从Siri看下一代智能系统的变革与商机.doc

从Siri看下一代智能系统的变革与商机关键字： iPhone-4S Siri 智能系统 今天，所谓的“智能系统”(smart system)，能够直观地处理自动化任务、整合资料中心的分析数据和嵌入式计算机，为企业和消费者创造价值。 我们赋予这些计算机人类的名字Watson、Siri告诉我们自己，他们有多们像我们。今天的智能系统已经能够直观地处理迄今仍无法实现实时自动化的任务。透过汇聚来自全球数十亿的信息串流，这些智能系统提供的分析科学能力，可以创造甚至超越其系统本身的价值。 今年稍早，在Jeopardy游戏中战胜人类冠军的IBM Watson超级电脑获得了广泛关注。苹果(Apple) iPhone-4S的Siri能用接近自然的英文来回答使用者问题，在一部任何人都能够买得起的手机中，已经搭载了如此惊人的智能系统。虽然这些系统得到广大赞誉，不过，在几乎所有的电子产业汽车、工业、通讯、计算机、交通、能源、医疗和个人保健等领域中，或多或少都已经开始运用智能系统了。事实上，根据美国总统的科学和技术顾问委员会表示，“网络实体系统”(cyber-physical systems)最终将串连全球50%左右的电子产品。 美国国家标准与技术策员会(NIST)最近因而针对互操作性定义了一个接口，可作为智能系统之间的性能比较指针及标准化。现阶段所做的努力，是协助美国企业自主开发设计智能系统，但这些设计会在海外进行低成本生产。 这个赌注非常巨大。市调公司IDC近日指出，每年的智能系统销售量近20亿部，市场规模达1兆美元，IDC预测到2015年该市场将成长一倍到40亿部，规模达2兆美元。 而据IDC的分析，最具价值的智能系统，是能针对实时信息串流进行分析的服务。 “资料是全新型态的货币，”IDC半导体研究副总裁Mario Morales说。“像IBM、惠普(HP)、英特尔(Intel)、微软(Microsoft)、德州仪器(TI)、飞思卡尔(Freescale)和甲骨文(Oracle)等公司，都早已明白资料的价值，而且致力于开发能进行资料分析以获得最大价值的基础设备。” “过去三年来，我们不仅看到计算机在转型，网络也是，甚至包括使用者与智能设备互动的方式也在转变。企业尚未找出这些资料可以怎样转换成利润，但其中确实蕴藏着巨大的机会，促使有远见企业投资在分析软件和服务领域，以便与其智能硬件结合。 10多年来，IDC一直将相关设备定义在嵌入式计算机领域，最近才开始将智能系统定义为嵌入式领域的继任者。IDC并不是唯一一家声称智能系统是未来大势的市场研究公司。另一家位于纽约的Applied Business Intelligence Inc.,最近也激活了一项名为智能城市电网的研究服务。IBM引领智能系统发展 IBM或许是对当前的智能系统有着最深刻理解的公司。在全球各地，已经有数十个该公司称之为智能行星(smart planet)的系统用于解决各式各样的基础设施问题，如位于瑞典斯德哥尔摩的智能运输系统；马耳他的国家级智能电网(也是全球第一个)；以及设置在纽约大都会博物馆，用于保护艺术品的无线传感器网络等。 “智能系统每天所产生的资料比美国所有图书馆的资料加起来还要多出8倍而其中有85%都是非结构化的，”IBM院士暨技术长及系统部门技术策略副总裁Jai Menon说。“目前的商业情报仍难以对所有非结构化资料进行分析并进一步获取价值，而Watson则是一个很好的例子，因为它能快速回答有关非结构化资料的问题。” 传统的IT分析都执行结构化资料，透过数据库整理所有资料，可以轻易地进行搜索、排序，并使用知名的数学公式进行分析。但Watson证实凌乱的、非结构化的资料，也能借着巧妙制作分析优势，在最佳化的系统架构上轻易地进行搜索。 “针对金融市场的分析如商品价格预测是透过已知周期性模式进行的。然而，要预测基础设计的失效风险，如水管究竟有多长，而最后这就是我们所说的非结构化问题，”IBM研究中心工程师暨商业分析总监Arun Hampapur说。 IBM最近运用智能系统解决非结构性问题的例子，主要是利用Watson为医疗保健、银行和金融、零售、法律和政府监控等领域建立自动化顾问。“我们每天都接到各行各业领导人的电话，他们都希望Watson能展开更多新应用，举例来说，如何能更快速、更方便地订机票等，”Hampapur说。 另一个例子，全美最大型的保健服务供货商WellPoint公司最近宣布，将采用Watson所衍生的智能系统，透过从数百万的医疗记录、期刊文献和最新医学研究结果中，获取和患者症状匹配的资料来简化并加速医疗诊断。 Watson是以IBM所建构的技术为基础，这些技术是为了解决智能城市开发项目中非结构化问题所开发的。IBM的探索行动是从运用其传统资中心分析的长处开始，而后不断朝智能系统的方向进行开发。这家公司不断朝着可运用嵌入式处理器本身进行分析的边缘连接网络方向努力。Menon指出，芝加哥警察“在边缘网络使用了智能分析，能自动将保全摄影镜头转向qiang响的方向，因此当接到911报案电话时，他们已经能够获得qiang枝口径读数和摄影镜头所转向的方位等信息了。” 过去几年内，IBM已经花费超过150亿美元，用于收购具备专业分析知识的公司，该公司希望为可融合来自多个感测输入的智能系统，开发新一代的感知计算机(cognitive-computer)芯片。惠普：人人有机会 在此同时，惠普也正在转变其业务模式，希望能运用无线传感器网络提供智能系统，让惠普的云端服务器能与多种不同的实时信息串流通讯，以执行各种资料分析工作，预测从公共电力中断到个人心脏病发作等一切事物。 “我们并未试着模仿IBM，但我们发现，我们与他们面对着相同的机会，很明显，对一个完整的系统而言，资料分析的价值愈来愈高，”HP资深院士Stanley Williams说。“我们大量投资在资料分析上，因为这能将0与1转变为意义的东西，它能创造并提供知识和意识，让人们能够迅速对任何情况做出反应，以预防不良后果。” 在建立第一代智能系统时，惠普仅侧重在能源和保健领域，这家公司从头开始建构系统，包括了传感器芯片到执行在云端的分析软件。 “智能系统代表了极为庞大的发展过程，它涉及了信息技术的各个领域，但我们决定，仅运用我们首个垂直整合的平台进入两个领域，”Williams说。“我们希望至少进入两个应用领域，如此我们就能比较和对照不同的应用，以了解何者是共通的，何者又是需要差异化的。而后，一旦我们在这两个领域建立了基础，我们便可以再转向其它的垂直细分市场。” 惠普是透过与壳牌石油(Shell Oil)这类客户合作而首次将其原型扩展至大规模应用，壳牌石油已经与HP签署了无线传感器网络合约，可运用该技术进行智能地震成像。在HP服务器上执行的分析工作，会将来自于数千个HP地震传感器的资料串流转化为实际可用的情报资料，告诉这些公司哪里可以进行石油开采。 HP同时负责制造专门针对地震感测应用的专用MEMS加速器，以及负责将资料串流送回服务器进行分析的无线传感器节点。英特尔：先做好本地处理 英特尔正透过增加本地分析能力来推动智能系统业务，随着嵌入式系统的自然演进，该公司也更加专注在软件领域，让OEM能在Intel X86及Atom处理器上执行分析，而不是直接将原始资料串流送到云端上。 “随着传感器日益普及，嵌入式系统已经开始创造大量信息，这些信息都会汇流到云端，”英特尔副总裁暨嵌入式通讯集团总经理Ton Steenman说。“我们认为一切都要送到云端上的想法并不合理，事实上，我们建议，应该在嵌入式处理器上执行需要实时分析的任何问题。” 英特尔去年收购了CognoVision Solutions Inc.的不记名视频分析(anonymous video analytics, AVA)的技术，该技术可执行在x86处理器上，英特尔将让技术重新命名为Intel Audience Impression Metrics Suite (AIM)。