Learning to Work with Intelligent Machines

by?Matt Beane（馬特·比恩）

John W. Tomac

It’s 6:30 in the morning,?and Kristen?is wheeling her prostate patient into the OR. She’s a senior resident, a surgeon in training. Today she’s hoping to do some of the procedure’s delicate, nerve-sparing dissection herself. The attending physician is by her side, and their four hands are mostly in the patient, with Kristen leading the way under his watchful guidance. The work goes smoothly, the attending backs away, and Kristen closes the patient by 8:15, with a junior resident looking over her shoulder. She lets him do the final line of sutures. She feels great: The patient’s going to be fine, and she’s a better surgeon than she was at 6:30.

早上六點半，克里斯汀正用輪椅把前列腺病人推進(jìn)手術(shù)室。她是一名高級住院醫(yī)生，即實習(xí)外科醫(yī)生。今天她想親自操刀手術(shù)中精細(xì)的神經(jīng)剝離環(huán)節(jié)。主治醫(yī)生站在她旁邊，兩個人的四只手都在病人體內(nèi)操作。在主治醫(yī)生的悉心指導(dǎo)下，克里斯汀全程主刀。手術(shù)進(jìn)展順利，主治醫(yī)生退后，克里斯汀在八點一刻完成了縫合，一名初級住院醫(yī)生在她身后觀察學(xué)習(xí)，并完成了最后的縫合?？死锼雇「杏X棒極了：病人將會康復(fù)，而她和兩小時前相比，外科技藝又精進(jìn)了不少。

Fast-forward six months. It’s 6:30?AM?again, and Kristen is wheeling another patient into the OR, but this time for robotic prostate surgery. The attending leads the setup of a thousand-pound robot, attaching each of its four arms to the patient. Then he and Kristen take their places at a control console 15 feet away. Their backs are to the patient, and Kristen just watches as the attending remotely manipulates the robot’s arms, delicately retracting and dissecting tissue. Using the robot, he can do the entire procedure himself, and he largely does. He knows Kristen needs practice, but he also knows she’d be slower and would make more mistakes. So she’ll be lucky if she operates more than 15 minutes during the four-hour surgery. And she knows that if she slips up, he’ll tap a touch screen and resume control, very publicly banishing her to watch from the sidelines.

近六個月后，又是在早上六點半，克里斯汀又用輪椅將另一名病人推進(jìn)手術(shù)室，但這一次做前列腺手術(shù)的是機器人。主治醫(yī)師負(fù)責(zé)設(shè)置1000磅重的機器人，將四個機器臂架在病人身上。然后他和克里斯汀回到15英尺之外的操作臺上，背朝病人。主治醫(yī)生遠(yuǎn)程控制機器臂靈巧地伸縮，并解剖組織，克里斯汀從旁觀察。利用機器人，主治醫(yī)生一個人就能完成手術(shù)，事實也基本如此。他知道克里斯汀需要練習(xí)，但他也很清楚，她手術(shù)的速度更慢，而且更易出錯。因此，時長4小時的手術(shù)中，她如能親手操刀15分鐘就很幸運了。如果她失手，他可以輕點觸屏恢復(fù)控制，公然將她“驅(qū)逐出場”。

Surgery may be extreme work, but until recently surgeons in training learned their profession the same way most of us learned how to do our jobs: We watched an expert, got involved in the easier work first, and then progressed to harder, often riskier tasks under close supervision until we became experts ourselves. This process goes by lots of names: apprenticeship, mentorship, on-the-job learning (OJL). In surgery it’s called?See one, do one, teach one.

外科手術(shù)可能是個極端的例子，但此前實習(xí)外科醫(yī)生和其他人掌握工作技能的方法別無二致。我們觀摩專家，先參與較簡單的工作，然后在嚴(yán)格監(jiān)督下進(jìn)入難度更高，且往往風(fēng)險更大的環(huán)節(jié)，直到我們自己成為專家。這一過程有多種稱謂：學(xué)徒期、導(dǎo)師制、在崗學(xué)習(xí)（OJL）。在外科中的行話是：看一、做一、教一（See one, do one,teach one.）。

Critical as it is, companies tend to take on-the-job learning for granted; it’s almost never formally funded or managed, and little of the?estimated $366 billion?companies spent globally on formal training in 2018 directly addressed it. Yet?decades of research?show that although employer-provided training is important, the lion’s share of the skills needed to reliably perform a specific job can be learned only by doing it. Most organizations depend heavily on OJL: A 2011?Accenture survey,?the most recent of its kind and scale, revealed that only one in five workers had learned any new job skills through formal training in the previous five years.

盡管在崗學(xué)習(xí)至關(guān)重要，公司卻往往對其熟視無睹。很少有公司對在崗學(xué)習(xí)進(jìn)行正式的管理或投入，2018年全球公司在正式培訓(xùn)上投入約3660億美元，然而鮮有資金用于在崗學(xué)習(xí)。數(shù)十年來的研究顯示，盡管雇主提供的培訓(xùn)十分重要，但要想真正掌握特定職能所需的技能，必須靠親身實踐。大多組織嚴(yán)重依賴在崗學(xué)習(xí)。2011年埃森哲進(jìn)行了迄今規(guī)模最大的在崗學(xué)習(xí)調(diào)查，發(fā)現(xiàn)只有20%的員工在過去5年里通過正式培訓(xùn)獲得了新工作技能。

Today OJL is under threat. The headlong introduction of sophisticated analytics, AI, and robotics into many aspects of work is fundamentally disrupting this time-honored and effective approach. Tens of thousands of people will lose or gain jobs every year as those technologies automate work, and hundreds of millions will have to learn?new skills?and ways of working. Yet broad evidence demonstrates that companies’ deployment of intelligent machines often blocks this critical learning pathway: My colleagues and I have found that it moves trainees away from learning opportunities and experts away from the action, and overloads both with a mandate to master old and new methods simultaneously.

