TED2015
Jim Simons: The mathematician who cracked Wall Street
Jim Simons: Jedinstven intervju s matematičarem koji je "provalio" u Wall Street
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Jim Simons je bio matematičar i kriptograf kada je shvatio: kompleksna matematika koju je koristio da razbije kodove bi mogla pomoći u razumijevanju uzoraka u svijetu financija. Milijarde poslije, radi na podupiranju sljedeće generacije profesora matematike. Chris Anderson iz TED-a razgovarao je s njim o njegovom nevjerojatnom životu u brojevima.
Jim Simons - Philanthropist, mathematician
After astonishing success as a mathematician, code breaker and billionaire hedge fund manager, Jim Simons is mastering yet another field: philanthropy. Full bio
After astonishing success as a mathematician, code breaker and billionaire hedge fund manager, Jim Simons is mastering yet another field: philanthropy. Full bio
Double-click the English transcript below to play the video.
00:12
Chris Anderson: You were something
of a mathematical phenom.
of a mathematical phenom.
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Chris Anderson: Bili ste svojevrsni
matematički fenomen.
matematički fenomen.
00:15
You had already taught at Harvard
and MIT at a young age.
and MIT at a young age.
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Predavali ste na Harvardu i MIT-u
već kao mladić.
već kao mladić.
00:18
And then the NSA came calling.
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I onda vas je zvala NSA.
00:21
What was that about?
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Zbog čega?
00:23
Jim Simons: Well the NSA --
that's the National Security Agency --
that's the National Security Agency --
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Jim Simons: Pa, NSA -- to je
Nacionalna sigurnosna agencija --
Nacionalna sigurnosna agencija --
00:27
they didn't exactly come calling.
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nisu me baš zvali.
00:29
They had an operation at Princeton,
where they hired mathematicians
where they hired mathematicians
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Imali su operaciju na Princetonu,
gdje su zapošljavali matematičare
gdje su zapošljavali matematičare
00:33
to attack secret codes
and stuff like that.
and stuff like that.
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da napadaju tajne kodove
i takve stvari.
i takve stvari.
00:37
And I knew that existed.
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I ja sam znao da to postoji.
00:39
And they had a very good policy,
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Oni su imali veoma dobru politiku,
00:41
because you could do half your time
at your own mathematics,
at your own mathematics,
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jer ste mogli pola svog vremena provoditi
radeći svoju matematiku
radeći svoju matematiku
00:45
and at least half your time
working on their stuff.
working on their stuff.
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i barem pola radeći na njihovim
zadacima.
zadacima.
00:49
And they paid a lot.
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I puno su plaćali.
00:51
So that was an irresistible pull.
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Tako da je to bio neodoljiv poziv.
00:54
So, I went there.
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Pa sam otišao tamo.
00:56
CA: You were a code-cracker.
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CA: Bili ste razbijač kodova.
00:57
JS: I was.
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JS: Tako je.
00:58
CA: Until you got fired.
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CA: Dok vam nisu dali otkaz.
00:59
JS: Well, I did get fired. Yes.
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JS: Pa, da, dobio sam otkaz. Da.
01:01
CA: How come?
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CA: Kako to?
01:03
JS: Well, how come?
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JS: Pa, kako to?
01:05
I got fired because,
well, the Vietnam War was on,
well, the Vietnam War was on,
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Dobio sam otkaz, jer
je bilo vrijeme Vijetnamskog rata
je bilo vrijeme Vijetnamskog rata
01:10
and the boss of bosses in my organization
was a big fan of the war
was a big fan of the war
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i šef šefova u mojoj organizaciji je
bio veliki obožavatelj rata
bio veliki obožavatelj rata
01:16
and wrote a New York Times article,
a magazine section cover story,
a magazine section cover story,
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i napisao je članak za New York Times,
naslovnu priču za sekciju časopisa,
naslovnu priču za sekciju časopisa,
01:20
about how we would win in Vietnam.
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o tome kako ćemo pobijediti u Vijetnamu.
01:22
And I didn't like that war,
I thought it was stupid.
I thought it was stupid.
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A meni se nije svidio taj rat,
smatrao sam da je glup.
smatrao sam da je glup.
01:25
And I wrote a letter to the Times,
which they published,
which they published,
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I napisao sam pismo Timesu,
koje su objavili,
koje su objavili,
01:28
saying not everyone
who works for Maxwell Taylor,
who works for Maxwell Taylor,
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u kojem sam napisao da se,
svi koji rade za Maxwella Taylora,
svi koji rade za Maxwella Taylora,
01:32
if anyone remembers that name,
agrees with his views.
agrees with his views.
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ako se itko sjeća tog imena,
ne slažu s njegovim pogledima.
ne slažu s njegovim pogledima.
01:37
And I gave my own views ...
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I dao sam svoje mišljenje ...
01:39
CA: Oh, OK. I can see that would --
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CA: Oh, OK. To bi prouzročilo --
01:41
JS: ... which were different
from General Taylor's.
from General Taylor's.
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JS: ... koje je bilo drukčije od
mišljenja generala Taylora.
mišljenja generala Taylora.
01:44
But in the end, nobody said anything.
