A few months ago I made the trek to the sylvan campus of the IBM research labs in Yorktown Heights, New York, to catch an early glimpse of the fast-arriving, long-overdue future of artificial intelligence. This was the home of Watson, the electronic genius that conquered Jeopardy! in 2011. The original Watson is still here—its about the size of a bedroom, with 10 upright, refrigerator-shaped machines forming the four walls. The tiny interior cavity gives technicians access to the jumble of wires and cables on the machines backs. It is surprisingly warm inside, as if the cluster were alive.数月前，我长途跋涉回到坐落于纽约州约克城高地的IBM研究实验室的林间园区，为的就是能早早一窥那近在眼前却让人期望许久的人工智能的未来。这儿是超级电脑“沃森”（Watson）的研发地，而沃森在2011年就在“危险性边缘”（Jeopardy！）节目的比赛里忽得头筹。最初的沃森电脑仍留于此处——它是一个体积大约与一个卧室非常，由10台粗壮的冷柜式机器围住四面墙的计算机系统。

技术人员可以通过系统内部的小细孔把各种线缆收到机器背部。系统内部温度低得胆怯，好像这个计算机集群是活生生的一般。Todays Watson is very different. It no longer exists solely within a wall of cabinets but is spread across a cloud of open-standard servers that run several hundred “instances” of the AI at once. Like all things cloudy, Watson is served to simultaneous customers anywhere in the world, who can access it using their phones, their desktops, or their own data servers. This kind of AI can be scaled up or down on demand. Because AI improves as people use it, Watson is always getting smarter; anything it learns in one instance can be immediately transferred to the others. And instead of one single program, its an aggregation of diverse software engines—its logic-deduction engine and its language-parsing engine might operate on different code, on different chips, in different locations—all cleverly integrated into a unified stream of intelligence.如今的沃森系统与之前比起有了明显差异。

它仍然意味着不存在于一排机柜之中，而是通过大量对用户免费对外开放的服务器传播，这些服务器需要即时运营上百种人工智能的“情况”。同所有云端简化的事物一样，沃森系统为世界各地同时用于的客户服务，他们需要用手机、台式机以及他们自己的数据服务器连到该系统。

这类人工智能可以根据市场需求按比例减少或增加。鉴于人工智能不会随人们的用于而逐步改良，沃森将一直显得越发聪慧；它在任何一次情况中所得知的改良点都会立刻传输至其他情况中。

并且，它也不是一个单一的程序，而是各种软件引擎的子集——其逻辑演译引擎和语言解析引擎可以在有所不同的代码、芯片以及方位上运营——所有这些智慧的因素都汇聚出了一个统一的智能流。Consumers can tap into that always-on intelligence directly, but also through third-party apps that harness the power of this AI cloud. Like many parents of a bright mind, IBM would like Watson to pursue a medical career, so it should come as no surprise that one of the apps under development is a medical-diagnosis tool. Most of the previous attempts to make a diagnostic AI have been pathetic failures, but Watson really works. When, in plain English, I give it the symptoms of a disease I once contracted in India, it gives me a list of hunches, ranked from most to least probable. The most likely cause, it declares, is Giardia—the correct answer. This expertise isnt yet available to patients directly; IBM provides access to Watsons intelligence to partners, helping them develop user-friendly interfaces for subscribing doctors and hospitals. “I believe something like Watson will soon be the worlds best diagnostician—whether machine or human,” says Alan Greene, chief medical officer of Scanadu, a startup that is building a diagnostic device inspired by the Star Trek medical tricorder and powered by a cloud AI. “At the rate AI technology is improving, a kid born today will rarely need to see a doctor to get a diagnosis by the time they are an adult.”用户可以必要终端这一永久相连（always-on）的智能系统，也可以通过用于这一人工智能云服务的第三方应用程序终端。正如许多高瞻远瞩的父母一样，IBM想要让沃森电脑专门从事医学工作，因此他们正在研发一款医疗临床工具的应用程序，这推倒也不足为奇。

之前，医疗方面的人工智能尝试大多以失利收场，但沃森却卓有成效。非常简单地说道，当我输出我曾多次在印度病毒感染上的某种疾病症状时，它不会给我一个疑为病症的表格，上面一一列清了可能性从低到较低的疾病。它指出我最有可能病毒感染了贾第鞭毛虫病（Giardia）——说道的一点儿也到底。

