作者本人写的书评及翻译《Automation Makes Us Dumb》《自动化让我们变蠢》
子非鱼 2014-12-02
4
作者本人的原文链接如下：
http://online.wsj.com/articles/automation-makes-us-dumb-1416589342
（是的，需要翻墙，不过英文原文见下方）
Automation Makes Us Dumb
Human intelligence is withering as computers do more, but there’s a solution.
Computers are taking over the kinds of knowledge work long considered the preserve of well-educated, well-trained professionals. ENLARGE
Computers are taking over the kinds of knowledge work long considered the preserve of well-educated, well-trained professionals. LUCI GUTIÉRREZ
By NICHOLAS CARR
Nov. 21, 2014 12:02 p.m. ET
190 COMMENTS
Artificial intelligence has arrived. Today’s computers are discerning and sharp. They can sense the environment, untangle knotty problems, make subtle judgments and learn from experience. They don’t think the way we think—they’re still as mindless as toothpicks—but they can replicate many of our most prized intellectual talents. Dazzled by our brilliant new machines, we’ve been rushing to hand them all sorts of sophisticated jobs that we used to do ourselves.
But our growing reliance on computer automation may be exacting a high price. Worrisome evidence suggests that our own intelligence is withering as we become more dependent on the artificial variety. Rather than lifting us up, smart software seems to be dumbing us down.
It has been a slow process. The first wave of automation rolled through U.S. industry after World War II, when manufacturers began installing electronically controlled equipment in their plants. The new machines made factories more efficient and companies more profitable. They were also heralded as emancipators. By relieving factory hands of routine chores, they would do more than boost productivity. They would elevate laborers, giving them more invigorating jobs and more valuable talents. The new technology would be ennobling.
Then, in the 1950s, a Harvard Business School professor named James Bright went into the field to study automation’s actual effects on a variety of industries, from heavy manufacturing to oil refining to bread baking. Factory conditions, he discovered, were anything but uplifting. More often than not, the new machines were leaving workers with drabber, less demanding jobs. An automated milling machine, for example, didn’t transform the metalworker into a more creative artisan; it turned him into a pusher of buttons.
Bright concluded that the overriding effect of automation was (in the jargon of labor economists) to “de-skill” workers rather than to “up-skill” them. “The lesson should be increasingly clear,” he wrote in 1966. “Highly complex equipment” did not require “skilled operators. The ‘skill’ can be built into the machine.”
We are learning that lesson again today on a much broader scale. As software has become capable of analysis and decision-making, automation has leapt out of the factory and into the white-collar world. Computers are taking over the kinds of knowledge work long considered the preserve of well-educated, well-trained professionals: Pilots rely on computers to fly planes; doctors consult them in diagnosing ailments; architects use them to design buildings. Automation’s new wave is hitting just about everyone.
A professor from Harvard Medical School wrote in a journal article that when doctors become ‘screen-driven,’ following a computer’s prompts rather than ‘the patient’s narrative thread,’ their thinking can become constricted. In the worst cases, they may miss important diagnostic signals. ENLARGE
A professor from Harvard Medical School wrote in a journal article that when doctors become ‘screen-driven,’ following a computer’s prompts rather than ‘the patient’s narrative thread,’ their thinking can become constricted. In the worst cases, they may miss important diagnostic signals. GETTY IMAGES
Computers aren’t taking away all the jobs done by talented people. But computers are changing the way the work gets done. And the evidence is mounting that the same de-skilling effect that ate into the talents of factory workers last century is starting to gnaw away at professional skills, even highly specialized ones. Yesterday’s machine operators are today’s computer operators.
Just look skyward. Since their invention a century ago, autopilots have helped to make air travel safer and more efficient. That happy trend continued with the introduction of computerized “fly-by-wire” jets in the 1970s. But now, aviation experts worry that we’ve gone too far. We have shifted so many cockpit tasks from humans to computers that pilots are losing their edge—and beginning to exhibit what the British aviation researcher Matthew Ebbatson calls “skill fade.”
In 2007, while working on his doctoral thesis at Cranfield University’s School of Engineering, Mr. Ebbatson conducted an experiment with a group of airline pilots. He had them perform a difficult maneuver in a flight simulator—bringing a Boeing jet with a crippled engine in for a landing in rough weather—and measured subtle indicators of their skill, such as the precision with which they maintained the plane’s airspeed.
