What I learned from 2,000 obituaries | Lux Narayan

163,985 views ・ 2017-03-23

TED


請雙擊下方英文字幕播放視頻。

譯者: 庭芝 梁 審譯者: ZHENG Shu
00:12
Joseph Keller used to jog around the Stanford campus,
0
12699
4072
約瑟夫·凱勒習慣在 史丹福大學校園周圍慢跑,
00:16
and he was struck by all the women jogging there as well.
1
16795
4717
在那裡慢跑的其他女性, 引發了他的好奇:
00:21
Why did their ponytails swing from side to side like that?
2
21536
3589
為什麼她們的馬尾總是左右晃動著?
00:25
Being a mathematician, he set out to understand why.
3
25687
3138
身為一名數學家, 他決定要弄清楚原因。
00:28
(Laughter)
4
28849
1151
(笑聲)
00:30
Professor Keller was curious about many things:
5
30024
2306
凱勒教授對許多事情都很好奇:
00:32
why teapots dribble
6
32354
1967
為什麼茶水會順著壺嘴滴下來,
00:34
or how earthworms wriggle.
7
34345
1830
或是蚯蚓如何蠕動。
00:36
Until a few months ago, I hadn't heard of Joseph Keller.
8
36667
3048
幾個月之前, 我還不知道約瑟夫·凱勒是誰。
00:40
I read about him in the New York Times,
9
40401
2852
我在紐約時報看到他的消息,
00:43
in the obituaries.
10
43277
1432
在訃聞版。
00:44
The Times had half a page of editorial dedicated to him,
11
44733
3772
紐約時報的編輯 用了半個版面來向他致敬。
00:48
which you can imagine is premium space for a newspaper of their stature.
12
48529
3922
你可以想像得到, 對一家大報社來說,
這代表著極高的尊崇。
00:53
I read the obituaries almost every day.
13
53188
2342
我幾乎每天都會閱讀訃聞版。
00:56
My wife understandably thinks I'm rather morbid
14
56510
3022
我的妻子曉得我這個 有點病態的習慣:
00:59
to begin my day with scrambled eggs and a "Let's see who died today."
15
59556
4400
每天早晨,我會一邊吃著炒蛋, 一邊閱讀訃聞版:
「我們來看看今天有誰去世了」。
01:03
(Laughter)
16
63980
1150
(笑聲)
01:05
But if you think about it,
17
65845
1292
但是如果你仔細想想,
01:07
the front page of the newspaper is usually bad news,
18
67161
3413
報紙的頭版通常刊登壞消息,
01:10
and cues man's failures.
19
70598
1975
這暗示我們某人失敗了。
01:12
An instance where bad news cues accomplishment
20
72597
2666
然而有一種情況: 壞消息卻暗示了某人的成就,
01:15
is at the end of the paper, in the obituaries.
21
75287
3235
那就是在報紙的最後一版, 在訃聞版。
01:19
In my day job,
22
79225
1364
我平常的工作,
01:20
I run a company that focuses on future insights
23
80613
2476
是經營一間企管顧問公司, 我們關注未來的發展趨勢,
01:23
that marketers can derive from past data --
24
83113
2420
並分析過去所累積的數據──
01:25
a kind of rearview-mirror analysis.
25
85557
2944
這是一種稱為「回顧分析」的技術。
01:28
And we began to think:
26
88912
1155
我們開始思考:
01:30
What if we held a rearview mirror to obituaries from the New York Times?
27
90091
5118
如果我們對紐約時報的訃聞版, 進行回顧分析?
01:36
Were there lessons on how you could get your obituary featured --
28
96334
3468
能否從裡面學到 「如何讓訃聞變得更為獨特」──
01:39
even if you aren't around to enjoy it?
29
99826
1977
即使你以後也看不到自己的訃聞?
01:41
(Laughter)
30
101827
1484
(笑聲)
01:43
Would this go better with scrambled eggs?
31
103335
2628
這樣做能讓訃聞更適合搭配炒蛋嗎?
01:45
(Laughter)
32
105987
1150
(笑聲)
01:47
And so, we looked at the data.
33
107983
2998
所以,我們檢視了數據。
01:51
2,000 editorial, non-paid obituaries
34
111689
4494
我們分析了總共 2000 篇 由編輯部刊登,非付費的訃聞,
01:56
over a 20-month period between 2015 and 2016.
35
116207
3642
範圍是 2015 到 2016 年的 20 個月之間。
01:59
What did these 2,000 deaths -- rather, lives -- teach us?
36
119873
4824
究竟這 2000 個死亡 ──應該說是生命──
教導了我們什麼?
02:04
Well, first we looked at words.
37
124721
2033
好,首先來看訃聞的用字。
02:06
This here is an obituary headline.
38
126778
1761
這是一篇訃聞的標題。
02:08
This one is of the amazing Lee Kuan Yew.
39
128563
2296
這一位是傳奇人物李光耀。
02:10
If you remove the beginning and the end,
40
130883
2522
移除開頭和結尾後的內容,
02:13
you're left with a beautifully worded descriptor