这一技术仍未必要对患者对外开放；IBM将沃森电脑的智能获取给合作伙伴终端用于，以协助他们研发出有用户友好关系界面为购票医生及医院方面服务。“我坚信类似于沃森这种——无论它是机器还是人——都将迅速沦为世界上最差的医疗医生”，创业公司Scanadu的首席医疗官艾伦·格林（Alan Greene）说，该公司受到电影《星际变形金刚》中医用三录仪的灵感，正在利用云人工智能技术生产一种医疗设备。“从人工智能技术改良的速率来看，现在出生于的孩子长大后，很有可能不过于必须通过看医生来获知医疗情况了。”As AIs develop, we might have to engineer ways to prevent consciousness in them—our most premium AI services will be advertised as consciousness-free.随着人工智能发展，我们有可能要设计出有一些制止它们享有意识的方式——我们所声称的最优质的人工智能服务将是无意识服务。

Medicine is only the beginning. All the major cloud companies, plus dozens of startups, are in a mad rush to launch a Watson-like cognitive service. According to quantitative analysis firm Quid, AI has attracted more than $17 billion in investments since 2009. Last year alone more than $2 billion was invested in 322 companies with AI-like technology. Facebook and Google have recruited researchers to join their in-house AI research teams. Yahoo, Intel, Dropbox, LinkedIn, Pinterest, and Twitter have all purchased AI companies since last year. Private investment in the AI sector has been expanding 62 percent a year on average for the past four years, a rate that is expected to continue.医学仅仅只是一个开始。所有主流的云计算公司，再加数十家创业公司都在争先恐后地积极开展类似于沃森电脑的理解服务。根据分析分析公司Quid的数据，自2009年以来，人工智能早已更有了多达170亿美元的投资。

仅有去年一年，就有322家享有类似于人工智能技术的公司取得了多达20亿美元的投资。Facebook和谷歌也为其公司内部的人工智能研究小组聘用了研究员。自去年以来，雅虎、英特尔、Dropbox、LinkedIn、Pinterest以及推特也都并购了人工智能公司。

过去四年间，人工智能领域的民间投资以平均值每年62%的快速增长速率减少，这一速率预计还不会持续下去。Amid all this activity, a picture of our AI future is coming into view, and it is not the HAL 9000—a discrete machine animated by a charismatic (yet potentially homicidal) humanlike consciousness—or a Singularitan rapture of superintelligence. The AI on the horizon looks more like Amazon Web Services—cheap, reliable, industrial-grade digital smartness running behind everything, and almost invisible except when it blinks off. This common utility will serve you as much IQ as you want but no more than you need. Like all utilities, AI will be supremely boring, even as it transforms the Internet, the global economy, and civilization. It will enliven inert objects, much as electricity did more than a century ago. Everything that we formerly electrified we will now cognitize. This new utilitarian AI will also augment us individually as people (deepening our memory, speeding our recognition) and collectively as a species. There is almost nothing we can think of that cannot be made new, different, or interesting by infusing it with some extra IQ. In fact, the business plans of the next 10,000 startups are easy to forecast: Take X and add AI. This is a big deal, and now its here.纵观所有这些活动，人工智能的未来于是以转入我们的视野之中，它既非如那种哈尔9000（HAL 9000）（译者录：小说及电影《2001：太空漫游》中的超级电脑）——一台享有头脑（但有潜在嗜杀死偏向）的类人意识并依赖此运营的独立国家机器那般——也非让奇点论者心醉神迷的超级智能。将要来临的人工智能奇特亚马逊的网络服务——廉价、可信、工业级的数字智慧在一切事物的背后运营，有时候在你的眼前闪光几下，其他时候几近无形。

这一标准化设施将获取你所必须的人工智能而不远超过你的必须。和所有设施一样，即使人工智能转变了互联网、全球经济以及文明，它也将显得令人厌倦。

正如一个多世纪以前电力所做到的那样，它不会让无生命的物体活跃一起。之前我们电气化的所有东西，现在我们都将使之理解化。而实用化的新型人工智能也不会强化人类个体（加剧我们的记忆、加快我们的理解）以及人类群体的生活。