When he compared the simulator readings with the aviators’ actual flight records, he found a close connection between a pilot’s adroitness at the controls and the amount of time the pilot had recently spent flying planes manually. “Flying skills decay quite rapidly towards the fringes of ‘tolerable’ performance without relatively frequent practice,” Mr. Ebbatson concluded. But computers now handle most flight operations between takeoff and touchdown—so “frequent practice” is exactly what pilots are not getting.
Even a slight decay in manual flying ability can risk tragedy. A rusty pilot is more likely to make a mistake in an emergency. Automation-related pilot errors have been implicated in several recent air disasters, including the 2009 crashes of Continental Flight 3407 in Buffalo and Air France Flight 447 in the Atlantic Ocean, and the botched landing of Asiana Flight 214 in San Francisco in 2013.
Late last year, a report from a Federal Aviation Administration task force on cockpit technology documented a growing link between crashes and an overreliance on automation. Pilots have become “accustomed to watching things happen, and reacting, instead of being proactive,” the panel warned. The FAA is now urging airlines to get pilots to spend more time flying by hand.
As software improves, the people using it become less likely to sharpen their own know-how. Applications that offer lots of prompts and tips are often to blame; simpler, less solicitous programs push people harder to think, act and learn.
Ten years ago, information scientists at Utrecht University in the Netherlands had a group of people carry out complicated analytical and planning tasks using either rudimentary software that provided no assistance or sophisticated software that offered a great deal of aid. The researchers found that the people using the simple software developed better strategies, made fewer mistakes and developed a deeper aptitude for the work. The people using the more advanced software, meanwhile, would often “aimlessly click around” when confronted with a tricky problem. The supposedly helpful software actually short-circuited their thinking and learning.
The philosopher Hubert Dreyfus of the University of California, Berkeley, wrote in 2002 that human expertise develops through “experience in a variety of situations, all seen from the same perspective but requiring different tactical decisions.” In other words, our skills get sharper only through practice, when we use them regularly to overcome different sorts of difficult challenges.
The goal of modern software, by contrast, is to ease our way through such challenges. Arduous, painstaking work is exactly what programmers are most eager to automate—after all, that is where the immediate efficiency gains tend to lie. In other words, a fundamental tension ripples between the interests of the people doing the automation and the interests of the people doing the work.
Nevertheless, automation’s scope continues to widen. With the rise of electronic health records, physicians increasingly rely on software templates to guide them through patient exams. The programs incorporate valuable checklists and alerts, but they also make medicine more routinized and formulaic—and distance doctors from their patients.
In a study conducted in 2007-08 in upstate New York, SUNY Albany professor Timothy Hoff interviewed more than 75 primary-care physicians who had adopted computerized systems. The doctors felt that the software was impoverishing their understanding of patients, diminishing their “ability to make informed decisions around diagnosis and treatment.”
Harvard Medical School professor Beth Lown, in a 2012 journal article written with her student Dayron Rodriquez, warned that when doctors become “screen-driven,” following a computer’s prompts rather than “the patient’s narrative thread,” their thinking can become constricted. In the worst cases, they may miss important diagnostic signals.
The risk isn’t just theoretical. In a recent paper published in the journal Diagnosis, three medical researchers—including Hardeep Singh, director of the health policy, quality and informatics program at the Veterans Administration Medical Center in Houston—examined the misdiagnosis of Thomas Eric Duncan, the first person to die of Ebola in the U.S., at Texas Health Presbyterian Hospital Dallas. They argue that the digital templates used by the hospital’s clinicians to record patient information probably helped to induce a kind of tunnel vision. “These highly constrained tools,” the researchers write, “are optimized for data capture but at the expense of sacrificing their utility for appropriate triage and diagnosis, leading users to miss the forest for the trees.” Medical software, they write, is no “replacement for basic history-taking, examination skills, and critical thinking.”
Even creative trades are increasingly suffering from automation’s de-skilling effects. Computer-aided design has helped architects to construct buildings with unusual shapes and materials, but when computers are brought into the design process too early, they can deaden the aesthetic sensitivity and conceptual insight that come from sketching and model-building.
