TEDxMidAtlantic
Albert-László Barabási: The real relationship between your age and your chance of success
Albert-László Barabási: Hubungan sebenarnya antara usia dan kemungkinan sukses Anda
Filmed:
Readability: 4
2,762,222 views
Didukung dengan analisis matematis, ahli teori jaringan Albert-László Barabási mengeksplorasi mekanisme tersembunyi yang mendorong kesuksesan - apapun bidang Anda - dan mengungkap hubungan menarik antara usia dan kemungkinan Anda mencapai kesuksesan.
Albert-László Barabási - Network scientist
A pioneer in network science, Albert-László Barabási uncovers the hidden order behind complex systems. Full bio
A pioneer in network science, Albert-László Barabási uncovers the hidden order behind complex systems. Full bio
Double-click the English transcript below to play the video.
00:12
Today, actually, is
a very special day for me,
a very special day for me,
0
249
2266
Hari ini amat berarti bagi saya,
00:14
because it is my birthday.
1
2539
2121
karena hari ini hari ulang tahun saya
00:16
(Applause)
2
4684
3973
(Tepuk tangan)
00:20
And so, thanks to all of you
for joining the party.
for joining the party.
3
8681
3441
Terima kasih telah menghadiri pesta saya.
00:24
(Laughter)
4
12146
1167
(Tawa)
00:25
But every time you throw a party,
there's someone there to spoil it. Right?
there's someone there to spoil it. Right?
5
13337
4786
Tapi tiap kali Anda mengadakan pesta,
selalu ada yang merusaknya. Benar, 'kan?
selalu ada yang merusaknya. Benar, 'kan?
00:30
(Laughter)
6
18147
1072
(Tawa)
00:31
And I'm a physicist,
7
19243
1359
Dan saya adalah ahli fisika,
00:32
and this time I brought
another physicist along to do so.
another physicist along to do so.
8
20626
4157
kali ini saya membawa seorang
ahli fisika lain untuk menemani saya.
ahli fisika lain untuk menemani saya.
00:36
His name is Albert Einstein --
also Albert -- and he's the one who said
also Albert -- and he's the one who said
9
24807
4562
Namanya Albert Einstein - juga
Albert - dan dia yang mengatakan
Albert - dan dia yang mengatakan
00:41
that the person who has not made
his great contributions to science
his great contributions to science
10
29393
4830
orang yang belum berkontribusi besar
di bidang ilmu pengetahuan
di bidang ilmu pengetahuan
00:46
by the age of 30
11
34247
1559
pada umur 30 tahun
00:47
will never do so.
12
35830
1396
tak akan pernah
berkontribusi.
berkontribusi.
00:49
(Laughter)
13
37250
1012
(Tawa)
00:50
Now, you don't need to check Wikipedia
14
38286
2340
Anda tidak perlu mengecek
Wikipedia
Wikipedia
00:52
that I'm beyond 30.
15
40650
1571
bahwa umur saya di atas 30.
00:54
(Laughter)
16
42245
1416
(Tawa)
00:55
So, effectively, what
he is telling me, and us,
he is telling me, and us,
17
43685
3606
Jadi, singkatnya apa yang ia katakan
kepada saya, dan kita,
kepada saya, dan kita,
00:59
is that when it comes to my science,
18
47315
2544
ialah jika berkaitan dengan ilmu saya,
01:01
I'm deadwood.
19
49883
1203
saya orang tak berguna.
01:04
Well, luckily, I had my share
of luck within my career.
of luck within my career.
20
52078
5586
Untungnya saya cukup beruntung
sepanjang karier saya.
sepanjang karier saya.
01:10
Around age 28, I became
very interested in networks,
very interested in networks,
21
58132
3822
Pada umur 28, saya sangat tertarik
dengan jaringan,
dengan jaringan,
01:13
and a few years later, we managed
to publish a few key papers
to publish a few key papers
22
61978
4076
dan beberapa tahun kemudian, kami
mempublikasikan sejumlah makalah penting
mempublikasikan sejumlah makalah penting
01:18
that reported the discovery
of scale-free networks
of scale-free networks
23
66078
4097
mengenai penemuan jaringan bebas skala
01:22
and really gave birth to a new discipline
that we call network science today.
that we call network science today.
24
70199
4578
dan melahirkan disiplin baru yang sekarang
kita kenal sebagai ilmu jaringan.
kita kenal sebagai ilmu jaringan.
01:26
And if you really care about it,
you can get a PhD now in network science
you can get a PhD now in network science
25
74801
3678
Jika Anda berminat, Anda bisa
meraih gelar PhD di bidang ilmu jaringan
meraih gelar PhD di bidang ilmu jaringan
01:30
in Budapest, in Boston,
26
78503
2028
di Budapest, di Boston,
01:32
and you can study it all over the world.
27
80555
2308
Anda dapat mempelajarinya
di seluruh dunia.
di seluruh dunia.
01:35
A few years later,
28
83466
1595
Beberapa tahun kemudian,
01:37
when I moved to Harvard
first as a sabbatical,
first as a sabbatical,
29
85085
3230
saat saya pindah ke Harvard
pertama-tama sebagai cuti panjang,
pertama-tama sebagai cuti panjang,
01:40
I became interested
in another type of network:
in another type of network:
30
88339
3092
saya menjadi tertarik dengan
jaringan tipe lain:
jaringan tipe lain:
01:43
that time, the networks within ourselves,
31
91455
3027
saat itu,
jaringan dalam diri kita sendiri,
jaringan dalam diri kita sendiri,
01:46
how the genes and the proteins
and the metabolites link to each other
and the metabolites link to each other
32
94506
3726
bagaimana gen dan protein dan metabolit
saling berkaitan satu sama lain
saling berkaitan satu sama lain
01:50
and how they connect to disease.
33
98256
2493
dan bagaimana hubungannya dengan penyakit.
01:53
And that interest led
to a major explosion within medicine,
to a major explosion within medicine,
34
101368
4592
Ketertarikan ini membawa ledakan besar
dalam pengobatan,
dalam pengobatan,
01:57
including the Network Medicine
Division at Harvard,
Division at Harvard,
35
105984
3979
termasuk Divisi Kedokteran Jaringan
di Harvard,
di Harvard,
02:01
that has more than 300 researchers
who are using this perspective
who are using this perspective
36
109987
3395
di mana lebih dari 300 peneliti
menggunakan sudut pandang ini
menggunakan sudut pandang ini
02:05
to treat patients and develop new cures.
