Technology has brought us so much: the moon landing, the Internet, the ability to sequence the human genome. But it also taps into a lot of our deepest fears, and about 30 years ago, the culture critic Neil Postman wrote a book called "Amusing Ourselves to Death," which lays this out really brilliantly. And here's what he said, comparing the dystopian visions of George Orwell and Aldous Huxley. He said, Orwell feared we would become a captive culture. Huxley feared we would become a trivial culture. Orwell feared the truth would be concealed from us, and Huxley feared we would be drowned in a sea of irrelevance. In a nutshell, it's a choice between Big Brother watching you and you watching Big Brother. (Laughter)
科技帶給我們很多美好的事物: 登陸月球、網路、 人類基因組定序。 但也挖掘出我們內心深處的許多恐懼。 大約 30 年前, 文化評論家尼爾.波茲曼寫了一本書, 叫做《娛樂至死》, 書中把這個現象說得很妙。 他是這樣說的: 比較歐威爾和赫胥黎的兩種反烏托邦, 他說,歐威爾擔心我們會成為 圈養的文化。 赫胥黎則擔心我們會成為庸俗的文化。 歐威爾擔心真相會被隱瞞, 赫胥黎則擔心我們會被瑣碎的汪洋吞沒。 簡單點說, 我們可以選擇「老大哥監視你」 或是「你監視老大哥」 (觀眾笑聲)
But it doesn't have to be this way. We are not passive consumers of data and technology. We shape the role it plays in our lives and the way we make meaning from it, but to do that, we have to pay as much attention to how we think as how we code. We have to ask questions, and hard questions, to move past counting things to understanding them. We're constantly bombarded with stories about how much data there is in the world, but when it comes to big data and the challenges of interpreting it, size isn't everything. There's also the speed at which it moves, and the many varieties of data types, and here are just a few examples: images, text, video, audio. And what unites this disparate types of data is that they're created by people and they require context.
其實不必這樣, 我們不是被動地消費資料和科技, 我們可以決定科技在生活中扮演的角色, 和它對我們的意義。 但是要這麼做, 我們必須重視思考的方式, 不只重視編碼的方式。 我們必須問問題,難解的問題, 超越單純的算術, 試圖去了解。 我們不斷聽到世界上有多少資料, 但是談到大數據, 以及詮釋這些數據資料的挑戰, 光看數量是不夠的, 還必須關注資料成長的速度, 以及眾多不同的資料類型。 我略舉幾個例子: 圖像、 文字、 [請稍候,直到你有用處的時候,謝謝。] 影片、 聲音。 這些不同資料類型的共通處在於 它們都是人建立的, 也都不能斷章取義來詮釋。
Now, there's a group of data scientists out of the University of Illinois-Chicago, and they're called the Health Media Collaboratory, and they've been working with the Centers for Disease Control to better understand how people talk about quitting smoking, how they talk about electronic cigarettes, and what they can do collectively to help them quit. The interesting thing is, if you want to understand how people talk about smoking, first you have to understand what they mean when they say "smoking." And on Twitter, there are four main categories: number one, smoking cigarettes; number two, smoking marijuana; number three, smoking ribs; and number four, smoking hot women. (Laughter)
舉例,有一個資料科學家小組, 成員來自伊利諾大學芝加哥分校, 這小組叫做「衛生媒體合作實驗室」。 他們和美國疾病管制中心合作, 想要更了解 人們怎樣談論戒菸、 怎樣談論電子香煙, 以及怎樣一起幫助吸菸者戒菸。 有趣的是, 若要了解人們如何談論抽菸 smoking, 就要先了解人們說 smoking 是什麼意思。 在推特上大致分成四類: 第一類,抽菸; 第二類,抽大麻; 第三類,煙熏肋排; 第四類,嗆辣正妹; (觀眾笑聲) 接著要思考,
So then you have to think about, well, how do people talk about electronic cigarettes? And there are so many different ways that people do this, and you can see from the slide it's a complex kind of a query. And what it reminds us is that language is created by people, and people are messy and we're complex and we use metaphors and slang and jargon and we do this 24/7 in many, many languages, and then as soon as we figure it out, we change it up.