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Ali na kraju, nitko nije ništa rekao.
01:45
But then, I was 29 years old at this time,
and some kid came around
and some kid came around
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Ali tada, imao sam 29 godina,
neki klinac je došao
neki klinac je došao
01:49
and said he was a stringer
from Newsweek magazine
from Newsweek magazine
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i rekao da je freelancer za
časopis Newsweek
časopis Newsweek
01:52
and he wanted to interview me
and ask what I was doing about my views.
and ask what I was doing about my views.
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i htio me intervjuirati i
pitati što radim sa svojim mišljenjem.
pitati što radim sa svojim mišljenjem.
01:58
And I told him, "I'm doing
mostly mathematics now,
mostly mathematics now,
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I reako sam mu: "Sada uglavnom radim
matematiku,
matematiku,
02:02
and when the war is over,
then I'll do mostly their stuff."
then I'll do mostly their stuff."
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a kad rat završi, onda ću
raditi većinom njihove stvari."
raditi većinom njihove stvari."
02:06
Then I did the only
intelligent thing I'd done that day --
intelligent thing I'd done that day --
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Tada sam napravio jedinu inteligentnu
stvar tog dana --
stvar tog dana --
02:08
I told my local boss
that I gave that interview.
that I gave that interview.
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rekao sam svom lokalnom šefu
da sam dao taj intervju.
da sam dao taj intervju.
02:13
And he said, "What'd you say?"
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I on je pitao: " Što si rekao?"
02:14
And I told him what I said.
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I rekao sam mu što sam rekao.
02:16
And then he said,
"I've got to call Taylor."
"I've got to call Taylor."
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I onda je on rekao: "Moram
nazvati Taylora."
nazvati Taylora."
02:18
He called Taylor; that took 10 minutes.
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Nazvao je Taylora; to
je trajalo 10 minuta.
je trajalo 10 minuta.
02:20
I was fired five minutes after that.
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Pet minuta nakon toga dobio
sam otkaz.
sam otkaz.
02:23
CA: OK.
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CA: OK.
02:24
JS: But it wasn't bad.
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JS: Ali nije bilo loše.
02:26
CA: It wasn't bad,
because you went on to Stony Brook
because you went on to Stony Brook
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CA: Nije bilo loše, jer ste otišli
u Stony Brook
u Stony Brook
02:28
and stepped up your mathematical career.
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i uznapredovali u svojoj matematičkoj
karijeri.
karijeri.
02:31
You started working with this man here.
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Počeli ste raditi s ovim čovjekom.
02:34
Who is this?
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Tko je to?
02:36
JS: Oh, [Shiing-Shen] Chern.
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JS: Oh, Shiing-Shen Chern.
02:37
Chern was one of the great
mathematicians of the century.
mathematicians of the century.
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Chern je bio jedan od najboljih
matematičara stoljeća.
matematičara stoljeća.
02:40
I had known him when
I was a graduate student at Berkeley.
I was a graduate student at Berkeley.
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Upoznao sam ga dok sam bio student
na Berkeleyju.
na Berkeleyju.
02:46
And I had some ideas,
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Imao sam neke ideje,
02:48
and I brought them to him
and he liked them.
and he liked them.
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donio sam ih njemu i svidjele su mu se.
02:50
Together, we did this work
which you can easily see up there.
which you can easily see up there.
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Zajedno smo radili na ovome što
lako možete vidjeti tamo gore.
lako možete vidjeti tamo gore.
02:57
There it is.
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Eto ga.
02:59
CA: It led to you publishing
a famous paper together.
a famous paper together.
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CA: To je dovelo do vaše
poznate objavljene studije s njim.
poznate objavljene studije s njim.
03:02
Can you explain at all what that work was?
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Možete li imalo objasniti o čemu
je bila?
je bila?
03:07
JS: No.
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JS: Ne.
03:08
(Laughter)
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(Smijeh)
03:10
JS: I mean, I could
explain it to somebody.
explain it to somebody.
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JS: Mislim, mogao bih ju objasniti nekome.
03:13
(Laughter)
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(Smijeh)
03:15
CA: How about explaining this?
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CA: A da objasnite ovo?
03:17
JS: But not many. Not many people.
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JS: Ne mnogima. Ne mnogim ljudima.
03:21
CA: I think you told me
it had something to do with spheres,
it had something to do with spheres,
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CA: Mislim da ste mi rekli da ima veze sa
sferama
sferama
03:23
so let's start here.
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pa hajdemo tu započeti.
03:25
JS: Well, it did,
but I'll say about that work --
but I'll say about that work --
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JS: Pa, ima, ali reći ću o tom radu --
03:29
it did have something to do with that,
but before we get to that --
but before we get to that --
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imalo je neke veze s tim, ali
prije nego što dođemo do toga --
prije nego što dođemo do toga --
03:32
that work was good mathematics.
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taj rad je bila dobra matematika.
03:36
I was very happy with it; so was Chern.
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Bio sam jako zadovoljan s tim; i Chern
također.
također.
03:39
It even started a little sub-field
that's now flourishing.
that's now flourishing.
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Time je čak započelo malo pod-područje
koje sada cvijeta.
koje sada cvijeta.