37
113406
2897
untuk mengobati pasien dan
mengembangkan pengobatan baru.
mengembangkan pengobatan baru.
02:09
And a few years ago,
38
117457
1770
Beberapa tahun yang lalu,
02:11
I thought that I would take
this idea of networks
this idea of networks
39
119251
2526
saya berpikir untuk membawa
ide mengenai jaringan ini
ide mengenai jaringan ini
02:13
and the expertise we had in networks
40
121801
1766
dan keahlian kami di
bidang jaringan
bidang jaringan
02:15
in a different area,
41
123591
1392
ke bidang yang lain,
02:17
that is, to understand success.
42
125007
1982
yaitu, untuk memahami sukses.
02:19
And why did we do that?
43
127704
1210
Kenapa kami
melakukan itu?
melakukan itu?
02:20
Well, we thought that, to some degree,
44
128938
2281
Kami berpikir bahwa,
sedikit banyak,
sedikit banyak,
02:23
our success is determined
by the networks we're part of --
by the networks we're part of --
45
131243
3377
kesuksesan kita ditentukan oleh
jaringan yang jadi bagian kita -
jaringan yang jadi bagian kita -
02:26
that our networks can push us forward,
they can pull us back.
they can pull us back.
46
134644
3847
bahwa jaringan dapat mendorong kita maju,
atau menarik kita ke belakang.
atau menarik kita ke belakang.
02:30
And I was curious if we could use
the knowledge and big data and expertise
the knowledge and big data and expertise
47
138925
4128
Saya ingin tahu apakah kita bisa memakai
ilmu dan mahadata dan keahlian
ilmu dan mahadata dan keahlian
02:35
where we develop the networks
48
143077
1403
di mana kita bangun jaringan
02:36
to really quantify
how these things happen.
how these things happen.
49
144504
3296
untuk benar-benar mengukur bagaimana
hal ini bisa terjadi.
hal ini bisa terjadi.
02:40
This is a result from that.
50
148404
1342
Ini ialah hasilnya.
02:41
What you see here is a network
of galleries in museums
of galleries in museums
51
149770
2947
Yang Anda lihat di sini ialah
jaringan galeri di museum
jaringan galeri di museum
02:44
that connect to each other.
52
152741
1632
yang saling berhubungan.
02:46
And through this map
that we mapped out last year,
that we mapped out last year,
53
154806
4055
Melalui peta yang kami buat tahun lalu,
02:50
we are able to predict very accurately
the success of an artist
the success of an artist
54
158885
4848
kami dapat memprediksikan kesuksesan
seorang seniman dengan amat akurat
seorang seniman dengan amat akurat
02:55
if you give me the first five exhibits
that he or she had in their career.
that he or she had in their career.
55
163757
4021
jika Anda memberitahu saya lima pameran
pertama seniman tersebut.
pertama seniman tersebut.
03:01
Well, as we thought about success,
56
169404
2706
Saat memikirkan kesuksesan,
03:04
we realized that success
is not only about networks;
is not only about networks;
57
172134
3067
kami menyadari bahwa sukses
bukan hanya mengenai jaringan;
bukan hanya mengenai jaringan;
03:07
there are so many
other dimensions to that.
other dimensions to that.
58
175225
2396
ada banyak dimensi lain dari
kesuksesan.
kesuksesan.
03:10
And one of the things
we need for success, obviously,
we need for success, obviously,
59
178145
3247
Jelas salah satu hal yang kita butuhkan
untuk mencapai sukses
untuk mencapai sukses
03:13
is performance.
60
181416
1170
ialah kinerja.
03:14
So let's define what's the difference
between performance and success.
between performance and success.
61
182610
3504
Mari definisikan apa perbedaan antara
kinerja dan sukses.
kinerja dan sukses.
03:18
Well, performance is what you do:
62
186465
1997
Kinerja adalah apa yang Anda lakukan:
03:20
how fast you run,
what kind of paintings you paint,
what kind of paintings you paint,
63
188486
3032
seberapa cepat Anda berlari,
lukisan apa yang Anda buat,
lukisan apa yang Anda buat,
03:23
what kind of papers you publish.
64
191542
1881
makalah apa yang Anda publikasikan.
03:25
However, in our working definition,
65
193835
2614
Namun, definisi kerja kami,
03:28
success is about what the community
notices from what you did,
notices from what you did,
66
196473
4205
sukses adalah mengenai apa yang komunitas
lihat dari apa yang Anda lakukan,
lihat dari apa yang Anda lakukan,
03:32
from your performance:
67
200702
1612
dari kinerja Anda:
03:34
How does it acknowledge it,
and how does it reward you for it?
and how does it reward you for it?
68
202338
4132
Bagaimana komunitas mengakui sukses dan
menghadiahi Anda?
menghadiahi Anda?
03:38
In other terms,
69
206494
1182
Dengan kata lain,
03:39
your performance is about you,
but your success is about all of us.
but your success is about all of us.
70
207700
4596
kinerja Anda adalah tentang Anda,
sukses Anda adalah tentang kita semua.
sukses Anda adalah tentang kita semua.
03:45
And this was a very
important shift for us,
important shift for us,
71
213392
3334
Ini merupakan pergeseran yang
sangat penting bagi kita,
sangat penting bagi kita,
03:48
because the moment we defined success
as being a collective measure
as being a collective measure
72
216750
4024
karena pada saat kita mendefinisikan
sukses sebagai ukuran kolektif
sukses sebagai ukuran kolektif
03:52
that the community provides to us,
73
220798
2106
dari komunitas kepada kita,
03:54
it became measurable,
74
222928
1510
sukses menjadi dapat diukur,
03:56
because if it's in the community,
there are multiple data points about that.
there are multiple data points about that.
75
224462
4510
karena jika suatu hal ada dalam komunitas,
maka ada berbagai titik data mengenainya.
maka ada berbagai titik data mengenainya.
04:00
So we go to school,
we exercise, we practice,
we exercise, we practice,
76
228996
5280
Jadi kita pergi ke sekolah,
kita berolah raga, kita berlatih,
kita berolah raga, kita berlatih,
04:06
because we believe
that performance leads to success.
that performance leads to success.
77
234300
2991
karena kita yakin bahwa
kinerja membawa kesuksesan.
kinerja membawa kesuksesan.
04:09
But the way we actually
started to explore,
started to explore,
78
237832
2015
Namun cara kami mulai mengeksplorasi,
04:11
we realized that performance and success
are very, very different animals
are very, very different animals
79
239871
3527
kami sadar bahwa kinerja dan sukses
adalah dua hal yang amat berbeda
adalah dua hal yang amat berbeda
04:15
when it comes to
the mathematics of the problem.
the mathematics of the problem.