Working by hand, psychological studies have found, is better for unlocking designers’ originality, expands their working memory and strengthens their tactile sense. A sketchpad is an “intelligence amplifier,” says Nigel Cross, a design professor at the Open University in the U.K.
When software takes over, manual skills wane. In his book “The Thinking Hand,” the Finnish architect Juhani Pallasmaa argues that overreliance on computers makes it harder for designers to appreciate the subtlest, most human qualities of their buildings. “The false precision and apparent finiteness of the computer image” narrow a designer’s perspective, he writes, which can mean technically stunning but emotionally sterile work. As University of Miami architecture professor Jacob Brillhart wrote in a 2011 paper, modern computer systems can translate sets of dimensions into precise 3-D renderings with incredible speed, but they also breed “more banal, lazy, and uneventful designs that are void of intellect, imagination and emotion.”
We do not have to resign ourselves to this situation, however. Automation needn’t remove challenges from our work and diminish our skills. Those losses stem from what ergonomists and other scholars call “technology-centered automation,” a design philosophy that has come to dominate the thinking of programmers and engineers.
When system designers begin a project, they first consider the capabilities of computers, with an eye toward delegating as much of the work as possible to the software. The human operator is assigned whatever is left over, which usually consists of relatively passive chores such as entering data, following templates and monitoring displays.
This philosophy traps people in a vicious cycle of de-skilling. By isolating them from hard work, it dulls their skills and increases the odds that they will make mistakes. When those mistakes happen, designers respond by seeking to further restrict people’s responsibilities—spurring a new round of de-skilling.
Because the prevailing technique “emphasizes the needs of technology over those of humans,” it forces people “into a supporting role, one for which we are most unsuited,” writes the cognitive scientist and design researcher Donald Norman of the University of California, San Diego.
There is an alternative.
In “human-centered automation,” the talents of people take precedence. Systems are designed to keep the human operator in what engineers call “the decision loop”—the continuing process of action, feedback and judgment-making. That keeps workers attentive and engaged and promotes the kind of challenging practice that strengthens skills.
In this model, software plays an essential but secondary role. It takes over routine functions that a human operator has already mastered, issues alerts when unexpected situations arise, provides fresh information that expands the operator’s perspective and counters the biases that often distort human thinking. The technology becomes the expert’s partner, not the expert’s replacement.
Pushing automation in a more humane direction doesn’t require any technical breakthroughs. It requires a shift in priorities and a renewed focus on human strengths and weaknesses.
ENLARGE
LUCI GUTIÉRREZ
Airlines, for example, could program cockpit computers to shift control back and forth between computer and pilot during a flight. By keeping the aviator alert and active, that small change could make flying even safer.
In accounting, medicine and other professions, software could be far less intrusive, giving people room to exercise their own judgment before serving up algorithmically derived suggestions.
When it comes to the computerization of knowledge work, writes John Lee of the University of Iowa, “a less-automated approach, which places the automation in the role of critiquing the operator, has met with much more success” than the typical practice of supplanting human judgment with machine calculations. The best decision-support systems provide professionals with “alternative interpretations, hypotheses, or choices.”
Human-centered automation doesn’t constrain progress. Rather, it guides progress onto a more humanistic path, providing an antidote to the all-too-common, misanthropic view that venerates computers and denigrates people.
One of the most exciting examples of the human-focused approach is known as adaptive automation. It employs cutting-edge sensors and interpretive algorithms to monitor people’s physical and mental states, then uses that information to shift tasks and responsibilities between human and computer. When the system senses that an operator is struggling with a difficult procedure, it allocates more tasks to the computer to free the operator of distractions. But when it senses that the operator’s interest is waning, it ratchets up the person’s workload to capture their attention and build their skills.
We are amazed by our computers, and we should be. But we shouldn’t let our enthusiasm lead us to underestimate our own talents. Even the smartest software lacks the common sense, ingenuity and verve of the skilled professional. In cockpits, offices or examination rooms, human experts remain indispensable. Their insight, ingenuity and intuition, honed through hard work and seasoned real-world judgment, can’t be replicated by algorithms or robots.
If we let our own skills fade by relying too much on automation, we are going to render ourselves less capable, less resilient and more subservient to our machines. We will create a world more fit for robots than for us.