41
133429
3334
只剩短短的幾句話, 一些優美的描述辭彙,
02:16
that tries to, in just a few words, capture an achievement or a lifetime.
42
136787
4675
能讓你捕捉到亡者的成就, 或是他的一生。
02:21
Just looking at these is fascinating.
43
141486
2161
看著這些詞彙就夠令人著迷了。
02:24
Here are a few famous ones, people who died in the last two years.
44
144121
3295
這裡有幾位, 在這兩年內過世的名人。
02:27
Try and guess who they are.
45
147440
1319
試著猜猜看他們是誰。
02:28
[An Artist who Defied Genre]
46
148783
1440
「一位顛覆形式的藝術家」
02:30
That's Prince.
47
150247
1185
這是王子。
02:32
[Titan of Boxing and the 20th Century]
48
152317
1837
「二十世紀的拳擊巨星」
02:34
Oh, yes.
49
154178
1160
是的,
02:35
[Muhammad Ali]
50
155362
1224
拳王阿里。
02:36
[Groundbreaking Architect]
51
156610
1546
「開創未來的建築師」
02:38
Zaha Hadid.
52
158180
1251
札哈.哈蒂。
02:40
So we took these descriptors
53
160663
1748
因此,我們找出這些描述詞,
02:42
and did what's called natural language processing,
54
162435
2524
進行所謂的自然語言處理。
02:44
where you feed these into a program,
55
164983
1771
也就是你將文字輸入程式,
02:46
it throws out the superfluous words --
56
166778
1865
它能剔除不必要的文字, 例如 「the」--
02:48
"the," "and," -- the kind of words you can mime easily in "Charades," --
57
168667
4223
並且剔除在玩「比手畫腳」遊戲時, 很容易以手勢表示的文字,
02:52
and leaves you with the most significant words.
58
172914
2193
最後留下最重要的詞彙。
02:55
And we did it not just for these four,
59
175131
1821
我們不只分析上面這四則,
02:56
but for all 2,000 descriptors.
60
176976
2519
而是分析了所有 2000 則 訃聞的描述詞彙。
02:59
And this is what it looks like.
61
179519
1743
我們來看看結果是什麼樣子。
03:02
Film, theatre, music, dance and of course, art, are huge.
62
182824
4827
電影,戲劇,音樂,舞蹈。 當然「藝術」是最明顯的。
03:08
Over 40 percent.
63
188305
1946
出現的頻率多出 40%。
03:10
You have to wonder why in so many societies
64
190275
2528
你不得不驚訝的是, 為什麼在大多數的社會中,
03:12
we insist that our kids pursue engineering or medicine or business or law
65
192827
4435
我們一直認為讓孩子讀工程、 醫學、商業或法律科系,
03:17
to be construed as successful.
66
197286
1587
才是所謂的成功。
03:19
And while we're talking profession,
67
199691
1693
當我們關注職業時,
03:21
let's look at age --
68
201408
1151
也來看看年齡──
03:22
the average age at which they achieved things.
69
202583
2510
這些人功成名就的平均年齡。
03:25
That number is 37.
70
205117
1846
這個數字是37年。
03:28
What that means is, you've got to wait 37 years ...
71
208094
3656
這意味著什麼? 就是你平均必須等待 37 年……
03:31
before your first significant achievement that you're remembered for --
72
211774
3395
才能獲得第一個成就,
03:35
on average --
73
215193
1151
44 年後,
03:36
44 years later, when you die at the age of 81 --
74
216368
2478
當你過世時才會被紀念,
03:38
on average.
75
218870
1168
平均年齡是 81 歲。
03:40
(Laughter)
76
220062
1001
(笑聲)
03:41
Talk about having to be patient.
77
221087
1684
這告訴我們要有耐心。
03:42
(Laughter)
78
222795
1057
(笑聲)
03:43
Of course, it varies by profession.
79
223876
2089
當然,這會因職業而異。
03:46
If you're a sports star,
80
226386
1193
如果你是體育明星,
03:47
you'll probably hit your stride in your 20s.
81
227603
2127
你可能會在 20 多歲打破紀錄。
03:49
And if you're in your 40s like me,
82
229754
2645
如果你和我一樣已經 40 多歲了,
03:52
you can join the fun world of politics.
83
232423
1991
你可以加入有趣的政治圈。
03:54
(Laughter)
84
234438
1056
(笑聲)
03:55
Politicians do their first and sometimes only commendable act in their mid-40s.
85
235518
3915
政治家完成他們的第一項成就, 可能也是唯一的一次,
大約是在45歲左右。
03:59
(Laughter)
86
239457
1257
(笑聲)
04:00
If you're wondering what "others" are,
87
240738
1937
如果你想知道「其他職業」是什麼,
04:02
here are some examples.
