编者按：在计算机领域，每隔 30 年，就会卷起一场计算运用新浪潮。上世纪 50 年代，人们为物质世界建模；80年代，人们设法利用计算机增强的彼此的联系；在新世纪的 2010 年，计算应用翻开了与物质世界交互利用的新篇章。未来的计算机科学发展将为我们带来什么？它又需要什么样的设备、方法和技术的支持？看图灵奖获得者 Butler Lampson 对这些问题的思考。
本文译自 Butler Lampson 在纪念图灵诞辰 100 周年大会的发言稿
“What Computers Do: Model, Connect, and Engage”
译者：任艳
The first uses of computers, around 1950, were to model or simulate other things. Whether the target is a nuclear weapon or a payroll, the method is the same: build a computer system that behaves in some important ways like the target, observe the system, and infer something about the behavior of the target. The key idea is abstraction: there is an ideal system, often defined by a system of equations, which behaves like both the target system and the computer model. Modeling has been enormously successful; today it is used to understand, and often control, galaxies, proteins, inventories, airplanes in flight and many other systems, both physical and conceptual, and it has only begun to be exploited.
1950年左右，计算机最初用来对事物“建模”或者模拟事物。无论对象是核武器还是工资单，方法都是一样：创建一个和目标对象在某些重要方面相似的电脑系统，然后观察这个系统，根据对象的行为作出推断。这样做的核心概念是提取：这里有一个理想的系统，常常由方程组来定义，它既像是目标系统，又像电脑模型。建模取得了巨大的成功，现在它已经用来理解，甚至常常用以控制物质和概念上的系统，比如银河系、蛋白质、目录清单、飞行中的飞机等系统，而且建模才刚刚开始被利用。
Models can be very simple or enormously complex， quite sketchy or very detailed, so they can be adapted to the available hardware capacity even when it is very small. Using early computers to connect people was either impossible or too expensive, compared to letters, telephones and meetings. But around 1980 Moore’s law improvements in digital hardware made it economic to use computers for word processing, e-mail, mobile phones, the web, search, music, social networks, e-books, and video. Much of this communication is real time, but even more involves stored information, often many petabytes of it.
模型可以很简单，也可以非常复杂，可能很粗糙，也可能要求非常详细，所以就算非常小，它们也能够配合可利用的硬件容量。和书信、电话还有面谈相比，利用早期计算机来“联系”人们，要么无法实现，要么费用昂贵。但是在 1980 年左右，摩尔定律在数字硬件方面的进步让计算机变得经济实用，可用于文字处理、电子邮件、移动手机、网络、搜索、音乐、社交网络、电子图书、视频等领域。很多这样的通讯都是实时的，但更多涉及到信息存储，数量常常达到许多 PB (petabyte)。
So modeling and connection are old stories—there must be little more to do. Not so. Both the physical and the conceptual worlds are enormously complex， and there are great opportunities to model them more accurately: chemical reactions, airplane wings, disposable diapers, economies, and social networks are still far from being well understood. Telepresence is still much worse than face-to-face meetings between people, real time translation of spoken language is primitive, and the machine can seldom understand what the user doing a search is actually looking for. So there’s still lots of opportunity for innovations in modeling and connection. This is especially true in education, where computers could provide teachers with power tools.