Mr. Carr is the author of “The Shallows: What the Internet Is Doing to Our Brains” and most recently, of “The Glass Cage: Automation and Us.”
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中文翻译由36氪提供，见下方
http://www.36kr.com/p/217399.html
自动化让我们变蠢
注：Mark Anderson说软件正在蚕食世界，他或许是从一个风投家的角度看到了无尽商机。但《浅薄（The Shallows: What the Internet Is Doing to Our Brains）》以及《玻璃笼子：自动化与我们（The Glass Cage: Automation and Us）》两本书的作者Nicholas Carr却感受到的是对人类未来的忧患—软件导致的知识工作自动化会让人类变蠢。人类智能会因为机器做得越来越多而变得枯竭。我们会不会像电影《机器人瓦力》里面的人类那样，沉迷于虚幻的现实、肥胖到无法直立行走，不会做任何事情吗？Carr的看法是，未必，但这必须要求我们采取另一种自动化的方案。
人工智能已经来临。现在的计算机不仅目光敏锐而且行动敏捷。它们可以感知环境，解决棘手问题，做出微妙判断，并懂得学习经验。它们已经能够复制许多我们当中最有价值的智力人才，我们现在已经把过去许多自己做的复杂工作交到了它们手上。
但是人类对计算机自动化的依赖程度不断加深可能要付出高昂代价。令人不安的证据表明，在我们越来越依赖于各种人造物以后，人类自身的智能正在变弱。智能软件似乎不能提高我们的智能，而是令我们变蠢了。
这是一个缓慢的过程。第一波自动化浪潮始于二战后，制造业开始在工厂安装电子控制设备，从而显著提升了生产效率和企业利润。不仅如此，自动化被誉为是解放者，让被替代的工人得以从事更有活力的工作，发挥更有价值的才能。
然后到了 1950 年代，哈佛商学院的 James Bright 教授开始研究自动化在不同行业（从重工业、炼油业到面包烘烤等）中收到的实效。结果他发现，工厂情况并未得到好转。新机器并没有把人往上赶，而是往往把人挤到了更单调乏味、要求更低的岗位。比方说，自动化铣床并没有把金属加工工人变成了富于创意的艺术家，而是把他变成了一个按按钮的人。
Bright 因此得出结论认为，自动化对工人产生的最重要效应不是“提升技能”而是“去技术化”。“经验教训越来越清晰，”他在 1966 年写道：“高度复杂的设备并不需要熟练的操作者。‘技能’已经被植入到机器里面了。”
今天，我们正在更广的范围内再次感受到这一教训。随着软件拥有了分析和决策能力，自动化已经飞跃出了工厂，进入到白领世界。计算机正在接管长久以来被认为是受到过良好教育以及训练有素的专业人士才能从事的知识工作：飞行员得靠计算机开飞机；医生要向它们请教诊断结论；建筑师用它们来设计建筑。自动化的这波新浪潮席卷了每一个人。
计算机并没有夺走了有才能的人的所有工作，但是却改变了工作的完成方式。专业技能同样的去技术化迹象正在不断累积，甚至连高度专业的领域亦是如此。今天的计算机操作者就像昨天的机器操作者。
比方说天上的自动驾驶。发明 1 个世纪以来，自动驾驶已经让飞行更高效更安全。随着 1970 年代计算机化“飞控系统”的引入，这一趋势仍在延续。但是现在航空专家担心我们走得太远了。我们已经把太多的驾驶舱任务交给了计算机，以至于飞行员正在失去优势，并开始出现英国飞行研究者 Matthew Ebbatson 所谓的“技能退化”。
2007 年，准备博士论文的 Ebbatson 对一组飞行员进行了一项实验。实验内容是让飞行员在飞行模拟中做出一项难度很高的机动动作—让引擎失效的波音飞机在恶劣天气下着陆，希望借此评估一下这些人的技能（比方说评估空速的精确度等）。