88
242699
1476
這裡有一些例子。
04:04
Isn't it fascinating, the things people do
89
244641
2116
這些人所做的,
04:06
and the things they're remembered for?
90
246781
1882
和他們被紀念的事蹟, 是不是很令人著迷?
04:08
(Laughter)
91
248687
1752
(笑聲)
04:11
Our curiosity was in overdrive,
92
251956
1844
我們的好奇心被點燃了,
04:13
and we desired to analyze more than just a descriptor.
93
253824
3788
我們不只想要分析描述詞。
04:18
So, we ingested the entire first paragraph of all 2,000 obituaries,
94
258818
4946
所以,我們輸入了 2000 則 訃聞的第一段全文,
04:23
but we did this separately for two groups of people:
95
263788
2774
但是將亡者分為兩群:
04:26
people that are famous and people that are not famous.
96
266586
2777
知名人士,以及非知名人士。
04:29
Famous people -- Prince, Ali, Zaha Hadid --
97
269387
2689
知名人士例如:王子、 阿里、札哈.哈蒂。
04:32
people who are not famous are people like Jocelyn Cooper,
98
272100
4235
非知名人士例如:喬斯林庫柏、
04:36
Reverend Curry
99
276359
1154
嘉里牧師
04:37
or Lorna Kelly.
100
277537
1169
或羅娜.凱利。
04:38
I'm willing to bet you haven't heard of most of their names.
101
278730
3188
我敢打賭,你絕對沒聽過 大多數這些人的名字。
04:41
Amazing people, fantastic achievements, but they're not famous.
102
281942
3812
這些人有著令人驚訝,稀奇古怪的成就,
但是他們並不出名。
04:46
So what if we analyze these two groups separately --
103
286540
2788
因此,如果我們分析一下這兩群人,
04:49
the famous and the non-famous?
104
289352
1525
知名和非知名人士,
04:50
What might that tell us?
105
290901
1419
可能得到什麼結果?
04:52
Take a look.
106
292344
1240
我們來看一下。
04:56
Two things leap out at me.
107
296376
1469
有兩個結果讓我驚訝。
04:58
First:
108
298389
1170
第一個:
04:59
"John."
109
299926
1198
「約翰」。
05:01
(Laughter)
110
301148
1300
(笑聲)
05:03
Anyone here named John should thank your parents --
111
303734
3388
如果這裡有人也叫約翰的, 應該感謝你的父母──
05:07
(Laughter)
112
307146
1329
(笑聲)
05:08
and remind your kids to cut out your obituary when you're gone.
113
308499
3082
而且記得提醒你的孩子, 當你過世時要把訃聞剪下來。
05:12
And second:
114
312881
1356
另一個結果是:
05:15
"help."
115
315669
1154
「幫助」。
05:18
We uncovered, many lessons from lives well-led,
116
318344
3465
我們發現了,這些已經逝去, 在報紙上令我們緬懷的事蹟,
05:21
and what those people immortalized in print could teach us.
117
321833
2836
教導我們許多事情, 教導我們如何好好活著。
05:24
The exercise was a fascinating testament to the kaleidoscope that is life,
118
324693
4738
這次的實驗就是 萬花筒般生命的迷人見證。
05:29
and even more fascinating
119
329455
2715
甚至更迷人的是,
05:32
was the fact that the overwhelming majority of obituaries
120
332194
3068
在大多數的訃聞中,
05:35
featured people famous and non-famous,
121
335286
2998
無論是知名或非知名人士,
05:38
who did seemingly extraordinary things.
122
338308
2433
他們所做的不平凡事蹟。
05:41
They made a positive dent in the fabric of life.
123
341394
3110
他們在不停編織的人生中, 留下了有意義的印記。
05:44
They helped.
124
344528
1237
他們幫助他人。
05:46
So ask yourselves as you go back to your daily lives:
125
346592
2591
所以問問自己, 當你回到日常生活中:
05:49
How am I using my talents to help society?
126
349207
2920
我如何運用我的才華, 幫助這個社會?
05:52
Because the most powerful lesson here is,
127
352151
2973
因為在這裡,最重要的一課是:
05:55
if more people lived their lives trying to be famous in death,
128
355148
4336
如果有更多的人, 在活著時努力過著自己的人生,
而能在過世時變得知名,
05:59
the world would be a much better place.
129
359508
2605
這個世界將會變得更加美好。
06:02
Thank you.
130
362882
1169
謝謝大家。
06:04
(Applause)
131
364075
2848
(掌聲)
關於本網站

本網站將向您介紹對學習英語有用的 YouTube 視頻。 您將看到來自世界各地的一流教師教授的英語課程。 雙擊每個視頻頁面上顯示的英文字幕,從那裡播放視頻。 字幕與視頻播放同步滾動。 如果您有任何意見或要求,請使用此聯繫表與我們聯繫。

https://forms.gle/WvT1wiN1qDtmnspy7