TEDNYC
Lux Narayan: What I learned from 2,000 obituaries
Lakss Narajans: Ko es uzzināju no 2000 nekrologiem
Filmed:
Readability: 4.4
1,705,669 views
Lakss Narajans dienu uzsāk ar olu kulteni un jautājumu: "Kurš šodien nomiris?" Kāpēc? Analizējot 2000 "New York Times" publicētu nekrologu 20 mēnešu periodā, Narajans dažos vārdos apkopojis, kā izskatās dzīves laikā sasniegtais. Šeit viņš dalās ar to, ko drukā iemūžinātie var iemācīt mums par labi nodzīvotu dzīvi.
Lux Narayan - Entrepreneur
Lux Narayan is a perpetual learner of various things -- from origami and molecular gastronomy to stand-up and improv comedy. Full bio
Lux Narayan is a perpetual learner of various things -- from origami and molecular gastronomy to stand-up and improv comedy. Full bio
Double-click the English transcript below to play the video.
00:12
Joseph Keller used to jog
around the Stanford campus,
around the Stanford campus,
0
879
4072
Džozefs Kellers mēdza skriet
pa Stenfordas Universitātes teritoriju,
pa Stenfordas Universitātes teritoriju,
00:16
and he was struck by all the women
jogging there as well.
jogging there as well.
1
4975
4717
un viņu pārsteidza daudzās sievietes,
kas arī tur skrēja.
kas arī tur skrēja.
00:21
Why did their ponytails swing
from side to side like that?
from side to side like that?
2
9716
3589
Kāpēc viņu zirgastes tā šūpojās
no vienas puses uz otru?
no vienas puses uz otru?
00:25
Being a mathematician,
he set out to understand why.
he set out to understand why.
3
13867
3138
Kā matemātiķis viņš nolēma
noskaidrot, kāpēc tas tā.
noskaidrot, kāpēc tas tā.
00:29
(Laughter)
4
17029
1151
(Smiekli)
00:30
Professor Keller was curious
about many things:
about many things:
5
18204
2306
Profesoru Kelleru interesēja daudz kas:
00:32
why teapots dribble
6
20534
1967
kāpēc tējkannas pil
00:34
or how earthworms wriggle.
7
22525
1830
vai kā sliekas lokās?
00:36
Until a few months ago,
I hadn't heard of Joseph Keller.
I hadn't heard of Joseph Keller.
8
24847
3048
Līdz dažu mēnešu nesenai pagātnei
es par Džozefu Kelleru nebiju dzirdējis.
es par Džozefu Kelleru nebiju dzirdējis.
00:40
I read about him in the New York Times,
9
28581
2852
Es par viņu izlasīju New York Times
00:43
in the obituaries.
10
31457
1432
nekrologā.
00:44
The Times had half a page
of editorial dedicated to him,
of editorial dedicated to him,
11
32913
3772
Laikraksts viņam bija veltījis
puslappusi redakcijas slejas,
puslappusi redakcijas slejas,
00:48
which you can imagine is premium space
for a newspaper of their stature.
for a newspaper of their stature.
12
36709
3922
kas, kā varat iedomāties,
ir prestiža vieta šāda mēroga laikrakstam.
ir prestiža vieta šāda mēroga laikrakstam.
00:53
I read the obituaries almost every day.
13
41368
2342
Es lasu nekrologus gandrīz katru dienu.
00:56
My wife understandably thinks
I'm rather morbid
I'm rather morbid
14
44690
3022
Mana sieva, saprotams, uzskata,
ka ar mani nav viss kārtībā,
ka ar mani nav viss kārtībā,
00:59
to begin my day with scrambled eggs
and a "Let's see who died today."
and a "Let's see who died today."
15
47736
4400
jo es dienu uzsāku ar olu kulteni
un "paskatīsimies, kurš šodien nomiris".
un "paskatīsimies, kurš šodien nomiris".
01:04
(Laughter)
16
52160
1150
(Smiekli)
01:06
But if you think about it,
17
54025
1292
Bet, ja tā padomā,
01:07
the front page of the newspaper
is usually bad news,
is usually bad news,
18
55341
3413
laikraksta titullapā
parasti ir sliktas ziņas
parasti ir sliktas ziņas
01:10
and cues man's failures.
19
58778
1975
un norādījumi uz cilvēku kļūdām.