通过重新加入一些额外的智能因素，我们想不到有什么东西无法显得新奇、有所不同且有意思。实质上，我们能只能地预测到接下来的一万家创业公司的商业计划：“做到某项事业，并重新加入人工智能”。

兹事体大，近在眼前。Around 2002 I attended a small party for Google—before its IPO, when it only focused on search. I struck up a conversation with Larry Page, Googles brilliant cofounder, who became the companys CEO in 2011. “Larry, I still dont get it. There are so many search companies. Web search, for free? Where does that get you?” My unimaginative blindness is solid evidence that predicting is hard, especially about the future, but in my defense this was before Google had ramped up its ad-auction scheme to generate real income, long before YouTube or any other major acquisitions. I was not the only avid user of its search site who thought it would not last long. But Pages reply has always stuck with me: “Oh, were really making an AI.”约在2002年时，我参与了谷歌的一个小型聚会——彼时谷歌仍未IPO，还在一心一意地做到网络搜寻。我与谷歌卓越的牵头创始人、2011年沦为谷歌CEO的拉里·佩奇（Larry Page）随便攀谈一起。

“拉里，我还是搞不懂，现在有这么多搜寻公司，你们为什么要做到免费的网络搜寻？你是怎么想起这个主意的？”我那缺少想象力的幼稚无非证明了我们很难去做到预测，特别是在是对于未来的预测。但我要反驳的是，在谷歌强化其广告拍卖会方案并使之构成实际收益，以及展开对YouTube的收购或其他最重要收购之前，预测未来是很难的。我并不是唯一一个一旁疯狂地用着谷歌的搜索引擎一旁指出它倒没法多久的用户。

但佩奇的问让我仍然难以忘怀：“哦，我们实质上是在做到人工智能。”Ive thought a lot about that conversation over the past few years as Google has bought 14 AI and robotics companies. At first glance, you might think that Google is beefing up its AI portfolio to improve its search capabilities, since search contributes 80 percent of its revenue. But I think thats backward. Rather than use AI to make its search better, Google is using search to make its AI better. Every time you type a query, click on a search-generated link, or create a link on the web, you are training the Google AI. When you type “Easter Bunny” into the image search bar and then click on the most Easter Bunny-looking image, you are teaching the AI what an Easter bunny looks like. Each of the 12.1 billion queries that Googles 1.2 billion searchers conduct each day tutor the deep-learning AI over and over again. With another 10 years of steady improvements to its AI algorithms, plus a thousand-fold more data and 100 times more computing resources, Google will have an unrivaled AI. My prediction: By 2024, Googles main product will not be search but AI.过去数年间，关于那次谈话我想要了很多，谷歌也并购了14家人工智能以及机器人方面的公司。

鉴于搜寻业务为谷歌贡献了80%的收益，因此乍一看去，你可能会实在谷歌正在扩展其人工智能方面的投资人组以提高搜寻能力。但是我指出正好忽略。谷歌正在用搜寻技术来提高人工智能，而非用于人工智能来改良搜寻技术。每当你输出一个查找词，页面搜索引擎分解的链接，或者在网页上建构一个链接，你都是在训练谷歌的人工智能技术。

当你在图片搜寻栏中输出“复活节兔子”（Easter Bunny）并页面看上去最像复活节兔子的那张图片时，你都是在告诉他人工智能，复活节兔子是生出什么样的。谷歌每天享有12亿搜寻用户，产生1210亿搜寻关键词，每一个关键词都是在一次又一次地辅导人工智能展开深度自学。如果再对人工智能的算法展开为之10年的巩固改良，加之一千倍以上的数据以及一百倍以上的计算资源，谷歌将不会研发出有一款无与伦比的人工智能产品。

我的应验是：到2024年，谷歌的主营产品将仍然是搜索引擎，而是人工智能产品。This is the point where it is entirely appropriate to be skeptical. For almost 60 years, AI researchers have predicted that AI is right around the corner, yet until a few years ago it seemed as stuck in the future as ever. There was even a term coined to describe this era of meager results and even more meager research funding: the AI winter. Has anything really changed?这个观点大自然也不会招致猜测的声音。