如今在崗學(xué)習(xí)面臨挑戰(zhàn)。復(fù)雜分析技術(shù)、人工智能和機器人突然闖入了職場的方方面面，從根本上顛覆了這一由來已久的有效學(xué)習(xí)方式。隨著技術(shù)讓工作越來越自動化，每年都有數(shù)以萬計的人離職或就業(yè)，數(shù)以億計的人必須學(xué)習(xí)新技能和新工作方式。但更廣泛的證據(jù)表明，公司部署智能機器會阻礙這一關(guān)鍵的學(xué)習(xí)渠道：我和我的同事發(fā)現(xiàn)，人工智能會讓新手失去學(xué)習(xí)機會，讓老手減少實踐機會，迫使兩者必須同時掌握新方法和舊方法，令他們不堪重負(fù)。

John W. Tomac

How, then, will employees learn to work alongside these machines? Early indications come from observing learners engaged in norm-challenging practices that are pursued out of the limelight and tolerated for the results they produce. I call this widespread and informal process?shadow learning.

那么，員工能否學(xué)會和這些機器共事呢？此前的一些觀察來自參與挑戰(zhàn)常規(guī)實踐的學(xué)習(xí)者，這些實踐并非重點，而且人們對其結(jié)果的容忍度高。我將這一廣泛存在且非正式的流程稱為“暗中學(xué)習(xí)”。

Obstacles to Learning

學(xué)習(xí)的障礙

My discovery of shadow learning came from two years of watching surgeons and surgical residents at 18 top-rated teaching hospitals in the United States. I studied learning and training in two settings: traditional (“open”) surgery and robotic surgery. I gathered data on the challenges robotic surgery presented to senior surgeons, residents, nurses, and scrub technicians (who prep patients, help glove and gown surgeons, pass instruments, and so on), focusing particularly on the few residents who found new, rule-breaking ways to learn. Although this research concentrated on surgery, my broader purpose was to identify learning and training dynamics that would show up in many kinds of work with intelligent machines.

我在美國18家頂級教學(xué)醫(yī)院中，對外科醫(yī)生和外科住院醫(yī)生進(jìn)行了為期兩年的觀察，從中發(fā)現(xiàn)了暗中學(xué)習(xí)。我研究了兩種情景下的學(xué)習(xí)和訓(xùn)練：傳統(tǒng)（“開放式”）手術(shù)和機器人手術(shù)。我收集了機器人手術(shù)為資深外科醫(yī)生、住院醫(yī)生、護(hù)士和手術(shù)技術(shù)員（為患者做術(shù)前準(zhǔn)備，幫助外科醫(yī)生戴手套和穿手術(shù)服，傳遞手術(shù)器械等）所帶來挑戰(zhàn)的相關(guān)數(shù)據(jù)，重點關(guān)注那些發(fā)現(xiàn)了突破傳統(tǒng)學(xué)習(xí)方法的少數(shù)住院醫(yī)生。雖然研究重點是手術(shù)，但我更宏大的目的在于：發(fā)現(xiàn)可能會在與智能機器共事時出現(xiàn)的學(xué)習(xí)和培訓(xùn)動態(tài)。

To this end, I connected with a small but growing group of field researchers who are studying how people work with smart machines in settings such as internet start-ups, policing organizations, investment banking, and online education. Their work reveals dynamics like those I observed in surgical training. Drawing on their disparate lines of research, I’ve identified four widespread obstacles to acquiring needed skills. Those obstacles drive shadow learning.

為此我與少數(shù)實地研究人員建立了聯(lián)系，他們的成員在不斷增加。他們正在研究人們?nèi)绾卧诨ヂ?lián)網(wǎng)初創(chuàng)企業(yè)、警務(wù)組織、投資銀行和在線教育等場景中使用智能機器。他們的發(fā)現(xiàn)與我在外科手術(shù)培訓(xùn)中觀察到的現(xiàn)象相似。根據(jù)他們的幾類不同研究，我發(fā)現(xiàn)了獲取所需技能的四大普遍障礙。這些障礙觸發(fā)了暗中學(xué)習(xí)。

1. Trainees are being moved away from their “l(fā)earning edge.”

1.新手正在失去“學(xué)習(xí)優(yōu)勢”。

Training people in any kind of work can incur costs and decrease quality, because novices move slowly and make mistakes. As organizations introduce intelligent machines, they often manage this by reducing trainees’ participation in the risky and complex portions of the work, as Kristen found. Thus trainees are being kept from situations in which they struggle near the boundaries of their capabilities and recover from mistakes with limited help—a requirement for learning new skills.

在任何工作中，培訓(xùn)員工都會產(chǎn)生成本并降低質(zhì)量，因為新手行動緩慢且易犯錯。正如克里斯汀所發(fā)現(xiàn)的那樣，組織迎來智能機器，通常會讓受培訓(xùn)者減少參與風(fēng)險和復(fù)雜度高的部分，以此作為管理之策。因此，受培訓(xùn)者將無法獲得擴充能力范圍邊界，并在有限幫助下從錯誤中成長的機會——而這些恰恰是學(xué)習(xí)新技能的必要條件。

The same phenomenon can be seen in?investment banking?New York University’s Callen Anthony found that junior analysts in one firm were increasingly being separated from senior partners as those partners interpreted algorithm-assisted company valuations in M&As. The junior analysts were tasked with simply pulling raw reports from systems that scraped the web for financial data on companies of interest and passing them to the senior partners for analysis.

投資銀行里也有同樣現(xiàn)象。紐約大學(xué)的卡倫·安東尼（Callen Anthony）在某投行中發(fā)現(xiàn)，合伙人用算法來協(xié)助公司并購并解讀估值，使得初級分析師與高級合伙人越離越遠(yuǎn)。初級分析師的任務(wù)僅是從系統(tǒng)中提取原始報告（在網(wǎng)絡(luò)上對感興趣公司的財務(wù)數(shù)據(jù)進(jìn)行收集），然后將其傳遞給高級合伙人進(jìn)行分析。

The implicit rationale for this division of labor? First, reduce the risk that junior people would make mistakes in doing sophisticated work close to the customer; and second, maximize senior partners’ efficiency: The less time they needed to explain the work to junior staffers, the more they could focus on their higher-level analysis. This provided some short-term gains in efficiency, but it moved junior analysts away from challenging, complex work, making it harder for them to learn the entire valuation process and diminishing the firm’s future capability.

