The learning machine
学习机器
The online commercial empire rests on a low-key approach to artificial intelligence
这家互联网商业帝国在人工智能的发展上选择了一条低调的路
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.
亚马逊的六页备忘录十分出名，执行官们每年必须按要求写一页，详细阐述自己未来的商业计划。但不太出名的一点是，每一封信函必须回答一个具体的问题：你打算怎么利用机器学习？如果你的回答是“没什么可说的”，根据亚马逊管理层的说法，这种答案是不允许出现的。
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.
机器学习是人工智能的一种实现途径，它主要包括特定类型的数据挖掘，主要目的是对未来趋势进行预测。1999年当杰夫·维尔克（Jeff Wilke）加入公司的时候，这一想法就开始落地了。维尔克先生是亚马逊公司的第二把交椅，他组建了一个人工智能专家组，主要负责亚马逊内部工作流程的研究，目的在于提高员工的工作效率。他将科学家们安排在各企事业部门，将不断循环的自我评价和提高过程固定为一个默认模式，很快这个循环就加入了算法；第一代算法可以向顾客推荐他们喜欢的图书。随着贝佐斯先生的野心越来越膨胀，这种全自动的算法推荐模式也显得越来越重要。
Yet whereas its fellow tech titans flaunt
其他科技巨头有什么可炫耀的
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.
科技巨头们抓住一切机会展现自己在AI方面的实力：脸书推出了面部识别软件，苹果拥有语音助手Siri，谷歌推出了无人驾驶和阿尔法Go。和这些公司相比，亚马逊在机器学习上选择了一条低调的路。Alexa（亚历克斯）是亚马逊公司推出的一项人工智能服务，它的主要竞争对手是苹果的Siri。依靠Alexa的云平台，亚马逊可以为用户提供预测服务。这款人工智能背后的算法颇具特色，它能够不断将自己的操作流程精简处理，但这款AI服务的反馈回路和其客户端AI类似：发起一项服务，吸引目标客户，收集用户信息，让计算机学习这些数据，并且处理的数据规模是人力无法企及的。
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.
我们可以了解一下亚马逊的“执行中心”。它们其实是大型的仓库，在北美地区超过100座，还有60多座分布在世界各地。可以说这些仓库就是这家公司强有力的心脏，它们驱动了亚马逊价值2070亿美金的在线购物贸易。这些仓库用于存储和调配货物，亚马逊再把它们卖给顾客。位于西雅图市郊的一座仓库里，传送带以机车的速度传送着包装用品，你很难听到一点儿噪音，并且这些设施基本实现了全自动操作。在围栏围住的一个区域，一块差不多足球场大小的地方存放着一些黄色方块状货架，每一个货架的高度约为1.8米，亚马逊把它们称为“小型货仓”。这些“货仓”们整齐排列成一排，数百个机器人穿梭其中，把它们移出来又移进去。在人类看来，这些货品，比如牙膏，书籍和袜子被随机地放置在货架上，着实让人难以理解。但是在算法的引导下，这一过程又显得极其合理。
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.
人类员工，或亚马逊公司所称的“人类伙伴”，主要为机器人提供辅助服务，他们的工作场所位于围栏间的站台处，围栏内部就是所谓的“机器人地带”。机器人不停地搬运小型货仓，有的员工从上面取下货物，有的又把货物放回空的货仓。但无论员工是取出还是放回，他们都会使用条形码仪对商品以及对应的货架进行扫描，这样软件系统就可以记录该商品的运行路径。
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）是这些算法背后的主要开发者和管理者，同时也是亚马逊公司的首席机器人科学家。他组建的团队是维尔克先生队伍的优化版本，主要服务对象是执行中心。波特先生主要关注如何缩小小型货仓间的间隙，以及如何减少人类员工在他们站台等待机器人运送货物的时间。对人类员工而言，更少以及更小的间隙意味着更短的装卸时间，更加迅速的货物运输流程，以及更加快捷的配送服务。一直以来，波特先生的团队都在对新型优化策略进行试验，但每一次的推广都十分小心谨慎，因为“机器人地带”的交通堵塞是一个非常严重和可怕的问题。
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.