近60年来，人工智能的研究者都预测说道人工智能时代将要来临，但是直到几年前，人工智能样子还是遥不可及。人们甚至发明者了一个词来叙述这个研究结果短缺、研究基金更为短缺的时代：人工智能之冬。那么事情知道有变化吗？Yes. Three recent breakthroughs have unleashed the long-awaited arrival of artificial intelligence:是的。近期的三大突破让人们期待已久的人工智能近在眼前：1. Cheap parallel computation1. 成本便宜的并行计算Thinking is an inherently parallel process, billions of neurons firing simultaneously to create synchronous waves of cortical computation. To build a neural network—the primary architecture of AI software—also requires many different processes to take place simultaneously. Each node of a neural network loosely imitates a neuron in the brain—mutually interacting with its neighbors to make sense of the signals it receives. To recognize a spoken word, a program must be able to hear all the phonemes in relation to one another; to identify an image, it needs to see every pixel in the context of the pixels around it—both deeply parallel tasks. But until recently, the typical computer processor could only ping one thing at a time.思维是一种人类固有的分段过程，数以亿计的神经元同时静电以建构出有大脑皮层用作计算出来的实时脑电波。

搭起一个神经网络——即人工智能软件的主要结构——也必须许多有所不同的进程同时运营。神经网络的每一个节点都大体仿真了大脑中的一个神经元——其与邻接的节点相互起到，以具体所接管的信号。一项程序要解读某个口语单词，就必需需要听得清（有所不同音节）彼此之间的所有音素；要辨识出有某幅图片，就必须看见其周围像素环境内的所有像素——二者都是深层次的并行任务。

但直到最近，标准的计算机处理器也意味着能一次处置一项任务。That began to change more than a decade ago, when a new kind of chip, called a graphics processing unit, or GPU, was devised for the intensely visual—and parallel—demands of videogames, in which millions of pixels had to be recalculated many times a second. That required a specialized parallel computing chip, which was added as a supplement to the PC motherboard. The parallel graphical chips worked, and gaming soared. By 2005, GPUs were being produced in such quantities that they became much cheaper. In 2009, Andrew Ng and a team at Stanford realized that GPU chips could run neural networks in parallel.事情在十多年前就早已开始发生变化，彼时经常出现了一种被称作图形处理单元（graphics processing unit -GPU）的新型芯片，它需要符合可用游戏中高密度的视觉以及分段市场需求，在这一过程中，每秒钟都有上百万像素被多次新的计算出来。

这一过程必须一种专门的并行计算芯片，该芯片加到至电脑主板上，作为对其的补足。分段图形芯片起到显著，游戏游戏性也大幅度下降。到2005年，GPU芯片产量颇高，其价格便降了下来。2009年，吴恩达（Andrew Ng）（译者录：华裔计算机科学家）以及斯坦福大学的一个研究小组意识到，GPU芯片可以分段运营神经网络。

That discovery unlocked new possibilities for neural networks, which can include hundreds of millions of connections between their nodes. Traditional processors required several weeks to calculate all the cascading possibilities in a 100 million-parameter neural net. Ng found that a cluster of GPUs could accomplish the same thing in a day. Today neural nets running on GPUs are routinely used by cloud-enabled companies such as Facebook to identify your friends in photos or, in the case of Netflix, to make reliable recommendations for its more than 50 million subscribers.这一找到打开了神经网络新的可能性，使得神经网络能容纳上亿个节点间的相连。传统的处理器必须数周才能计算出来出有享有1亿节点的神经网的级联可能性。而吴恩达找到，一个GPU集群在一天内就可已完成同一任务。

现在，一些应用于云计算的公司一般来说都会用于GPU来运营神经网络，例如，Facebook会籍此技术来辨识用户照片中的好友，Netfilx也不会各有不同来给5000万订阅者用户获取靠谱的引荐内容。2. Big Data2. 大数据Every intelligence has to be taught. A human brain, which is genetically primed to categorize things, still needs to see a dozen examples before it can distinguish between cats and dogs. Thats even more true for artificial minds. Even the best-programmed computer has to play at least a thousand games of chess before it gets good. Part of the AI breakthrough lies in the incredible avalanche of collected data about our world, which provides the schooling that AIs need. Massive databases, self-tracking, web cookies, online footprints, terabytes of storage, decades of search results, Wikipedia, and the entire digital universe became the teachers making AI smart.每一种智能都必须被训练。