所以建模和连接都有些过时了——它们看似已经没有发展空间了。但其实并非如此。物质世界和虚拟世界都极为复杂，我们现在有很大的契机做更精确的建模：化学反应、机翼、纸尿裤、经济以及社交网络都还远远未被充分理解。远程呈现仍然没有面对面交谈的效果好；口语的实时翻译还比较粗糙；机器很难明白用户真正想搜索的是什么……所以建模和连接仍然还有很多创新的机会，特别是在教育领域，因为计算机能为老师们提供功能强大的工具。
Nonetheless, I think that the most exciting applications of computing in the next 30 years will engage with the physical world in a non-trivial way. Put another way， computers will become embodied. Today this is in its infancy， with surgical robots and airplanes that are operated remotely by people, autonomous vacuum cleaners, adaptive cruise control for cars, and cellphone-based sensor networks for traffic data. In a few years we will have cars that drive themselves, prosthetic eyes and ears, health sensors in our homes and bodies, and effective automated personal assistants. I have a very bad memory for people’s names and faces, so my own dream (easier than a car) is a tiny camera I can clip to my shirt that will whisper in my ear, “That’s John Smith, you met him in Los Angeles last year.” In addition to saving many lives, these systems will have vast economic consequences. Autonomous cars alone will make the existing road system much more productive, as well as freeing drivers to do something more useful or pleasant, and using less fuel.
然而，我认为，在未来的 30 年中，最激动人心的计算应用莫过于与物质世界不寻常的交互利用了。换句话说，计算机将会变得更形象具体。如今这一应用还处于萌芽阶段：人们远程操控的外科手术机器人和飞机、自动吸尘器、车辆巡航控制、还有针对交通数据开发的基于手机的传感器网络等。几年后，我们也许开发出自动驾驶的汽车、人工眼睛和耳朵、在家里和人体内放置的健康传感器、以及高效的自动化私人助理等。我很难记住人们的名字和容貌，所以我个人的梦想（比拥有一辆自动驾驶的汽车更容易）就是有一台微型照相机，可以别在我的衬衫上轻轻提醒我，“这是约翰?史密斯，你去年在洛杉矶见过他。”除了治病救命，这些系统还能带来巨大的经济效应。自动驾驶的汽车会让现存的道路系统更高效，同时还把驾驶者解放出来，去做更多有用或者愉快的事情，而且节约了能源。
What is it that determines when a new application of computing is feasible? Usually it’s improvements in the underlying hardware, driven by Moore’s law (2× gain / 18 months). Today’s what-you-see-is-what-you-get word processors were not possible in the 1960s, because the machines were too slow and expensive. The first machine that was recognizably a modern PC was the Xerox Alto in 1973, and it could support a decent word processor or spreadsheet, but it was much too small and slow to handle photographs or video, or to store music or books. Engagement needs vision, speech recognition, world modeling, planning, processing of large scale data, and many other things that are just beginning to become possible at reasonable cost. It’s not clear how to compare the capacity of a human brain with that of a computer, but the brain’s 1015 synapses (connections) and cycle time of 5 ms yield 2×1017 synapse events/sec, compared to 1012 bit events/sec for a 2 GHz， 8 core, 64 bit processor. It will take another 27 years of Moore’s law to make these numbers equal, but a mouse has only 1012 synapses, so perhaps we’ll have a digital mouse in 12 years (but it will draw more power than a real mouse).
当一个计算应用可能实现时，什么是决定性的因素呢？通常在于基础硬件的提高，根据摩尔定律，硬件性能每 18 个月提升一倍。在 20 世纪 60 年代，“所见即所得”的文字处理器还没有开发出来，因为那时的机器运行速度太慢，价格太高。第一台被公认为现代计算机的机器是 1973 年研发的 Xerox Alto，它可以完成不错的文字处理或者电子表格，但是它太小太慢，不能处理图片、视频，或者存储音乐、图书。交互利用需要场景、语音识别、世界建模、规划、大规模的数据处理、以及很多工作，这些技术才刚开始在合理的成本下变得可能。现在还不清楚如何比较人脑和电脑的容量，但大脑的 1015次神经键（连接）和 5 毫秒的循环时间能处理每秒2×1017次神经键事件，而一台 2GHz 内存、8核、64位处理器的电脑能达到每秒 1012 比特的处理速度。根据摩尔定律，这些数据还需要 27 年的时间才能对等，但是一只老鼠只有 1012 次神经键，那么也许在 12 年内我们可以研发一只数字鼠（但是它可比一只真正的老鼠消耗更多的能量）。
Hardware is not the whole story， of course. It takes software to make a computer do anything, and the intellectual foundations of software are algorithms (for making each machine cycle do more useful work) and abstraction (for mastering complexity). We measure a computer or communication system externally by its bandwidth (jobs done per unit time)， latency (start to finish time for one job) and availability (probability that a job gets done on time). Internally we measure the complexity， albeit much less precisely; it has something to do with how many component parts there are, how many and how complex are the connections between parts, and how well we can organize groups of parts into a single part with only a few external connections.