人們怎麼談論電子香菸? 講法五花八門, 就像這張投影片所列的, 這種檢索非常複雜。 這提醒我們, 語言是人創造的, 而人是複雜、亂無章法的, 我們會用隱喻、俚語、行話, 無時無刻的製造,各式各樣的語言, 好不容易破解語言,就立刻又改變了。
So did these ads that the CDC put on, these television ads that featured a woman with a hole in her throat and that were very graphic and very disturbing, did they actually have an impact on whether people quit? And the Health Media Collaboratory respected the limits of their data, but they were able to conclude that those advertisements — and you may have seen them — that they had the effect of jolting people into a thought process that may have an impact on future behavior. And what I admire and appreciate about this project, aside from the fact, including the fact that it's based on real human need, is that it's a fantastic example of courage in the face of a sea of irrelevance.
那麼,疾管中心拍的這些戒菸文宣, 電視廣告裡,一名女子喉嚨破了大洞, 畫面驚悚嚇人, 這些廣告真的有效嗎? 真的讓人戒菸了嗎? 衛生媒體合作實驗室尊重其數據的限制, 但仍能做出結論, 認為這些廣告—也許你們看過, 成功地刺激人們開始反省, 可能影響未來的行為。 這個計畫讓我最欽佩、欣賞的地方是, 除了它是在解決人的實際需要以外, 同時它提供了絕佳的典範, 展現了人類面對瑣碎汪洋的勇氣。
And so it's not just big data that causes challenges of interpretation, because let's face it, we human beings have a very rich history of taking any amount of data, no matter how small, and screwing it up. So many years ago, you may remember that former President Ronald Reagan was very criticized for making a statement that facts are stupid things. And it was a slip of the tongue, let's be fair. He actually meant to quote John Adams' defense of British soldiers in the Boston Massacre trials that facts are stubborn things. But I actually think there's a bit of accidental wisdom in what he said, because facts are stubborn things, but sometimes they're stupid, too.
所以,詮釋的挑戰不只因為資料龐大, 因為,老實說,歷史上有很多的例子顯示, 無論資料再少,我們向來很能把它搞砸。 大家可能記得,很多年前, 前總統雷根曾被痛罵, 因為他說,事實是愚笨的東西。 憑良心說,他只是一時口誤, 他其實是想引用約翰.亞當斯在 為因波士頓慘案受審的英軍辯護時說的: 事實是固執難拗、不容改變的。 但我其實認為, 這口誤可能湊巧講出幾分智慧, 因為事實確實很固執, 但是有時也真的很愚笨。
I want to tell you a personal story about why this matters a lot to me. I need to take a breath. My son Isaac, when he was two, was diagnosed with autism, and he was this happy, hilarious, loving, affectionate little guy, but the metrics on his developmental evaluations, which looked at things like the number of words — at that point, none — communicative gestures and minimal eye contact, put his developmental level at that of a nine-month-old baby. And the diagnosis was factually correct, but it didn't tell the whole story. And about a year and a half later, when he was almost four, I found him in front of the computer one day running a Google image search on women, spelled "w-i-m-e-n." And I did what any obsessed parent would do, which is immediately started hitting the "back" button to see what else he'd been searching for. And they were, in order: men, school, bus and computer. And I was stunned, because we didn't know that he could spell, much less read, and so I asked him, "Isaac, how did you do this?" And he looked at me very seriously and said, "Typed in the box."