這種分工的隱含邏輯是什么？首先，降低初級員工在面向客戶的復(fù)雜工作中犯錯的風(fēng)險；第二，最大化高級合伙人的效率：向初級員工解釋工作的時間越少，他們就越能專注于更高級別的分析。這樣做短期內(nèi)效率有所提高，但卻剝奪了初級分析師挑戰(zhàn)復(fù)雜工作的機會，使他們更難以了解整個估值過程，并削弱了公司未來的能力。

2. Experts are being distanced from the work.

2.專家與工作疏遠(yuǎn)了。

Sometimes intelligent machines get between trainees and the job, and other times they’re deployed in a way that prevents experts from doing important hands-on work. In robotic surgery, surgeons don’t see the patient’s body or the robot for most of the procedure, so they can’t directly assess and manage critical parts of it. For example, in traditional surgery, the surgeon would be acutely aware of how devices and instruments impinged on the patient’s body and would adjust accordingly; but in robotic surgery, if a robot’s arm hits a patient’s head or a scrub is about to swap a robotic instrument, the surgeon won’t know unless someone tells her. This has two learning implications: Surgeons can’t practice the skills needed to make holistic sense of the work on their own, and they must build new skills related to making sense of the work through others.

有時，智能機器會夾在受培訓(xùn)者和工作之間，有時則妨礙專家進(jìn)行重要實踐工作。機器人操作的手術(shù)中，外科醫(yī)生在手術(shù)過程的大多數(shù)時間都看不到患者的身體或機器人，因此無法直接評估和管理關(guān)鍵環(huán)節(jié)。例如，在傳統(tǒng)手術(shù)中，外科醫(yī)生會敏銳地意識到裝置和器械如何碰觸患者的身體并進(jìn)行相應(yīng)調(diào)整。但是在機器人手術(shù)中，如果機器臂撞到病人的頭部，或者清潔臂即將替換器械，外科醫(yī)生必須依靠他人提醒才能知道。這對學(xué)習(xí)有兩重影響：外科醫(yī)生無法磨練全面了解自己工作所需的技能，以及必須通過他人才能獲得此類新技能。

Benjamin Shestakofsky, now at the University of Pennsylvania, described a?similar phenomenon?at a pre-IPO start-up that used machine learning to match local laborers with jobs and that provided a platform for laborers and those hiring them to negotiate terms. At first the algorithms weren’t making good matches, so managers in San Francisco hired people in the Philippines to manually create each match. And when laborers had difficulty with the platform—for instance, in using it to issue price quotes to those hiring, or to structure payments—the start-up managers outsourced the needed support to yet another distributed group of employees, in Las Vegas. Given their limited resources, the managers threw bodies at these problems to buy time while they sought the money and additional engineers needed to perfect the product. Delegation allowed the managers and engineers to focus on business development and writing code, but it deprived them of critical learning opportunities: It separated them from direct, regular input from customers—the laborers and the hiring contractors—about the problems they were experiencing and the features they wanted.

目前供職于賓夕法尼亞大學(xué)的本杰明·肖斯塔科夫斯基（BenjaminShestakofsky）介紹了某一還未上市初創(chuàng)公司中的類似現(xiàn)象。公司使用機器學(xué)習(xí)來匹配勞動者和職位，為勞動者和雇主提供了商討條款的平臺。起初算法匹配不準(zhǔn)，因此舊金山的經(jīng)理在菲律賓雇人來手動調(diào)整每一配對。當(dāng)勞動者在平臺上遇到困難時——例如利用平臺向招聘人員報價或發(fā)起付款時，初創(chuàng)公司的經(jīng)理將所需支持外包給拉斯維加斯的另一批員工。由于資源有限，管理人員在這些問題上增加人手爭取時間，同時尋找額外的資金和工程師來完善產(chǎn)品。外包讓管理者和工程師專注于業(yè)務(wù)拓展和編程，但卻剝奪了他們的關(guān)鍵學(xué)習(xí)機會，讓他們無法獲得客戶（勞動者和雇傭承包商）的直接定期反饋，比如他們正在經(jīng)歷哪些問題和他們想要的功能。

3. Learners are expected to master both old and new methods.

3.學(xué)習(xí)者必須掌握新舊兩種方法。

Robotic surgery comprises a radically new set of techniques and technologies for accomplishing the same ends that traditional surgery seeks to achieve. Promising greater precision and ergonomics, it was simply added to the curriculum, and residents were expected to learn robotic as well as open approaches. But the curriculum didn’t include enough time to learn both thoroughly, which often led to a worst-case outcome: The residents mastered neither. I call this problem?methodological overload.

機器人手術(shù)用一套全新的技巧和技術(shù)來實現(xiàn)傳統(tǒng)手術(shù)試圖達(dá)到的效果。它保證更高的精確度和更優(yōu)人體工程學(xué)，直接被納入了課程中，住院醫(yī)生被要求學(xué)習(xí)機器人知識和傳統(tǒng)方法。但課程沒有足夠的時間讓他們兩者兼通，這往往會導(dǎo)致最壞的結(jié)果：哪種都沒有掌握。我將這一難題稱為方法超載（methodological overload）。

Shreeharsh Kelkar, at UC Berkeley, found that?something similar?happened to many professors who were using a new technology platform called edX to develop massive open online courses (MOOCs). EdX provided them with a suite of course-design tools and instructional advice based on fine-grained algorithmic analysis of students’ interaction with the platform (clicks, posts, pauses in video replay, and so on). Those who wanted to develop and improve online courses had to learn a host of new skills—how to navigate the edX user interface, interpret analytics on learner behavior, compose and manage the course’s project team, and more—while keeping “old school” skills sharp for teaching their traditional classes. Dealing with this tension was difficult for everyone, especially because the approaches were in constant flux: New tools, metrics, and expectations arrived almost daily, and instructors had to quickly assess and master them. The only people who handled both old and new methods well were those who were already technically sophisticated and had significant organizational resources.

