字幕列表 影片播放 已審核 字幕已審核 列印所有字幕 列印翻譯字幕 列印英文字幕 Technology has brought us so much 科技帶來了巨大變革 The moon landing, the internet, the ability to sequence the human genome 登陸月球、網際網路、人體基因排序 but it also taps into a lot of humans’ fear 同時也引發了人類的恐懼 and about thirty years ago, the cultural critic Neil Postman wrote a book call the amusing ourselves to death 大約30年前 著名的文化評論家尼爾˙波茲曼寫了本書《娛樂至死》 Which lay this out really brilliantly 完美呈現了此概念 And here’s what he said comparing the dystopian visions of George Orwell and Aldous Huxley 他比較喬治˙奧威爾和奧爾德斯˙赫胥黎的反烏托邦思想: He said ‘Orwell fear that we will become a captive culture; Huxley fear that we would become a trivial culture.’ 「奧威爾認為我們會被禁錮、束縛;赫胥黎則擔心人類文化會漸趨瑣碎」 Orwell fear the truth would be conceal from us and Huxley fear we would be drown in the sea of irrelevance. 「奧威爾擔心事實被掩蓋;赫胥黎則煩惱人類會迷失在無關緊要的數據資料裡」 In a nut shell, it’s a choice between big brother watching you and you watching big brother. 簡而言之 就是在「老大哥監督你」和「你監督老大哥」之間選擇(註: 此處暗指英格蘭社會主義的監視活動與極權統治vs看"Big Brother"這個實境節目) But it doesn’t have to be this way, we are not passive consumer of data and technology. 其實不必如此 我們不是資訊和科技的被動接收者 We shape the role it place in our life and the way we made meaning from it. 我們能定義科技在生活中的角色 But to do that, we have to pay as much attention to how we think as how we code 這時留心思考模式以及撰寫程式語言的過程 就變得同等重要 We have to ask question and hard question to move pass counting things to understanding them 發掘更深入的問題 才能超越單純數算 盡一步了解資訊本身 We’re constantly bombarded with stories about how much data there is in the world 我們不斷聽到:「現在資訊爆炸到非常誇張的程度」 But when it comes to big data, and the challenge is interpreting it 面對大量數據還有資訊 最困難的是解讀 Size isn’t everything. 不是數據最多就贏了 There’s also the speed of which it moves 還得考慮資料成長的速率、 And the many variety of data types 不同類型的數據 And here are just the few examples, Images 舉幾個例: 影像、 Texts 文字、 Video 視頻、 Audio 音頻 And what unites these despair it types of data 它們有個共通點: Is that they are created by people 全都是人類創造的 And they require context 其意義也都取決於不同情境與狀況 Now, there’s a group of data scientists at the university of Illinos at Chicago 一群芝加哥伊利諾州大學的數據科學家 And they are called the Health Media collaboratory. 組成了「衛生媒體合作實驗室」 And they have been working with the center for disease control 他們持續和疾病管制局合作 To better understand, how people talk about quitting smoking 想要瞭解人們怎麼談戒菸、 How they talk about electronic cigarets. 對電子香菸有何見解、 And what they can do collectively to help them quit. 還有如何攜手讓癮君子成功戒菸 The interesting thing is if you wanna understand how people talk about smoking 了解大家怎麼談論「smoking」(註: 本意為抽菸) 其實很有趣 First you have to understand what they mean, when they say smoking 你首先得知道他口中的「smoking」所指為何 And on Tweeter, they are four main categories. 根據推特 總共有四大類 First one, smoking cigarets. 第一個是抽雪茄 Number two, smoking marijuana. 第二個是吸大麻 Number three, smoking ribs. 第三種指的是煙燻肋排(smoking ribs) And number four, smoking hot women 第四種則是嗆辣美眉(smoking hot women) So…then you have to think about what have the people talk about electronic cigarets? 確定之後 下一步是找出大家對電子香菸的看法 And there are so many different ways that people do this 方法五花八門 You can see from the slide. 你可以參考那張投影片 It’s a complex kind of queery. 