英語報(bào)紙解讀：Data may disrupt a peculiar business
原本想做精致些，整理出一周閱讀素材的處理方法，每日一篇，留日后反思。現(xiàn)在就跟著小編一起來了解一下《時(shí)尚玄學(xué)或遭數(shù)據(jù)瓦解》吧。
In the film “The Devil Wears Prada”, the character of Miranda Priestly, whose role is based on a feared Vogue editor, scolds her new assistant for not understanding fashion. Fashion, she tells her, is whatever a select group of designers and critics says it is. What she does not say, however, is that their judgments are themselves often influenced by another group: fashion forecasters, who predict what will be “in”. Might these seers of style in turn be undone by artificial intelligence (AI)?
電影《穿普拉達(dá)的女王》中的米蘭達(dá)取材于一位令人聞風(fēng)喪膽的Vogue主編，影片中她訓(xùn)斥新來的助理不懂什么是時(shí)尚，并告訴助理，一群頂尖設(shè)計(jì)師和評(píng)論家湊到一起，說時(shí)尚是什么，時(shí)尚就是什么。但她未提及，這些人的判斷還常常受到另一群人的影響:預(yù)言未來流行趨勢(shì)的時(shí)尚預(yù)測(cè)師。那會(huì)不會(huì)有一天也輪到這些潮流先知們被人工智能(AI)取代呢?
Fashion forecasting has always been a peculiar profession. The business came into its own in Paris in the 1960s when agencies began releasing “trend books”, collections of fabrics and design ideas. Retailers use these books for inspiration as they put together designs.
時(shí)尚預(yù)測(cè)向來是個(gè)玄乎的行當(dāng)。上世紀(jì)六十年代在巴黎，這一行自立門戶，開始發(fā)行“流行趨勢(shì)解讀”，內(nèi)容整合了各種流行面料和設(shè)計(jì)亮點(diǎn)，零售商在拼湊設(shè)計(jì)時(shí)讀這些書以尋求靈感。
The biggest of these forecasting firms is WGSN, with a market share of 50%. It employs 150 forecasters who scour the world’s catwalks, bars and clubs to spot the next big thing. Their findings are then combined with other data, from economic indicators to political sentiment. Petah Marian, a senior editor at WGSN, is confident that the methodology works. She says her colleagues often exclaim “I forecast that!” when visiting clothing shops.
最大的時(shí)尚預(yù)測(cè)公司是WGSN，占有50%的`市場(chǎng)份額。其麾下有150名時(shí)尚預(yù)測(cè)師，在全世界的秀場(chǎng)，酒吧，夜店里沙里淘金，尋找下一個(gè)時(shí)尚熱點(diǎn)。繼而他們的發(fā)現(xiàn)將與其他數(shù)據(jù)整合起來，從經(jīng)濟(jì)指標(biāo)到政治情感，無一例外。WGSN高級(jí)編輯佩塔·瑪麗安對(duì)這套方法信心滿滿。她說，她的同事們逛服裝店的時(shí)候經(jīng)常會(huì)興奮地叫“我預(yù)測(cè)到了這個(gè)!”
Ms Marian’s confidence may seem surprising, given the lack of clear correlations between fashion and macroeconomic data. Not much evidence supports the theory of George Taylor, an economist, that hemlines rise with stocks, and Leonard Lauder’s suggestion that lipstick sales increase during a downturn. Even the cofounder of WGSN, Marc Worth, who sold the firm to set up a rival service, once stated: “Nobody can really predict or forecast trends.” If forecasters can claim accuracy rates of up to 80%, it is because their predictions are often self-fulfilling. Most major retailers buy trend books. For designers, they are a form of insurance: as long as they are widely used, the risk of being wildly out of step with the market is modest.