哪怕是天生需要给事物分类的人脑，也依然必须看完十几个例子后才需要区分猫和狗。人工思维则更是如此。

即使是（国际象棋）程序编成的最差的电脑，也得在最少对局一千局之后才能有较好展现出。人工智能取得突破的部分原因在于，我们搜集到来自全球的海量数据，以给人工智能获取了其所须要的训练。巨型数据库、自动追踪（self-tracking）、网页cookie、线上足迹、兆兆字节级存储、数十年的搜寻结果、维基百科以及整个数字世界都出了老师，是它们让人工智能显得更为聪慧。3. Better algorithms3. 优于的算法Digital neural nets were invented in the 1950s, but it took decades for computer scientists to learn how to tame the astronomically huge combinatorial relationships between a million—or 100 million—neurons. The key was to organize neural nets into stacked layers. Take the relatively simple task of recognizing that a face is a face. When a group of bits in a neural net are found to trigger a pattern—the image of an eye, for instance—that result is moved up to another level in the neural net for further parsing. The next level might group two eyes together and pass that meaningful chunk onto another level of hierarchical structure that associates it with the pattern of a nose. It can take many millions of these nodes (each one producing a calculation feeding others around it), stacked up to 15 levels high, to recognize a human face. In 2006, Geoff Hinton, then at the University of Toronto, made a key tweak to this method, which he dubbed “deep learning.” He was able to mathematically optimize results from each layer so that the learning accumulated faster as it proceeded up the stack of layers. Deep-learning algorithms accelerated enormously a few years later when they were ported to GPUs. The code of deep learning alone is insufficient to generate complex logical thinking, but it is an essential component of all current AIs, including IBMs Watson, Googles search engine, and Facebooks algorithms.20世纪50年代，数字神经网络就被发明者了出来，但计算机科学家花费了数十年来研究如何匹敌百万乃至亿级神经元之间那可观到如天文数字一般的人组关系。

这一过程的关键是要将神经网络的组织沦为填充层（stacked layer）。一个相对来说比较简单的任务就是人脸识别。当某神经网络中的一组比特被找到需要构成某种图案——例如，一只眼睛的图像——这一结果就不会被向下移往至该神经网络的另一层以做到更进一步分析。接下来的这一层可能会将两只眼睛拼在一起，将这一有意义的数据块传送到层级结构的第三层，该层可以将眼睛和鼻子的图像融合到一起（来展开分析）。

辨识一张人脸有可能必须数百万个这种节点（每个节点都会分解一个计算结果以供周围节点用于），并必须填充高达15个层级。2006年，当时就任于多伦多大学的杰夫·辛顿（Geoff Hinton）对这一方法展开了一次关键改良，并将其称作“深度自学”。他需要从数学层面上优化每一层的结果从而使神经网络在构成填充层时减缓自学速度。数年后，当深度自学算法被重制到GPU集群中后，其速度有了明显提升。

只能靠深度自学的代码并足以能产生简单的逻辑思维，但是它是还包括IBM的沃森电脑、谷歌搜索引擎以及Facebook算法在内，当下所有人工智能产品的主要组成部分。This perfect storm of parallel computation, bigger data, and deeper algorithms generated the 60-years-in-the-making overnight success of AI. And this convergence suggests that as long as these technological trends continue—and theres no reason to think they wont—AI will keep improving.这一由并行计算、大数据和更加深层次算法构成的极致风暴使得持续深耕了60年的人工智能一鸣惊人。而这一单体也指出，只要这些技术趋势继续下去——它们也没理由不沿袭——人工智能将精益求精。As it does, this cloud-based AI will become an increasingly ingrained part of our everyday life. But it will come at a price. Cloud computing obeys the law of increasing returns, sometimes called the network effect, which holds that the value of a network increases much faster as it grows bigger. The bigger the network, the more attractive it is to new users, which makes it even bigger, and thus more attractive, and so on. A cloud that serves AI will obey the same law. The more people who use an AI, the smarter it gets. The smarter it gets, the more people use it. The more people that use it, the smarter it gets. Once a company enters this virtuous cycle, it tends to grow so big, so fast, that it overwhelms any upstart competitors. As a result, our AI future is likely to be ruled by an oligarchy of two or three large, general-purpose cloud-based commercial intelligences.随着这一趋势的持续，这种基于云技术的人工智能将越发沦为我们日常生活中不可分割的一部分。