03:44
But, more interestingly,
it happened to apply to physics,
it happened to apply to physics,
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Ali, još zanimljivije, ispalo je da je
primijenjivo na fiziku,
primijenjivo na fiziku,
03:49
something we knew nothing about --
at least I knew nothing about physics,
at least I knew nothing about physics,
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nešto o čemu nismo imali pojma --
barem ja nisam imao pojma o fizici,
barem ja nisam imao pojma o fizici,
03:54
and I don't think Chern
knew a heck of a lot.
knew a heck of a lot.
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a mislim da ni Chern nije znao previše.
03:56
And about 10 years
after the paper came out,
after the paper came out,
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I otprilike 10 godina nakon izlaska
studije,
studije,
04:00
a guy named Ed Witten in Princeton
started applying it to string theory
started applying it to string theory
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čovjek zvan Ed Witten s Princetona je
počeo primijenjivati to na teoriju struna
počeo primijenjivati to na teoriju struna
04:05
and people in Russia started applying it
to what's called "condensed matter."
to what's called "condensed matter."
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i ljudi u Rusiji su je počeli
primijenjivati na "kondenziranu tvar."
primijenjivati na "kondenziranu tvar."
04:09
Today, those things in there
called Chern-Simons invariants
called Chern-Simons invariants
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Danas, te stvari nazvane
Chern-Simons invarijante,
Chern-Simons invarijante,
04:14
have spread through a lot of physics.
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su se proširile na mnoga područja
fizike.
fizike.
04:16
And it was amazing.
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I bilo je nevjerojatno.
04:17
We didn't know any physics.
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Nismo uopće poznavali
fiziku.
fiziku.
04:19
It never occurred to me
that it would be applied to physics.
that it would be applied to physics.
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Nikad mi nije palo na pamet da će se
primijeniti u fizici.
primijeniti u fizici.
04:22
But that's the thing about mathematics --
you never know where it's going to go.
you never know where it's going to go.
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Ali tako je s matematikom --
nikad ne znate kamo će otići.
nikad ne znate kamo će otići.
04:26
CA: This is so incredible.
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CA: To je nevjerojatno.
04:27
So, we've been talking about
how evolution shapes human minds
how evolution shapes human minds
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Pričali smo kako evolucija
oblikuje ljudski um
oblikuje ljudski um
04:32
that may or may not perceive the truth.
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koji može ili ne može percipirati
istinu.
istinu.
04:34
Somehow, you come up
with a mathematical theory,
with a mathematical theory,
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Vi ste nekako došli do matematičke
teorije,
teorije,
04:38
not knowing any physics,
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bez da ste znali imalo fizike,
04:40
discover two decades later
that it's being applied
that it's being applied
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otkrijete dva desetljeća poslije
da se koristi
da se koristi
04:42
to profoundly describe
the actual physical world.
the actual physical world.
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u detaljnim opisima stvarnog
fizičkog svijeta.
fizičkog svijeta.
04:45
How can that happen?
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Kako se to dogodi?
04:46
JS: God knows.
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JS: Samo Bog zna.
04:47
(Laughter)
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(Smijeh)
04:50
But there's a famous physicist
named [Eugene] Wigner,
named [Eugene] Wigner,
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Ali postoji poznati fizičar,
Eugene Wigner,
Eugene Wigner,
04:54
and he wrote an essay on the unreasonable
effectiveness of mathematics.
effectiveness of mathematics.
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koji je napisao esej o nerazumljivoj
učinkovitosti matematike.
učinkovitosti matematike.
04:59
Somehow, this mathematics,
which is rooted in the real world
which is rooted in the real world
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Ta matematika, koja je ukorijenjena
u stvarnom svijetu na neki način --
u stvarnom svijetu na neki način --
05:03
in some sense -- we learn to count,
measure, everyone would do that --
measure, everyone would do that --
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učimo brojati, mjerimo, svi to rade --
05:08
and then it flourishes on its own.
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onda sama procvijeta.
05:10
But so often it comes
back to save the day.
back to save the day.
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Ali često se vraća da nas spasi.
05:14
General relativity is an example.
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Opća relativnost je primjer.
05:16
[Hermann] Minkowski had this geometry,
and Einstein realized,
and Einstein realized,
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Hermann Minkowski je razvio tu geometriju
i Einstein je shvatio
i Einstein je shvatio
05:19
"Hey! It's the very thing
in which I can cast general relativity."
in which I can cast general relativity."
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"Hey! To je točno ono čime
mogu prikazati opću relativnost."
mogu prikazati opću relativnost."
05:23
So, you never know. It is a mystery.
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Dakle, nikad ne zante. To je misterij.
05:27
It is a mystery.
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Misterij.
05:28
CA: So, here's a mathematical
piece of ingenuity.
piece of ingenuity.
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CA: Dakle, ovdje je matematički
dio dosjetljivosti.
dio dosjetljivosti.
05:31
Tell us about this.
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Recite nam nešto o tome.
05:32
JS: Well, that's a ball -- it's a sphere,
and it has a lattice around it --
and it has a lattice around it --
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JS: Pa, to je kugla -- sfera koja
ima rešetku oko sebe --
ima rešetku oko sebe --
05:38
you know, those squares.