加州大學(xué)伯克利分校的施里哈什·克爾卡（Shreeharsh Kelkar）發(fā)現(xiàn)，許多教授正在使用一種名為edX的新技術(shù)平臺來開發(fā)大規(guī)模開放式在線課程（MOOCs）。 EdX根據(jù)對學(xué)生與平臺互動（點擊、帖子和視頻播放中的暫停等）的細(xì)粒度算法分析，為學(xué)生提供了一套課程設(shè)計工具和教學(xué)建議。想開發(fā)和改進(jìn)在線課程的人必須學(xué)習(xí)一系列新技能：如何瀏覽edX用戶界面，解讀學(xué)習(xí)者行為分析數(shù)據(jù)，組件和管理課程的項目團(tuán)隊，等等，同時還要保留“老派”技能，來講授傳統(tǒng)課程。處理這種矛盾對每個人來說都很困難，況且方法還在不斷變化，新的工具、指標(biāo)和期望幾乎每天都出現(xiàn)，教師必須快速評估和掌握它們。唯一能夠很好地處理新舊方法的人，是那些已經(jīng)熟悉技術(shù)并擁有重要組織資源的人。

4. Standard learning methods are presumed to be effective.

4.標(biāo)準(zhǔn)學(xué)習(xí)方法被默認(rèn)為有效。

Decades of research and tradition hold trainees in medicine to the?See one, do one, teach one?method, but as we’ve seen, it doesn’t adapt well to robotic surgery. Nonetheless, pressure to rely on approved learning methods is so strong that deviation is rare: Surgical-training research, standard routines, policy, and senior surgeons all continue to emphasize traditional approaches to learning, even though the method clearly needs updating for robotic surgery.

幾十年的研究和傳統(tǒng)讓實習(xí)醫(yī)生遵循“看一、做一、教一”的方法。但如我們所見，它不適應(yīng)機器人手術(shù)。盡管如此，依賴?yán)吓蓪W(xué)習(xí)方法的壓力非常大，“離經(jīng)叛道”者寥寥：外科培訓(xùn)研究、標(biāo)準(zhǔn)程序、政策和高級外科醫(yī)生都繼續(xù)強調(diào)傳統(tǒng)的學(xué)習(xí)方法，哪怕該方法顯然已不適用于機器人手術(shù)。

Sarah Brayne, at the University of Texas, found a?similar mismatch?between learning methods and needs among police chiefs and officers in Los Angeles as they tried to apply traditional policing approaches to beat assignments generated by an algorithm. Although the efficacy of such “predictive policing” is unclear, and its ethics are controversial, dozens of police forces are becoming deeply reliant on it. The LAPD’s?PredPol?system breaks the city up into 500-foot squares, or “boxes,” assigns a crime probability to each one, and directs officers to those boxes accordingly. Brayne found that it wasn’t always obvious to the officers—or to the police chiefs—when and how the former should follow their AI-driven assignments. In policing, the traditional and respected model for acquiring new techniques has been to combine a little formal instruction with lots of old-fashioned learning on the beat.

類似的，得克薩斯大學(xué)的莎拉·布雷恩（Sarah Brayne）發(fā)現(xiàn)洛杉磯警長和警官的學(xué)習(xí)方法和需求之間也存在不匹配。他們試圖將傳統(tǒng)警務(wù)方法應(yīng)用于算法產(chǎn)生的巡邏任務(wù)。盡管這種“預(yù)測性警務(wù)”的效果尚不清晰，而且在道德上存在爭議，但數(shù)十支警隊越來越依賴它。洛杉磯警察局的PredPol系統(tǒng)將城市劃分成500英尺的方格，即“箱子”，計算每個箱子的犯罪概率，將警察分派到每個方格中。布雷恩發(fā)現(xiàn)，警官或警長有時并不明白何時以及如何遵循人工智能驅(qū)動的任務(wù)。在警務(wù)方面，傳統(tǒng)且受尊重的獲取新技術(shù)的模式，是將少部分正式課程與大部分傳統(tǒng)學(xué)習(xí)有機結(jié)合起來。

Many chiefs therefore presumed that officers would mostly learn how to incorporate crime forecasts on the job. This dependence on traditional OJL contributed to confusion and resistance to the tool and its guidance. Chiefs didn’t want to tell officers what to do once “in the box,” because they wanted them to rely on their experiential knowledge and discretion. Nor did they want to irritate the officers by overtly reducing their autonomy and coming across as micromanagers. But by relying on the traditional OJL approach, they inadvertently sabotaged learning: Many officers never understood how to use PredPol or its potential benefits, so they wholly dismissed it—yet they were still held accountable for following its assignments. This wasted time, decreased trust, and led to miscommunication and faulty data entry—all of which undermined their policing.

因此，許多警長認(rèn)為，警官們基本能夠在工作中運用犯罪預(yù)測。對傳統(tǒng)OJL的依賴導(dǎo)致了對人工智能工具及其指導(dǎo)的困惑和抵制。局長不想告訴警官一旦被分配到方格里需要做什么，希望他們依靠經(jīng)驗知識和自己的判斷行事。局長也不想減少他們的裁量權(quán)，被當(dāng)作微觀管理者，從而激怒他們。但是，依靠傳統(tǒng)的OJL方法，他們無意中破壞了學(xué)習(xí)：許多警官從未弄清如何使用PredPol或其潛在的好處，因此對其完全無視。然而他們?nèi)匀粚χ悄芊峙涞娜蝿?wù)負(fù)責(zé)。這浪費了時間，減少了信任，導(dǎo)致溝通誤會和數(shù)據(jù)輸入錯誤——所有這些都給警務(wù)活動造成了惡劣影響。

Shadow Learning Responses

暗中學(xué)習(xí)的回應(yīng)

Faced with such barriers, shadow learners are bending or breaking the rules out of view to get the instruction and experience they need. We shouldn’t be surprised. Close to a hundred years ago, the sociologist Robert Merton showed that when legitimate means are no longer effective for achieving a valued goal, deviance results. Expertise—perhaps the ultimate occupational goal—is no exception。

面臨上述阻礙，暗中學(xué)習(xí)者悄悄繞過或打破規(guī)則來獲得所需的指導(dǎo)和經(jīng)驗，自然不足為奇。約100年前，社會學(xué)家羅伯特·莫頓（Robert Merton）就發(fā)現(xiàn)，當(dāng)合法手段對達(dá)成有價值的目標(biāo)不再奏效時，就會出現(xiàn)非常手段。對于專業(yè)知識（或許是職業(yè)的終極目標(biāo)）也不例外。

Given the barriers I’ve described, we should expect people to find deviant ways to learn key skills. Their approaches are often ingenious and effective, but they can take a personal and an organizational toll: Shadow learners may be punished (for example, by losing practice opportunities and status) or cause waste and even harm. Still, people repeatedly take those risks, because their learning methods work well where approved means fail. It’s almost always a bad idea to uncritically copy these deviant practices, but organizations do need to learn from them.