我要講一個自己的故事, 解釋為什麼這對我這麼重要。 我要先吸一口氣。 我兒子艾薩克兩歲的時候, 被診斷為自閉兒。 但他是個快樂、搞笑、 有愛心、喜歡親密的孩子, 但是他的發展評估測驗數據 檢視的是: 他當時會說幾個字?零個。 只靠手勢溝通, 眼神接觸也極少, 讓他的發展程度 被評為九個月大的嬰兒。 按照數據,診斷並沒有錯, 卻跟實際狀況有落差。 大概過了一年半,兒子快滿四歲, 有一天,我看到他坐在電腦前面, 在用 Google 搜尋女性的照片, 他把女性 (women) 拼成 "w-i-m-e-n"。 我的反應跟任何偏執妄想的父母一樣, 立刻開始按瀏覽器的「返回」按鈕, 看看他還搜尋過什麼。 結果發現他依序搜尋過:男性 (men)、 學校 (school)、公車 (bus)、 和電腦(錯拼成 cpyutr)。 我很吃驚, 因為我們根本不知道他會拼字, 更別說閱讀。 所以我問他: 「艾薩克,你怎麼辦到的?」 他認真的看著我,說: 「在搜尋欄裡打字啊!」
He was teaching himself to communicate, but we were looking in the wrong place, and this is what happens when assessments and analytics overvalue one metric — in this case, verbal communication — and undervalue others, such as creative problem-solving. Communication was hard for Isaac, and so he found a workaround to find out what he needed to know. And when you think about it, it makes a lot of sense, because forming a question is a really complex process, but he could get himself a lot of the way there by putting a word in a search box.
他在教自己溝通, 只是我們都找錯方向了。 會發生這種情況, 是因為評量和分析太重視單一面向, 就像他的自閉症評量, 單看口語表達, 而忽視其他要素, 例如,創造性地解決問題。 溝通對艾薩克來說很困難, 所以他找到了替代方法, 來找解答。 想想很有道理, 因為問問題是很複雜的過程, 但他只要在搜尋欄輸入一個字, 就成功了一大半。
And so this little moment had a really profound impact on me and our family because it helped us change our frame of reference for what was going on with him, and worry a little bit less and appreciate his resourcefulness more.
於是這個小小的時刻 對我影響深遠, 對我們全家都是。 因為,這改變了我們的判斷標準, 用全新的眼光看待兒子的狀況, 比較不那麼擔憂, 轉而欣賞他解決問題的能力。
Facts are stupid things. And they're vulnerable to misuse, willful or otherwise. I have a friend, Emily Willingham, who's a scientist, and she wrote a piece for Forbes not long ago entitled "The 10 Weirdest Things Ever Linked to Autism." It's quite a list. The Internet, blamed for everything, right? And of course mothers, because. And actually, wait, there's more, there's a whole bunch in the "mother" category here. And you can see it's a pretty rich and interesting list. I'm a big fan of being pregnant near freeways, personally. The final one is interesting, because the term "refrigerator mother" was actually the original hypothesis for the cause of autism, and that meant somebody who was cold and unloving.
事實,真的是愚笨的。 事實也很容易被誤用, 不論是有心或無意。 我的朋友艾蜜莉.威靈漢是個科學家, 她不久前為《富比士》寫了一篇文章, 叫做〈 自閉症怪異印象十大排行榜〉, 內容挺可怕的: 「網路」,萬惡淵藪,對吧? 當然「媽媽」也上榜, 不言自明。 等等,還有, 這裡有一大類,都跟「媽媽」有關係, 你可以看到,原因很多、很有意思。 我最喜歡的是 「在高速公路附近受孕」。 最後一項很有趣, 因為「冰箱母親」這個封號 是自閉症原因最早的假說, 用來描述冷漠沒有愛心的母親。
And at this point, you might be thinking, "Okay, Susan, we get it, you can take data, you can make it mean anything." And this is true, it's absolutely true, but the challenge is that we have this opportunity to try to make meaning out of it ourselves, because frankly, data doesn't create meaning. We do. So as businesspeople, as consumers, as patients, as citizens, we have a responsibility, I think, to spend more time focusing on our critical thinking skills. Why? Because at this point in our history, as we've heard many times over, we can process exabytes of data at lightning speed, and we have the potential to make bad decisions far more quickly, efficiently, and with far greater impact than we did in the past. Great, right? And so what we need to do instead is spend a little bit more time on things like the humanities and sociology, and the social sciences, rhetoric, philosophy, ethics, because they give us context that is so important for big data, and because they help us become better critical thinkers. Because after all, if I can spot a problem in an argument, it doesn't much matter whether it's expressed in words or in numbers. And this means teaching ourselves to find those confirmation biases and false correlations and being able to spot a naked emotional appeal from 30 yards, because something that happens after something doesn't mean it happened because of it, necessarily, and if you'll let me geek out on you for a second, the Romans called this "post hoc ergo propter hoc," after which therefore because of which.
