What Does It Really Mean to Learn?
到底學習是什麼意思？

I read “Middlemarch” for the first time during my sophomore year of college. I didn’t get it. Why would Dorothea, a young and intelligent woman, marry that annoying old man? How could she be so stupid? No one else in the class seemed to get it, either, and this pushed our professor over the edge. “Of course you don’t understand,” he roared, swilling a Diet Coke. “Trust me, you’ll read this book again when you’re forty, after your first divorce, and you’ll say, ‘Oh, I see!’ ”
我在大學二年級時第一次讀《米德爾馬奇》。我不懂。為什麼多洛西亞，一位年輕聰明的女人，要嫁給那個討厭的老人？她怎麼能這麼愚蠢？班上沒有其他人似乎也不懂，這讓我們的教授惱火了。“當然你們不懂，” 他吼道，一邊喝著一罐飲料。“相信我，當你們四十歲時，經歷第一次離婚後，你們會再讀這本書，然後會說，‘哦，我懂了！’”

Arguably, it’s one of the tragedies of humanities education that so much of it occurs between the ages of eighteen and twenty-two. We don’t teach people to drive at twelve, when they’re carless; why should we make them read novels about life’s regrets when they have none? Yet there’s a theory behind the assignment of “Middlemarch” to sophomores: it’s that knowledge acquired too early gets stored away. Patterns of thinking established now will be retraced later; ideas encountered first in art will prime us for the rest of life. This sounds chancy and vague, until you reflect on the fact that knowledge almost never arrives at the moment of its application. You take a class in law school today only to argue a complicated case years later; you learn C.P.R. years before saving a drowning man; you read online about how to deter a charging bear, because you never know. In the mid-twentieth century, Toyota pioneered a methodology called just-in-time manufacturing, according to which car parts were constructed and delivered as close as possible to the hour of assembly. This was maximally efficient because it reduced waste and the cost of storage. But the human mind doesn’t work that way. Knowledge must often molder in our mental warehouses for decades until we figure out what to do with it.
可以說，人文教育的一大悲劇是很多教育發生在十八到二十二歲之間。我們不會在十二歲時教人開車，因為那時他們還沒有車；為什麼要讓他們在沒有遺憾的時候讀有關人生遺憾的小說呢？然而，將《米德爾馬契》指定給大二學生背後有一個理論：那就是過早獲得的知識會被遺忘。現在建立的思維模式將來會被重新追溯；在藝術中首次遇到的想法將為我們的餘生做好準備。這聽起來很冒險和模糊，直到你反思知識幾乎永遠不會在應用的那一刻到來這個事實。你今天在法學院上課只是為了幾年後辯論一個複雜的案件；你幾年前學習心肺復甦術，幾年後才救了一個快淹死的人；你在網上閱讀如何防止熊襲擊，因為你永遠不知道。在二十世紀中葉，豐田開創了一種叫做 “及時生產” 的方法論，根據這種方法，汽車零部件會在組裝的前一刻被製造和交付。這樣做是極其高效的，因為它減少了浪費和存儲成本。但人類的思維並不是這樣運作的。 知識常常必須在我們的心智倉庫中腐爛數十年，直到我們弄清楚該如何應用它。

Leslie Valiant, an eminent computer scientist who teaches at Harvard, sees this as a strength. He calls our ability to learn over the long term “educability,” and in his new book, “The Importance of Being Educable,” he argues that it’s key to our success. When we think about what makes our minds special, we tend to focus on intelligence. But if we want to grasp reality in all its complexity, Valiant writes, then “cleverness is not enough.” We need to build capacious and flexible theories about the world—theories that will serve us in new, unanticipated, and strange circumstances—and we do that by gathering diverse kinds of knowledge, often in a slow, additive, serendipitous way, and knitting them together. Through this process, we acquire systems of beliefs that are broader and richer than the ones we can create through direct personal experience. This is how, after our first divorce, we find that we can draw on wisdom borrowed from English literature.
萊斯利・瓦利安特（Leslie Valiant）是一位著名的計算機科學家，任教於哈佛大學，他認為這是一種優勢。他稱我們長期學習的能力為 “可教性”，在他的新書《可教性的重要性》中，他認為這是我們成功的關鍵。當我們思考我們的思想特殊之處時，我們往往專注於智慧。但是，如果我們想要理解現實的所有複雜性，瓦利安特寫道，那麼 “聰明是不夠的”。我們需要建立關於世界的寬廣且靈活的理論，這些理論將在新的、意想不到的和奇異的情況下為我們服務，我們通過收集各種不同類型的知識來實現這一點，通常是以緩慢、累積和偶然的方式，然後將它們編織在一起。通過這個過程，我們獲得了比通過直接個人經驗創建的信念系統更廣泛和豐富的信念系統。這就是我們在第一次離婚後發現，我們可以借鑒英國文學中的智慧。

