharth Suri, “Conducting Behavioral Research on Amazon’s Mechanical Turk,”Behavior Research Methods 44, no. 1(March 2012): 1–23, https://doi.org/10.3758/s13428-011-0124-6.
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第一章 环路中的人类
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1．M. Six Silberman, “Human-Centered Computing and the Future of Work: Lessons from Mechanical Turk and Turkopticon, 2008–2015”(PhD diss., University of California, Irvine, 2015).
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2．Brad Stone, The Everything Store: Jeff Bezos and the Age of Amazon (New York: Little, Brown, 2013).
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3．Ibid.
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4．Ibid.
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5．据说，MTurk是杰夫·贝佐斯最喜欢的项目，他把MTurk当成一个工具，不仅可以内部使用，还可以作为一个新的市场给商家创造收入。商家也需要清理自己的清单。参阅Stone，The Everything Store。
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6．Daniel W. Barowy et al., “VoxPL: Programming with the Wisdom of the Crowd,”in CHI ’17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (New York: ACM, 2017), 2347–58, https://doi.org/10.1145/3025453; Suri, “Computing with the Crowd,”101.
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7．很多人批评亚马逊给工作平台取名过于随便，但我们还不清楚最初亚马逊是否打算公布MTurk。从2005年发布以来，MTurk一直处于测试状态。
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8．Kevin P. Murphy, Machine Learning: A Probabilistic Perspective (Cambridge, MA: MIT Press, 2012).
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9．Fei-Fei Li, “ImageNet: Where Have We Been? Where Are We Going?,”ACM Learning Webinar, https://learning.am.org/, accessed September 21, 2017; Deng et al., “ImageNet: A Large-Scale Hierarchical Image Database,”in 2009 IEEE Conference on Computer Vision and Pattern Recognition (Piscataway, NJ: IEEE), 248–55.
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10．从2010年到2016年，精度从72%提高到97%。
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11．事实上，亚历山大·威斯纳–格罗斯指出：“限制人工智能发展的因素可能是数据集，而非算法。”参阅Alexander Wissner-Gross，“2016：What Do You Consider the Most Interesting Recent［Scientific］News？What Makes It Important？”Edge，https://www.edge.org/response-detail/26587，访问于2018年10月21日。
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12．为了激励研究人员使用这些数据集，李飞飞和她的同事每年组织一场挑战赛，从世界各地的研究团队中选出最佳的图像识别算法。科学家朝着这一目标所取得的进展是惊人的。过去8年，在一年一度的ImageNet挑战赛中，错误减少到约1/10，识别图像的精度提高了约3倍。最终，视觉算法的错误率比人工还要低。科学家在8年的挑战赛中取得的算法和工程上的进步，在很大程度上推动了最近神经网络的成功，即所谓的“深度学习革命”，它将影响多个领域和多种问题。
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13．Djellel Difallah, Elena Filatova, and Panos Ipeirotis, “Demographics and Dynamics of Mechanical Turk Workers,”in Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (New York: ACM, 2018), 135–43, https://doi.org/10.1145/3159652.3159661.
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14．Ibid.
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15．Panos Ipeirotis, “How Many Mechanical Turk Workers Are There?,”A Computer Scientist in Business School (blog), January 29, 2018, http://www.behind-the-enemy-lines.com/.
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