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znate, one kvadrate.
05:42
What I'm going to show here was
originally observed by [Leonhard] Euler,
originally observed by [Leonhard] Euler,
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Ono što ću ovdje pokazati je
prvi uočio Leonhard Euler,
prvi uočio Leonhard Euler,
05:47
the great mathematician, in the 1700s.
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veliki matematičar, 1700-ih.
05:50
And it gradually grew to be
a very important field in mathematics:
a very important field in mathematics:
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I to je postupno preraslo
u važno polje matematike:
u važno polje matematike:
05:55
algebraic topology, geometry.
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algebarska topologija, geometrija.
05:59
That paper up there had its roots in this.
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Ta studija tamo gore ima svoje korijene
u ovome.
u ovome.
06:03
So, here's this thing:
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Evo o čem se radi:
06:05
it has eight vertices,
12 edges, six faces.
12 edges, six faces.
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ima osam točaka, 12 rubova,
šest strana.
šest strana.
06:09
And if you look at the difference --
vertices minus edges plus faces --
vertices minus edges plus faces --
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I ako pogledate razliku --
točke minus rubovi plus strane --
točke minus rubovi plus strane --
06:13
you get two.
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dobijete dva.
06:14
OK, well, two. That's a good number.
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OK, dakle, dva. To je dobar broj.
06:17
Here's a different way of doing it --
these are triangles covering --
these are triangles covering --
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Evo drugog načina da to napravite --
ovo su trokuti --
ovo su trokuti --
06:21
this has 12 vertices and 30 edges
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12 točaka i 30 rubova
06:25
and 20 faces, 20 tiles.
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i 20 strana, 20 pločica.
06:30
And vertices minus edges
plus faces still equals two.
plus faces still equals two.
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Točke minus rubovi plus
strane je još uvijek dva.
strane je još uvijek dva.
06:35
And in fact, you could do this
any which way --
any which way --
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I zapravo biste ovo mogli napraviti
na bilo koji način --
na bilo koji način --
06:38
cover this thing with all kinds
of polygons and triangles
of polygons and triangles
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pokriti ovo sa svim vrstama poligona
i trokuta
i trokuta
06:41
and mix them up.
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i pomiješati ih.
06:42
And you take vertices minus edges
plus faces -- you'll get two.
plus faces -- you'll get two.
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I onda točke minus rubovi plus
strane -- i dobijete dva.
strane -- i dobijete dva.
06:46
Here's a different shape.
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Evo drugog oblika.
06:48
This is a torus, or the surface
of a doughnut: 16 vertices
of a doughnut: 16 vertices
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Ovo je torus, ili površina krafne: 16
točaka
točaka
06:53
covered by these rectangles,
32 edges, 16 faces.
32 edges, 16 faces.
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pokrivenih ovim pravokutnicima, 32
ruba, 16 strana.
ruba, 16 strana.
06:58
Vertices minus edges comes out to be zero.
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Točke minus rubovi je nula.
07:01
It'll always come out to zero.
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Uvijek će biti nula.
07:02
Every time you cover a torus
with squares or triangles
with squares or triangles
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Svaki put kad pokrijete torus
kvadratima ili trokutima
kvadratima ili trokutima
07:07
or anything like that,
you're going to get zero.
you're going to get zero.
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ili nečim sličnim, dobit ćete nulu.
07:12
So, this is called
the Euler characteristic.
the Euler characteristic.
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Ovo se zove Eulerova
karakteristika.
karakteristika.
07:14
And it's what's called
a topological invariant.
a topological invariant.
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I to je ono što se zove
topološka invarijanta.
topološka invarijanta.
07:18
It's pretty amazing.
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Doista je nevjerojatno.
07:20
No matter how you do it,
you're always get the same answer.
you're always get the same answer.
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Kako god da napravite to,
uvijek ćete dobiti isti odgovor.
uvijek ćete dobiti isti odgovor.
07:22
So that was the first sort of thrust,
from the mid-1700s,
from the mid-1700s,
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Dakle, to je bio prvi prodor, iz
1700-ih,
1700-ih,
07:29
into a subject which is now called
algebraic topology.
algebraic topology.
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u predmet koji se dans zove
algebarska topologija.
algebarska topologija.
07:32
CA: And your own work
took an idea like this and moved it
took an idea like this and moved it
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CA: I vaš je rad preuzeo ovu ideju
i promaknuo je
i promaknuo je
07:35
into higher-dimensional theory,
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u više-dimenzionalnu teoriju,
07:38
higher-dimensional objects,
and found new invariances?
and found new invariances?
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više-dimenzionalne predmete,
i našao nove invarijante?
i našao nove invarijante?
07:41
JS: Yes. Well, there were already
higher-dimensional invariants:
higher-dimensional invariants:
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JS: Da. Pa, već su postojale
više-dimenzionalne invarijante:
više-dimenzionalne invarijante:
07:46
Pontryagin classes --
actually, there were Chern classes.
actually, there were Chern classes.
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Pontryaginove klase --
zapravo, bile su Chernove klase.
zapravo, bile su Chernove klase.
07:50
There were a bunch
of these types of invariants.
of these types of invariants.