既複雜又古怪 And what that reminds us is that 這個例子提醒了我們 Languages created by people. 語言是人類創造出來的 And people are messy and were complex and we use metaphors and slang and jargon 而人類混亂、難懂 我們還使用比喻、俚語和黑話 And we do this twenty-four seven and many many languages. 用這些東西進行溝通 許多語言都是這樣 And then as soon as we figure it out, we change it up. 單字意思確立後 馬上又有衍伸意義 So…did this ads that cdcs put on these television ads that feature a woman with a hole in her throat 疾管局在電視節目中放送的廣告: 吸菸的女人喉嚨破了個洞 And that were very graphic and very disturbing 這樣逼真噁心的畫面 Did they actually have an impact on whether people quit? 真的有用嗎? And helpfully, the collaboratory respect the limits of their data 雖然衛生媒體合作實驗室了解手上數據有其限制 But they were able to conclude that those advertisements and you may have seen them 他們仍能結論: 那些你或許也看過的戒菸文宣 They have the affect of jotting people into a thought process. 的確讓人們進行了反思 That may have an impact on future behavior. 還可能影響其未來行動 And…what I admire, and appreciate about this project design from the fact, including the fact 我很欣賞這個計畫 他們選擇了「抽菸」這個現實作為出發點 That’s base on real human need is that 目標是解決人的實際需要 It’s a fantastic example of courage and the face of the sea of relevance. 它還展現了面對瑣碎資訊所需的勇氣 堪稱絕佳典範 And so…it’s not just big data that causes challenge and interpretation. 不只有龐大的資料數據會帶來挑戰和解讀困難 Because let’s face it. We human-beings have a very rich stream of taking any among of data 老實說 就算是少少的資料 No matter how small and screwing it up. 人類一樣能出紕漏、捅簍子 So…many years ago you may remember 有人也許記得 多年前 That formal president Ronald Reagan was very criticize for making a statement the facts are stupid things 前總統雷根因為說出「事實是蠢笨的東西」而廣受批評 And it was a slip of the tongue. Let’s be fair. 那是個口誤 He actually meant to quote John Adams’ defense British soldiers in the Boston Massacre trial 他本來要引述波士頓大屠殺案審判中 約翰˙亞當斯為英國士兵辯護的名言 That facts are stubborn things. 「事實是不容改變的」 But I actually think there’s a bit of accidental wisdom in what he said. 但我覺得雷根還真說對了 Because facts are stubborning things. 事實的確不容改變 But sometimes they are stupid too. 不過它卻也很愚蠢 When I tell you a personal story about why this matters a lot to me 這句話對我意義重大 因為我的親身經歷印證了同個道理 I need to take a breath. 讓我先深呼吸一下 My son Isaac when he was two, he is diagnose with autism. 我兒子以撒兩歲時 被診斷為自閉症 And he was happy, hilarious, loving and affectionate little guy. 他是個充滿愛、喜悅、情感豐富的快樂小傢伙 but the metrics on his developmental evaluations, which looked at things like the number of words — at that point, none 發展評估是用「會說幾個字」作為標準 當時的他一個字都講不出來 Communicate with gestures and minimum eye contact put his developmental level at that of a nine months old baby. 僅能以手勢溝通、稀少的眼神接觸 讓他的發展程度被評為9個月大的嬰兒 And the diagnosis fact is actually correct but it didn’t tell the whole story. 以數據來看 診斷沒錯 但並非故事全貌 And about a year and a half later, he was almost four. 時隔約一年半 他快四歲了 I found him in front of the computer one day. 有天我發現他在用電腦 Running a google search on woman google「woman」(註: 女人)的圖片 Spell w-i-m-e-m 不過他拼: w「i」m「e」m And I did what any you know…upset parents will do 我和大多數氣惱的父母一樣 just immediate started hitting the back bottom to see what else he has been searching for 馬上衝過去按「上一頁」 看他還搜尋了什麼 And they were in order men, school, bus and computer (cpyutr) 結果依序是男人(men)、學校(school)、公車(bus)和電腦(computer誤拼為cpyutr) And I was stunned. 