80
243422
2444
jika berkaitan dengan sisi matematisnya.
04:18
And let me illustrate that.
81
246429
1432
Mari saya ilustrasikan.
04:20
So what you see here is
the fastest man on earth, Usain Bolt.
the fastest man on earth, Usain Bolt.
82
248329
4947
Apa yang Anda lihat di sini ialah
manusia tercapat di dunia, Usain Bolt.
manusia tercapat di dunia, Usain Bolt.
04:25
And of course, he wins most of
the competitions that he enters.
the competitions that he enters.
83
253832
3910
Tentu saja ia memenangkan mayoritas
pertandingan yang ia ikuti.
pertandingan yang ia ikuti.
04:30
And we know he's the fastest on earth
because we have a chronometer
because we have a chronometer
84
258393
3175
Kita tahu ia yang tercepat karena
kita punya kronometer untuk
kita punya kronometer untuk
04:33
to measure his speed.
85
261592
1160
mengukur kecepatannya
04:34
Well, what is interesting about him
is that when he wins,
is that when he wins,
86
262776
4119
Yang menarik mengenai ia ialah
saat ia menang,
saat ia menang,
04:38
he doesn't do so by really significantly
outrunning his competition.
outrunning his competition.
87
266919
5502
ia tidak menang dengan berlari jauh
lebih cepat daripada saingannya.
lebih cepat daripada saingannya.
04:44
He's running at most a percent faster
than the one who loses the race.
than the one who loses the race.
88
272445
4519
Paling banyak, ia berlari 1 persen lebih
cepat daripada orang yang kalah.
cepat daripada orang yang kalah.
04:49
And not only does he run only
one percent faster than the second one,
one percent faster than the second one,
89
277631
3638
ia tidak hanya berlari 1 persen
lebih cepat daripada pemenang kedua,
lebih cepat daripada pemenang kedua,
04:53
but he doesn't run
10 times faster than I do --
10 times faster than I do --
90
281293
2849
ia tidak berlari 10 kali lebih cepat
daripada saya -
daripada saya -
04:56
and I'm not a good runner,
trust me on that.
trust me on that.
91
284166
2181
dan saya bukan pelari yang cepat,
percayalah.
percayalah.
04:58
(Laughter)
92
286371
1197
(Tawa)
04:59
And every time we are able
to measure performance,
to measure performance,
93
287592
3502
Setiap saat kami mengukur kinerja,
05:03
we notice something very interesting;
94
291118
2050
kami menemukan sesuatu yang
sangat menarik:
sangat menarik:
05:05
that is, performance is bounded.
95
293192
2511
kinerja itu terbatas.
05:07
What it means is that there are
no huge variations in human performance.
no huge variations in human performance.
96
295727
3757
Artinya tidak ada variasi besar dalam
kinerja manusia.
kinerja manusia.
05:11
It varies only in a narrow range,
97
299508
3432
Variasinya hanya dalam kisaran sempit,
05:14
and we do need the chronometer
to measure the differences.
to measure the differences.
98
302964
3279
dan kami memerlukan kronometer
untuk mengukur perbedaannya.
untuk mengukur perbedaannya.
05:18
This is not to say that we cannot
see the good from the best ones,
see the good from the best ones,
99
306267
3168
Tidak berarti kita tidak bisa membedakan
yang baik dan terbaik,
yang baik dan terbaik,
05:21
but the best ones
are very hard to distinguish.
are very hard to distinguish.
100
309459
2733
tetapi yang terbaik
sangat sulit dibedakan.
sangat sulit dibedakan.
05:24
And the problem with that
is that most of us work in areas
is that most of us work in areas
101
312216
2992
Masalahnya kebanyakan dari kita
bekerja dalam bidang
bekerja dalam bidang
05:27
where we do not have a chronometer
to gauge our performance.
to gauge our performance.
102
315232
3922
di mana kita tidak punya kronometer
untuk mengukur kinerja kita.
untuk mengukur kinerja kita.
05:31
Alright, performance is bounded,
103
319178
1564
Baiklah, kinerja itu terbatas,
05:32
there are no huge differences between us
when it comes to our performance.
when it comes to our performance.
104
320766
3532
tidak ada perbedaan besar di antara kita
dalam hal kinerja.
dalam hal kinerja.
05:36
How about success?
105
324322
1157
Bagaimana dengan sukses?
05:37
Well, let's switch to
a different topic, like books.
a different topic, like books.
106
325995
2930
Mari kita berubah topik, mengenai
buku misalnya.
buku misalnya.
05:40
One measure of success for writers is
how many people read your work.
how many people read your work.
107
328949
5015
Salah satu ukuran sukses penulis ialah
berapa banyak orang membaca karya mereka.
berapa banyak orang membaca karya mereka.
05:46
And so when my previous book
came out in 2009,
came out in 2009,
108
334662
4410
Jadi saat buku saya dipublikasikan
pada tahun 2009,
pada tahun 2009,
05:51
I was in Europe talking with my editor,
109
339096
1902
saya di Eropa bicara dengan
editor saya,
editor saya,
05:53
and I was interested:
Who is the competition?
Who is the competition?
110
341022
2462
saya ingin tahu:
Siapa saingan saya?
Siapa saingan saya?
05:56
And I had some fabulous ones.
111
344253
2735
Saya memiliki sejumlah
kompetitor yang hebat.
kompetitor yang hebat.
05:59
That week --
112
347012
1169
Minggu itu -
06:00
(Laughter)
113
348205
1024
(Tawa)
06:01
Dan Brown came out with "The Lost Symbol,"
114
349253
3557
Dan Brown mempublikasikan
"The Lost Symbol,"
"The Lost Symbol,"
06:04
and "The Last Song" also came out,
115
352834
2982
dan "The Last Song" juga dipublikasikan,
06:07
Nicholas Sparks.
116
355840
1429
karya Nicholas Sparks.
06:09
And when you just look at the list,
117
357293
2988
Saat Anda melihat daftarnya,
06:12
you realize, you know, performance-wise,
there's hardly any difference
there's hardly any difference
118
360305
3453
Anda sadar, dalam hal kinerja,
hampir tidak ada perbedaan
hampir tidak ada perbedaan
06:15
between these books or mine.
119
363782
1598
antara dua buku itu
atau buku saya
atau buku saya
06:17
Right?