瑪麗安女士的信心看上去有點(diǎn)不可思議，畢竟時(shí)尚和宏觀經(jīng)濟(jì)數(shù)據(jù)之間并無明顯的聯(lián)系。經(jīng)濟(jì)學(xué)家喬治·泰勒的理論稱裙子越短，股市越好，這一說法也沒什么證據(jù)支持，萊納德·勞德所說的經(jīng)濟(jì)下滑時(shí)口紅銷量會(huì)上漲亦是如此。WSGN創(chuàng)始人之一的馬克·沃斯，賣掉WSGN后成立了一家競(jìng)爭(zhēng)機(jī)構(gòu)，他曾說：“沒有人能真正預(yù)測(cè)流行趨勢(shì)。”如果預(yù)測(cè)師能確保準(zhǔn)確率在80%以上，則是因?yàn)樗麄兊念A(yù)測(cè)通�？梢宰晕覍�(shí)現(xiàn)。主要零售商是流行趨勢(shì)預(yù)言的受眾。對(duì)設(shè)計(jì)師們來說，這些預(yù)言提供了某種形式的保障：只要預(yù)言廣而泛之，自己過分偏離市場(chǎng)的幾率就微乎其微。
The business of forecasting is menaced by data-driven analysis, however. The clothing industry’s supply chain is becoming more digital and more flexible: Inditex and H&M, for example, aim to take an idea and turn it into a finished product ready for mass production in two weeks. In response, forecasting agencies are making use of data collated from retailers’ IT systems and have added short-term predictions to their portfolio of services. In 2013 WGSN launched INstock, a retail-analytics service, which uses past sales figures to predict upcoming bestsellers. EDITED, a competing service, provides “solid metrics” in fashion, claiming to use machine learning, an AI technique, in order to predict short-term sales trends.
然而，以數(shù)據(jù)為主的分析法對(duì)商業(yè)預(yù)測(cè)構(gòu)成了威脅。在時(shí)裝行業(yè)，供應(yīng)鏈越發(fā)數(shù)字化，體系也更加靈活。像印地迪克和H&M兩大經(jīng)銷商就打算一旦確定某個(gè)想法，就得在兩周之內(nèi)制出成品，用于大規(guī)模生產(chǎn)。相應(yīng)地，預(yù)測(cè)機(jī)構(gòu)對(duì)零售商信息系統(tǒng)中的數(shù)據(jù)整理利用，并由此在其服務(wù)項(xiàng)目中添加了短期預(yù)測(cè)這一項(xiàng)。2013年，WGSN發(fā)布了INstock軟件，該軟件運(yùn)用以往的銷售數(shù)據(jù)對(duì)未來暢銷品進(jìn)行預(yù)測(cè)，因此可用于零售分析。INstock的競(jìng)爭(zhēng)對(duì)手EDITED號(hào)稱運(yùn)用人工智能中的機(jī)器學(xué)習(xí)法，能對(duì)短期銷售走勢(shì)進(jìn)行預(yù)測(cè)，從而為時(shí)尚業(yè)提供“可靠準(zhǔn)則”。
Such offerings notwithstanding, the marriage of AI and fashion is still in its infancy. A study in 2014 found that the best predictive models get it wrong nearly half the time. But forecasters are likely to face rising competition as technology firms enter the market. Google, an online giant, now has a “Trend spotting” division. It releases a regular “Fashion Trends Report” based on the firm’s vast trove of search data. So far the results are basic: in 2016 slim “mom jeans” were on the rise while baggier “boyfriend jeans” were on the way out. But Olivier Zimmer, the project’s data scientist, says that the goal is to produce more sophisticated combinations of search and other data.
人工智能和時(shí)尚的結(jié)合，雖說孕育了上述產(chǎn)品服務(wù)，卻仍處于初級(jí)階段。2014年的一項(xiàng)研究發(fā)現(xiàn)，即便最好的預(yù)測(cè)模型，出錯(cuò)率仍接近百分之五十。隨著技術(shù)公司的介入，預(yù)測(cè)機(jī)構(gòu)還可能面臨更大的挑戰(zhàn)。網(wǎng)絡(luò)巨頭“谷歌”現(xiàn)在建立了“潮流觀測(cè)”部。該部門基于谷歌強(qiáng)大的搜索數(shù)據(jù)庫，定期發(fā)布“時(shí)尚潮流報(bào)告”。目前來講，報(bào)告得出的結(jié)論尚屬淺顯，比方說里面會(huì)給出“2016年，緊身的“媽媽牛仔褲”興起而寬松的“男友牛仔褲”正逐漸落伍”這樣的結(jié)論。但負(fù)責(zé)這些報(bào)告的數(shù)據(jù)學(xué)家奧利維·季默稱，更加科學(xué)合理地結(jié)合搜索數(shù)據(jù)與其他數(shù)據(jù)才是目的所在。
【本文作者：徐州七中彭向梅。(公眾號(hào)：草根英語行思教)】
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