我嚇到了 Because we didn’t know that he could spell much less read 我們沒想過他能拼字 甭談閱讀 So I ask him. Isaac, how do you do this? 我問他: 「以撒 你怎麼辦到的?」 And he looked at me very seriously and said ‘type in the box’ 他認真的看著我 說: 「在搜尋欄打字」 He was teaching himself to communicate. 他自己學習如何溝通 But we were looking at the wrong place. 我們卻完全沒發現 And this is what happens when assessment and analytics over value one matrix in this case verbal communication 這就是單一指標和分析產生的盲點 只看口語表達 And undervalue others’ such as creating problem solving. 而低估了其他才能 諸如創造力和問題解決 Communication was hard for Isaac. 溝通對以撒來說是一大挑戰 And so he found a work around to find out what he needed to know. 所以他自己摸索、尋找答案 And when you think about it, it makes a lot of sense. 仔細想想也很合理 Because forming a question is really complex process. 問問題是很複雜的過程 But he can get himself a lot of way there. By putting a word in the search box. 但只要在搜尋欄裡輸入文字 他就能進步神速 And so this little moment had a really profound impact on me. 那個瞬間對我和家人 影響都非常深遠 In our family. Because it helps us change our reference for what’s going on for him. 因為這改變了我們對於他狀況的看法 And worry of a little bit less and appreciate his resource more. 我們學會了不過分擔憂他的情況 且多欣賞他另外的天賦 Facts are stupid things. 事實不僅愚蠢 And they’re vulnerable to misuse willful or otherwise. 還容易被無心或有意的誤用 I have a friend - Emily Willingham who’s a scientist. 我有個朋友 名叫艾蜜莉˙韋玲翰 她是科學家 And she wrote a piece for forbes not long ago. 不久前為富比士寫了篇文章 Entitled the ten weirdest things ever linked to autism. 標題是「大眾對自閉兒的十個怪印象」 It’s quite a list. 還真不少 The internet link for everything, right? 什麼都有網路的份 And of course mother. Because an actually way, there’s more others the whole bunch in the mother category here. 當然還有媽媽 其實母親這一項 還可再細分 And you can see, it’s a pretty rich and interesting list. 如你所見 滿多種的 有些很好玩 I’m a big fan of you know…being pregnant in a free way, personally. 我個人最喜歡「在高速公路附近懷孕」這一項 The final one is interesting because the term of ‘refrigerator’ mother was actually the original hypothesis for the cause of autism. 而最後一個是自閉症的最初假設-「冰箱媽媽」 And that meant somebody was cold and unloving. 指的是冷漠、沒有母愛的媽媽 And at this point, you might be thinking…okay…Susan we get it. 你可能正在想「好啦 我們懂妳意思了 You can take data. You can make it mean anything and this is true. 你可以自由解讀一切數據資料」 It’s absolutely true. 沒錯 But the challenge is that 不過重點是隨之而來的挑戰 We have this opportunity to try make meaning out of ourselves. 既然我們有機會賦予萬物意義 Because frankly, data doesn’t create meaning, we do. 資料不可能自己生出意義來 人才有辦法 So as business people, as consumers, as patients, as citizens 那商人、消費者、病患、公民 We have our responsibility, I think. 每個人都有責任 To spend more time focus on our critical thinking skills. 訓練自己批判思考 Why? 為什麼呢? Because at this point in our history as we heard, many times over we can process Exabyte in lightening speed. 今天這個世代 可以用光速處理好幾EB的資訊量 And we have potential to make bad decisions far more quickly, efficiently and far greater impact than we did in the past. 大家更容易做錯決定 而後果不容小覷 Great, right? 很可怕吧? And so what we need to do instead is spend a little bit more time on things like the humanities. 