亚马逊网络服务（AWS）是其核心基础设施的另一个组成部件。它的存在维持了亚马逊价值2600亿美元的云计算业务。利用这一网络系统，公司们可以在没有服务器的基础上开设自己的网站或开发自己的应用程序。
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在机器学习方面的主要用途是预测计算需求。当互联网用户涌入客户端时，计算能力缺乏就会产生很多错误，比如用户进入错误页面，交易只好被迫取消。“我们不能说我们没有存货。”安迪·杰西（Andy Jassy）是AWS的老板，他表示，为了保证这一网络系统永远不出错误，他的团队收集并分析了大量顾客的数据。虽然亚马逊方面无法得知服务器上的内容，但它可以检测到顾客获取了多少流量，他们与服务器间的连接持续了多长时间，以及这一连接的质量如何。在亚马逊公司的执行中心，机器学习模型依靠这些元数据的输入继而运转起来，这些模型的功能主要是预测AWS系统在何时何地有可能产生计算需求。
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最大的客户之一就是亚马逊自己。同时，亚马逊其他业务对于AWS的需求也集中在它的预测能力这一块。由于计算量巨大，研究者为AWS设计了一款新的芯片来处理这些任务，它被称为Inferentia。杰西先生表示，这款芯片将为亚马逊在机器学习的各类任务上节省不少钱，同时又能吸引更多的客户选择其云服务。杰西先生还表示“Inferentia将给公司的成本效率带来数量级的提升。”能够辨识声音，理解人类语言的Alexa将为其本身的算法发展带来无穷的好处。
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，这家公司最新成果是亚马逊Go，它是一家不设置收银员的杂货店。店内数百台摄像头无时无刻地从上方监控着顾客行为，并将采集到的视觉数据转换成三维用户信息，这些数据的用途是跟踪顾客在拿取货品时的手臂动作。如此一来，这一算法系统就可以知道顾客拿了哪些商品，并在顾客离店时，把这些商品的账单自动发送到顾客的亚马逊账号中。迪里普·库玛（Dilip Kumar）是负责亚马逊Go项目的老板，他强调这个系统的目的是追踪顾客的身体动作，并没有使用面部识别来辨识顾客信息以连接其亚马逊账户。这个系统就是机器学习的“颂歌”，它从数百台摄像头那里采集信息，从而断定顾客究竟拿了什么。也许你打算偷拿一件商品，但这些摄像头系统可不会被轻易骗到。
Fit for purpose
量体裁衣
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.
人工智能动作追踪在执行中心内部也有用武之地。亚马逊公司推出了一项试验计划，在公司内部，它被称为“耐克意图探测“系统，它在执行中心的运转原理和亚马逊Go一样：追踪货物在货架上取出和放回的轨迹。这一想法主要是为了淘汰以前的手握条形码扫描仪，因为这样的录入工作很浪费员工的时间，操作起来也十分麻烦。理想情况是，在系统的监控和追踪下，员工可以把货物放在任何货架上。亚马逊的目标总是提高效率，最大化产品的流通速率，用波特先生的话说，“我们所有人类员工都觉得这一过程十分自然。”
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.
在数据采集方面，亚马逊选择了一天十分谨慎的道路，因此，和脸书以及谷歌相比，政府相关部门对于亚马逊的审查力度要小很多，有些部分甚至可以免除。主要原因在于，亚马逊采集和处理的用户信息仅仅用于提高用户的操作体验，在满足使用者和消费者的需求之间并没有什么灰色地带。数据使用者和制造者（消费者）之间的差异通常很明显：人们能够使用社交媒体或免费的搜索引擎，那是因为广告商通过向谷歌和亚马逊支付广告费，使得他们的广告可以接触到消费者。对亚马逊而言，这两者基本上是同一个人（尽管亚马逊不是很在乎广告收益）。但亚马逊也面临一些监管层面的担忧，比如它在线上购物和云计算这两大商业领域的垄断地位。但这一地位的确立正是建立在强大的机器学习基础上的，没有迹象表明，它们处于衰退之中。