01:12
An instance where bad news
cues accomplishment
cues accomplishment
20
60777
2666
Piemērs, kur sliktas ziņas
norāda uz sasniegumiem,
norāda uz sasniegumiem,
01:15
is at the end of the paper,
in the obituaries.
in the obituaries.
21
63467
3235
ir laikraksta beigās – nekrologos.
01:19
In my day job,
22
67405
1364
Mans pamatdarbs
01:20
I run a company that focuses
on future insights
on future insights
23
68793
2476
ir uzņēmumā, kas pievēršas
nākotnes tendencēm,
nākotnes tendencēm,
01:23
that marketers can derive
from past data --
from past data --
24
71293
2420
ko tirgus pētnieki var izsecināt
no pagātnes datiem,
no pagātnes datiem,
01:25
a kind of rearview-mirror analysis.
25
73737
2944
sava veida atpakaļskata spoguļa analīze.
01:29
And we began to think:
26
77092
1155
Un mēs sākām domāt: kā būtu,
ja mēs uzstādītu atpakaļskata spoguli
ja mēs uzstādītu atpakaļskata spoguli
01:30
What if we held a rearview mirror
to obituaries from the New York Times?
to obituaries from the New York Times?
27
78271
5118
New York Times nekrologiem?
01:36
Were there lessons on how you could get
your obituary featured --
your obituary featured --
28
84514
3468
Vai no tā varētu izsecināt,
kā izcelt savu nekrologu,
kā izcelt savu nekrologu,
01:40
even if you aren't around to enjoy it?
29
88006
1977
pat ja vairs nebūsiet dzīvs,
lai to izbaudītu?
lai to izbaudītu?
01:42
(Laughter)
30
90007
1484
(Smiekli)
01:43
Would this go better with scrambled eggs?
31
91515
2628
Vai šis derētu pie olu kulteņa?
01:46
(Laughter)
32
94167
1150
(Smiekli)
01:48
And so, we looked at the data.
33
96163
2998
Tā nu mēs ieskatījāmies datos.
01:51
2,000 editorial, non-paid obituaries
34
99869
4494
2000 redakcijas sagatavotu,
neapmaksātu nekrologu
neapmaksātu nekrologu
01:56
over a 20-month period
between 2015 and 2016.
between 2015 and 2016.
35
104387
3642
20 mēnešu periodā
no 2015. līdz 2016. gadam.
no 2015. līdz 2016. gadam.
02:00
What did these 2,000 deaths --
rather, lives -- teach us?
rather, lives -- teach us?
36
108053
4824
Ko šīs 2000 nāves,
vai drīzāk dzīves, mums māca?
vai drīzāk dzīves, mums māca?
02:04
Well, first we looked at words.
37
112901
2033
Pirmkārt, mēs apskatījām vārdus.
02:06
This here is an obituary headline.
38
114958
1761
Šis ir nekrologa virsraksts.
02:08
This one is of the amazing Lee Kuan Yew.
39
116743
2296
Šis te ir par apbrīnojamo Lī Kvanu Jū.
02:11
If you remove the beginning and the end,
40
119063
2522
Noņemot sākumu un beigas,
02:13
you're left with a beautifully
worded descriptor
worded descriptor
41
121609
3334
paliek vien vārdos
skaisti noformulēts deskriptors,
skaisti noformulēts deskriptors,
02:16
that tries to, in just a few words,
capture an achievement or a lifetime.
capture an achievement or a lifetime.
42
124967
4675
kas mēģina dažos vārdos
ietvert visā mūžā sasniegto.
ietvert visā mūžā sasniegto.
02:21
Just looking at these is fascinating.
43
129666
2161
To lasīt ir aizraujoši.
02:24
Here are a few famous ones,
people who died in the last two years.
people who died in the last two years.
44
132301
3295
Lūk, daži slavenākie ļaudis,
kas miruši pēdējos divos gados.
kas miruši pēdējos divos gados.
Mēģiniet uzminēt, kuri tie ir!
02:27
Try and guess who they are.
45
135620
1319
[Mākslinieks, kas nepakļāvās žanram]
02:28
[An Artist who Defied Genre]
46
136963
1440
02:30
That's Prince.
47
138427
1185
Tas ir Prinss.
02:32
[Titan of Boxing and the 20th Century]
48
140497
1837
[Boksa un 20. gadsimta titāns]
O, jā!