Valiant won the 2010 Turing Award, his discipline’s version of the Nobel Prize, for developing ideas that underpin artificial intelligence and distributed computation, in which many computers work together to solve problems. In his book, he contrasts A.I.’s way of learning with ours. An A.I. can be astonishingly smart, and even think intuitively, kind of like a person. But A.I. systems, Valiant argues, are not as flexible as human minds because they are not yet educable. Even the most state-of-the-art A.I.s learn through a rigid process, in which they are trained, at great expense, and don’t really get any smarter after that, no matter how much new information they ingest. It’s as though their minds freeze on graduation day. Yet human beings constantly improve their own minds through an unfolding, open-ended process that connects newly acquired facts and ideas to ones collected long ago. We “combine pieces of knowledge gained years apart” into “theories of considerable complexity that have many and disparate parts.”
Valiant 在 2010 年獲得圖靈獎，這是他所在學科的諾貝爾獎版本，以表彰他對人工智慧和分散計算的基本理念的貢獻，即許多電腦共同解決問題。在他的著作中，他對比了人工智慧的學習方式與我們的方式。Valiant 認為，人工智慧可以驚人地聰明，甚至可以直覺地思考，有點像人類。但 Valiant 主張，人工智慧系統不如人類思維靈活，因為它們尚未具有可教性。即使是最先進的人工智慧系統也是通過一個嚴格的過程學習，雖然耗費巨大，但在此之後並不會變得更聰明，無論它們吸收多少新信息。就好像它們的思維在畢業那天就凍結了一樣。然而，人類通過一個不斷發展的、開放式的過程不斷改進自己的思維，將新獲得的事實和想法與很久以前收集的事實和想法相連結，形成 “將多年來獲得的知識片段結合成具有許多不同部分的相當複雜的理論”。

Valiant says that he tries not to use the word “intelligent” to describe people (in fact, he is “sometimes taken aback” when he hears others use it); instead, he is drawn to “valuable abilities that somehow involve learning and are not well captured by conventional notions of IQ.” An educable mind, he writes, can learn from books, lectures, conversations, experiences, and Zen koans—from anything, really—and notice when relevant aspects of almost forgotten knowledge reveal themselves. We admire aspects of someone’s educability when we say that they are a quick study, or identify them as “coachable,” but what really makes them educable is that they apply insights “for purposes not foreseen at the time of the study or the coaching”; educability is something like “street smarts”—a term which connotes the “uncanny ability to negotiate the practicalities of life”—and is closely related to having common sense. When people strike us as particularly “well-educated,” this might mean that they’ve had lots of school, Valiant writes, but it could also mean that they’re exceptionally educable, with the ability to “take good advantage of whatever educational opportunities arise, whether formal or informal.”
Valiant 表示，他試著不使用「聰明」這個詞來描述人（事實上，當他聽到別人使用這個詞時，他「有時會感到驚訝」）；相反，他被「有價值的能力所吸引，這些能力在某種程度上涉及學習，並且無法被傳統的智商觀念所捕捉」。他寫道，一個可教導的心靈可以從書籍、講座、對話、經驗和禪宗公案中學習 —— 實際上，可以從任何事物中學習 —— 並且當幾乎被遺忘的知識的相關方面展現時，能夠注意到。當我們說某人是一個快速學習者，或者將他們認定為「可教導的」時，我們欣賞他們可教導性的方面，但真正使他們可教導的是，他們將洞察力應用於「在學習或指導時未預見到的目的」；可教導性有點像「街頭智慧」—— 這個術語暗示了「在處理生活實際問題時的神秘能力」—— 並且與具有常識密切相關。 當人們給我們留下特別 “受過良好教育” 的印象時，這可能意味著他們上過很多學校，Valiant 寫道，但這也可能意味著他們具有非凡的教育能力，能夠 “善加利用出現的任何教育機會，無論是正式還是非正式的。”