当然硬件只是一方面，软件可以让电脑做任何事，而算法（让每次的机器工作周期完成更多有用的工作）和提取（用以掌控复杂性）又是软件的智力基础。要从外部衡量一台电脑或通讯系统的性能，我们会看它的带宽（每单位时间完成的工作）、延时（完成一件任务的时间）以及可利用性（及时完成工作的概率）。从内部看，我们衡量复杂性，虽然不太精确。它涉及到组成部分的多少、各部分间连接的多少和复杂程度，还有利用少数的外部连接将组件编为单个部分的完善程度。
There are many methods for building systems, but most of them fit comfortably under one of three headings: Approximate, Increment, and Divide and conquer—AID for short.
我们有许多建立系统的方法，但大多数都可归到三类：近似（Approximate），增量（Increment），分治（Divide and conquer）——缩写为 AID。
An approximate result is usually a good first step that’s easy to take, and often suffices. Even more important, there are many systems in which there is no right answer, or in which timeliness and agility are more important than correctness: internet packet delivery， search engines, social networks, even retail web sites. These systems are fundamentally different from the flight control, accounting, word processing and email systems that are the traditional bread and butter of computing.
得出一个近似值往往是容易踏出的第一步，而且也经常能够满足需求。更重要的是，很多系统没有正确的答案，或者时效性和灵活性比正确性更重要：网络数据包传输、搜索引擎、社交网络、甚至零售网站。比起传统和主要的计算，这些系统和飞行控制、统计、文字处理以及电子邮件系统有着本质的不同。
Incrementally adjusting the state as conditions change, rather than recomputing it from scratch, is the best way to speed up a system (lacking a better algorithm). Caches in their many forms, copy on write, load balancing, dynamic scale out, and just in time compilation are a few examples. In development, it’s best to incrementally change and test a functioning system. Device drivers, apps, browser plugins and JavaScript incrementally extend a platform, and plug and play and hot swapping extend the hardware.
当情况变化时，比起重新抓取信息，重新计算，通过增量调整状态是加快系统速度的最佳方法（缺乏一个更好的算法）。比如，各种形式的缓存：写时拷贝（Copy on write）、负载平衡（load balancing）、动态扩容（dynamic scale out）、即时编译（just in time compilation）。在开发中，最好是通过增量来改变和测试一个功能系统。设备驱动、（应用程序）、浏览器插件以及 JavaScript 这些组件增量式地扩展了平台，而且即插即用、热调接等技术也扩展了硬件。
Divide and conquer is the best single rule: break a big problem down into smaller pieces. Recursion, path names such as file or DNS names, redo logs for failure recovery， transactions, striping and partitioning, and replication are examples. Modern systems are structured hierarchically， and they are built out of big components such as an operating system, database, a browser or a vision system such as Kinect.
分治是最好的单一原则：将一个大问题拆成很多小部分，例如，递归、文件或 DNS 的路径名称、记录前次更新失败的日志、硬盘的分区、复制等。现代系统分层明确，它们有由操作系统等大组件、数据库、浏览器或视觉系统（如 Kinect）构成。
For engagement, algorithms and abstraction are not enough. Probability is also essential, since the machine’s model of the physical world is necessarily uncertain. We are just beginning to learn how to write programs that can handle uncertainty. They use the techniques of statistics, Bayesian inference and machine learning to combine models of the connections among random variables, both observable and hidden, with observed data to learn parameters of the models and then to infer hidden variables such as the location of vehicles on a road from observations such as the image data from a camera.