随后他把模拟结果与飞行员的实际飞行记录进行了对比。对比发现飞行员对控制的熟练程度与该飞行员最近手工操作飞机的时间具有紧密联系（注：熟能生巧嘛，这个研究似乎多此一举）。越是不需要频繁练习的可容错操作，技能就退化得越快。可是现在飞机的起飞和降落大部分已经由计算机包办了，因此这两块恰恰正是飞行员缺乏练习的地方。
而人工操作能力的一点点退化都可能造成悲剧事故。手艺生疏的飞行员更有可能在紧急情况下犯错。最近的多次飞行事故均牵扯到自动化相关的飞行员人为错误，其中就包括 2009 年美国大陆航空 3407 航班在布法罗的坠机事故以及法航 447 在大西洋的坠机，还是就是 2013 年韩亚航空在旧金山的着落失败事故等。
去年，美国联邦航空管理局（FAA）驾驶舱技术特别调查小组发布了一份报告，报告记录了坠机与过度信赖自动化之间存在着越来越多的关联。飞行员已经“习惯于先观察发生了什么事情再去做出反应而不是保持前瞻性。”现在 FAA 已经敦促航空公司要让飞行员花更多的时间去联系人工操作。
软件改进后，使用软件的人也就不大愿意去磨练自己的专门技能。这应该归咎于那些提供了大量提示的应用；而那些不那么热心的、更简单一些的应用却会促使人更努力去思考、行动和学习。
10 年前，荷兰乌特勒支大学的信息科学家让一群人执行复杂的分析和规划任务，这些人有的用不提供任何辅助的基本软件，有的则用提供了大量辅助的复杂软件。研究人员发现，使用简单软件的人反而制定出了更好的策略，所犯错误更少并且形成了更出色的工作技能。而使用更先进软件的那些人往往却在遭遇棘手问题时“毫无目标地乱点鼠标”。本该是有帮助的软件实际上却令这部分人头脑短路，不懂学习。
加州大学伯克利分校的哲学家 Hubert Dreyfus 在 2002 年写道，人类的专门知识是“经历了各种情况后而形成的，观察角度都是一样的，但是需要不同的策略决策。”换句话说，熟能生巧，只有不断地使用这些技能才能克服各种不同的挑战。
相反，现代软件的目标则是让我们轻松面对这些挑战。艰苦、费力的工作正是程序员渴望自动化的—毕竟这是立竿见影的东西。换言之，进行自动化的人的兴趣点以及做工作的人的兴趣点之间存在着根本性的紧张关系。
但无论如何，自动化的范围还在继续扩大。电子病历的推广使得医生越来越依赖于在软件模板的指导下进行病人身体检查。软件引进了有用的检查列表和告警信息，但是也使得医学愈发的程式化，并且疏远了医生与病人之间的距离。
2007-08 年间，纽约州立大学奥尔巴尼分校的 Timothy Hoff 进行了一项研究，研究调查了 75 名采用计算机化系统进行初级诊疗的医生。医生认为软件弱化了自己对病人的了解，令其“对诊疗做出明智决策的能力”受到削弱。
哈佛医学院教授 Beth Lown 2012 年的一篇合著的期刊文章也对医生受到“屏幕驱动”的现象提出了警告，文章认为医生按照计算机指示而非病人的病情叙述进行的思考是狭隘的。最糟糕的情况有可能会导致错失重要的诊断信号。
这种风险并非理论上存在而已。诊断杂志最近的一篇论文对美国的首个埃博拉死亡病例的误诊进行了检查。他们认为，医院临床医生用来记录病人信息的数字化模板有可能对误诊起到了误导的作用。研究人员认为，这些工具为了能捕捉数据而进行了优化，但却牺牲了分类诊断的实用性，所以存在很大的局限性，导致用户只见树木不见森林。其结论是，医疗软件无法替代基本的病史采集、检查技能以及批判性思维。
甚至连创意性的工作也不断受到自动化去技能化的影响。计算机辅助设计帮助建筑师利用不同常规的形状和材料来构造建筑，但是设计流程引入计算机过早的话，会损害到画草图和建模过程中产生的美学敏感性以及概念性洞见。
心理学研究已经发现，通过手工能够更好地激发设计师的原创力，拓展其工作记忆以及增强他们的触感。英国开放大学的设计学教授 Nigel Cross 说，画板就是“智能放大器”。
