The learning machine

學習機器

The online commercial empire rests on a low-key approach to artificial intelligence

這家互聯(lián)網(wǎng)商業(yè)帝國在人工智能的發(fā)展上選擇了一條低調(diào)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(zhí)行官們每年必須按要求寫一頁，詳細闡述自己未來的商業(yè)計劃。但不太出名的一點是，每一封信函必須回答一個具體的問題：你打算怎么利用機器學習？如果你的回答是“沒什么可說的”，根據(jù)亞馬遜管理層的說法，這種答案是不允許出現(xiàn)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一種實現(xiàn)途徑，它主要包括特定類型的數(shù)據(jù)挖掘，主要目的是對未來趨勢進行預測。1999年當杰夫·維爾克（Jeff Wilke）加入公司的時候，這一想法就開始落地了。維爾克先生是亞馬遜公司的第二把交椅，他組建了一個人工智能專家組，主要負責亞馬遜內(nèi)部工作流程的研究，目的在于提高員工的工作效率。他將科學家們安排在各企事業(yè)部門，將不斷循環(huán)的自我評價和提高過程固定為一個默認模式，很快這個循環(huán)就加入了算法；第一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

其他科技巨頭有什么可炫耀的

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.

科技巨頭們抓住一切機會展現(xiàn)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一條低調(diào)的路。Alexa（亞歷克斯）是亞馬遜公司推出的一項人工智能服務，它的主要競爭對手是蘋果的Siri。依靠Alexa的云平臺，亞馬遜可以為用戶提供預測服務。這款人工智能背后的算法頗具特色，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(fā)起一項服務，吸引目標客戶，收集用戶信息，讓計算機學習這些數(shù)據(jù)，并且處理的數(shù)據(jù)規(guī)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.

我們可以了解一下亞馬遜的“執(zhí)行中心”。它們其實是大型的倉庫，在北美地區(qū)超過100座，還有60多座分布在世界各地?？梢哉f這些倉庫就是這家公司強有力的心臟，它們驅(qū)動了亞馬遜價值2070億美金的在線購物貿(mào)易。這些倉庫用于存儲和調(diào)配貨物，亞馬遜再把它們賣給顧客。位于西雅圖市郊的一座倉庫里，傳送帶以機車的速度傳送著包裝用品，你很難聽到一點兒噪音，并且這些設施基本實現(xiàn)了全自動操作。在圍欄圍住的一個區(qū)域，一塊差不多足球場大小的地方存放著一些黃色方塊狀貨架，每一個貨架的高度約為1.8米，亞馬遜把它們稱為“小型貨倉”。這些“貨倉”們整齊排列成一排，數(shù)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，這一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“人類伙伴”，主要為機器人提供輔助服務，他們的工作場所位于圍欄間的站臺處，圍欄內(nèi)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(tǒng)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）是這些算法背后的主要開發(fā)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(yōu)化版本，主要服務對象是執(zhí)行中心。波特先生主要關(guān)注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。一直以來，波特先生的團隊都在對新型優(yōu)化策略進行試驗，但每一次的推廣都十分小心謹慎，因為“機器人地帶”的交通堵塞是一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(wǎng)絡服務（AWS）是其核心基礎設施的另一個組成部件。它的存在維持了亞馬遜價值2600億美元的云計算業(yè)務。利用這一網(wǎng)絡系統(tǒng)，公司們可以在沒有服務器的基礎上開設自己的網(wǎng)站或開發(fā)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(lián)網(wǎng)用戶涌入客戶端時，計算能力缺乏就會產(chǎn)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·杰西（Andy Jassy）是AWS的老板，他表示，為了保證這一網(wǎng)絡系統(tǒng)永遠不出錯誤，他的團隊收集并分析了大量顧客的數(shù)據(jù)。雖然亞馬遜方面無法得知服務器上的內(nèi)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(xù)了多長時間，以及這一連接的質(zhì)量如何。在亞馬遜公司的執(zhí)行中心，機器學習模型依靠這些元數(shù)據(jù)的輸入繼而運轉(zhuǎn)起來，這些模型的功能主要是預測AWS系統(tǒng)在何時何地有可能產(chǎn)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最大的客戶之一就是亞馬遜自己。同時，亞馬遜其他業(yè)務對于AWS的需求也集中在它的預測能力這一塊。由于計算量巨大，研究者為AWS設計了一款新的芯片來處理這些任務，它被稱為Inferentia。杰西先生表示，這款芯片將為亞馬遜在機器學習的各類任務上節(jié)省不少錢，同時又能吸引更多的客戶選擇其云服務。杰西先生還表示“Inferentia將給公司的成本效率帶來數(shù)量級的提升?！蹦軌虮孀R聲音，理解人類語言的Alexa將為其本身的算法發(fā)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，它是一家不設置收銀員的雜貨店。店內(nèi)數(shù)百臺攝像頭無時無刻地從上方監(jiān)控著顧客行為，并將采集到的視覺數(shù)據(jù)轉(zhuǎn)換成三維用戶信息，這些數(shù)據(jù)的用途是跟蹤顧客在拿取貨品時的手臂動作。如此一來，這一算法系統(tǒng)就可以知道顧客拿了哪些商品，并在顧客離店時，把這些商品的賬單自動發(fā)送到顧客的亞馬遜賬號中。迪里普·庫瑪（Dilip Kumar）是負責亞馬遜Go項目的老板，他強調(diào)這個系統(tǒng)的目的是追蹤顧客的身體動作，并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(tǒng)就是機器學習的“頌歌”，它從數(shù)百臺攝像頭那里采集信息，從而斷定顧客究竟拿了什么。也許你打算偷拿一件商品，但這些攝像頭系統(tǒng)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(zhí)行中心內(nèi)部也有用武之地。亞馬遜公司推出了一項試驗計劃，在公司內(nèi)部，它被稱為“耐克意圖探測“系統(tǒng)，它在執(zhí)行中心的運轉(zhuǎn)原理和亞馬遜Go一樣：追蹤貨物在貨架上取出和放回的軌跡。這一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(tǒng)的監(jiān)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(chǎn)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一過程十分自然?！?/p>

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(shù)據(jù)采集方面，亞馬遜選擇了一天十分謹慎的道路，因此，和臉書以及谷歌相比，政府相關(guān)部門對于亞馬遜的審查力度要小很多，有些部分甚至可以免除。主要原因在于，亞馬遜采集和處理的用戶信息僅僅用于提高用戶的操作體驗，在滿足使用者和消費者的需求之間并沒有什么灰色地帶。數(shù)據(jù)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，這兩者基本上是同一個人（盡管亞馬遜不是很在乎廣告收益）。但亞馬遜也面臨一些監(jiān)管層面的擔憂，比如它在線上購物和云計算這兩大商業(yè)領域的壟斷地位。但這一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，它們處于衰退之中。