对于交互利用来说，算法和提取就不够了。概率也是根本，因为机器对于物质世界的建模必然是不确定的。我们才刚刚开始学习写程序去处理这种不确定性。他们利用很多方法在随机的变量中合并连接模型，包括统计学、贝叶斯推断和机器学习技术。这些变量有的可见，有的隐藏。人们利用可见的数据学习模型的参数，然后推断隐藏的变量，例如，通过观察如相机拍摄的图片数据，来推断道路上车辆的位置。
Some applications of engagement are safety critical, such as driving a car or performing sur-gery， and these need to be much more dependable than typical computer systems. There are methods for building dependable systems: writing careful specifications of their desired behav-ior, giving more or less formal proofs that their code actually implements the specs, and using replicated state machines to ensure that the system will work even when some of its components fail. Today these methods only work for fairly simple systems. There’s much to be learned about how to scale them up, and also about how to design systems so that the safety critical part is small enough to be dependable.
关于交互利用的一些应用对安全性至关重要，如驾驶汽车或者做外科手术，相比典型的计算机系统，这些应用需要更安全可靠。有一些方法可以构建可靠的系统：为想要的行为撰写细致的规范说明；给出或多或少的形式证明他们的代码确实落实了那些说明；利用复制状态的机器来保证系统即使在某些组件停止工作的情况下也能运作。如今，这些方法仅仅适用于相对简单的系统。如何才能扩容？怎样设计系统才能尽量减少安全性瓶颈，从而提高可靠性？关于这些问题我们还要不断学习。
Engagement can be very valuable to users, and when it is they will put up with a lot of hassle to get the value; consider an artificial eye for a blind person, for example. But other applications, such as a system that tells you which of your friends are nearby， are examples of ubiquitous computing that although useful, have only modest value. These systems have to be very well engineered, so that the hassle of using them is less than their modest value. Many such systems have failed because they didn’t meet this requirement.
对于用户来说，交互利用可以变得非常重要，而且当他们想要得到这种价值的时候，需要应对许多困难，如人造眼之于盲人。其他的应用就是随处可见的计算，虽然有用，但价值有限，如一个可以告诉你哪个朋友在附近的系统。这些系统必须要构建得很好，才能让其有限的价值多过使用它们的争论。许多这样的系统都失败了，因为没能满足用户的需求。
The computing systems of the next few decades will expand the already successful application domains that model the world and connect people, and exploit the new domain that engages computers with the physical world in non-trivial ways. They will continue to be a rich source of value to their users, who will include almost everyone in the world, and an exciting source of problems, both intellectual and practical, for their builders.
未来几十年的计算系统将扩大现有已成功的应用领域：为世界建模，联系人们以及利用新的领域从而让计算机与物质世界进行不寻常地交互利用。这些系统将继续为用户和创建者提供丰富的价值源泉，而这里的用户几乎包括了世界上的每一个人、每一个激动人心的问题，不论是智力上的还是实际存在的问题。
作者简介：Butler Lampson，知名计算机科学家，1992年图灵奖获得者。现任微软技术院士（Technical Fellow）。他是 Xerox PARC 的创始人之一，参与设计了 SDS 940 分时系统、Alto 个人分布式计算系统、Xerox 9700 激光打印机、两阶段提交协议、Autonet 局域网、网络安全的 SPKI 系统、微软公司台式计算机软件、微软公司的 Palladium 高保证堆栈存储器以及多种编程语言。他从事的工作内容包括计算机体系结构、局域网、光栅打印机、页面描述语言、操作系统、远程程序通话、编程语言及其语义、大规模编程、容错计算、事务处理、计算机安全、所见即所得编辑程序以及台式计算机等。曾获 1984 年美国计算机协会软件系统奖，1996年 IEEE 计算机先驱奖，2001年获得冯·诺依曼勋章，2004年国家工程院的德雷珀奖等众多表彰与荣誉。