02:34
Oh, yes.
49
142358
1160
02:35
[Muhammad Ali]
50
143542
1224
[Muhameds Ali]
02:36
[Groundbreaking Architect]
51
144790
1546
[Revolucionāra arhitekte]
02:38
Zaha Hadid.
52
146360
1251
Zaha Hadīda.
02:40
So we took these descriptors
53
148843
1748
Mēs savācām šos deskriptorus
02:42
and did what's called
natural language processing,
natural language processing,
54
150615
2524
un veicām tā saucamo
dabiskās valodas apstrādi,
dabiskās valodas apstrādi,
02:45
where you feed these into a program,
55
153163
1771
kur datus ievada programmā,
kas atmet liekos vārdus
02:46
it throws out the superfluous words --
56
154958
1865
– "tas", "un" un tiem līdzīgos,
kas viegli iekļaujas šarādēs, –
kas viegli iekļaujas šarādēs, –
02:48
"the," "and," -- the kind of words
you can mime easily in "Charades," --
you can mime easily in "Charades," --
57
156847
4223
02:53
and leaves you with the most
significant words.
significant words.
58
161094
2193
un atstāj tikai svarīgākos.
02:55
And we did it not just for these four,
59
163311
1821
Mēs neaprobežojāmies
tikai ar šiem četriem,
tikai ar šiem četriem,
02:57
but for all 2,000 descriptors.
60
165156
2519
bet iekļāvām visus 2000 deskriptorus.
02:59
And this is what it looks like.
61
167699
1743
Un, lūk, kā tas izskatās.
03:03
Film, theatre, music, dance
and of course, art, are huge.
and of course, art, are huge.
62
171004
4827
Filmas, tēatris, mūzika, deja
un, protams, māksla ir pārsvarā.
un, protams, māksla ir pārsvarā.
03:08
Over 40 percent.
63
176485
1946
Vairāk nekā 40 procenti.
03:10
You have to wonder
why in so many societies
why in so many societies
64
178455
2528
Jābrīnās, kāpēc tik daudzās sabiedrībās
03:13
we insist that our kids pursue
engineering or medicine or business or law
engineering or medicine or business or law
65
181007
4435
mēs uzstājam, lai bērni studē inženieriju,
medicīnu, uzņēmējdarbību vai jurisprudenci
medicīnu, uzņēmējdarbību vai jurisprudenci
un tiecas pēc sasniegumiem tajās.
03:17
to be construed as successful.
66
185466
1587
03:19
And while we're talking profession,
67
187871
1693
Un, runājot par profesiju,
palūkosimies arī uz vecumu.
palūkosimies arī uz vecumu.
03:21
let's look at age --
68
189588
1151
Vidējais vecums, kurā gūst panākumus,
03:22
the average age at which
they achieved things.
they achieved things.
69
190763
2510
03:25
That number is 37.
70
193297
1846
ir 37 gadi.
03:28
What that means is,
you've got to wait 37 years ...
you've got to wait 37 years ...
71
196274
3656
Tas nozīmē, ka jāgaida 37 gadi...
03:31
before your first significant achievement
that you're remembered for --
that you're remembered for --
72
199954
3395
līdz sasniegsiet kaut ko,
par ko jūs atcerēsies,
par ko jūs atcerēsies,
vidēji aritmētiski,
03:35
on average --
73
203373
1151
44 gadus pēc tam,
ja nomirstat 81 gada vecumā,
ja nomirstat 81 gada vecumā,
03:36
44 years later, when you
die at the age of 81 --
die at the age of 81 --
74
204548
2478
03:39
on average.
75
207050
1168
vidēji aritmētiski.
03:40
(Laughter)
76
208242
1001
(Smiekli)
03:41
Talk about having to be patient.
77
209267
1684
Lūk, tā ir pacietība.
03:42
(Laughter)
78
210975
1057
(Smiekli)
03:44
Of course, it varies by profession.
79
212056
2089
Protams, atšķirības ir arī profesiju ziņā.
03:46
If you're a sports star,
80
214566
1193
Ja esat sporta zvaigzne, jūs droši vien
sasniegsiet virsotni jau pēc 20.
sasniegsiet virsotni jau pēc 20.
03:47
you'll probably hit
your stride in your 20s.
your stride in your 20s.