120
365404
1175
Bukan demikian?
06:18
So maybe if Nicholas Sparks's team
works a little harder,
works a little harder,
121
366603
4668
Jika tim Nicholas Sparks bekerja
sedikit lebih keras,
sedikit lebih keras,
06:23
he could easily be number one,
122
371295
1722
dengan mudah ia bisa
jadi nomor satu,
jadi nomor satu,
06:25
because it's almost by accident
who ended up at the top.
who ended up at the top.
123
373041
2898
siapa yang berada di puncak
ditentukan dengan tidak sengaja.
ditentukan dengan tidak sengaja.
06:28
So I said, let's look at the numbers --
I'm a data person, right?
I'm a data person, right?
124
376486
3153
Mari kita lihat angkanya -
saya adalah orang yang senang data, kan?
saya adalah orang yang senang data, kan?
06:31
So let's see what were
the sales for Nicholas Sparks.
the sales for Nicholas Sparks.
125
379663
4318
Mari kita lihat jumlah penjualan
untuk Nicholas Sparks.
untuk Nicholas Sparks.
06:36
And it turns out that
that opening weekend,
that opening weekend,
126
384005
2054
Ternyata pada pembukaan
di akhir minggu itu,
di akhir minggu itu,
06:38
Nicholas Sparks sold more than
a hundred thousand copies,
a hundred thousand copies,
127
386083
2975
Nicholas Sparks menjual lebih dari
seratus ribu eksemplar,
seratus ribu eksemplar,
06:41
which is an amazing number.
128
389082
1705
jumlah yang mengagumkan.
06:42
You can actually get to the top
of the "New York Times" best-seller list
of the "New York Times" best-seller list
129
390811
3396
Anda dapat mencapai puncak
daftar buku paling laku "New York Times"
daftar buku paling laku "New York Times"
06:46
by selling 10,000 copies a week,
130
394231
2110
dengan menjual 10.000
eksemplar tiap minggu,
eksemplar tiap minggu,
06:48
so he tenfold overcame
what he needed to be number one.
what he needed to be number one.
131
396365
3752
jadi ia 10 kali lipat melebihi jumlah
yang dibutuhkan untuk menjadi nomor satu.
yang dibutuhkan untuk menjadi nomor satu.
06:52
Yet he wasn't number one.
132
400141
1430
Tapi ia bukan normor satu.
06:53
Why?
133
401595
1308
Kenapa?
06:54
Because there was Dan Brown,
who sold 1.2 million copies that weekend.
who sold 1.2 million copies that weekend.
134
402927
4078
Karena Dan Brown menjual
1,2 juta eksemplar di akhir minggu itu.
1,2 juta eksemplar di akhir minggu itu.
06:59
(Laughter)
135
407029
2136
(Tawa)
07:01
And the reason I like this number
is because it shows that, really,
is because it shows that, really,
136
409189
3971
Kenapa saya menyukai angka ialah karena
angka menunjukkan bahwa, sungguh,
angka menunjukkan bahwa, sungguh,
07:05
when it comes to success, it's unbounded,
137
413184
3730
sukses itu tidak terbatas,
07:08
that the best doesn't only get
slightly more than the second best
slightly more than the second best
138
416938
5861
yang terbaik bukan hanya mendapat sedikit
lebih dari yang terbaik kedua
lebih dari yang terbaik kedua
07:14
but gets orders of magnitude more,
139
422823
2697
tetapi mendapat
tingkat besaran lebih banyak,
tingkat besaran lebih banyak,
07:17
because success is a collective measure.
140
425544
2794
karena sukses adalah ukuran kolektif.
07:20
We give it to them, rather than
we earn it through our performance.
we earn it through our performance.
141
428362
4376
Kita memberikan, bukan mendapatkan sukses
melalui kinerja.
melalui kinerja.
07:24
So one of things we realized is that
performance, what we do, is bounded,
performance, what we do, is bounded,
142
432762
5376
Salah satu hal yang kami sadari ialah
kinerja, apa kita lakukan, itu terbatas
kinerja, apa kita lakukan, itu terbatas
07:30
but success, which is
collective, is unbounded,
collective, is unbounded,
143
438162
2682
namun sukses, yang kolektif, itu
tidak terbatas,
tidak terbatas,
07:32
which makes you wonder:
144
440868
1312
membuat Anda bertanya:
07:34
How do you get these
huge differences in success
huge differences in success
145
442204
2911
Bagaimana bisa ada perbedaan besar
dalam kesuksesan
dalam kesuksesan
07:37
when you have such tiny
differences in performance?
differences in performance?
146
445139
2906
jika hanya ada perbedaan amat kecil
dalam kinerja?
dalam kinerja?
07:40
And recently, I published a book
that I devoted to that very question.
that I devoted to that very question.
147
448537
3787
Baru-baru ini saya mempublikasi buku
untuk membahas pertanyaan ini.
untuk membahas pertanyaan ini.
07:44
And they didn't give me enough time
to go over all of that,
to go over all of that,
148
452348
2839
Saya tidak punya cukup waktu
untuk membahasnya,
untuk membahasnya,
07:47
so I'm going to go back
to the question of,
to the question of,
149
455211
2071
jadi kembali ke pertanyaan,
07:49
alright, you have success;
when should that appear?
when should that appear?
150
457306
3135
baik, kesuksesan:
kapan harus muncul?
kapan harus muncul?
07:52
So let's go back to the party spoiler
and ask ourselves:
and ask ourselves:
151
460465
3758
Mari kembali ke perusak pesta
dan bertanya pada diri sendiri:
dan bertanya pada diri sendiri:
07:57
Why did Einstein make
this ridiculous statement,
this ridiculous statement,
152
465215
3339
Mengapa Einstein membuat
pernyataan bodoh ini,
pernyataan bodoh ini,
08:00
that only before 30
you could actually be creative?
you could actually be creative?
153
468578
3156
bahwa Anda hanya bisa kreatif
sebelum usia 30?
sebelum usia 30?
08:03
Well, because he looked around himself
and he saw all these fabulous physicists
and he saw all these fabulous physicists
154
471758
4680
Karena ia melihat sekelilingnya dan ia
melihat semua ahli fisika hebat ini
melihat semua ahli fisika hebat ini
08:08
that created quantum mechanics
and modern physics,
and modern physics,
155
476462
2587
yang menemukan mekanika kuantum
dan fisika modern,
dan fisika modern,
08:11
and they were all in their 20s
and early 30s when they did so.
and early 30s when they did so.
