机器人程序的崛起

    有家叫Savage Beast的公司很能说明问题。该公司创立于上世纪90年代，其它的运作方式是付费请数百名音乐人听歌，然后依据400项音乐特质（包括节奏、音调及众多其他要素）对其进行归类。Savage Beast试图向Tower Records及百思买（Best Buy）之类音乐相关产品零售商销售其音乐推荐服务，但销路惨淡。该公司差点就没能活过2000年的网络股泡沫破灭潮，2005年时已经奄奄一息。此后，它转用算法、而不是非真正的音乐人来生成音乐推荐信息，并摇身一变，改名为Pandora。，2011年，这家该公司上市，市值高达30亿美元。

    eLoyalty是另一家发展历程体现了算法威力的公司。这家客户管理咨询公司从事的业务平庸无奇——给呼叫中心提供建议。eLoyalty的算法能通过扫描一个拥有约200万说话方式的数据库，界定来电者的个性。，如此，销售代表或服务专员能立即了解来电客户较为情绪化还是相对理性，并采取相应的销售或服务技巧。沃达丰（Vodaphone）签约使用了eLoyalty的服务，此后，其接线员就可有针对性的提供服务。，比如，面对情绪化的顾客时，需要用小道消息套近乎，才能让他们对升级服务感兴趣；而面对更善于分析的客户时，只需谈谈服务的价值定位就行了。应用eLoyalty服务后，沃达丰的升级服务率提升了8,600%。

    尽管斯坦纳援引了大量案例，但他似乎并不是这个新奇世界的优秀向导。由于该书未能专注于华尔街或医药界之类的某一个特定领域，讲清楚算法到底是如何颠覆其原有模式的。，它只好覆盖太多不同行业，结果是有些材料显得陈腐。比如书中有一章讲述音乐品味的自动化，当中连上世纪90年代和21世纪初的报纸上的报道都摘录了。同样，美国国家航空航天局（NASA）开发个性检测系统以便利太空任务宇航员团队的遴选还是上世纪七八十年代的事情。

    我真心希望能爱上这本书，因为这个充满互联网机器人程序的新世界既令人担忧，又引人入胜。我们这个世界的运作，越来越取决于华尔街、Facebook、谷歌（Google）和亚马逊（Amazon）如何部署其算法。可是，尽管斯坦纳撰写了很多例子，讲述机器人程序对我们生活的影响，该书的特质和叙述方式仍然无法使人爱不释手。相反，它读起来就像一篇写的太长的读书报告。

    《自动化：算法统治世界》出版的时机（8月30日上市）既可说幸运，也可说不幸。很多美国人仍在热议骑士资本（Knight Capital）造成的混乱，该。这家公司的交易算法出错，一夜之间就造成了几亿美元的损失。可是，骑士资本事件所提出的问题，该书并未回答。骑士资本的算法问题只是影响了几只股票而已，可要是医疗保健行业最终也部署机器人程序来给我们开药方，那系统会不会还出故障？很少有人深入探讨过算法普及的缺陷，而斯坦纳也放过了这个话题。

    机器人程序一旦进驻，就不会撤走。不管人们将其应用到哪个领域，算法都能带来效率、巧妙与速度。可与绝大多数其他突破性创新一样，它们已开始体验到成长中的阵痛。既然算法已经统治世界，那紧接着就应该担心它们的缺陷是否会毁掉世界。

    译者:小宇

    There's Savage Beast, a 1990s startup that paid hundreds of musicians to listen to songs and classify them according to some 400 musical attributes, including rhythm, tonality, and much more. Savage Beast tried without luck to sell its music recommendation service to music retailers like Tower Records and Best Buy (BBY). The company barely survived the 2000 dotcom bust and was on life support by 2005, when it started to produce music recommendations using algorithms instead of live musicians. Along the way, Savage Beast changed its name to Pandora (P). In 2011 it went public with a $3 billion valuation.

    ELoyalty is another company whose story shows the power of algorithms. The customer management consultant deals in the stodgy business of advising call centers. ELoyalty's algorithms scan a database of about two million speech patterns to classify callers by personality. As a result, sales and service reps can instantly tell if a customer is more emotional or more thought-driven, and tailor their pitches accordingly. Vodaphone (VOD) signed on to eLoyalty's program, and afterward its operators knew if they were talking to an emotional customer who needed chummy gossip to get interested in upgrades, as opposed to more analytic clients who only wanted to hear about the value proposition. After adopting eLoyalty, Vodaphone's sales upgrades increased by 8,600%.

    Despite his wealth of case material, Steiner turns out to be an uncertain guide to this newfangled world. Because the book lacks a narrow focus on how algos are upending, say, Wall Street or the medical field, it tries to cover too many industries. As a result, some of the material feels stale. A chapter on the automation of musical taste, for instance, includes stories told in newspapers in the 1990s and early 2000s. Similarly, NASA's personality-detecting system, which helped the space program pick teams of astronauts, was developed in the 1970s and 1980s.

    I really wanted to fall in love with this book, for the new world of bots is at once alarming and engrossing. Increasingly, our world is being shaped by how Wall Street, Facebook (FB), Google, and Amazon (AMZN) deploy their algorithms. But while Steiner has written an exhaustive account of the bots powering our lives, the book lacks the characters and narrative to be a page-turner. Instead it feels like a book report that ran long.

    The timing of Automate This (available Aug. 30) is both lucky and unlucky. Half of America is still talking about the fiasco at Knight Capital, where trading algorithms went haywire and caused the firm to lose several hundred million dollars overnight. Yet the Knight Capital story raises questions the book doesn't answer. Knight's algo issues only affected a few stocks. But if the health care industry eventually deploys bots to prescribe our medicines, for example, can we expect similar glitches? There's a downside to this story that's rarely been explored, and Steiner lets it pass.

    Once bots move in, they don't move out. Algorithms have brought efficiency, craftiness, and speed to nearly everything that humans have tasked them with. But as with most breakthrough innovations, they have experienced growing pains. Now that algorithms rule the world, the next story will be how their shortcomings might destroy it.