透读经济学人-亚马逊的AI

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6页备忘录很有名。 高管必须每年写一篇，制定他们的商业计划书。 鲜为人知的是，这些信件必须总是特别回答一个问题：你打算如何使用机器学习？ 据亚马逊经理称，“不多”的回应令人气馁。

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.【boffin: tech guy] {as.... so did...}机器学习是人工智能（AI）的一种形式，它挖掘可用于进行预测的模式的数据。 1999年Jeff Wilke加入公司时，它在亚马逊上扎根。 威尔克今天是杰夫贝索斯的副指挥官，他成立了一个科学家团队来研究亚马逊的内部流程，以提高他们的效率。 他将自己的boffins编织成业务部门，将自我评估和改进的循环转变为默认模式。 很快，这个循环涉及机器学习算法; 客户可能喜欢的第一本推荐书籍。 随着贝佐斯先生的雄心壮志，自动化数据洞察的重要性也在增长。

Yet whereas its fellow tech titans flaunt 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 labour could not emulate.然而，当亚马逊的同伴科技巨头在每个机会都炫耀他们的人工智能 - Facebook的面部识别软件，Apple的Siri数字助理或者Alphabet的自动驾驶汽车和大师级玩家 - 亚马逊采用了低调的机器学习方法。 它的Alexa与Siri竞争，该公司在其云中提供预测服务。 但对公司成功最关键的算法是它用来不断简化自身运营的算法。 反馈循环看起来与面向消费者的AI相同：构建服务，吸引客户，收集数据，让计算机从这些数据中学习，所有这些都是人工无法模仿的。

Mr Porter’s algorithms

Consider Amazon’s fulfilment centres. 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, packages hurtle 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 sit thousands of yellow, cuboid shelving units, each six feet (1.8 metres) tall. Amazon calls them pods. Hundreds of robots 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.想想亚马逊的执行中心，这些庞大的仓库，北美100多个，全球60多个，是其2070亿美元在线购物业务的核心。 他们存储和发送亚马逊销售的商品。 在西雅图郊区的一个地方，包裹沿着轻便摩托车的速度沿着传送带流动。 噪音震耳欲聋 - 设施似乎没有人类。 相反，在一个围栏区内，足球场的大小上有数千个黄色长方形搁架单元，每个单元高6英尺（1.8米）。 亚马逊称他们为pods。 数以百计的机器人将这些机器人整齐地拖入其中，在它们下方滑动并拖动它们。 牙膏，书籍和袜子以对人类观察者来说随机的方式堆叠。 然而，通过指导过程的算法镜头，这一切都具有“至高无上”的意义。

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.人类工作者，或公司白话中的“伙伴”，围绕着这个“机器人领域”的围栏间隙。 一些从机器人带来的pods中拾取物品; 其他人将物品装入空舱中，然后旋转并存放。 无论何时他们挑选或放置物品，他们都会使用条形码阅读器扫描产品和相关的货架，以便软件可以跟踪。

The man in charge of developing these algorithms is Brad Porter, Amazon’s chief roboticist. His team is Mr Wilke’s optimisation squad for fulfilment centres. 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 optimisations, but rolls them out with caution. Traffic jams in the robot field can be hellish.负责开发这些算法的人是亚马逊首席机器人专家布拉德波特。 他的团队是威尔克先生的履行中心优化小组。 波特先生关注“pod间隙”，或人类工作人员在将机器人拖到他们的工作站之前必须等待的时间。 更短和更短的间隙意味着更少的人工停工时间，更快的货物流通过仓库，最终亚马逊更快速地送到您家门口。 波特先生的团队正在不断尝试新的优化措施，但要谨慎对待。 机器人领域的交通堵塞可能是地狱般的后果。

Amazon Web Services (AWS) is the other piece of core infrastructure. It underpins Amazon’s $26bn cloud-computing business, which allows companies to host websites and apps without servers of their own.亚马逊网络服务（AWS）是核心基础设施的另一部分。 它支撑着亚马逊价值260亿美元的云计算业务，该业务允许公司在没有自己的服务器的情况下托管网站和应用程序。

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 errors—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主要使用机器学习来预测计算需求。 当互联网用户涌向客户的服务时，计算能力不足可能会导致错误，并在用户遇到错误页面时丢失销售。 “我们不能说我们已经缺货，”AWS的老板Andy Jassy说。 为了确保他们不会缺货，Jassy先生的团队处理大量客户数据。 亚马逊无法看到其服务器上托管的内容，但它可以监控每个客户获得的流量，连接的持续时间以及它们的稳固性。 与其履行中心一样，这些元数据提供机器学习模型，可预测AWS何时何地需要。

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 recognise voices and understand human language in Alexa will be one big beneficiary.AWS最大的客户之一是亚马逊。 其他亚马逊企业想要的主要内容之一就是预测。 需求如此之高以至于AWS设计了一款名为Inferentia的新芯片来处理这些任务。 Jassy先生表示，Inferentia将为所有需要运行的机器学习任务节省亚马逊资金，并吸引客户使用其云服务。 “我们相信它的成本和效率至少可以提高一个数量级，”他说。 在Alexa中识别语音和理解人类语言的算法将是一个很大的受益者。

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.该公司最新的算法项目是亚马逊Go，一个无收银员的杂货店。 一组数百个摄像头从上方观察购物者，将视觉数据转换为3D轮廓，用于在处理产品时跟踪手和手臂。 系统会查看购物者在离开商店时收取哪些商品并将其记入亚马逊帐户。 Amazon Go的老板Dilip Kumar强调，该系统正在跟踪购物者身体的运动。 他说，它没有使用面部识别识别它们并将它们与亚马逊帐户相关联。 相反，这是通过在门上滑动条形码来完成的。 系统将该3D配置文件的后续操作归因于刷过的亚马逊帐户。 这是对机器学习的颂歌，从数百台摄像机处理数据以确定购物者的需求。 无论怎样尝试，无人能欺骗系统并盗窃物品。

Fit for purpose

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, maximising the rate at which products flow. “It feels very natural to the associates,” says Mr Porter.AI身体跟踪也在履行中心内部出现。 该公司有一个试点项目，内部称为“Nike意图检测”系统，该系统为亚马逊Go为购物者提供的履行中心员工提供服务：它跟踪他们在货架上挑选和放置的东西。 我们的想法是摆脱手持式条形码阅读器。 这种手动扫描需要时间，对工人来说很麻烦。 理想情况下，他们可以将物品放在他们喜欢的任何架子上，同时系统会监视并跟踪。 与以往一样，目标是提高效率，最大限度地提高产品流动速度。 “这对同事来说非常自然，”波特先生说。

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.亚马逊对数据收集的谨慎态度使其不受Facebook和谷歌最近面临的政府审查的影响。 亚马逊收集和处理客户数据的唯一目的是改善客户的体验。 它不在满足用户和客户之间的灰色区域中运行。 这两者通常是截然不同的：人们可以免费获得社交媒体或搜索，因为广告商向Facebook和Google支付费用以便访问用户。 对于亚马逊来说，他们大多是同一个（虽然它是在玩弄广告销售）。 监管机构确实提出担忧的是亚马逊在其在线购物和云计算核心业务中的主导地位。 这种力量建立在机器学习的基础上。 它丝毫没有显示出减弱的迹象。