鑒于我描述的障礙，我們應(yīng)理解人們會采取其他方式學(xué)習(xí)關(guān)鍵技能。這些方式一般靈活有效，卻往往會讓個人和組織付出代價：暗中學(xué)習(xí)者可能會受到懲罰，例如失去實踐機會或地位或造成浪費甚至構(gòu)成傷害。但人們依然一再鋌而走險，因為當(dāng)合規(guī)的方式失敗時，他們的學(xué)習(xí)方法奏效。不加鑒別地效仿這些非常手段自然不對，但它們確實有組織值得學(xué)習(xí)之處。

Following are the shadow learning practices that I and others have observed:

以下是我和他人觀察到的一些暗中學(xué)習(xí)實踐。

Seeking struggle.

尋找難點。

Recall that robotic surgical trainees often have little time on task. Shadow learners get around this by looking for opportunities to operate near the edge of their capability and with limited supervision. They know they must struggle to learn, and that many attending physicians are unlikely to let them. The subset of residents I studied who did become expert found ways to get the time on the robots they needed. One strategy was to seek collaboration with attendings who weren’t themselves seasoned experts. Residents in urology—the specialty having by far the most experience with robots—would rotate into departments whose attendings were less proficient in robotic surgery, allowing the residents to leverage the halo effect of their elite (if limited) training. The attendings were less able to detect quality deviations in their robotic surgical work and knew that the urology residents were being trained by true experts in the practice; thus they were more inclined to let the residents operate, and even to ask for their advice. But few would argue that this is an optimal learning approach.

讓我們回顧一下在機器人手術(shù)中沒有足夠時間學(xué)習(xí)的外科實習(xí)生。暗中學(xué)習(xí)者通過尋求接近他們能力極限，且受到很少監(jiān)督的機會來達(dá)到目的。他們知道自己在學(xué)習(xí)中一定會遇到難點，而且很多主治醫(yī)生不太會給他們學(xué)習(xí)的機會。我研究的一小群成為專家的實習(xí)醫(yī)生設(shè)法找機會操作機器人。一大策略是，與那些本身經(jīng)驗不足的主治醫(yī)生尋求合作。目前泌尿科的實習(xí)醫(yī)生在機器人手術(shù)上經(jīng)驗最豐富，他們可以輪崗到那些主治醫(yī)生不太熟悉機器人手術(shù)的科室，盡管自身的培訓(xùn)有限，泌尿科實習(xí)醫(yī)生也可以利用本科室的（在機器人手術(shù)上的）光環(huán)效應(yīng)。其他科室的主治醫(yī)生不太有能力判斷他們在機器人手術(shù)上的水平差異，會覺得泌尿科實習(xí)醫(yī)生受到過真正的專家培訓(xùn)；因此他們更愿意讓泌尿科的實習(xí)醫(yī)生做手術(shù)，甚至咨詢他們意見。但很少有人認(rèn)為這是最佳學(xué)習(xí)之道。

What about those junior analysts who were cut out of complex valuations? The junior and senior members of one group engaged in shadow learning by disregarding the company’s emerging standard practice and working together. Junior analysts continued to pull raw reports to produce the needed input, but they worked alongside senior partners on the analysis that followed.

而那些無法參與負(fù)責(zé)估值的初級分析師呢？同組的初級和高級成員也會暗中學(xué)習(xí)——無視公司中出現(xiàn)的標(biāo)準(zhǔn)操作，一起工作。初級分析師繼續(xù)準(zhǔn)備原始報告來生成所需的數(shù)據(jù)，但也與高級合伙人一同處理隨后的分析。

In some ways this sounds like a risky business move. Indeed, it slowed down the process, and because it required the junior analysts to handle a wider range of valuation methods and calculations at a breakneck pace, it introduced mistakes that were difficult to catch. But the junior analysts developed a deeper knowledge of the multiple companies and other stakeholders involved in an M&A and of the relevant industry and learned how to manage the entire valuation process. Rather than function as a cog in a system they didn’t understand, they engaged in work that positioned them to take on more-senior roles. Another benefit was the discovery that, far from being interchangeable, the software packages they’d been using to create inputs for analysis sometimes produced valuations of a given company that were billions of dollars apart. Had the analysts remained siloed, that might never have come to light.

從某種程度說，這種舉動聽起來風(fēng)險頗高，事實上也確實拖延了進(jìn)程。而且初級分析師由于被要求飛速處理多種估值方法和計算，所以會導(dǎo)致很難被發(fā)現(xiàn)的錯誤。但因為初級分析師從中獲得了多家公司、相關(guān)行業(yè)和其他參與并購利益相關(guān)方的更深刻知識，以及學(xué)會如何管理完整估值流程，他們不再僅僅是自己一無所知的系統(tǒng)中的一顆螺絲釘，而是能參與更高一級的工作。另一個有益發(fā)現(xiàn)是，他們用來生成輸入分析數(shù)據(jù)的軟件包不僅不能互相替換，甚至在進(jìn)行同一家公司的估值時會出現(xiàn)幾十億美元的差距。要不是分析師們走出了孤島，可能永遠(yuǎn)意識不到這點。

Tapping frontline know-how.

利用前線知識。

As discussed, robotic surgeons are isolated from the patient and so lack a holistic sense of the work, making it harder for residents to gain the skills they need. To understand the bigger picture, residents sometimes turn to scrub techs, who see the procedure in its totality: the patient’s entire body; the position and movement of the robot’s arms; the activities of the anesthesiologist, the nurse, and others around the patient; and all the instruments and supplies from start to finish. The best scrubs have paid careful attention during thousands of procedures. When residents shift from the console to the bedside, therefore, some bypass the attending and go straight to these “superscrubs” with technical questions, such as whether the intra-abdominal pressure is unusual, or when to clear the field of fluid or of smoke from cauterization. They do this despite norms and often unbeknownst to the attending.