81
215783
2127
03:49
And if you're in your 40s like me,
82
217934
2645
Ja jums ir pāri 40 kā man,
03:52
you can join the fun world of politics.
83
220603
1991
varat nodoties politikas priekiem.
03:54
(Laughter)
84
222618
1056
(Smiekli)
03:55
Politicians do their first and sometimes
only commendable act in their mid-40s.
only commendable act in their mid-40s.
85
223698
3915
Politiķi veic savu pirmo un dažkārt
vienīgo slavējamo darbu pēc 40.
vienīgo slavējamo darbu pēc 40.
03:59
(Laughter)
86
227637
1257
(Smiekli)
04:00
If you're wondering what "others" are,
87
228918
1937
Ja gribat zināt, kas ir "citi",
šeit ir daži piemēri:
šeit ir daži piemēri:
04:02
here are some examples.
88
230879
1476
[Vudū priesteris, šuvējs 9 prezidentiem]
04:04
Isn't it fascinating, the things people do
89
232821
2116
Vai nav aizraujoši uzzināt,
ko cilvēki dara un par ko viņus atceras?
ko cilvēki dara un par ko viņus atceras?
04:06
and the things they're remembered for?
90
234961
1882
[Atkailinātu krūšu
izklaides programmu pioniere]
izklaides programmu pioniere]
04:08
(Laughter)
91
236867
1752
(Smiekli)
04:12
Our curiosity was in overdrive,
92
240136
1844
Mūsu ziņkārība auga augumā,
04:14
and we desired to analyze
more than just a descriptor.
more than just a descriptor.
93
242004
3788
un mēs gribējām analizēt
vairāk nekā deskriptoru vien.
vairāk nekā deskriptoru vien.
04:18
So, we ingested the entire
first paragraph of all 2,000 obituaries,
first paragraph of all 2,000 obituaries,
94
246998
4946
Tāpēc mēs ievadījām
visu 2000 nekrologu pirmo rindkopu,
visu 2000 nekrologu pirmo rindkopu,
04:23
but we did this separately
for two groups of people:
for two groups of people:
95
251968
2774
bet to mēs izdarījām
divām cilvēku grupām atsevišķi:
divām cilvēku grupām atsevišķi:
04:26
people that are famous
and people that are not famous.
and people that are not famous.
96
254766
2777
slavenajiem cilvēkiem
un sabiedrībā nepazīstamajiem.
un sabiedrībā nepazīstamajiem.
04:29
Famous people -- Prince,
Ali, Zaha Hadid --
Ali, Zaha Hadid --
97
257567
2689
Slavenie ir tādi kā Prinss,
Ali, Zaha Hadīda.
Ali, Zaha Hadīda.
04:32
people who are not famous
are people like Jocelyn Cooper,
are people like Jocelyn Cooper,
98
260280
4235
Nepazīstamie ir tādi kā Džoselīna Kūpere,
04:36
Reverend Curry
99
264539
1154
tēvs Karijs
04:37
or Lorna Kelly.
100
265717
1169
vai Lorna Kelija.
Varu derēt, ka par vairumu no viņiem
neesat dzirdējuši neko.
neesat dzirdējuši neko.
04:38
I'm willing to bet you haven't heard
of most of their names.
of most of their names.
101
266910
3188
04:42
Amazing people, fantastic achievements,
but they're not famous.
but they're not famous.
102
270122
3812
Apbrīnojami ļaudis,
fantastiski sasniegumi, bet bez slavas.
fantastiski sasniegumi, bet bez slavas.
04:46
So what if we analyze
these two groups separately --
these two groups separately --
103
274720
2788
Tātad, ko mēs iegūstam,
ja analizējam šīs grupas atsevišķi:
ja analizējam šīs grupas atsevišķi:
04:49
the famous and the non-famous?
104
277532
1525
slavenos un nepazīstamos?
04:51
What might that tell us?
105
279081
1419
Ko tas mums pavēsta?
04:52
Take a look.
106
280524
1240
Palūkosimies!
04:56
Two things leap out at me.
107
284556
1469
Acīs krīt divas lietas.
04:58
First:
108
286569
1170
Pirmā:
05:00
"John."
109
288106
1198
"Džons."