156
479073
3736
mereka semua berusia 20-an dan awal 30-an
saat mereka menemukan hal-hal itu.
saat mereka menemukan hal-hal itu.
08:15
And it's not only him.
157
483730
1220
Jadi bukan hanya ia.
08:16
It's not only observational bias,
158
484974
1623
Ini bukan hanya
bias observasional,
bias observasional,
08:18
because there's actually
a whole field of genius research
a whole field of genius research
159
486621
3997
karena sesungguhnya
ada bidang penelitian jenius
ada bidang penelitian jenius
08:22
that has documented the fact that,
160
490642
2256
yang mendokumentasikan fakta bahwa,
08:24
if we look at the people
we admire from the past
we admire from the past
161
492922
3160
jika kita melihat orang-orang yang
kita kagumi di masa lalu
kita kagumi di masa lalu
08:28
and then look at what age
they made their biggest contribution,
they made their biggest contribution,
162
496106
3358
dan pada umur berapa mereka
memberikan kontribusi terbesar mereka,
memberikan kontribusi terbesar mereka,
08:31
whether that's music,
whether that's science,
whether that's science,
163
499488
2096
baik dalam bidang musik,
ilmu pengetahuan,
ilmu pengetahuan,
08:33
whether that's engineering,
164
501608
1619
tehnik,
08:35
most of them tend to do so
in their 20s, 30s, early 40s at most.
in their 20s, 30s, early 40s at most.
165
503251
6123
kebanyakan berkontribusi pada usia
20-an, 30-an, paling lambat awal 40-an.
20-an, 30-an, paling lambat awal 40-an.
08:41
But there's a problem
with this genius research.
with this genius research.
166
509914
2791
Namun penelitian jenius ini
memiliki masalah.
memiliki masalah.
08:45
Well, first of all, it created
the impression to us
the impression to us
167
513197
3280
Pertama, penelitian ini
menciptakan pandangan
menciptakan pandangan
08:48
that creativity equals youth,
168
516501
3479
bahwa kreativitas sama dengan usia muda,
08:52
which is painful, right?
169
520004
1610
menyakitkan, bukan?
08:53
(Laughter)
170
521638
1951
(Tawa)
08:55
And it also has an observational bias,
171
523613
4088
Penelitian ini juga memiliki
bias observasional,
bias observasional,
08:59
because it only looks at geniuses
and doesn't look at ordinary scientists
and doesn't look at ordinary scientists
172
527725
4962
karena hanya melihat orang-orang jenius
dan tidak melihat para ilmuwan biasa
dan tidak melihat para ilmuwan biasa
09:04
and doesn't look at all of us and ask,
173
532711
1965
tidak melihat kita dan bertanya,
09:06
is it really true that creativity
vanishes as we age?
vanishes as we age?
174
534700
3185
benarkah kreativitas berkurang sejalan
dengan bertambahnya usia?
dengan bertambahnya usia?
09:10
So that's exactly what we tried to do,
175
538382
1877
Inilah yang sebenarnya
ingin kami coba,
ingin kami coba,
09:12
and this is important for that
to actually have references.
to actually have references.
176
540283
3803
dan memiliki acuan adalah penting.
09:16
So let's look at an ordinary
scientist like myself,
scientist like myself,
177
544110
2643
Mari kita melihat ilmuwan biasa
seperti saya,
seperti saya,
09:18
and let's look at my career.
178
546777
1522
mari kita lihat karier saya.
09:20
So what you see here is all the papers
that I've published
that I've published
179
548323
3202
Ini ialah semua publikasi ilmiah saya
09:23
from my very first paper, in 1989;
I was still in Romania when I did so,
I was still in Romania when I did so,
180
551549
5115
dari yang pertama, di tahun 1989;
saya masih di Romania saat itu,
saya masih di Romania saat itu,
09:28
till kind of this year.
181
556688
1593
sampai kurang lebih tahun ini.
09:30
And vertically, you see
the impact of the paper,
the impact of the paper,
182
558940
2518
Secara vertikal,
dampak publikasi ilmiah itu,
dampak publikasi ilmiah itu,
09:33
that is, how many citations,
183
561482
1403
yakni berapa banyak kutipan,
09:34
how many other papers
have been written that cited that work.
have been written that cited that work.
184
562909
3988
berapa banyak publikasi lain
yang mengutip hasil kerja saya
yang mengutip hasil kerja saya
09:39
And when you look at that,
185
567397
1300
Jika Anda lihat,
09:40
you see that my career
has roughly three different stages.
has roughly three different stages.
186
568721
2813
karier saya secara kasar
memiliki tiga stadium berbeda.
memiliki tiga stadium berbeda.
09:43
I had the first 10 years
where I had to work a lot
where I had to work a lot
187
571558
2435
10 tahun pertama di mana saya
harus banyak bekerja
harus banyak bekerja
09:46
and I don't achieve much.
188
574017
1276
dan tidak banyak hasil.
09:47
No one seems to care
about what I do, right?
about what I do, right?
189
575317
2118
Tak ada yang peduli apa
yang saya lakukan.
yang saya lakukan.
09:49
There's hardly any impact.
190
577459
1681
Hampir tidak ada dampaknya.
09:51
(Laughter)
191
579164
1404
(Tawa)
09:52
That time, I was doing material science,
192
580592
2887
Saat itu saya sedang sibuk
dengan ilmu material.
dengan ilmu material.
09:55
and then I kind of discovered
for myself networks
for myself networks
193
583503
3691
dan tidak sengaja saya menemukan
ilmu jaringan
ilmu jaringan
09:59
and then started publishing in networks.
194
587218
1947
dan mulai menulis mengenai jaringan.
10:01
And that led from one high-impact
paper to the other one.
paper to the other one.
195
589189
3073
Dari karya berdampak tinggi yang satu
ke yang lain.
ke yang lain.
10:04
And it really felt good.
That was that stage of my career.
That was that stage of my career.
196
592286
3104
Sangat senang rasanya.
Di stadium karier saya saat itu.
Di stadium karier saya saat itu.
10:07
(Laughter)
197
595414
1282
(Tawa)
10:08
So the question is,
what happens right now?
what happens right now?
198
596720
3208
Pertanyaannya, apa yang
terjadi sekarang?
terjadi sekarang?