We’d probably like it if our political leaders were more educable: they “often need to make judgments on matters well beyond the knowledge and experience they had when elected.” Valiant suggests that we value educability in doctors, too. Imagine that you feel a pain in your side. Is it appendicitis? It would be unwise, he writes, to rely on what you know about the disease, based on what a few people who’ve had it have told you; you’d want to talk to a physician who’s seen a thousand cases. A medical A.I. could also train by looking at thousands of cases; in fact, “if it has seen a million cases, a situation well beyond individual human experience, then it may make predictions that are stunningly better than our intuitions can even comprehend.” And yet “the reason we go to doctors is not just that they have seen a thousand cases of a disease,” Valiant argues. “It is that we believe doctors deliver further results.” The basis of that extra value is educability.
我們可能會喜歡我們的政治領袖更具教育性：他們 “經常需要對超出當選時的知識和經驗範圍的事情做出判斷。” Valiant 也建議我們在醫生身上看重教育性。想像一下，你感到側腹疼痛。這是闌尾炎嗎？他寫道，依賴你對這種疾病的了解是不明智的，這種了解僅基於幾個告訴你他們得過這種病的人所說的話；你會想要與見過一千例病例的醫生交談。醫學人工智能也可以通過觀察數千例病例來進行訓練；事實上，“如果它看過一百萬例病例，這是遠超個人經驗範圍的情況，那麼它可能做出比我們的直覺更令人驚訝的預測。” 然而，“我們去看醫生的原因不僅僅是因為他們見過一千例疾病病例，” Valiant 辯稱。“而是我們相信醫生能夠帶來進一步的成果。” 這種額外價值的基礎是教育性。

Valiant doesn’t get into details, but we can imagine for ourselves the value that a hypothetical doctor’s educability might provide. Such a doctor might draw on a range of ideas and connections spanning years of learning. She learned about appendicitis in medical school, of course—and quickly concludes that you don’t have it. But maybe her brother happens to be an avid cyclist, and she notices you drinking from a water bottle emblazoned with a logo she recognizes from his bike. Wasn’t there an article online about how dangerous the city’s bike lanes have become? More generally, she’s also developed a new theory: she ought to ask more questions about the particulars of her patients’ lives. She asks if you’re a cyclist, discovers that you are, and eventually pinpoints the real issue—a bruised rib acquired in a fall, which you’ve not allowed to heal properly. Being educable, the doctor actually regards your case as an opportunity to learn, and is a better physician for it.
Valiant 沒有深入細節，但我們可以想像一下一位假想醫生的可教性可能提供的價值。這樣的醫生可能會借鑑多年學習的各種想法和聯繫。她在醫學院學到了闌尾炎，當然 —— 並迅速得出結論，你並沒有得這個病。但也許她的兄弟碰巧是一位狂熱的自行車手，她注意到你正在喝一個印有他自行車上認識的標誌的水瓶。在線上不是有一篇文章談到城市自行車道變得多麼危險嗎？更一般地說，她還發展了一個新理論：她應該問更多關於病人生活細節的問題。她問你是否是自行車手，發現你是，最終找到了真正的問題 —— 一根瘀傷的肋骨，是你沒有好好讓它癒合。作為一個可教的人，這位醫生實際上將你的病例視為一次學習的機會，因此成為了一位更好的醫生。

