學習機器
The online commercial empire rests on a low-key approach to artificial intelligence
這家互聯網商業帝國在人工智能的發展上選擇了壹條低調的路
Amazon’s six-page memos are famous. Executives must write one every year, laying out their business plan. Less well known is that these missives must always answer one question in particular: how are you planning to use machine learning? Responses like “not much” are, according to Amazon managers, discouraged.
亞馬遜的六頁備忘錄十分出名，執行官們每年必須按要求寫壹頁，詳細闡述自己未來的商業計劃。但不太出名的壹點是，每壹封信函必須回答壹個具體的問題：妳打算怎麽利用機器學習？如果妳的回答是“沒什麽可說的”，根據亞馬遜管理層的說法，這種答案是不允許出現的。
Machine learning is a form of artificial intelligence (ai) which mines data for patterns that can be used to make predictions. It took root at Amazon in 1999 when Jeff Wilke joined the firm. Mr Wilke, who today is second-in-command to Jeff Bezos, set up a team of scientists to study Amazon’s internal processes in order to improve their efficiency. He wove his boffins into business units, turning a cycle of self-assessment and improvement into the default pattern. Soon the cycle involved machine- learning algorithms; the first one recommended books that customers might like. As Mr Bezos’s ambitions grew, so did the importance of automated insights.
機器學習是人工智能的壹種實現途徑，它主要包括特定類型的數據挖掘，主要目的是對未來趨勢進行預測。1999年當傑夫·維爾克（Jeff Wilke）加入公司的時候，這壹想法就開始落地了。維爾克先生是亞馬遜公司的第二把交椅，他組建了壹個人工智能專家組，主要負責亞馬遜內部工作流程的研究，目的在於提高員工的工作效率。他將科學家們安排在各企事業部門，將不斷循環的自我評價和提高過程固定為壹個默認模式，很快這個循環就加入了算法；第壹代算法可以向顧客推薦他們喜歡的圖書。隨著貝佐斯先生的野心越來越膨脹，這種全自動的算法推薦模式也顯得越來越重要。
Yet whereas its fellow tech titans flaunt
其他科技巨頭有什麽可炫耀的
their ai prowess at every opportunity—Facebook’s facial-recognition software, Apple’s Siri digital assistant or Alphabet’s self- driving cars and master go player—Amazon has adopted a lower-key approach to machine learning. Yes, its Alexa competes with Siri and the company offers predictive services in its cloud. But the algorithms most critical to the company’s success are those it uses to constantly streamline its own operations. The feedback loop looks the same as in its consumer-facing ai: build a service, attract customers, gather data, and let computers learn from these data, all at a scale that human labor could not emulate.
科技巨頭們抓住壹切機會展現自己在AI方面的實力：臉書推出了面部識別軟件，蘋果擁有語音助手Siri，谷歌推出了無人駕駛和阿爾法Go。和這些公司相比，亞馬遜在機器學習上選擇了壹條低調的路。Alexa（亞歷克斯）是亞馬遜公司推出的壹項人工智能服務，它的主要競爭對手是蘋果的Siri。依靠Alexa的雲平臺，亞馬遜可以為用戶提供預測服務。這款人工智能背後的算法頗具特色，它能夠不斷將自己的操作流程精簡處理，但這款AI服務的反饋回路和其客戶端AI類似：發起壹項服務，吸引目標客戶，收集用戶信息，讓計算機學習這些數據，並且處理的數據規模是人力無法企及的。
Mr Porter’s algorithms
波特先生的算法
Consider Amazon’s fulfilment centers. These vast warehouses, more than 100 in North America and 60-odd around the world, are the beating heart of its $207bn online-shopping business. They store and dispatch the goods Amazon sells. Inside one on the outskirts of Seattle, package shuttle along conveyor belts at the speed of a moped. The noise is deafening—and the facility seemingly bereft of humans. Instead, inside a fenced-off area the size of a football field sits thousands of yellow, cuboid shelving units, each six feet (1.8 meters) tall. Amazon calls them pods. Hundreds of robot shuffle these in and out of neat rows, sliding beneath them and dragging them around. Toothpaste, books and socks are stacked in a manner that appears random to a human observer. Through the lens of the algorithms guiding the process, though, it all makes supreme sense.