10:12
And we don't know, because there
hasn't been enough time passed yet
hasn't been enough time passed yet
199
600587
3239
Kita tidak tahu, karena belum cukup
waktu berlalu
waktu berlalu
10:15
to actually figure out how much impact
those papers will get;
those papers will get;
200
603850
2987
untuk mengukur dampak karya-karya
ilmiah tersebut;
ilmiah tersebut;
10:18
it takes time to acquire.
201
606861
1227
ini membutuhkan waktu.
10:20
Well, when you look at the data,
202
608112
1569
Jika Anda melihat datanya,
10:21
it seems to be that Einstein,
the genius research, is right,
the genius research, is right,
203
609705
2854
tampaknya Einstein,
penelitian jenius, benar,
penelitian jenius, benar,
10:24
and I'm at that stage of my career.
204
612583
1811
dan karier saya ada di stadium itu.
10:26
(Laughter)
205
614418
2308
(Tawa)
10:28
So we said, OK, let's figure out
how does this really happen,
how does this really happen,
206
616750
5974
Jadi kami memutuskan mari
cari tahu bagaimana ini terjadi,
cari tahu bagaimana ini terjadi,
10:34
first in science.
207
622748
1778
pertama di bidang
ilmu pengetahuan.
ilmu pengetahuan.
10:36
And in order not to have
the selection bias,
the selection bias,
208
624550
3632
Agar tidak ada bias seleksi, yaitu
10:40
to look only at geniuses,
209
628206
1337
hanya melihat para jenius,
10:41
we ended up reconstructing the career
of every single scientist
of every single scientist
210
629567
3716
kami merekonstruksi
karier semua ilmuwan
karier semua ilmuwan
10:45
from 1900 till today
211
633307
2502
dari tahun 1900 sampai sekarang
10:47
and finding for all scientists
what was their personal best,
what was their personal best,
212
635833
3712
dan menentukan apa
pencapaian terbaik mereka,
pencapaian terbaik mereka,
10:51
whether they got the Nobel Prize
or they never did,
or they never did,
213
639569
2812
pernah menang hadiah Nobel atau tidak,
10:54
or no one knows what they did,
even their personal best.
even their personal best.
214
642405
3407
atau tiada yang tahu usaha mereka,
bahkan pencapaian terbaik mereka.
bahkan pencapaian terbaik mereka.
10:57
And that's what you see in this slide.
215
645836
1915
Inilah yang Anda lihat di salindia ini.
10:59
Each line is a career,
216
647775
1573
Tiap garis menunjukkan karier,
11:01
and when you have a light blue dot
on the top of that career,
on the top of that career,
217
649372
3003
dan titik biru muda
di atas karier itu,
di atas karier itu,
11:04
it says that was their personal best.
218
652399
2040
menunjukkan pencapaian terbaik mereka.
11:06
And the question is,
219
654463
1155
Pertanyaannya,
11:07
when did they actually make
their biggest discovery?
their biggest discovery?
220
655642
3568
kapan mereka membuat
penemuan terbesar mereka?
penemuan terbesar mereka?
11:11
To quantify that,
221
659234
1165
Untuk kuantifikasinya,
11:12
we look at what's the probability
that you make your biggest discovery,
that you make your biggest discovery,
222
660423
3376
kami melihat probabilitas
penemuan terbesar Anda,
penemuan terbesar Anda,
11:15
let's say, one, two, three
or 10 years into your career?
or 10 years into your career?
223
663823
2672
satu, dua, tiga atau
10 tahun setelah memulai karier?
10 tahun setelah memulai karier?
11:18
We're not looking at real age.
224
666519
1480
Tidak melihat usia sebenarnya.
11:20
We're looking at
what we call "academic age."
what we call "academic age."
225
668023
2134
Kami melihat apa yang
disebut "usia akademis"
disebut "usia akademis"
11:22
Your academic age starts
when you publish your first papers.
when you publish your first papers.
226
670181
3250
Usia akademis Anda dimulai
saat Anda menerbitkan makalah pertama.
saat Anda menerbitkan makalah pertama.
11:25
I know some of you are still babies.
227
673455
1779
Sebagian dari Anda masih bayi.
11:27
(Laughter)
228
675258
1397
(Tawa)
11:28
So let's look at the probability
229
676679
2706
Mari lihat probabilitasnya
11:31
that you publish
your highest-impact paper.
your highest-impact paper.
230
679409
2066
Anda mempublikasikan makalah
berdampak besar
berdampak besar
11:33
And what you see is, indeed,
the genius research is right.
the genius research is right.
231
681499
3071
Anda akan melihat bahwa
penelitian jenius benar.
penelitian jenius benar.
11:36
Most scientists tend to publish
their highest-impact paper
their highest-impact paper
232
684594
3024
Kebanyakan ilmuwan mempublikasikan
makalah berdampak besar
makalah berdampak besar
11:39
in the first 10, 15 years in their career,
233
687642
2899
dalam 10, 15 tahun pertama karier mereka,
11:42
and it tanks after that.
234
690565
3133
dan kemudian kualitasnya menurun.
11:45
It tanks so fast that I'm about --
I'm exactly 30 years into my career,
I'm exactly 30 years into my career,
235
693722
5107
Menurun amat cepat hingga saya -
karier saya sudah 30 tahun,
karier saya sudah 30 tahun,
11:50
and the chance that I will publish a paper
that would have a higher impact
that would have a higher impact
236
698853
3540
kemungkinan saya mempublikasikan
makalah berdampak lebih besar
makalah berdampak lebih besar
11:54
than anything that I did before
237
702417
1940
daripada sebelumnya
11:56
is less than one percent.
238
704381
1353
kurang dari satu persen.
11:57
I am in that stage of my career,
according to this data.
according to this data.
239
705758
3049
Menurut data ini, saya ada
pada stadium karier tersebut.
pada stadium karier tersebut.
12:01
But there's a problem with that.
240
709648
1843
Tapi ada masalah.
12:03
We're not doing controls properly.
241
711515
3675
Kita tidak melakukan kontrol
dengan benar.
dengan benar.
12:07
So the control would be,
242
715214
1417
Kontrolnya ialah,
12:08
what would a scientist look like
who makes random contribution to science?
who makes random contribution to science?
243
716655
4607
ilmuwan seperti apa yang berkontribusi
acak bagi ilmu pengetahuan?
acak bagi ilmu pengetahuan?
12:13
Or what is the productivity
of the scientist?
of the scientist?
244
721286
2995
Atau bagaimanakah
produktivitas ilmuwan tersebut?
produktivitas ilmuwan tersebut?
12:16
When do they write papers?
245
724305
2006
Kapan mereka menulis makalah?