AIM可执行在本地数字看板系统上，并依照观众类型来更换播放的广告内容。 “过去，数字看板和配备低阶处理器的媒体播放器没什么两样，”Steenman说。“但现在数字看板已经添加了相机，可在本地进行智能分析来识别观众的性别和年龄，然后配合观众来更改播放的广告。” 英特尔还收购了McAfee和嵌入式操作系统供货商Wind River。这些收购而来的解决方案都与英特尔的远程程管理工具搭配，将PC类的安全策略扩展到智能嵌入式系统中。 英特尔的数字看板提供了能与消费者互动的显示器，如阿迪达斯的球鞋广告，可对观众的性别、年龄及兴趣进行本地分析，以确定要发送的资料。微软：将所有的智能特性整合起来 微软公司也藉由扩展其智能系统的软件兼容性，来扩展嵌入式业务，目前其Windows已经能执行在 ARM、MIPS和x86嵌入式处理器到更高阶的Xeon服务器上。截至目前，该公司称Windows平台已有超过300万个嵌入式系统，并希望运用此一成果进入智能系统。 “我们的策略优势，是将Windows环境从嵌入式智能系统再升级，用户目前透过我们的系统收集资料，传回云端Windows服务器，”微软Windows Embedded行销部资深总监Barb Edson 表示。 举例来说，微软现在有一家大型企业客户，在装载食物的货箱内设置了传感器，来感测果实的成熟情况，因此当货抵达码头时，不同熟成度的水果便可以分开运送到正确的目的地，可能被送到一个必须要人工熟成的仓库，或是将已成熟的水果直接送到生产厂，从而节省了人工检查步骤。 “在所有这些智能嵌入式系统中，情报是最重要的关键，”Edson说。“我们相信，一切都会被连接到网际网络，最终演变成智能系统。”In a smart-system world, datas the new currency R. Colin Johnson We give them human namesWatson, Sirithat suggest how much “like us” they are. Todays smart systems can intuitively handle tasks that until now have been impossible to automate in real-time. And by mining the resultant sea of real-time data coming in from billions of streams worldwide, analytics science is creating services that have even more value than the smart systems themselves. IBMs Watson supercomputer captured the publics attention earlier this year when it beat human champions at Jeopardy. Siri, the intelligent agent on the Apple iPhone-4s, answers users ad hoc questions about almost anything in natural, conversational English, putting a scary-smart system in the pocket of anyone who can afford the phone. While those systems get the glory, theres a seething mass of smart systems already at work in virtually every electronics sector: automotive, industrial, communications, computing, transportation, energy, medical and personal health maintenance. In fact, according to the U.S. Presidents Council of Advisors on Science and Technology, such “cyber-physical systems” will eventually constitute 50 percent of all electronics worldwide, making them a U.S. strategic asset. In response, the National Institute of Standards and Technology recently announced a standardization effort to define interfaces for interoperability, as well as metrics and methods for measuring and comparing performance among smart systems. Such efforts set the stage for U.S. entrepreneurs to build successful smart systems from homegrown designs, but to realize those designs with electronics that are manufactured at low cost overseas (see sidebar, final page). The stakes are huge. Market watcher International Data Corp. (Framingham, Mass.) recently reported that nearly 2 billion smart systems per year are already being sold, making for a $1 trillion market that IDC predicts will grow to 4 billion units and $2 trillion by 2015. The most valuable services performed by smart systems, according to IDC, result from the application of analytics to real-time data streams. “Data is the new currency,” said Mario Morales, vice president of semiconductor research at IDC. “And the companies that understand this are the ones already developing the analytics and infrastructure to extract that valuecompanies like IBM, HP, Intel, Microsoft, TI, Freescale and Oracle. “Over the last three years, we have seen a transformation not merely in computing, but also in networking, and even in