我們可以了解壹下亞馬遜的“執行中心”。它們其實是大型的倉庫，在北美地區超過100座，還有60多座分布在世界各地。可以說這些倉庫就是這家公司強有力的心臟，它們驅動了亞馬遜價值2070億美金的在線購物貿易。這些倉庫用於存儲和調配貨物，亞馬遜再把它們賣給顧客。位於西雅圖市郊的壹座倉庫裏，傳送帶以機車的速度傳送著包裝用品，妳很難聽到壹點兒噪音，並且這些設施基本實現了全自動操作。在圍欄圍住的壹個區域，壹塊差不多足球場大小的地方存放著壹些黃色方塊狀貨架，每壹個貨架的高度約為1.8米，亞馬遜把它們稱為“小型貨倉”。這些“貨倉”們整齊排列成壹排，數百個機器人穿梭其中，把它們移出來又移進去。在人類看來，這些貨品，比如牙膏，書籍和襪子被隨機地放置在貨架上，著實讓人難以理解。但是在算法的引導下，這壹過程又顯得極其合理。
Human workers, or “associates” in company vernacular, man stations at gaps in the fence that surrounds this “robot field”. Some pick items out of pods brought to them by a robot; others pack items into empty pods, to be whirred away and stored. Whenever they pick or place an item, they scan the product and the relevant shelf with a bar-code reader, so that the software can keep track.
人類員工，或亞馬遜公司所稱的“人類夥伴”，主要為機器人提供輔助服務，他們的工作場所位於圍欄間的站臺處，圍欄內部就是所謂的“機器人地帶”。機器人不停地搬運小型貨倉，有的員工從上面取下貨物，有的又把貨物放回空的貨倉。但無論員工是取出還是放回，他們都會使用條形碼儀對商品以及對應的貨架進行掃描，這樣軟件系統就可以記錄該商品的運行路徑。
The man in charge of developing these algorithms is Brad Porter, Amazon’s chief roboticist. His team is Mr Wilke’s optimization squad for fulfilment centers. Mr Porter pays attention to “pod gaps”, or the amount of time that the human workers have to wait before a robot drags a pod to their station. Fewer and shorter gaps mean less down time for the human worker, faster flow of goods through the warehouse, and ultimately speedier Amazon delivery to your doorstep. Mr Porter’s team is constantly experimenting with new optimizations, but rolls them out with caution. Traffic jams in the robot field can be hellish.
布拉德·波特（Brad Porter）是這些算法背後的主要開發者和管理者，同時也是亞馬遜公司的首席機器人科學家。他組建的團隊是維爾克先生隊伍的優化版本，主要服務對象是執行中心。波特先生主要關註如何縮小小型貨倉間的間隙，以及如何減少人類員工在他們站臺等待機器人運送貨物的時間。對人類員工而言，更少以及更小的間隙意味著更短的裝卸時間，更加迅速的貨物運輸流程，以及更加快捷的配送服務。壹直以來，波特先生的團隊都在對新型優化策略進行試驗，但每壹次的推廣都十分小心謹慎，因為“機器人地帶”的交通堵塞是壹個非常嚴重和可怕的問題。
Amazon Web Services (aws) is the other piece of core infrastructure. It underpins Amazon’s $26bn cloud-computing business, which allows companies to host web- sites and apps without servers of their own.
亞馬遜網絡服務（AWS）是其核心基礎設施的另壹個組成部件。它的存在維持了亞馬遜價值2600億美元的雲計算業務。利用這壹網絡系統，公司們可以在沒有服務器的基礎上開設自己的網站或開發自己的應用程序。
aws’s chief use of machine learning is to forecast demand for computation. Insufficient computing power as internet users flock to a customer’s service can engender error and lost sales as users encounter error pages. “We can’t say we’re out of stock,” says Andy Jassy, aws’s boss. To ensure they never have to, Mr Jassy’s team crunches customer data. Amazon cannot see what is hosted on its servers, but it can monitor how much traffic each of its customers gets, how long the connections last and how solid they are. As in its fulfilment centres, these metadata feed machine- learning models which predict when and where aws is going to see demand.
AWS在機器學習方面的主要用途是預測計算需求。當互聯網用戶湧入客戶端時，計算能力缺乏就會產生很多錯誤，比如用戶進入錯誤頁面，交易只好被迫取消。“我們不能說我們沒有存貨。”安迪·傑西（Andy Jassy）是AWS的老板，他表示，為了保證這壹網絡系統永遠不出錯誤，他的團隊收集並分析了大量顧客的數據。雖然亞馬遜方面無法得知服務器上的內容，但它可以檢測到顧客獲取了多少流量，他們與服務器間的連接持續了多長時間，以及這壹連接的質量如何。在亞馬遜公司的執行中心，機器學習模型依靠這些元數據的輸入繼而運轉起來，這些模型的功能主要是預測AWS系統在何時何地有可能產生計算需求。
One of aws’s biggest customers is Amazon itself. And one of the main things other Amazon businesses want is predictions. Demand is so high that aws has designed a new chip, called Inferentia, to handle these tasks. Mr Jassy says that Inferentia will save
Amazon money on all the machine-learning tasks it needs to run in order to keep the lights on, as well as attracting customers to its cloud services. “We believe it can be at least an order-of-magnitude improvement in cost and efficiency,” he says. The algorithms which recognize voices and understand human language in Alexa will be one big beneficiary.