12:18
So we measured the productivity,
246
726335
2444
Jadi kami mengukur produktivitas,
12:20
and amazingly, the productivity,
247
728803
2052
yang mengherankan, produktivitas,
12:22
your likelihood of writing a paper
in year one, 10 or 20 in your career,
in year one, 10 or 20 in your career,
248
730879
4131
probabilitas Anda menulis makalah
dalam satu, 10 atau 20 tahun karier Anda,
dalam satu, 10 atau 20 tahun karier Anda,
12:27
is indistinguishable from the likelihood
of having the impact
of having the impact
249
735034
3606
tidak dapat dibedakan dari
kemungkinan dampak yang terjadi
kemungkinan dampak yang terjadi
12:30
in that part of your career.
250
738664
1775
pada karier Anda di stadium itu.
12:33
And to make a long story short,
251
741026
1783
Singkatnya,
12:34
after lots of statistical tests,
there's only one explanation for that,
there's only one explanation for that,
252
742833
4228
setelah banyak tes statistik,
hanya ada satu penjelasan,
hanya ada satu penjelasan,
12:39
that really, the way we scientists work
253
747085
2894
bahwa, cara kerja kita para ilmuwan
12:42
is that every single paper we write,
every project we do,
every project we do,
254
750003
3633
setiap makalah yang kita tulis,
setiap proyek yang kita kerjakan,
setiap proyek yang kita kerjakan,
12:45
has exactly the same chance
of being our personal best.
of being our personal best.
255
753660
4160
semua memiliki kemungkinan sama
untuk menjadi yang terbaik.
untuk menjadi yang terbaik.
12:49
That is, discovery is like
a lottery ticket.
a lottery ticket.
256
757844
4953
Artinya, penemuan adalah tiket undian.
12:54
And the more lottery tickets we buy,
257
762821
2351
Semakin banyak tiket undian
yang kita beli,
yang kita beli,
12:57
the higher our chances.
258
765196
1507
semakin besar kemungkinannya.
12:58
And it happens to be so
259
766727
1559
Dan kebetulan
13:00
that most scientists buy
most of their lottery tickets
most of their lottery tickets
260
768310
2719
kebanyakan ilmuwan membeli
tiket undian mereka
tiket undian mereka
13:03
in the first 10, 15 years of their career,
261
771053
2460
dalam satu, 10, 15 tahun pertama
karier mereka,
karier mereka,
13:05
and after that,
their productivity decreases.
their productivity decreases.
262
773537
3413
dan setelah itu,
produktivitas mereka menurun.
produktivitas mereka menurun.
13:09
They're not buying
any more lottery tickets.
any more lottery tickets.
263
777411
2084
Mereka tidak lagi
membeli tiket undian.
membeli tiket undian.
13:11
So it looks as if
they would not be creative.
they would not be creative.
264
779519
3444
Seolah-olah mereka
tidak lagi kreatif.
tidak lagi kreatif.
13:14
In reality, they stopped trying.
265
782987
1999
Realitasnya, mereka berhenti mencoba.
13:17
So when we actually put the data together,
the conclusion is very simple:
the conclusion is very simple:
266
785509
3915
Jadi saat kami merangkum semua data,
kesimpulannya sederhana:
kesimpulannya sederhana:
13:21
success can come at any time.
267
789448
2331
sukses dapat terjadi kapan saja.
13:23
It could be your very first
or very last paper of your career.
or very last paper of your career.
268
791803
3735
Bisa jadi makalah pertama
atau terakhir Anda.
atau terakhir Anda.
13:27
It's totally random
in the space of the projects.
in the space of the projects.
269
795562
4288
Benar-benar acak
dalam cakupan proyek Anda.
dalam cakupan proyek Anda.
13:31
It is the productivity that changes.
270
799874
1931
Yang berubah ialah produktivitas.
13:33
Let me illustrate that.
271
801829
1252
Mari saya ilustrasikan.
13:35
Here is Frank Wilczek,
who got the Nobel Prize in Physics
who got the Nobel Prize in Physics
272
803105
3269
Ini ialah Frank Wilczek,
pemenang Hadiah Nobel Fisika
pemenang Hadiah Nobel Fisika
13:38
for the very first paper he ever wrote
in his career as a graduate student.
in his career as a graduate student.
273
806398
4101
untuk makalah pertamanya yang ia tulis
saat masih mahasiswa pasca sarjana.
saat masih mahasiswa pasca sarjana.
13:42
(Laughter)
274
810523
1007
(Tawa)
13:43
More interesting is John Fenn,
275
811554
3218
Yang lebih menarik ialah John Fenn,
13:46
who, at age 70, was forcefully retired
by Yale University.
by Yale University.
276
814796
4598
yang pada usia 70 dipaksa pensiun
oleh Universitas Yale.
oleh Universitas Yale.
13:51
They shut his lab down,
277
819418
2056
Mereka menutup lab-nya,
13:53
and at that moment, he moved
to Virginia Commonwealth University,
to Virginia Commonwealth University,
278
821498
3666
dan pada saat itu, ia pindah ke
Universitas Virginia Commonwealth,
Universitas Virginia Commonwealth,
13:57
opened another lab,
279
825188
1786
membuka lab lain,
13:58
and it is there, at age 72,
that he published a paper
that he published a paper
280
826998
3033
dan di sanalah, pada umur 72,
ia mempublikasikan makalah
ia mempublikasikan makalah
14:02
for which, 15 years later, he got
the Nobel Prize for Chemistry.
the Nobel Prize for Chemistry.
281
830055
3845
yang membuatnya memenangkan
Hadiah Nobel Kimia 15 tahun kemudian.
Hadiah Nobel Kimia 15 tahun kemudian.
14:06
And you think, OK,
well, science is special,
well, science is special,
282
834940
3042
Anda mungkin berpikir,
ilmu pengetahuan itu spesial,
ilmu pengetahuan itu spesial,
14:10
but what about other areas
where we need to be creative?
where we need to be creative?
283
838006
3463
bagaimana dengan area lain
yang memerlukan kreativitas?
yang memerlukan kreativitas?
14:13
So let me take another
typical example: entrepreneurship.
typical example: entrepreneurship.
284
841493
4936
Mari saya ambil contoh khas lain:
wirausaha.
wirausaha.
14:18
Silicon Valley,
285
846834
1579
Silicon Valley,
14:20
the land of the youth, right?
286
848437
2066
tanah kaum muda, bukan demikian?