現在,你可能會想: 「好了,蘇珊,我們懂了, 你可以對資料做任何詮釋。」 這也沒錯, 絕對正確。 但是挑戰在於, 我們自己有這個機會, 可以賦予資料意義, 因為老實說,資料不會自己產生意義。 我們才可以。 所以,身為商人、消費者、 病人、公民等等, 我想我們有責任 多花點時間 提升我們的批判性思考能力。 為什麼? 我們聽過很多次, 因為在歷史的這一刻, 已經能用光速 處理數十億 GB 的資料量, 可能更快速、更有效地 做出錯誤的決定, 影響之大可能更甚以往。 這下好了,對吧? 所以,我們反而必須 多花時間 發展人文、 社會學和社會科學, 修辭、哲學、倫理, 因為這些知識 構成我們的背景涵養, 對大數據非常重要, 也因為這能幫助我們更會思辨, 因為畢竟, 如果我能看出命題裡的問題, 那麼無論是 用文字或數據表達都可以。 這表示, 要教育我們自己 去發覺各種確認的偏見 和謬誤的關聯, 並且能對赤裸裸的情感訴求保持警覺。 因為甲事之後發生了乙事, 並不代表 甲事必定是乙事的肇因。 如果大家容我書呆一下, 羅馬人稱這現象為「後此謬誤」 "post hoc ergo propter hoc", 後此,故因此。
And it means questioning disciplines like demographics. Why? Because they're based on assumptions about who we all are based on our gender and our age and where we live as opposed to data on what we actually think and do. And since we have this data, we need to treat it with appropriate privacy controls and consumer opt-in, and beyond that, we need to be clear about our hypotheses, the methodologies that we use, and our confidence in the result. As my high school algebra teacher used to say, show your math, because if I don't know what steps you took, I don't know what steps you didn't take, and if I don't know what questions you asked, I don't know what questions you didn't ask. And it means asking ourselves, really, the hardest question of all: Did the data really show us this, or does the result make us feel more successful and more comfortable?
這表示要質疑像人口統計這樣的方法。 為什麼? 因為這些都假設 我們一定是某種人, 只憑我們的性別、年齡、居住地, 而忽視我們實際的思考和行為資料。 現在有了這些資料, 我們必須做好隱私權控管, 以及讓消費者自願參與。 再來, 我們必須很清楚我們的假設、 使用的方法, 以及我們對結果的信心。 就像我高中代數老師常說的: 「算給我看。 因為如果我不知道 你做了哪些步驟, 就不知道哪些步驟你沒有做。 如果我不知道你問了哪些問題, 就不知道哪些問題你沒有問。」 這表示我們要問自己 最難的一個問題: 「數據資料真的有這樣說嗎? 還是這種結果讓我們覺得 比較成功和自在?」
So the Health Media Collaboratory, at the end of their project, they were able to find that 87 percent of tweets about those very graphic and disturbing anti-smoking ads expressed fear, but did they conclude that they actually made people stop smoking? No. It's science, not magic.
衛生媒體合作實驗室在計畫結束時, 發現 87% 的推文 回應那些令人不安的戒菸廣告時, 表達了恐懼。 但是, 他們有說那些廣告讓人成功戒菸嗎? 沒有。這是科學,不是魔術。
So if we are to unlock the power of data, we don't have to go blindly into Orwell's vision of a totalitarian future, or Huxley's vision of a trivial one, or some horrible cocktail of both. What we have to do is treat critical thinking with respect and be inspired by examples like the Health Media Collaboratory, and as they say in the superhero movies, let's use our powers for good.
所以, 如果想要釋放數據的力量, 我們不必盲目地踏進 歐威爾預見的極權主義未來, 或是赫胥黎的瑣碎世界, 或是兩者的可怕綜合體。 我們必須做的是, 重視批判性思考, 並且向衛生媒體合作室 這樣的典範學習。 就像超級英雄電影常講的: 「讓我們把我們的力量用在正途。」
Thank you.
謝謝。
(Applause)
(觀眾掌聲)