如前所述，參與機器人手術(shù)的外科醫(yī)生與病人隔離，因此缺乏對工作的整體認(rèn)知，讓實習(xí)醫(yī)生更難掌握所需的技巧。為了解全局，實習(xí)醫(yī)生有時需要求助于全程跟進(jìn)手術(shù)的技術(shù)員：他/她能看到病人全身、機器臂的位置和移動、麻醉師、護(hù)士及其他病人旁邊的醫(yī)生的活動，以及所有的器械和用品。最優(yōu)秀的手術(shù)技術(shù)員仔細(xì)觀摩了幾千場手術(shù)。當(dāng)實習(xí)醫(yī)生從控制臺走到患者床邊時，有些人甚至繞過主治醫(yī)生，直接向這些“超級技師”提問，比如腹腔壓正常與否，或者什么時候清理創(chuàng)面液體或灼燒產(chǎn)生的煙霧。他們這樣做不合常規(guī)，而且往往主治醫(yī)生并不知情。

And what about the start-up managers who were outsourcing jobs to workers in the Philippines and Las Vegas? They were expected to remain laser focused on raising capital and hiring engineers. But a few spent time with the frontline contract workers to learn how and why they made the matches they did. This led to insights that helped the company refine its processes for acquiring and cleaning data—an essential step in creating a stable platform. Similarly, some attentive managers spent time with the customer service reps in Las Vegas as they helped workers contend with the system. These “ride alongs” led the managers to divert some resources to improving the user interface, helping to sustain the start-up as it continued to acquire new users and recruit engineers who could build the robust machine learning systems it needed to succeed.

那么將工作外包到菲律賓和拉斯維加斯的初創(chuàng)公司經(jīng)理呢？他們理應(yīng)聚焦于籌集資金和聘用工程師。但有幾名經(jīng)理與前線合同工進(jìn)行了接觸，學(xué)習(xí)了他們做出搭配的過程和原因，從而獲得了幫助公司改善捕捉和過濾數(shù)據(jù)的流程——讓平臺穩(wěn)定運轉(zhuǎn)的關(guān)鍵步驟。類似的，一些有心的經(jīng)理花時間了解了拉斯維加斯的客服代表如何提高求職者滿意度。這些“順便一做”的事情讓經(jīng)理能拿出部分資源改善用戶界面。因此，隨著新用戶增加，初創(chuàng)公司得以延續(xù)，并能聘用工程師讓機器學(xué)習(xí)系統(tǒng)更穩(wěn)健，為公司成功夯實基礎(chǔ)。

Redesigning roles.

重新設(shè)計角色。

The new work methods we create to deploy intelligent machines are driving a variety of shadow learning tactics that restructure work or alter how performance is measured and rewarded. A surgical resident may decide early on that she isn’t going to do robotic surgery as a senior physician and will therefore consciously minimize her robotic rotation. Some nurses I studied prefer the technical troubleshooting involved in robotic assignments, so they surreptitiously avoid open surgical work. Nurses who staff surgical procedures notice emerging preferences and skills and work around blanket staffing policies to accommodate them. People tacitly recognize and develop new roles that are better aligned with the work—whether or not the organization formally does so.

我們創(chuàng)造利用智能機器的新工作方法，推動了多種暗中學(xué)習(xí)策略，改變了工作邏輯或績效考核及獎勵方法。一名外科住院醫(yī)生可能很早就會決定未來成為高級醫(yī)師后不會涉獵機器人手術(shù)，因此有意盡可能減少機器人手術(shù)輪崗。我研究中的一些護(hù)士偏好在機器人任務(wù)中解決技術(shù)難題，因此他們盡量暗中避開傳統(tǒng)手術(shù)工作。負(fù)責(zé)配備外科手術(shù)人手的護(hù)士注意到了偏好和技術(shù)苗頭，設(shè)法繞開人力政策進(jìn)行協(xié)調(diào)。人們悄悄地意識到并設(shè)置新角色，以便更好地適應(yīng)工作——無論此前組織是否有正式規(guī)定。

Consider how some police chiefs reframed expectations for beat cops who were having trouble integrating predictive analytics into their work. Brayne found that many officers assigned to patrol PredPol’s “boxes” appeared to be less productive on traditional measures such as number of arrests, citations, and FIs (field interview cards—records made by officers of their contacts with citizens, typically people who seem suspicious). FIs are particularly important in AI-assisted policing, because they provide crucial input data for predictive systems even when no arrests result. When cops went where the system directed them, they often made no arrests, wrote no tickets, and created no FIs.

對那些有困難將預(yù)測性分析技術(shù)與工作結(jié)合的巡警，警長也會重新考量對他們的期待。布雷恩發(fā)現(xiàn)，很多被分配到“方框”里的警官按照逮捕、口供、FI（面談記錄卡，即警官調(diào)查可疑人員的記錄）等傳統(tǒng)指標(biāo)考核似乎效率欠佳。FI通常在人工智能輔助警務(wù)中非常重要，因為即使沒有逮捕結(jié)果，它們也為預(yù)測系統(tǒng)提供了關(guān)鍵輸入數(shù)據(jù)。警察到達(dá)系統(tǒng)提示的地點，往往不會進(jìn)行逮捕，也不會開具傳票，不寫FI。

Recognizing that these traditional measures discouraged beat cops from following PredPol’s recommendations, a few chiefs sidestepped standard practice and publicly and privately praised officers not for making arrests and delivering citations but for learning to work with the algorithmic assignments. As one captain said, “Good, fine, but we are telling you where the probability of a crime is at, so sit there, and if you come in with a zero [no crimes], that is a success.” These chiefs were taking a risk by encouraging what many saw as bad policing, but in doing so they were helping to move the law enforcement culture toward a future in which the police will increasingly collaborate with intelligent machines, whether or not PredPol remains in the tool kit.

意識到這些傳統(tǒng)方法會導(dǎo)致巡警不理會PredPol的推薦，一些警長繞開標(biāo)準(zhǔn)做法，并且在公開和私下對沒有達(dá)成逮捕和口供結(jié)果、但完成了智能出警任務(wù)的警官進(jìn)行表揚。正如一位副巡長所說：“好的，沒問題，我們只是告訴你這里發(fā)生犯罪的概率是多少，所以在那兒待一會兒，哪怕空手而歸也算成功?！边@些警長擔(dān)著風(fēng)險，鼓勵被很多人視為收效欠佳的出警。但他們這樣做能促進(jìn)執(zhí)法向著與智能機器緊密合作的未來邁進(jìn)，無論PredPol那時是否存在。

Curating solutions.

設(shè)計解決方案。

Trainees in robotic surgery occasionally took time away from their formal responsibilities to create, annotate, and share play-by-play recordings of expert procedures. In addition to providing a resource for themselves and others, making the recordings helped them learn, because they had to classify phases of the work, techniques, types of failures, and responses to surprises.