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Postojala je hrpa tih
vrsta invarijanti.
vrsta invarijanti.
07:54
I was struggling to work on one of them
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Mučio sam se radeći na jednoj
07:58
and model it sort of combinatorially,
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i modelirajući je kombinatorički,
08:02
instead of the way it was typically done,
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umjesto na način na koji se to tipično
radi,
radi,
08:05
and that led to this work
and we uncovered some new things.
and we uncovered some new things.
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i to je dovelo do ovog rada
i otkrili smo nove stvari.
i otkrili smo nove stvari.
08:10
But if it wasn't for Mr. Euler --
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Ali da nije bilo g.Eulera --
08:13
who wrote almost 70 volumes of mathematics
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koji je napisao gotovo 70 djela
o matematici
o matematici
08:17
and had 13 children,
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i imao 13 djece,
08:19
who he apparently would dandle on his knee
while he was writing --
while he was writing --
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koje je navodno njihao na koljenima
dok je pisao --
dok je pisao --
08:25
if it wasn't for Mr. Euler, there wouldn't
perhaps be these invariants.
perhaps be these invariants.
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da nije bilo g.Eulera, vjerojatno ne bi
bilo ovih invarijanti.
bilo ovih invarijanti.
08:32
CA: OK, so that's at least given us
a flavor of that amazing mind in there.
a flavor of that amazing mind in there.
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CA: OK, to nam je dalo barem uvid
u nevjerojatan um koji stoji iza toga.
u nevjerojatan um koji stoji iza toga.
08:36
Let's talk about Renaissance.
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Pričajmo o Renaissance-i.
08:38
Because you took that amazing mind
and having been a code-cracker at the NSA,
and having been a code-cracker at the NSA,
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Zato što ste promatrali taj nevjerojatan
um i zato što ste bili razbijač kodova
u NSA,
um i zato što ste bili razbijač kodova
u NSA,
08:44
you started to become a code-cracker
in the financial industry.
in the financial industry.
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počeli ste raditi kao razbijač kodova
u financijskoj industriji.
u financijskoj industriji.
08:47
I think you probably didn't buy
efficient market theory.
efficient market theory.
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Vjerojatno niste vjerovali u
učinkovitu tržišnu teoriju.
učinkovitu tržišnu teoriju.
08:50
Somehow you found a way of creating
astonishing returns over two decades.
astonishing returns over two decades.
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Nekako ste našli način za kreaciju
zadivljujućih prihoda u zadnjih 20 godina.
zadivljujućih prihoda u zadnjih 20 godina.
08:56
The way it's been explained to me,
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Ovako su mi to objasnili,
08:58
what's remarkable about what you did
wasn't just the size of the returns,
wasn't just the size of the returns,
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ono što je izvanredno u tome što ste
napravili, nisu samo prihodi,
napravili, nisu samo prihodi,
09:01
it's that you took them
with surprisingly low volatility and risk,
with surprisingly low volatility and risk,
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nego kako ste ih stvorili sa začuđujuće
niskim volatilitetom i rizikom,
niskim volatilitetom i rizikom,
09:05
compared with other hedge funds.
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uspoređujući s drugim hedge fondovima.
09:07
So how on earth did you do this, Jim?
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Kako ste, pobogu, to napravili, Jim?
09:10
JS: I did it by assembling
a wonderful group of people.
a wonderful group of people.
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JS: Napravio sam to skupivši
divnu grupu ljudi.
divnu grupu ljudi.
09:14
When I started doing trading, I had
gotten a little tired of mathematics.
gotten a little tired of mathematics.
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Kada sam se počeo baviti trgovinom, malo
sam se umorio od matematike.
sam se umorio od matematike.
09:18
I was in my late 30s,
I had a little money.
I had a little money.
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Bio sam u kasnim 30-im,
imao sam malo novca.
imao sam malo novca.
09:22
I started trading and it went very well.
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Počeo sam trgovati i išlo je dobro.
09:25
I made quite a lot of money
with pure luck.
with pure luck.
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Zaradio sam dosta novca čistom srećom.
09:27
I mean, I think it was pure luck.
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Barem ja mislim da je bila čista
sreća.
sreća.
09:29
It certainly wasn't mathematical modeling.
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Sigurno nije bilo matematičko
modeliranje.
modeliranje.
09:31
But in looking at the data,
after a while I realized:
after a while I realized:
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Ali kada sam pogledao podatke,
nakon nekog vremena sam shvatio:
nakon nekog vremena sam shvatio:
09:35
it looks like there's some structure here.
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Izgleda kao da postoji neka struktura
u tome.
u tome.
09:38
And I hired a few mathematicians,
and we started making some models --
and we started making some models --
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Zaposlio sam par matematičara
i počeli smo izrađivati neke modele --
i počeli smo izrađivati neke modele --
09:41
just the kind of thing we did back
at IDA [Institute for Defense Analyses].
at IDA [Institute for Defense Analyses].
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iste kakve smo radili na Institutu za
obrambenu analizu.
obrambenu analizu.
09:46
You design an algorithm,
you test it out on a computer.
you test it out on a computer.
