????有家叫Savage Beast的公司很能說(shuō)明問(wèn)題。該公司創(chuàng)立于上世紀(jì)90年代，其它的運(yùn)作方式是付費(fèi)請(qǐng)數(shù)百名音樂人聽歌，然后依據(jù)400項(xiàng)音樂特質(zhì)（包括節(jié)奏、音調(diào)及眾多其他要素）對(duì)其進(jìn)行歸類。Savage Beast試圖向Tower Records及百思買（Best Buy）之類音樂相關(guān)產(chǎn)品零售商銷售其音樂推薦服務(wù)，但銷路慘淡。該公司差點(diǎn)就沒能活過(guò)2000年的網(wǎng)絡(luò)股泡沫破滅潮，2005年時(shí)已經(jīng)奄奄一息。此后，它轉(zhuǎn)用算法、而不是非真正的音樂人來(lái)生成音樂推薦信息，并搖身一變，改名為Pandora。，2011年，這家該公司上市，市值高達(dá)30億美元。

????eLoyalty是另一家發(fā)展歷程體現(xiàn)了算法威力的公司。這家客戶管理咨詢公司從事的業(yè)務(wù)平庸無(wú)奇——給呼叫中心提供建議。eLoyalty的算法能通過(guò)掃描一個(gè)擁有約200萬(wàn)說(shuō)話方式的數(shù)據(jù)庫(kù)，界定來(lái)電者的個(gè)性。，如此，銷售代表或服務(wù)專員能立即了解來(lái)電客戶較為情緒化還是相對(duì)理性，并采取相應(yīng)的銷售或服務(wù)技巧。沃達(dá)豐（Vodaphone）簽約使用了eLoyalty的服務(wù)，此后，其接線員就可有針對(duì)性的提供服務(wù)。，比如，面對(duì)情緒化的顧客時(shí)，需要用小道消息套近乎，才能讓他們對(duì)升級(jí)服務(wù)感興趣；而面對(duì)更善于分析的客戶時(shí)，只需談?wù)劮?wù)的價(jià)值定位就行了。應(yīng)用eLoyalty服務(wù)后，沃達(dá)豐的升級(jí)服務(wù)率提升了8,600%。

????盡管斯坦納援引了大量案例，但他似乎并不是這個(gè)新奇世界的優(yōu)秀向?qū)?。由于該書未能專注于華爾街或醫(yī)藥界之類的某一個(gè)特定領(lǐng)域，講清楚算法到底是如何顛覆其原有模式的。，它只好覆蓋太多不同行業(yè)，結(jié)果是有些材料顯得陳腐。比如書中有一章講述音樂品味的自動(dòng)化，當(dāng)中連上世紀(jì)90年代和21世紀(jì)初的報(bào)紙上的報(bào)道都摘錄了。同樣，美國(guó)國(guó)家航空航天局（NASA）開發(fā)個(gè)性檢測(cè)系統(tǒng)以便利太空任務(wù)宇航員團(tuán)隊(duì)的遴選還是上世紀(jì)七八十年代的事情。

????我真心希望能愛上這本書，因?yàn)檫@個(gè)充滿互聯(lián)網(wǎng)機(jī)器人程序的新世界既令人擔(dān)憂，又引人入勝。我們這個(gè)世界的運(yùn)作，越來(lái)越取決于華爾街、Facebook、谷歌（Google）和亞馬遜（Amazon）如何部署其算法。可是，盡管斯坦納撰寫了很多例子，講述機(jī)器人程序?qū)ξ覀兩畹挠绊?，該書的特質(zhì)和敘述方式仍然無(wú)法使人愛不釋手。相反，它讀起來(lái)就像一篇寫的太長(zhǎng)的讀書報(bào)告。

????《自動(dòng)化：算法統(tǒng)治世界》出版的時(shí)機(jī)（8月30日上市）既可說(shuō)幸運(yùn)，也可說(shuō)不幸。很多美國(guó)人仍在熱議騎士資本（Knight Capital）造成的混亂，該。這家公司的交易算法出錯(cuò)，一夜之間就造成了幾億美元的損失。可是，騎士資本事件所提出的問(wèn)題，該書并未回答。騎士資本的算法問(wèn)題只是影響了幾只股票而已，可要是醫(yī)療保健行業(yè)最終也部署機(jī)器人程序來(lái)給我們開藥方，那系統(tǒng)會(huì)不會(huì)還出故障？很少有人深入探討過(guò)算法普及的缺陷，而斯坦納也放過(guò)了這個(gè)話題。

????機(jī)器人程序一旦進(jìn)駐，就不會(huì)撤走。不管人們將其應(yīng)用到哪個(gè)領(lǐng)域，算法都能帶來(lái)效率、巧妙與速度?？膳c絕大多數(shù)其他突破性創(chuàng)新一樣，它們已開始體驗(yàn)到成長(zhǎng)中的陣痛。既然算法已經(jīng)統(tǒng)治世界，那緊接著就應(yīng)該擔(dān)心它們的缺陷是否會(huì)毀掉世界。

????譯者: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.