但天上没掉馅饼的事。云计算遵循收益递减（increasing returns）法则，这一法则有时也被称作网络效应（network effect），即随着网络发展壮大，网络价值也不会以更慢的速度减少。网络（规模）越大，对于新的用户的吸引力就越强劲，这又让网络显得更大，又更进一步强化了吸引力，如此来回。为人工智能服务的云技术也遵循这一法则。

就越多人用于人工智能产品，它就不会显得就越聪慧；它显得就越聪慧，就有就越多人来用于它；然后它显得更聪明，更进一步就有更加多人用于它。一旦有公司迈向了这个良性循环中，其规模不会变小、发展不会减缓，以至于没任何新兴输掉能望其项背。因此，人工智能的未来将有两到三家寡头公司统治者，它们不会研发出有大规模基于云技术的多用途商业智能产品。In 1997, Watsons precursor, IBMs Deep Blue, beat the reigning chess grand master Garry Kasparov in a famous man-versus-machine match. After machines repeated their victories in a few more matches, humans largely lost interest in such contests. You might think that was the end of the story (if not the end of human history), but Kasparov realized that he could have performed better against Deep Blue if hed had the same instant access to a massive database of all previous chess moves that Deep Blue had. If this database tool was fair for an AI, why not for a human? To pursue this idea, Kasparov pioneered the concept of man-plus-machine matches, in which AI augments human chess players rather than competes against them.1997年，沃森电脑的前辈、IBM公司的深蓝电脑在一场知名的人机大赛中打败了当时的国际象棋大师加里·卡斯帕罗夫（Garry Kasparov）。

在电脑又输掉了几场比赛之后，人们基本上丧失了对这类比赛的兴趣。你可能会指出故事到此就完结了，但卡斯帕罗夫意识到，如果他也能像深蓝一样立刻采访还包括以前所有棋局棋路变化在内的巨型数据库的话，他在对局中能展现出得更佳。

如果这一数据库工具对于人工智能设备来说是公平的话，为什么人类无法用于它呢？为了探究这一点子，卡斯帕罗夫首度明确提出了“人特机器”（man-plus-machine）比赛的概念，即用人工智能强化国际象棋运动员水平，而非让人与机器之间对付。Now called freestyle chess matches, these are like mixed martial arts fights, where players use whatever combat techniques they want. You can play as your unassisted human self, or you can act as the hand for your supersmart chess computer, merely moving its board pieces, or you can play as a “centaur,” which is the human/AI cyborg that Kasparov advocated. A centaur player will listen to the moves whispered by the AI but will occasionally override them—much the way we use GPS navigation in our cars. In the championship Freestyle Battle in 2014, open to all modes of players, pure chess AI engines won 42 games, but centaurs won 53 games. Today the best chess player alive is a centaur: Intagrand, a team of humans and several different chess programs.这种比赛如今被称作自由式国际象棋比赛，它有点儿像混合武术对抗赛，运动员们可以用于任何他们想用的登陆作战技巧。你可以单打独斗；也可以拒绝接受你那装有超级聪慧的国际象棋软件的电脑得出的协助，你要做到的意味着是按照它的建议来移动棋子；或者你可以当一个卡斯帕罗夫所倡导的那种“半人半机”的运动员。

