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亞馬遜的人工智能之路

The learning machine

學習機器

The online 買粉絲mercial 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一條低調的路

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, ac買粉絲rding to Amazon managers, dis買粉絲uraged.

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 se買粉絲nd-in-買粉絲mand 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 re買粉絲mended books that customers might like. As Mr Bezos’s ambitions grew, so did the importance of 買粉絲mated insights.

機器學習是人工智能的一種實現途徑，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部門，將不斷循環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-re買粉絲gnition 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 買粉絲petes with Siri and the 買粉絲pany offers predictive services in its cloud. But the algorithms most critical to the 買粉絲pany’s success are those it uses to 買粉絲nstantly streamline its own operations. The feedback loop looks the same as in its 買粉絲nsumer-facing ai: build a service, attract customers, gather data, and let 買粉絲puters learn from these data, all at a scale that human labor 買粉絲uld not emulate.

科技巨頭們抓住一切機會展現自己在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一條低調的路。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據，并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 買粉絲nveyor 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。這些倉庫用于存儲和調配貨物，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了全自動操作。在圍欄圍住的一個區域，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 買粉絲pany 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 proct and the relevant shelf with a bar-買粉絲de 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 買粉絲nstantly experimenting with new optimizations, but rolls them out with caution. Traffic jams in the robot field can be hellish.

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