機器人手術(shù)的實習(xí)生偶爾會抽出時間，制作、注釋并分享帶解說的專業(yè)手術(shù)錄像，這類工作并不屬于他們的職責(zé)范疇。錄像不僅能給自己或別人提供資源，還能幫助他們學(xué)習(xí)，因為他們必須將工作、技術(shù)、失敗類型以及對意外的回應(yīng)進(jìn)行階段劃分。

Faculty members who were struggling to build online courses while maintaining their old-school skills used similar techniques to master the new technology. EdX provided tools, templates, and training materials to make things easier for instructors, but that wasn’t enough. Especially in the beginning, far-flung instructors in resource-strapped institutions took time to experiment with the platform, make notes and videos on their failures and successes, and share them informally with one another online. Establishing these connections was hard, especially when the instructors’ institutions were ambivalent about putting content and pedagogy online in the first place.

在設(shè)計在線課程中遇到困難，同時還需要保持傳統(tǒng)教學(xué)技能的教師，也利用類似的技巧來掌握新技術(shù)。EdX提供工具、模板和培訓(xùn)材料，降低教師學(xué)習(xí)的難度，但還不夠。尤其是一開始，分散在各地、資源緊缺的教師花時間實驗平臺，把自己的成敗經(jīng)驗做成筆記或視頻，然后在網(wǎng)上與其他人分享。建立這種聯(lián)系很難，更不要說教師所在的院校對在線教育抱持矛盾態(tài)度。

Shadow learning of a different type occurred among the original users of edX—well-funded, well-supported professors at topflight institutions who had provided early input during the development of the platform. To get the support and resources they needed from edX, they surreptitiously shared techniques for pitching desired changes in the platform, securing funding and staff support, and so on.

EdX初始用戶的暗中學(xué)習(xí)還有另外一種方式——來自資金和資源充足的一流院校教授，他們在平臺開發(fā)期間提供了早期投入。為了從edX獲得所需的支持和資源，他們私下分享技巧，來促成平臺上的理想變化，并保證資金和人員支持，等等。

Learning from shadow learners.

從暗中學(xué)習(xí)者身上吸取經(jīng)驗。

Obviously shadow learning is not the ideal solution to the problems it addresses. No one should have to risk getting fired just to master a job. But these practices are hard-won, tested paths in a world where acquiring expertise is becoming more difficult and more important.

顯然，暗中學(xué)習(xí)并非是問題的理想解決之道。沒有人愿意冒著被解雇的風(fēng)險來掌握一門技藝。但這些實踐來之不易，也是在獲得專業(yè)知識越來越難、越來越重要情況下的重要探索。

The four classes of behavior shadow learners exhibit—seeking struggle, tapping frontline know-how, redesigning roles, and curating solutions—suggest corresponding tactical responses. To take advantage of the lessons shadow learners offer, technologists, managers, experts, and workers themselves should:

以上四種暗中學(xué)習(xí)者的行為——尋找難點，利用前線知識，重新設(shè)計角色和設(shè)計解決方案，分別對應(yīng)了不同的策略。為了利用這些暗中學(xué)習(xí)者的經(jīng)驗，技術(shù)專家、經(jīng)理、專家和員工自身應(yīng)該做到：

● ensure that learners get opportunities to struggle near the edge of their capacity in real (not simulated) work so that they can make and recover from mistakes

? 保證學(xué)習(xí)者有機會在實際工作而非模擬任務(wù)中，觸及他們能力的邊界。因此他們能夠犯錯并改錯。

● foster clear channels through which the best frontline workers can serve as instructors and coaches

? ?創(chuàng)造清晰的渠道，讓最優(yōu)秀的前線員工能成為導(dǎo)師。

● restructure roles and incentives to help learners master new ways of working with intelligent machines

? ?重新調(diào)整職位結(jié)構(gòu)和獎勵機制，幫助學(xué)習(xí)者掌握與智能機器共事的新方法。

● build searchable, annotated, crowdsourced “skill repositories” containing tools and expert guidance that learners can tap and contribute to as needed

? ?建立可搜索、有注釋、眾包的“技能儲備庫”，其中配備學(xué)習(xí)者能夠利用并貢獻(xiàn)的必須工具和專業(yè)指導(dǎo)。

The specific approach to these activities depends on organizational structure, culture, resources, technological options, existing skills, and, of course, the nature of the work itself. No single best practice will apply in all circumstances. But a large body of managerial literature explores each of these, and outside consulting is readily available.

這些活動具體的方法取決于組織結(jié)構(gòu)、文化、資源、技術(shù)選擇、現(xiàn)有技術(shù)，以及工作本身的性質(zhì)。沒有一套放之四海而皆準(zhǔn)的最佳實踐。但很多管理文獻(xiàn)對上述各方面都有探索，而且也有外部咨詢可供選擇。

More broadly, my research, and that of my colleagues, suggests three organizational strategies that may help leverage shadow learning’s lessons:

更廣泛地，我和我同事的研究提供了三條組織戰(zhàn)略建議，有助于利用暗中學(xué)習(xí)的經(jīng)驗。

1. Keep studying it.

1.持續(xù)學(xué)習(xí)。

Shadow learning is evolving rapidly as intelligent technologies become more capable. New forms will emerge over time, offering new lessons. A cautious approach is critical. Shadow learners often realize that their practices are deviant and that they could be penalized for pursuing them. (Imagine if a surgical resident made it known that he sought out the least-skilled attendings to work with.) And middle managers often turn a blind eye to these practices because of the results they produce—as long as the shadow learning isn’t openly acknowledged. Thus learners and their managers may be less than forthcoming when an observer, particularly a senior manager, declares that he wants to study how employees are breaking the rules to build skills. A good solution is to bring in a neutral third party who can ensure strict anonymity while comparing practices across diverse cases. My informants came to know and trust me, and they were aware that I was observing work in numerous work groups and facilities, so they felt confident that their identities would be protected. That proved essential in getting them to open up.