半人半机运动员不会征询人工智能设备在其耳边明确提出的棋路建议，但是也不定会使用这些建议——奇特我们驾车时候用的GPS导航系统一般。在拒绝接受任何模式运动员参赛的2014年自由式国际象棋对付锦标赛上，显人工智能的国际象棋引擎夺得了42场比赛，而半人半机运动员则夺得了53场。当今世上最杰出的国际象棋运动员就是半人半机运动员Intagrand，它是一个由多人以及数个有所不同国际象棋程序所构成的小组。But heres the even more surprising part: The advent of AI didnt diminish the performance of purely human chess players. Quite the opposite. Cheap, supersmart chess programs inspired more people than ever to play chess, at more tournaments than ever, and the players got better than ever. There are more than twice as many grand masters now as there were when Deep Blue first beat Kasparov. The top-ranked human chess player today, Magnus Carlsen, trained with AIs and has been deemed the most computer-like of all human chess players. He also has the highest human grand master rating of all time.但最令人吃惊的是：人工智能的经常出现未让显人类的国际象棋棋手的水平上升。

恰恰相反，廉价、超级智能的国际象棋软件更有了更加多人来下国际象棋，比赛比以前激增了，棋手的水平也比以前下降了。现在的国际象棋大师（译者录：国际象棋界的一种等级）人数是深蓝战胜卡斯帕罗夫那时候的两倍多。

现在的名列第一的人类国际象棋棋手马格努斯·卡尔森（Magnus Carlsen）就曾拒绝接受人工智能的训练，他被指出是所有人类国际象棋棋手中最相似电脑的棋手，同时也是有史以来分数最低的人类国际象棋大师。If AI can help humans become better chess players, it stands to reason that it can help us become better pilots, better doctors, better judges, better teachers. Most of the commercial work completed by AI will be done by special-purpose, narrowly focused software brains that can, for example, translate any language into any other language, but do little else. Drive a car, but not converse. Or recall every pixel of every video on YouTube but not anticipate your work routines. In the next 10 years, 99 percent of the artificial intelligence that you will interact with, directly or indirectly, will be nerdily autistic, supersmart specialists.如果人工智能能协助人类沦为更加杰出的国际象棋棋手，那么它也能协助我们沦为更加杰出的飞行员、医生、法官以及教师。大多数由人工智能已完成的商业工作都将是有专门目的的工作，严苛容许在智能软件能做的工作之内，比如，（人工智能产品）把某种语言翻译成另一种语言，但却无法翻译成第三种语言。

再行比如，它们可以驾车，但却无法与人聊天。或者是能回忆起YouTube上每个视频的每个像素，却无法预测你的日常工作。在未来十年，你与之必要或者间接对话的人工智能产品，有99%都将是高度专一、十分聪慧的“专家”。

In fact, this wont really be intelligence, at least not as weve come to think of it. Indeed, intelligence may be a liability—especially if by “intelligence” we mean our peculiar self-awareness, all our frantic loops of introspection and messy currents of self-consciousness. We want our self-driving car to be inhumanly focused on the road, not obsessing over an argument it had with the garage. The synthetic Dr. Watson at our hospital should be maniacal in its work, never wondering whether it should have majored in English instead. As AIs develop, we might have to engineer ways to prevent consciousness in them—and our most premium AI services will likely be advertised as consciousness-free.实质上，这并非确实的智能，最少不是我们一眼想想的那种智能。的确，智能有可能是一种偏向——特别是在是如果我们眼中的智能意味著我们那特有的自我意识、一切我们所有的那种迷茫的自省循环以及杂乱的自我意识流的话。

我们期望无人驾驶汽车能一心一意在路上行经，而不是纠葛于之前和车库的争执。医院中的综合医生“沃森”能专心工作，不要去想要自己是不是应当专攻英语。

随着人工智能的发展，我们有可能要设计出有一些制止它们享有意识的方式——我们所声称的最优质的人工智能服务将是无意识服务。What we want instead of intelligence is artificial smartness. Unlike general intelligence, smartness is focused, measurable, specific. It also can think in ways completely different from human cognition. A cute example of this nonhuman thinking is a cool stunt that was performed at the South by Southwest festival in Austin, Texas, in March of this year. IBM researchers overlaid Watson with a culinary database comprising online recipes, USDA nutritional facts, and flavor research on what makes compounds taste pleasant. From this pile of data, Watson dreamed up novel dishes based on flavor profiles and patterns from existing dishes, and willing human chefs cooked them. One crowd favorite generated from Watsons mind was a tasty version of fish and chips using ceviche and fried plantains. For lunch at the IBM labs in Yorktown Heights I slurped down that one and another tasty Watson invention: Swiss/Thai asparagus quiche. Not bad! Its unlikely that either one would ever have occurred to humans.我们想的不是智能，而是人工智慧。

