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  • 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 humansfear

    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 saidOrwell 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

  • Were 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)

  • Sothen 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

  • Sodid 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

  • Andwhat 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 soit’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

  • Somany 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 Adamsdefense 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 wordsat 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 knowupset 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 saidtype 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 otherssuch 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 theyre 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 knowbeing 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 ofrefrigeratormother 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 thinkingokaySusan 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 thispost 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

Technology has brought us so much

科技帶來了巨大變革

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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?)

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    Go Tutor 發佈於 2014 年 11 月 12 日
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