05:01
(Laughter)
110
289328
1300
(Smiekli)
05:03
Anyone here named John
should thank your parents --
should thank your parents --
111
291914
3388
Ja šeit ir kāds Džons, jums vajadzētu
saviem vecākiem teikt paldies
saviem vecākiem teikt paldies
05:07
(Laughter)
112
295326
1329
(Smiekli)
05:08
and remind your kids to cut out
your obituary when you're gone.
your obituary when you're gone.
113
296679
3082
un atgādināt bērniem
izgriezt no avīzes jūsu nekrologu.
izgriezt no avīzes jūsu nekrologu.
05:13
And second:
114
301061
1356
Un otrkārt:
05:15
"help."
115
303849
1154
"palīdzība".
05:18
We uncovered, many lessons
from lives well-led,
from lives well-led,
116
306524
3465
No labi nodzīvotajām dzīvēm
mēs guvām daudz atziņu,
mēs guvām daudz atziņu,
05:22
and what those people immortalized
in print could teach us.
in print could teach us.
117
310013
2836
un drukā iemūžinātie ļaudis
mums daudz iemācīja.
mums daudz iemācīja.
05:24
The exercise was a fascinating testament
to the kaleidoscope that is life,
to the kaleidoscope that is life,
118
312873
4738
Šis vingrinājums bija aizraujošs
dzīves kaleidoskopa apliecinājums,
dzīves kaleidoskopa apliecinājums,
05:29
and even more fascinating
119
317635
2715
un vēl jo aizraujošāk
05:32
was the fact that the overwhelming
majority of obituaries
majority of obituaries
120
320374
3068
ir tas, ka vairums nekrologu
05:35
featured people famous and non-famous,
121
323466
2998
bija veltīti kā slaveniem,
tā arī nepazīstamiem cilvēkiem,
tā arī nepazīstamiem cilvēkiem,
05:38
who did seemingly extraordinary things.
122
326488
2433
kas paveikuši šķietami neparastas lietas.
05:41
They made a positive dent
in the fabric of life.
in the fabric of life.
123
329574
3110
Viņi ir atstājuši pozitīvu nospiedumu
dzīves audumā.
dzīves audumā.
05:44
They helped.
124
332708
1237
Viņi ir palīdzējuši.
05:46
So ask yourselves as you go
back to your daily lives:
back to your daily lives:
125
334772
2591
Tāpēc, atgriežoties ikdienā,
pajautājiet sev:
pajautājiet sev:
05:49
How am I using my talents to help society?
126
337387
2920
"Kā es izmantoju savus talantus,
lai palīdzētu sabiedrībai?"
lai palīdzētu sabiedrībai?"
05:52
Because the most powerful lesson here is,
127
340331
2973
Jo visnozīmīgākā mācība šeit ir:
05:55
if more people lived their lives
trying to be famous in death,
trying to be famous in death,
128
343328
4336
ja vairāk cilvēku dzīvotu tā,
lai kļūtu slaveni pēc nāves,
lai kļūtu slaveni pēc nāves,
05:59
the world would be a much better place.
129
347688
2605
pasaule būtu daudz labāka.
06:03
Thank you.
130
351062
1169
Paldies.
06:04
(Applause)
131
352255
2848
(Aplausi)
ABOUT THE SPEAKER
Lux Narayan - EntrepreneurLux Narayan is a perpetual learner of various things -- from origami and molecular gastronomy to stand-up and improv comedy.
Why you should listen
Lakshmanan aka Lux Narayan mans the helm of Unmetric, a social media intelligence company that helps digital marketers, social media analysts, and content creators harness social signals to track and analyze competitive content and campaigns, and to create better content and campaigns of their own.
Prior to founding Unmetric, Narayan was a co-founder at Vembu Technologies, an online data backup company. He also helped found and volunteered at ShareMyCake, a non-profit started by his wife that focuses on encouraging children to use their birthdays to channel monetary support towards a cause of their choosing.
As Unmetric's CEO, he leads a team of 70 people distributed across the company's operations in Chennai and New York City.
Outside of work, he is a perpetual learner of various things -- from origami and molecular gastronomy to stand-up and improv comedy. He enjoys reading obituaries and other non-fiction and watching documentaries with bad ratings. Narayan makes time every year for trekking in the Himalayas or scuba diving in tropical waters, and once he learns to fly, he hopes to spend more time off land than on it.
Lux Narayan | Speaker | TED.com