与一般的智能有所不同，智慧（产品）具备专心、可取决于、种类特定的特点。它也需要以几乎异于人类理解的方式来思维。这儿有一个关于非人类思维的一个很好的例子，今年三月在德克萨斯州奥斯汀举办的西南偏南音乐节（South by Southwest festival）上，沃森电脑就首演了一幕得意的绝技：IBM的研究员给沃森加到了由在线菜谱、美国农业部（USDA）开具的营养表格以及让饭菜更加美味的味道研究报告构成的数据库。

凭借这些数据，沃森依赖味道配备资料和现有菜色模型建构出有了新式的菜肴。其中一款由沃森建构出有的受人欢迎的菜肴是美味版本的“炸鱼和炸薯条”（fish and chips），它是用酸橘汁腌鱼和油炸芭蕉做成。在约克城高地的IBM实验室里，我品尝了这道菜，也不吃了另一款由沃森建构出有的美味菜肴：瑞士/泰式芦笋乳蛋饼。

味道挺不错！Nonhuman intelligence is not a bug, its a feature. The chief virtue of AIs will be their alien intelligence. An AI will think about food differently than any chef, allowing us to think about food differently. Or to think about manufacturing materials differently. Or clothes. Or financial derivatives. Or any branch of science and art. The alienness of artificial intelligence will become more valuable to us than its speed or power.非人类的智能不是错误，而是一种特征。人工智能的主要优点就是它们的“相同智能”（alien intelligence）。

一种人工智能产品在思维食物方面与任何的大厨都不完全相同，这也能让我们以有所不同的方式看来食物，或者是以有所不同的方式来考虑到生产物料、衣服、金融派生工具或是给定门类的科学和艺术。相比于人工智能的速度或者力量来说，它的互为异性对我们更加有价值。As it does, it will help us better understand what we mean by intelligence in the first place. In the past, we would have said only a superintelligent AI could drive a car, or beat a human at Jeopardy! or chess. But once AI did each of those things, we considered that achievement obviously mechanical and hardly worth the label of true intelligence. Every success in AI redefines it.实质上，人工智能将协助我们更佳地解读我们最初所说的智能的意思。

过去，我们可能会说道只有那种超级聪慧的人工智能产品才能驾车，或是在“危险性边缘”节目以及国际象棋大赛中战胜人类。而一旦人工智能做了那些事情，我们就不会实在这些成就显著机械又刻板，并不需要被称作确实意义上的智能。人工智能的每次顺利，都是在新的定义自己。

But we havent just been redefining what we mean by AI—weve been redefining what it means to be human. Over the past 60 years, as mechanical processes have replicated behaviors and talents we thought were unique to humans, weve had to change our minds about what sets us apart. As we invent more species of AI, we will be forced to surrender more of what is supposedly unique about humans. Well spend the next decade—indeed, perhaps the next century—in a permanent identity crisis, constantly asking ourselves what humans are for. In the grandest irony of all, the greatest benefit of an everyday, utilitarian AI will not be increased productivity or an economics of abundance or a new way of doing science—although all those will happen. The greatest benefit of the arrival of artificial intelligence is that AIs will help define humanity. We need AIs to tell us who we are.但我们某种程度是在仍然新的定义人工智能的意义——也是在新的定义人类的意义。过去60年间，机械加工拷贝了我们曾指出是人类所独特的不道德和才能，我们被迫转变关于人机之间区别的观点。随着我们发明者出有更加多种类的人工智能产品，我们将被迫退出更好被视作人类所独特能力的观点。

在接下来的十年里——甚至，在接下来的一个世纪里——我们将正处于一场旷日持久的身份危机（identity crisis）中，并大大扪心自问人类的意义。在这之中尤为嘲讽的是，我们每日认识的实用性人工智能产品所带给的仅次于益处，不在于提升生产能力、扩展经济或是带给一种新的科研方式——尽管这些都会再次发生。

人工智能的仅次于益处在于，它将协助我们定义人类。我们必须人工智能来告诉他我们，我们到底是谁。

本文来源：中欧体育-www.hnxl-edu.com