the way users are interacting with smart devices. Enterprises have yet to figure out exactly how to monetize all this data, but there is a tremendous amount of opportunity there, which is prompting visionaries to make huge investments in the analytics software and services that will couple to their intelligent hardware.” IDC has been covering embedded computers for over a decade but only recently started delineating “intelligent systems” as the successor to the embedded space. And IDC is not the only market forecaster claiming that smart systems are the future. Applied Business Intelligence Inc. (New York), for one, recently started a “smart cities and grids” research service. IBM ahead of curve IBM probably has the deepest understanding of smart systems today. Dozens of its so-called smarter-planet systems are already solving widespread infrastructure problems worldwide, including a smart transportation system in Stockholm, Sweden; a national smart gridthe worlds firstin Malta; and a smart wireless sensor network that protects paintings at the New York Metropolitan Museum of Art. “Smart systems are generating eight times more data every day than there is in all the U.S. libraries combined85 percent of which is unstructured,” said Jai Menon, IBM fellow, chief technology officer and vice president of technical strategy for the companys systems group. “Business intelligence has the problem of using analytics to derive value from all that unstructured data, and Watson is a good example of how to answer questions about unstructured data very quickly.” Traditional IT analytics were run on structured data that was carefully tailored by database experts into neat, isomorphic containers that could be easily searched, sorted and analyzed using well-known mathematical formulas. But Watson proved that messy, unstructured data can also be easily searched, by virtue of cleverly crafted analytics designed to run on optimized system architectures that preposition the technological capabilities needed to address specific unstructured problem domains. “Analytics for the financial marketssuch as predicting commodity pricesis a cyclical phenomenon driven by well-known patterns. But predicting the risk of failure in infrastructuresay, how long a water pipe will lastis what we call an unstructured problem,” said Arun Hampapur, distinguished engineer and director of business analytics at IBM Research. “And analytics for unstructured problems is best done by instrumenting a strategy that custom-tailors the analytics and architecture for a particular problem domain.” IBMs latest foray into addressing unstructured problem domains with smart systems is aimed at using Watson to create automated advisers for apps in health care, banking and finance, retailing, law and governmental regulation. “We get calls every day from industry leaders who want to repurpose Watson for new applications, such as helping to make airline reservations faster, better, easier,” said Hampapur. For instance, Wellpoint Inc. (Indianapolis), the nations largest health-care provider, recently announced that it would use a Watson-derived smart system to simplify and speed medical diagnoses by matching patients symptom sets with data from millions of medical records, journal articles and late-breaking