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Dizajnirate algoritme,
testirate ih na računalu.
testirate ih na računalu.
09:48
Does it work? Doesn't it work? And so on.
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Funkcionira? Ne funkcionira?
I tako dalje.
I tako dalje.
09:51
CA: Can we take a look at this?
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CA: Možemo li pogledati ovo?
09:52
Because here's a typical graph
of some commodity.
of some commodity.
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Zato što je ovdje tipični graf
neke robe.
neke robe.
09:58
I look at that, and I say,
"That's just a random, up-and-down walk --
"That's just a random, up-and-down walk --
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4041
Gledam u to i kažem:
"To je samo nasumičan hod gore-dolje --
"To je samo nasumičan hod gore-dolje --
10:02
maybe a slight upward trend
over that whole period of time."
over that whole period of time."
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možda mali trend povećanja
tijekom cijelog vremena."
tijekom cijelog vremena."
10:05
How on earth could you trade
looking at that,
looking at that,
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Kako bi, pobogu, mogli trgovati
gledajući u to
gledajući u to
10:07
and see something that wasn't just random?
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2326
i vidjeti nešto što nije samo
nasumično?
nasumično?
10:09
JS: In the old days -- this is
kind of a graph from the old days,
kind of a graph from the old days,
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JS: U starim ddanima -- ovo je graf
iz starih dana,
iz starih dana,
10:13
commodities or currencies
had a tendency to trend.
had a tendency to trend.
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roba ili valuta su imale tendenciju
rasta.
rasta.
10:17
Not necessarily the very light trend
you see here, but trending in periods.
you see here, but trending in periods.
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Ne nužno nizak rast kakav vidite ovdje,
ali periodični rast.
ali periodični rast.
10:23
And if you decided, OK,
I'm going to predict today,
I'm going to predict today,
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4056
I ako ste odlučili, OK,
predvidit ću danas,
predvidit ću danas,
10:27
by the average move in the past 20 days --
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4968
prema prosječnom pomicanju
u posljednjih 20 godina --
u posljednjih 20 godina --
10:32
maybe that would be a good prediction,
and I'd make some money.
and I'd make some money.
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možda bi to bilo dobro predviđanje
i zaradio bih novac.
i zaradio bih novac.
10:35
And in fact, years ago,
such a system would work --
such a system would work --
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I zapravo, prije mnogo godina,
ovakav bi sustav funkcionirao --
ovakav bi sustav funkcionirao --
10:41
not beautifully, but it would work.
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2391
ne lijepo, ali bi funkcionirao.
10:43
You'd make money, you'd lose
money, you'd make money.
money, you'd make money.
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2509
Zaradili biste, izgubili novac,
zaradili.
zaradili.
10:46
But this is a year's worth of days,
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2198
Ali ovo je godišnja vrijednost
10:48
and you'd make a little money
during that period.
during that period.
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4241
i zaradili biste malo novca
tijekom tog vremena.
tijekom tog vremena.
10:53
It's a very vestigial system.
197
641884
1958
To je veoma neprofitabilan sustav.
10:56
CA: So you would test
a bunch of lengths of trends in time
a bunch of lengths of trends in time
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3529
CA: Dakle, vi biste testirali hrpu
duljina porasta u vremenu
duljina porasta u vremenu
11:00
and see whether, for example,
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i vidjeli predviđa li, na primjer,
11:02
a 10-day trend or a 15-day trend
was predictive of what happened next.
was predictive of what happened next.
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3481
10-dnevni porast ili 15-dnevni porast što
će se sljedeće dogoditi.
će se sljedeće dogoditi.
11:06
JS: Sure, you would try all those things
and see what worked best.
and see what worked best.
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JS: Naravno, probali biste sve to
i vidjeli što najbolje funkcionira.
i vidjeli što najbolje funkcionira.
11:13
Trend-following would
have been great in the '60s,
have been great in the '60s,
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Praćenje rasta bi bilo odlično
60-ih
60-ih
11:16
and it was sort of OK in the '70s.
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2132
i bilo bi OK 70-ih.
11:19
By the '80s, it wasn't.
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1873
80-ih više nije bilo.
11:20
CA: Because everyone could see that.
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CA: Zato što su svi mogli vidjeti to.
11:23
So, how did you stay ahead of the pack?
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2782
Dakle, kako ste ostali ispred krda?
11:27
JS: We stayed ahead of the pack
by finding other approaches --
by finding other approaches --
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6132
JS: Ostali smo ispred krda, jer smo
pronašli druge pristupe --
pronašli druge pristupe --
11:33
shorter-term approaches to some extent.
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2741
kraće pristupe.
11:37
The real thing was to gather
a tremendous amount of data --
a tremendous amount of data --
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3347
Najvažnije je bilo prikupiti nevjerojatnu
količinu podataka --
količinu podataka --
11:40
and we had to get it by hand
in the early days.
in the early days.
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3578
u početku smo to morali raditi ručno.
11:44
We went down to the Federal Reserve
and copied interest rate histories
and copied interest rate histories
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Išli smo u Federalnu rezervu i kopirali
povijesne trendove rasta
povijesne trendove rasta
11:47
and stuff like that,
because it didn't exist on computers.
because it didn't exist on computers.