软件接管的事情手工技能就衰退。芬兰建筑师 Juhani Pallasmaa 在《The Thinking Hand》一书中指出，过度依赖计算机使得设计师更难欣赏自己建筑的那些最微妙的、最具人文素质的地方。“计算机图像的假精确以及显然的有限性”限制了设计师的视野，意味着做出来的东西有可能技术上极其出色但却缺乏情感。迈阿密大学的建筑学教授 Jacob Brillhart2011 年也写道，尽管现代计算机系统可以不可思议的速度将一组空间转化为精确的 3D 渲染，但也孕育出来“更多懒惰的、缺乏悟性、想象力以及情感的平庸之作。”
不过我们未必一定要听任这种情况发展。自动化不需要消除我们工作面临的挑战，不需要消灭我们的技能。那些损失源自人类工程学者等其他学者所谓的“以技术为中心的自动化”，这种设计哲学统治了程序员和工程师的思维。
系统设计师在开始项目的时候，首先考虑的是计算机的能力，想着把尽可能多的工作都交到软件身上。而分给人类操作者的则是剩下的事情，这些事情往往是相对被动的杂事，如输入数据、跟着模板做以及监视屏幕等。
这种哲学让人掉进了去技能化的恶性循环。通过让人与艰苦工作隔绝而钝化了人的技能并增加了犯错的几率。而一旦那些错误发生后，设计师的应对措施反而是进一步限制人的责任，从而导致新一轮的去技能化发生。
由于流行技术“强调技术需求胜过人的需求，”所以迫使人“进入到一个支撑的角色，但这种角色正好是我们最不擅长的角色之一，”加州大学学圣地亚哥分校的认知科学家以及设计研究者 Donald Norman 这样写道。
不过这里有一个替代方案。
“以人为中心的自动化，”人占据优先位置。系统在设计上把人类操作者放在一个工程师所谓的“决策环”—即行动、反馈及判断的持续过程上。这会让工人时刻留意、参与和促进了此类可强化技能的挑战性任务。
在这种模式下，软件扮演的是一个必不可少但却是次要的角色。这次它接管的是人类操作者早已掌握的常规职能，当非预期情况发生时发出告警，提供实时信息来拓展操作者视野，并抵制往往歪曲人类思维的偏差。这时候技术变成了专家的合作伙伴而不是取代专家。
把自动化推向一个更加人性化的方向并不需要任何技术突破。只需要优先次序的转移并重新关注于人类的优势和劣势。
比方说，航空公司可以在飞行期间设定控制权在计算机和飞行员之间不断转移。通过让飞行员保持警觉和活跃的方式来让飞行更加安全。
而会计、医疗等专业领域，软件远可以不用侵扰太多，给人留出更大的空间来实践和做出判断，而不是给出算法推导出来的建议。
爱荷华大学的 John Lee 认为，知识工作的计算机化可以采取一致不那么自动化的方案，让自动化担任操作者的评判者的角色，相对而言，这种方法比用机器计算来替代人类判断的做法要成功得多（例子参见Google想出了一个决定晋升的算法，然后……就没有然后了）。最好的决策支持系统向专业人士提供的是“替代性的解释、假设或选择。”
以人为中心的自动化并不对流程做出限制。相反，它引导流程进入到一条更加人文主义的路径，给一种太常见的推崇计算机厌恶人类的世界观提供了一剂解毒剂。
以人为本的最令人兴奋的例子之一是所谓的自适应自动化。它采用了先进的传感器和解释算法来监控人的身体和心理状态，然后利用这一信息来实现任务和责任在人与计算机之间的转移。一旦系统感觉到操作者挣正在与某一困难的任务做斗争时，就会分配更多的任务给计算机，让操作者避免分心。而当系统感觉到操作者兴趣正在减弱时，系统就会增加此人的工作量以便提高其注意力并开发他们的技能。
计算机令人赞叹。但我们不能让这种狂热导致我们低估了自己的才能。哪怕是最聪明的软件也缺乏熟练专业人士的常识，灵巧性以及神韵。在驾驶舱、办公室或者检查室内，人类专家依然是不可或缺的。他们通过艰苦工作打磨出来的洞见、灵活性和直觉，以及经验丰富的对现实世界的判断，是算法或者机器人所无法取代的。
如果我们让自己的技能随过度依赖自动化而退化的话，我们自身的能力就会下降，越来越缺少韧性，对机器也会越来越屈从。然后创造出一个更适合机器人而不是人类自己的世界。
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