因此我們應該更重視人文學科 And sociology, and the social sciences, rhetoric, philosophy, ethics. 好比說社會學、社會科學、修辭、哲學、道德倫理 Because it gives us context that is so important for big data. 如此一來 我們就更知道如何詮釋龐大的數據資料 Because they help us become better critical thinkers. 也讓人類的批判思考更上層樓 Because after all, if I can spot a problem in an argument, it doesn’t much matter whether it’s express in words or numbers 若我能在論據裡發現疑點 那麼不管是用文字或數字呈現都不會有影響 And this means, teaching ourselves. 所以說必須教育自己 To find those conformation by thesis and false correlations. 藉著探討特定主題與錯誤關連 偵測自身偏見 And being able to spot a naked emotional appeal from thirty yards. 培養發覺情感訴求的能力 Because something that happens after something doesn’t mean it happen because of it necessarily. 先發生的不一定就是原因 And if you let me geek out on your first second, the Romans call this ‘post hoc ergo propterhoc’ 容我稍微賣弄 羅馬人說這是「巧合關係」 After which therefore because of which. 後此,故因此。 And it means questioning disciplines like demographics 我們必須質疑人口統計這種方法 Why? Because they're based on assumptions about who we all are based on our gender 什麼意思? 因為人口統計假設我們都是某一種類的人-同個性別、 and our age and where we live as opposed to data on what we actually think and do 同個年齡、同居住地 而忽略了每個獨立個體的思想和行為 And since we have this data 有了資料之後 we need to treat it with appropriate privacy controls and consumer opt-in 必須保障個人隱私 吸引消費者參與 and beyond that, we need to be clear about our hypotheses, 此外 假設、使用的方法要清楚明確 the methodologies that we use, and our confidence in the result 對結果要有自信 As my high school algebra teacher used to say 就像我高中代數老師說: show your math, because if I don't know what steps you took 「算一次給我看 如果我不知道你採取哪些步驟, I don't know what steps you didn't take 就不知道哪些步驟你忘了用」 and if I don't know what questions you asked, I don't know what questions you didn't ask 「如果我不知道你問過哪些問題,就不知道哪些是你沒問過的」 And it means asking ourselves, really, the hardest question of all 我們得問自己最困難的問題: Did the data really show us this, or does the result make us feel more successful and more comfortable? 「從資料裡可以推知這個結論嗎? 或這個結果是為了讓我們感到更成功、更自在而人為捏造出來的?」 So the Health Media Collaboratory, at the end of their project 計畫接近尾聲時 衛生媒體合作實驗室 they were able to find that 87 percent of tweets about those very graphic and disturbing anti-smoking ads expressed fear 發現對於栩栩如生的可怕禁菸廣告87%的推特回應都傳達了恐懼 but did they conclude that they actually made people stop smoking? 但他們有結論「這些廣告助人戒菸」嗎? No. It's science, not magic. 並沒有 這是科學 不是魔術 So if we are to unlock the power of data 釋放資訊還有數據的威力 We don't have to go blindly into Orwell's vision of a totalitarian future 不必盲從奧威爾的極權主義未來 or Huxley's vision of a trivial one, or some horrible cocktail of both. 也不用篤信赫胥黎的瑣碎文化 或混合了兩種的可怕產物 What we have to do is treat critical thinking with respect and be inspired by examples like the Health Media Collaboratory 只須正視批判性思考 向衛生媒體合作實驗室之類的模範學習 and as they say in the superhero movies, let's use our powers for good. 就像電影裡的超級英雄們說: 「讓我們好好善用超能力」 Thank you. 謝謝
B1 中級 中文 美國腔 TED 數據 資料 人類 衛生 資訊 【TED】蘇珊‧艾特林格: 我們應該拿這些大數據怎麼辦? (Susan Etlinger: What do we do with all this big data?) 13518 928 Go Tutor 發佈於 2014 年 11 月 12 日 更多分享 分享 收藏 回報 影片單字