隨著智能技術(shù)變得更強大，暗中學(xué)習(xí)也在迅速發(fā)展。新形式將隨著時間的推移而出現(xiàn)，提供新的經(jīng)驗。保持謹(jǐn)慎至關(guān)重要。暗中學(xué)習(xí)者經(jīng)常意識到他們的做法不符合常規(guī)，并且他們可能因為自己的做法而受到懲罰。（試想如果一位外科住院醫(yī)生讓別人知道他/她想找最不熟練的主治醫(yī)師合作。）因為能產(chǎn)生效果，只要暗中學(xué)習(xí)者不公開承認(rèn)，中層管理者經(jīng)常對這些做法視而不見。當(dāng)觀察者，特別是高級管理者宣布想研究員工如何靠違反規(guī)則來獲得技能時，學(xué)習(xí)者及其管理者可能不愿意分享經(jīng)驗。比較好的解決方案是，引入中立的第三方，可以確保嚴(yán)格的匿名性，同時比較不同案例的做法。我的線人開始了解并信任我，他們意識到我在許多工作組和設(shè)施中觀察工作，因此他們確信自己的身份會受到保護(hù)。這對于讓他們說出真相至關(guān)重要。

2. Adapt the shadow learning practices you find to design organizations, work, and technology.

2.調(diào)整你發(fā)現(xiàn)的暗中學(xué)習(xí)實踐來適應(yīng)構(gòu)建組織、工作和技術(shù)。

Organizations have often handled intelligent machines in ways that make it easier for a single expert to take more control of the work, reducing dependence on trainees’ help. Robotic surgical systems allow senior surgeons to operate with less assistance, so they do. Investment banking systems allow senior partners to exclude junior analysts from complex valuations, so they do. All stakeholders should insist on organizational, technological, and work designs that improve productivity and enhance on-the-job learning. In the LAPD, for example, this would mean moving beyond changing incentives for beat cops to efforts such as redesigning the PredPol user interface, creating new roles to bridge police officers and software engineers, and establishing a cop-curated repository for annotated best practice use cases.

組織對智能機器的處置往往停留在讓個別專家控制工作，減少對受訓(xùn)者依賴的層面。機器人手術(shù)系統(tǒng)允許高級外科醫(yī)生在較少的幫助下操作，他們照做了。投資銀行系統(tǒng)允許高級合伙人將初級分析師從復(fù)雜的估值工作中排除，他們也照做了。所有利益相關(guān)者都應(yīng)堅持讓組織，技術(shù)和工作設(shè)計提高生產(chǎn)力和加強OJL。例如，在洛杉磯警察局中，這將意味著改變對巡警的激勵措施，重新設(shè)計PredPol用戶界面，創(chuàng)建新角色來連接警察和軟件工程師，以及由警察發(fā)起建立帶注釋的最佳實踐案例庫。

3. Make intelligent machines part of the solution.

3.使智能機器成為解決方案的一部分。

AI can be built to coach learners as they struggle, coach experts on their mentorship, and connect those two groups in smart ways. For example, when Juho Kim was a doctoral student at MIT, he built?ToolScape?and?LectureScape,?which allow for crowdsourced annotation of instructional videos and provide clarification and opportunities for practice where many prior users have paused to look for them. He called this?learnersourcing.?On the hardware side, augmented reality systems are beginning to bring expert instruction and annotation right into the flow of work.

人工智能可以在學(xué)習(xí)者遇到難題時提供幫助，為作為導(dǎo)師的專家提供培訓(xùn)，并巧妙地連接這兩個群體。例如，金柱赫（Juho Kim）在麻省理工學(xué)院讀博時建立了ToolScape和Lecture-Scape，可以眾包方式為教學(xué)視頻加注釋，并為之前暫停尋找注釋的用戶提供澄清解釋和機會。他將之稱為學(xué)習(xí)者采購。在硬件方面，增強現(xiàn)實系統(tǒng)開始將專家指導(dǎo)和注釋帶入工作流中。

Existing applications use tablets or smart glasses to overlay instructions on work in real time. More-sophisticated intelligent systems are expected soon. Such systems might, for example, superimpose a recording of the best welder in the factory on an apprentice welder’s visual field to show how the job is done, record the apprentice’s attempt to match it, and connect the apprentice to the welder as needed. The growing?community of engineers?in these domains have mostly been focused on formal training, and the deeper crisis is in on-the-job learning. We need to redirect our efforts there.

現(xiàn)有應(yīng)用程序使用平板電腦或智能眼鏡，將指導(dǎo)實時添加到工作上。預(yù)計很快就會有更復(fù)雜的智能系統(tǒng)。例如，這樣的系統(tǒng)可以在學(xué)徒焊工的視野中疊加工廠中模范焊工的錄像，顯示工作如何完成，記錄學(xué)徒的嘗試與之對比，并根據(jù)需要將學(xué)徒與模范焊工聯(lián)系起來。這些領(lǐng)域不斷增長的工程師社區(qū)大多專注于正式培訓(xùn)，更深層次的危機是OJL。我們需要重新分配在OJL上的精力。

For thousands of years, advances in technology have driven the redesign of work processes, and apprentices have learned necessary new skills from mentors. But as we’ve seen, intelligent machines now motivate us to peel apprentices away from masters, and masters from the work itself, all in the name of productivity. Organizations often unwittingly choose productivity over considered human involvement, and learning on the job is getting harder as a result. Shadow learners are nevertheless finding risky, rule-breaking ways to learn. Organizations that hope to compete in a world filling with increasingly intelligent machines should pay close attention to these “deviants.” Their actions provide insight into how the best work will be done in the future, when experts, apprentices, and intelligent machines work, and learn, together.

幾千年來，技術(shù)的進(jìn)步推動了工作流程的重新設(shè)計，學(xué)徒們從導(dǎo)師那里獲得了必要的新技能。但正如我們所見，現(xiàn)在智能機器正以生產(chǎn)率為名，迫使我們讓學(xué)徒與導(dǎo)師脫離，讓導(dǎo)師與工作脫離。組織通常在不經(jīng)意間選擇生產(chǎn)率而非員工參與，因此在工作中學(xué)習(xí)變得越來越困難。然而，暗中學(xué)習(xí)者正在尋找有風(fēng)險、打破常規(guī)的學(xué)習(xí)方法。想在智能機器世界中競爭的組織應(yīng)該密切關(guān)注這些“不按常理出牌的人”。他們的行動可以讓你深入了解，當(dāng)未來專家、學(xué)徒和智能機器共同工作和學(xué)習(xí)時，如何以最佳方式完成工作。

馬特·比恩是加州大學(xué)圣巴巴拉分校技術(shù)管理助理教授，也是麻省理工學(xué)院數(shù)字經(jīng)濟(jì)項目研究成員。