medical-research results. Watson is based on technologies that IBM created to solve unstructured problems in smart-city projects. IBM started its quest for such smart systems by leveraging its strengths in the data center, where traditional analytics are run. But it has been steadily working out toward the edge of the connectivity network, where analytics can be run on the embedded processors themselves. Menon noted that Chicago police “use smart analytics at the edge to automatically turn surveillance cameras toward a gunshot, so that by the time the 911 call comes in they already have a readout of the caliber of gun that was used and a camera pointing at the location from which it was fired.” IBM has spent more than $15 billion in the past few years acquiring companies with specialized analytics expertise, and it is building a new generation of cognitive-computer chips for smarter systems that can fuse the inputs from multiple sensors. HP sees the same opportunities Hewlett-Packard, meanwhile, is transforming its business model to deliver smart systems that harness wireless sensor networks in order to communicate vast streams of real-time data to HPs cloud-based servers, where analytics can be run to predict everything from public power outages to personal heart attacks. “We are not explicitly trying to emulate IBM, but we are finding the same type of opportunities as they are, because its clear that more and more of the value is in the system as a whole,” said senior HP fellow Stanley Williams. “The money will be in the analytics, because analytics is what turns ones and zeros into something meaningful; it creates the knowledge and awareness that allows people to quickly react to situations, and to prevent undesirable outcomes.” In creating its first generation of smart systems, HP is focusing on just two sectorsenergy and healthfor which it is building systems from scratch, from the sensor chips to the analytics software running in the cloud. “Smart systems represent a huge development effort, involving every aspect of information technology, but we have consciously decided to only enter two sectors with our first vertically integrated platform,” said Williams. “We wanted to enter at least two so that we could compare and contrast the different applications in order to understand what is common and what needs to be different. Then, once we have those two under our belt, well look to the other vertical market segments.” HP is prototyping its first widescale applications in cooperation with customers such as Shell Oil, which has contracted for a wireless sensor network that can perform smart seismic imaging. Analytics run on HP servers will turn data streams from thousands of HP seismic sensors into practical intelligence indicating where to drill. HP manufactures both a specialized MEMS accelerometer for seismic sensing and a wireless sensor node that streams the sensor data back to its servers, where analytics are run. Intel takes the local Intel Corp., for its part, is pursuing a smart-systems business that adds local analytics as the natural evolution from the embedded model, focusing on software that lets OEMs perform analytics on Intel X86 and Atom processors instead of diverting raw data streams up to the cloud. “As sensors become more perv