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i takve stvari, jer
to nije postojalo na računalima.
to nije postojalo na računalima.
11:50
We got a lot of data.
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1643
Dobili smo mnogo podataka.
11:52
And very smart people -- that was the key.
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4160
I mnogo pametnih ljudi -- to je bio ključ.
11:57
I didn't really know how to hire
people to do fundamental trading.
people to do fundamental trading.
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Nisam znao kako da zaposlim ljude
da se bave osnovnim trgovanjem.
da se bave osnovnim trgovanjem.
12:01
I had hired a few -- some made money,
some didn't make money.
some didn't make money.
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Zaposlio sam ih par -- neki su zarađivali,
a neki ne.
a neki ne.
12:04
I couldn't make a business out of that.
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1880
Nisam mogao stvoriti business iz toga.
12:06
But I did know how to hire scientists,
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2042
Ali znao sam kako zaposliti znanstvenike,
12:08
because I have some taste
in that department.
in that department.
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3389
jer sam se iskusio u tom području.
12:12
So, that's what we did.
220
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1838
Dakle, to smo i napravili.
12:13
And gradually these models
got better and better,
got better and better,
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3231
I postepeno su ti modeli
postajali sve bolji i bolji,
postajali sve bolji i bolji,
12:17
and better and better.
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1335
i bolji i bolji.
12:18
CA: You're credited with doing
something remarkable at Renaissance,
something remarkable at Renaissance,
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3214
CA: Zaslužni ste za nešto nevjerojatno
što ste napravili u Renaissence-u,
što ste napravili u Renaissence-u,
12:21
which is building this culture,
this group of people,
this group of people,
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2601
izgradili ste tu kulturu, tu
grupu ljudi,
grupu ljudi,
12:24
who weren't just hired guns
who could be lured away by money.
who could be lured away by money.
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3142
koji nisu bili zamo zaposlene puške
koje bi namamio novac.
koje bi namamio novac.
12:27
Their motivation was doing
exciting mathematics and science.
exciting mathematics and science.
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3912
Njihova je motivacija bila uzbudljiva
matematika i znanost.
matematika i znanost.
12:31
JS: Well, I'd hoped that might be true.
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JS: Pa, nadao bih se da je to istina.
12:34
But some of it was money.
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3580
Ali ipak je dio bio novac.
12:37
CA: They made a lot of money.
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1393
CA: Puno su zarađivali.
12:39
JS: I can't say that no one came
because of the money.
because of the money.
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2537
JS: Ne mogu reći da nitko nije došao
zbog novca.
zbog novca.
12:41
I think a lot of them
came because of the money.
came because of the money.
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2253
Mislim da ih je mnogo došlo zbog novca.
12:44
But they also came
because it would be fun.
because it would be fun.
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2021
Ali su također došli, jer
bi bilo zabavno.
bi bilo zabavno.
12:46
CA: What role did machine learning
play in all this?
play in all this?
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CA: Koju ulogu u svemu tome je imalo
mašinsko učenje?
mašinsko učenje?
12:48
JS: In a certain sense,
what we did was machine learning.
what we did was machine learning.
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JS: U određenom smislu, ono što smo
mi radili je bilo mašinsko učenje.
mi radili je bilo mašinsko učenje.
12:52
You look at a lot of data, and you try
to simulate different predictive schemes,
to simulate different predictive schemes,
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Pogledate puno podataka i pokušate
simulirati različite predviđajuće sheme,
simulirati različite predviđajuće sheme,
12:59
until you get better and better at it.
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2182
dok ne postanete sve bolji i bolji u tome.
13:01
It doesn't necessarily feed back on itself
the way we did things.
the way we did things.
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3767
Nije nužno dobiti povratnu informaciju
na način na koji smo mi radili.
na način na koji smo mi radili.
13:05
But it worked.
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2309
Ali funkcioniralo je.
13:08
CA: So these different predictive schemes
can be really quite wild and unexpected.
can be really quite wild and unexpected.
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4059
CA: Dakle, te različite predviđajuće sheme
mogu biti prilično divlje i neočekivane.
mogu biti prilično divlje i neočekivane.
13:12
I mean, you looked at everything, right?
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Mislim, vi ste sve gledali,
je li tako?
je li tako?
13:14
You looked at the weather,
length of dresses, political opinion.
length of dresses, political opinion.
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3317
Gledali ste vrijeme, duljinu haljina,
politička mišljenja.
politička mišljenja.
13:17
JS: Yes, length of dresses we didn't try.
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2837
JS: Da, duljinu haljina nismo probali.
13:20
CA: What sort of things?
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2057
CA: Kakve stvari?
13:22
JS: Well, everything.
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1158
JS: Pa, sve.
13:23
Everything is grist for the mill --
except hem lengths.
except hem lengths.
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3264
Sve je važno.
13:28
Weather, annual reports,
246
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2300
Vrijeme, godišnja izvješća,
13:31
quarterly reports, historic data itself,
volumes, you name it.
volumes, you name it.
247
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4732
kvartalna izvješća, povijesni podaci,
obujmi, što god.
obujmi, što god.
13:35
Whatever there is.
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1151
Što god postoji.