AWS最大的客戶之壹就是亞馬遜自己。同時，亞馬遜其他業務對於AWS的需求也集中在它的預測能力這壹塊。由於計算量巨大，研究者為AWS設計了壹款新的芯片來處理這些任務，它被稱為Inferentia。傑西先生表示，這款芯片將為亞馬遜在機器學習的各類任務上節省不少錢，同時又能吸引更多的客戶選擇其雲服務。傑西先生還表示“Inferentia將給公司的成本效率帶來數量級的提升。”能夠辨識聲音，理解人類語言的Alexa將為其本身的算法發展帶來無窮的好處。
The firm’s latest algorithmic venture is Amazon Go, a cashierless grocery. A bank of hundreds of cameras watches shoppers from above, converting visual data into a 3d profile which is used to track hands and arms as they handle a product. The system sees which items shoppers pick up and bills them to their Amazon account when they leave the store. Dilip Kumar, Amazon Go’s boss, stresses that the system is tracking the movements of shoppers’ bodies. It is not using facial recognition to identify them and to link them with their Amazon account, he says. Instead, this is done by swiping a bar code at the door. The system ascribes the subsequent actions of that 3d profile to the swiped Amazon account. It is an ode to machine learning, crunching data from hundreds of cameras to determine what a shopper takes. Try as he might, your correspondent could not fool the system and pilfer an item.
在算法探索方面，這家公司最新成果是亞馬遜Go，它是壹家不設置收銀員的雜貨店。店內數百臺攝像頭無時無刻地從上方監控著顧客行為，並將采集到的視覺數據轉換成三維用戶信息，這些數據的用途是跟蹤顧客在拿取貨品時的手臂動作。如此壹來，這壹算法系統就可以知道顧客拿了哪些商品，並在顧客離店時，把這些商品的賬單自動發送到顧客的亞馬遜賬號中。迪裏普·庫瑪（Dilip Kumar）是負責亞馬遜Go項目的老板，他強調這個系統的目的是追蹤顧客的身體動作，並沒有使用面部識別來辨識顧客信息以連接其亞馬遜賬戶。這個系統就是機器學習的“頌歌”，它從數百臺攝像頭那裏采集信息，從而斷定顧客究竟拿了什麽。也許妳打算偷拿壹件商品，但這些攝像頭系統可不會被輕易騙到。
Fit for purpose
量體裁衣
ai body-tracking is also popping up inside fulfilment centres. The firm has a pilot project, internally called the “Nike Intent Detection” system, which does for fulfilment- centre associates what Amazon Go does for shoppers: it tracks what they pick and place on shelves. The idea is to get rid of the hand-held bar-code reader. Such manual scanning takes time and is a bother for workers. Ideally they could place items on any shelf they like, while the system watches and keeps track. As ever, the goal is efficiency, maximizing the rate at which products flow. “It feels very natural to the associates,” says Mr Porter.
人工智能動作追蹤在執行中心內部也有用武之地。亞馬遜公司推出了壹項試驗計劃，在公司內部，它被稱為“耐克意圖探測“系統，它在執行中心的運轉原理和亞馬遜Go壹樣：追蹤貨物在貨架上取出和放回的軌跡。這壹想法主要是為了淘汰以前的手握條形碼掃描儀，因為這樣的錄入工作很浪費員工的時間，操作起來也十分麻煩。理想情況是，在系統的監控和追蹤下，員工可以把貨物放在任何貨架上。亞馬遜的目標總是提高效率，最大化產品的流通速率，用波特先生的話說，“我們所有人類員工都覺得這壹過程十分自然。”
Amazon’s careful approach to data collection has insulated it from some of the scrutiny that Facebook and Google have recently faced from governments. Amazon collects and processes customer data for the sole purpose of improving the experience of its customers. It does not operate in the grey area between satisfying users and customers. The two are often distinct: people get social media or search free of charge because advertisers pay Facebook and Google for access to users. For Amazon, they are mostly one and the same (though it is toying with ad sales). Where regulators do raise concerns is over Amazon’s dominance in its core business of online shopping and cloud computing. This power has been built on machine learning. It shows no signs of waning.
在數據采集方面，亞馬遜選擇了壹天十分謹慎的道路，因此，和臉書以及谷歌相比，政府相關部門對於亞馬遜的審查力度要小很多，有些部分甚至可以免除。主要原因在於，亞馬遜采集和處理的用戶信息僅僅用於提高用戶的操作體驗，在滿足使用者和消費者的需求之間並沒有什麽灰色地帶。數據使用者和制造者（消費者）之間的差異通常很明顯：人們能夠使用社交媒體或免費的搜索引擎，那是因為廣告商通過向谷歌和亞馬遜支付廣告費，使得他們的廣告可以接觸到消費者。對亞馬遜而言，這兩者基本上是同壹個人（盡管亞馬遜不是很在乎廣告收益）。但亞馬遜也面臨壹些監管層面的擔憂，比如它在線上購物和雲計算這兩大商業領域的壟斷地位。但這壹地位的確立正是建立在強大的機器學習基礎上的，沒有跡象表明，它們處於衰退之中。