That’s a rather schematic, explicit example of educability; sometimes, Valiant seems to be describing something more diffuse, and perhaps more powerful. To a degree, the connections, recombinations, and new applications of knowledge involved in being educable are useful precisely because they aren’t obvious. Every so often, I learn a lesson in one part of life that seems to apply to another: when I swim down the beach, for example, I tend to look at the umbrellas on the sand and think that I’m making very little progress, and yet, later, when I switch from the front crawl to the backstroke, I’m often surprised by the distance that I’ve travelled between my glances at the shore. The discovery that incremental progress feels faster when you let it accrue before judging it has been useful to me in my writing (and also in motivating me to clean out the garage). A civil-engineering class I took in college, which focussed on the structural forces shouldered by bridges and skyscrapers, comes back to me with great regularity when I think about all sorts of things. Wind exerts its force along the length of a skyscraper, causing it to bend. Similarly, a new source of stress in your life can’t be compartmentalized; it increases the pressure everywhere. It’s interesting to see one’s mind through the lens of educability. It makes you wonder what other cross-pollinations have occurred.
這是一個相當概要的、明確的可教性例子；有時，瓦利安特似乎在描述更加分散、也許更有力的東西。在可教性中涉及的知識連結、重組和新應用在某種程度上是有用的，正因為它們並不明顯。偶爾，我在生活的某個部分學到的一課似乎適用於另一個部分：例如，當我在海灘游泳時，我傾向於看著沙灘上的遮陽傘，覺得自己進展很少，然而，稍後當我從自由式切換到仰泳時，常常會對我在注視岸邊時所行進的距離感到驚訝。發現讓增量進步在評估之前累積起來時感覺更快對我在寫作中很有幫助（也激勵我清理車庫）。我在大學修過的土木工程課程，專注於橋樑和摩天大樓承受的結構力，當我思考各種事情時，這些內容經常浮現。風沿著摩天大樓的長度施加力量，使其彎曲。 同樣地，生活中的新壓力源無法劃分獨立；它會在各處增加壓力。通過可教性的鏡頭看待自己的思維是很有趣的。這讓你想知道其他什麼樣的交叉影響已經發生。

Valiant thinks it might be useful to promote educability as an ideal. We could try to figure out how to measure and teach it in schools, or to encourage it in adults; at a time when accelerating technological change means there’s always more to learn, we might seek to create a more educable society in general. (That change will further accelerate if Valiant’s proposals for A.I. capable of “artificial educability” prove workable.) After reading his book, I thought, on a less exalted scale, about how I might improve my own educability. I concluded that I would seek to learn about a wider range of subjects, and simply try more things, trusting that my mind would someday knit it all together. I also figured that it couldn’t hurt to remind myself of what I’d already learned. Down in the basement, a few big bookshelves hold my reading from college and graduate school. “Middlemarch” is there, along with many other books that I didn’t understand then but have come to value with the passage of time. Reading widely about things that don’t seem immediately or practically useful, in the hope that what you learn now may prove meaningful later—that’s pretty much the definition of a liberal-arts education. Who knew that one of its best defenders would turn out to be a computer scientist? ♦
Valiant 認為將教育能力提升為一種理想可能是有用的。我們可以嘗試找出如何在學校中衡量和教授它，或者在成年人中鼓勵它；在技術變革加速的時代，我們可能會尋求建立一個更具教育能力的社會。 （如果 Valiant 提出的具有 “人工教育能力” 的人工智能方案被證明可行，這種變革將進一步加速。）閱讀完他的書後，我想到，從一個較低的層次來看，我如何提高自己的教育能力。我得出結論，我將尋求學習更廣泛的主題，並嘗試更多事情，相信我的思維終將把一切編織在一起。我還想到，提醒自己已經學到的知識也不會有害。地下室裡，幾個大書架上擺滿了我大學和研究生時代的讀物。那裡有《米德爾馬奇》，還有許多其他我當時不懂但隨著時間流逝而珍視的書籍。 廣泛閱讀那些一時看似沒有立即或實際用處的事物，希望現在學到的知識將來可能會證明有意義 —— 這基本上就是自由藝術教育的定義。誰知道其中一位最好的辯護者竟然是一位電腦科學家？ ♦