14:22
And indeed, when you look at it,
287
850527
1595
Memang, jika Anda mencermatinya,
14:24
you realize that the biggest awards,
the TechCrunch Awards and other awards,
the TechCrunch Awards and other awards,
288
852146
4642
penghargaan terbesar TechCrunch
Awards dan penghargaan lain
Awards dan penghargaan lain
14:28
are all going to people
289
856812
2173
semua jatuh ke tangan orang-orang
14:31
whose average age
is late 20s, very early 30s.
is late 20s, very early 30s.
290
859009
5015
yang rata-rata berusia
akhir 20-an, awal 30-an.
akhir 20-an, awal 30-an.
14:36
You look at who the VCs give the money to,
some of the biggest VC firms --
some of the biggest VC firms --
291
864465
5602
Anda lihat pada siapa VC memberikan uang,
beberapa firma VC terbesar -
beberapa firma VC terbesar -
14:42
all people in their early 30s.
292
870091
2241
semua adalah orang-orang
berusia awal 30-an.
berusia awal 30-an.
14:44
Which, of course, we know;
293
872951
1265
Yang tentu saja,
kita tahu;
kita tahu;
14:46
there is this ethos in Silicon Valley
that youth equals success.
that youth equals success.
294
874240
4453
ada etos ini di Silicon Valley
bahwa usia muda berarti sukses.
bahwa usia muda berarti sukses.
14:51
Not when you look at the data,
295
879653
2183
Jika Anda melihat datanya,
14:53
because it's not only
about forming a company --
about forming a company --
296
881860
2304
karena tidak hanya tentang
mendirikan perusahaan-
mendirikan perusahaan-
14:56
forming a company is like productivity,
trying, trying, trying --
trying, trying, trying --
297
884188
3140
mendirikan perusahaan ibarat produktivitas
usaha, usaha, usaha --
usaha, usaha, usaha --
14:59
when you look at which
of these individuals actually put out
of these individuals actually put out
298
887352
3484
jika Anda melihat individu mana
yang benar-benar mendirikan
yang benar-benar mendirikan
15:02
a successful company, a successful exit.
299
890860
2782
perusahaan yang berhasil,
peluncuran yang berhasil.
peluncuran yang berhasil.
15:05
And recently, some of our colleagues
looked at exactly that question.
looked at exactly that question.
300
893666
3720
Baru-baru ini, sejumlah kolega kami
meneliti hal ini.
meneliti hal ini.
15:09
And it turns out that yes,
those in the 20s and 30s
those in the 20s and 30s
301
897410
3156
Hasilnya, ya, mereka yang
berusia 20-an dan 30-an
berusia 20-an dan 30-an
15:12
put out a huge number of companies,
form lots of companies,
form lots of companies,
302
900590
3348
mendirikan sejumlah besar perusahaan,
membentuk banyak perusahaan,
membentuk banyak perusahaan,
15:15
but most of them go bust.
303
903962
1531
tapi kebanyakan dari mereka
gagal
gagal
15:18
And when you look at the successful exits,
what you see in this particular plot,
what you see in this particular plot,
304
906089
4195
Jika Anda melihat peluncuran yang sukses,
yang dilihat di alur ini,
yang dilihat di alur ini,
15:22
the older you are, the more likely that
you will actually hit the stock market
you will actually hit the stock market
305
910308
3695
semakin tua Anda, semakin besar
kemungkinan mencapai pasar modal
kemungkinan mencapai pasar modal
15:26
or the sell the company successfully.
306
914027
2312
atau sukses menjual perusahaan.
15:28
This is so strong, actually,
that if you are in the 50s,
that if you are in the 50s,
307
916847
3113
Penemuan ini sangat kuat,
bahwa jika Anda berusia 50-an
bahwa jika Anda berusia 50-an
15:31
you are twice as likely
to actually have a successful exit
to actually have a successful exit
308
919984
3588
Anda 2 kali lebih mungkin
sukses melakukan peluncuran
sukses melakukan peluncuran
15:35
than if you are in your 30s.
309
923596
1890
daripada jika Anda berusia 30-an.
15:38
(Applause)
310
926613
4325
(Tepuk tangan)
15:43
So in the end, what is it
that we see, actually?
that we see, actually?
311
931645
3009
Pada akhirnya, apa yang
sebenarnya kita lihat?
sebenarnya kita lihat?
15:46
What we see is that creativity has no age.
312
934678
4083
Kita lihat bahwa kreativitas
tidak memiliki usia.
tidak memiliki usia.
15:50
Productivity does, right?
313
938785
2202
Tetapi produktivitas ya,
bukan demikian?
bukan demikian?
15:53
Which is telling me that
at the end of the day,
at the end of the day,
314
941424
4135
Ini menunjukkan saya bahwa akhirnya,
15:57
if you keep trying --
315
945583
2000
jika Anda terus berusaha -
15:59
(Laughter)
316
947607
2403
(Tawa)
16:02
you could still succeed
and succeed over and over.
and succeed over and over.
317
950034
3572
Anda dapat mencapai sukses berulang kali.
16:05
So my conclusion is very simple:
318
953630
2391
Jadi kesimpulan saya sangat sederhana:
16:08
I am off the stage, back in my lab.
319
956045
2093
Saya turun dari podium,
kembali ke lab saya.
kembali ke lab saya.
16:10
Thank you.
320
958162
1171
Terima kasih.
16:11
(Applause)
321
959357
3309
(Tepuk tangan)
ABOUT THE SPEAKER
Albert-László Barabási - Network scientistA pioneer in network science, Albert-László Barabási uncovers the hidden order behind complex systems.
Why you should listen
Albert-László Barabási is fascinated by a wide range of topics, from the structure of the brain and treating diseases with network medicine to the emergence of success in art and how science really works. His work uses the quantitative tools of network science, a research field that he pioneered, and led to the discovery of scale-free networks, helping explain the emergence of many natural, technological and social networks.
Barabási is the Robert Gray Dodge Professor of Network Science at Northeastern University and holds an appointment in the Department of Medicine at Harvard Medical School. He splits his time with Budapest, where he runs a European Research Council project at Central European University. A Hungarian born native of Transylvania, Romania, he received his masters in theoretical physics at the Eötvös University in Budapest, Hungary and his PhD three years later at Boston University.
Barabási’s latest book is The Formula: The Universal Laws of Success. He is also the author of Network Science, Linked and Bursts. He co-edited Network Medicine and The Structure and Dynamics of Networks. His books have been translated into over twenty languages.
Albert-László Barabási | Speaker | TED.com