13:37
We take in terabytes of data a day.
249
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2621
Uzimamo terabajte podataka
svaki dan.
svaki dan.
13:39
And store it away and massage it
and get it ready for analysis.
and get it ready for analysis.
250
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4124
I pohranjujemo ih te ih spremamo
za analizu.
za analizu.
13:45
You're looking for anomalies.
251
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1382
Tražite anomalije.
13:46
You're looking for -- like you said,
252
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2953
Tražite -- kako ste rekli,
13:49
the efficient market
hypothesis is not correct.
hypothesis is not correct.
253
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2452
učinkovitu tržišnu hipotezu
koja nije točna.
koja nije točna.
13:52
CA: But any one anomaly
might be just a random thing.
might be just a random thing.
254
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3467
CA: Ali jedna anomalija može biti
slučajna.
slučajna.
13:55
So, is the secret here to just look
at multiple strange anomalies,
at multiple strange anomalies,
255
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3658
Dakle, je li tajna ovdje samo gledati
više čudnih anomalija
više čudnih anomalija
13:59
and see when they align?
256
827478
1328
i vidjeti zašto se redaju?
14:01
JS: Any one anomaly
might be a random thing;
might be a random thing;
257
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3213
JS: Svaka anomalija može biti
slučajna;
slučajna;
14:04
however, if you have enough data
you can tell that it's not.
you can tell that it's not.
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ipak, ako imate dovoljno podataka možete
shvatiti da nije.
shvatiti da nije.
14:07
You can see an anomaly that's persistent
for a sufficiently long time --
for a sufficiently long time --
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Možete vidjeti anomaliju koja je
dugotrajna --
dugotrajna --
14:12
the probability of it being
random is not high.
random is not high.
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vjerojatnost da je slučajna nije visoka.
14:17
But these things fade after a while;
anomalies can get washed out.
anomalies can get washed out.
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Ali te stvare izblijede nakon nekog
vremena, anomalije se isperu.
vremena, anomalije se isperu.
14:22
So you have to keep on top
of the business.
of the business.
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Zato morate biti na vrhu businessa.
14:24
CA: A lot of people look
at the hedge fund industry now
at the hedge fund industry now
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CA: Mnogi ljudi gledaju
hedge fond industriju
hedge fond industriju
14:27
and are sort of ... shocked by it,
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i na neki način su šokirani njome,
14:31
by how much wealth is created there,
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koliko se tamo bogatstva stvara
14:34
and how much talent is going into it.
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i koliko talenta ide onamo.
14:37
Do you have any worries
about that industry,
about that industry,
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Imate li ikakve brige za tu industriju
14:41
and perhaps the financial
industry in general?
industry in general?
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ili možda za financijsku industriju
općenito?
općenito?
14:43
Kind of being on a runaway train that's --
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2704
Nekako je na tračnici koja --
14:46
I don't know --
helping increase inequality?
helping increase inequality?
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4030
ne znam --- pomaže u povećanju
nejednakosti?
nejednakosti?
14:50
How would you champion what's happening
in the hedge fund industry?
in the hedge fund industry?
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Kako biste opravdali sve što se događa
u hedge fond industriji?
u hedge fond industriji?
14:54
JS: I think in the last
three or four years,
three or four years,
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JS: Mislim da u
zadnje tri ili četiri godine
zadnje tri ili četiri godine
14:57
hedge funds have not done especially well.
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hedge fondovi nisu posebno dobro prošli.
14:59
We've done dandy,
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887387
1400
Mi smo odlično poslovali,
15:00
but the hedge fund industry as a whole
has not done so wonderfully.
has not done so wonderfully.
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4001
ali hedge fond industrija u cjelini,
ne baš odlično.
ne baš odlično.
15:04
The stock market has been on a roll,
going up as everybody knows,
going up as everybody knows,
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Dionice su divljale, povećavale
se kao što svi znaju
se kao što svi znaju
15:09
and price-earnings ratios have grown.
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i omjer zarada je porastao.
15:13
So an awful lot of the wealth
that's been created in the last --
that's been created in the last --
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3063
Dakle, ogromno bogatstvo koje je stvoreno
u posljednjih --
u posljednjih --
15:16
let's say, five or six years --
has not been created by hedge funds.
has not been created by hedge funds.
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904318
3350
ajmo reći, pet ili šest godina --
nisu stvorili hedge fondovi.
nisu stvorili hedge fondovi.
15:20
People would ask me,
"What's a hedge fund?"
"What's a hedge fund?"
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Ljudi bi me pitali: "Što su hedge
fondovi?"
fondovi?"
15:23
And I'd say, "One and 20."
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I ja bi reako :"Jedan i 20."
15:25
Which means -- now it's two and 20 --
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Što znači -- danas je dva i 20 --
15:29
it's two percent fixed fee
and 20 percent of profits.
and 20 percent of profits.
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917577
3353
to je dva posto fiksnog uloga i 20 posto
profita.
profita.
15:32
Hedge funds are all
different kinds of creatures.
different kinds of creatures.
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Hedge fondovi su različite vrste bića.
15:35
CA: Rumor has it you charge
slightly higher fees than that.
slightly higher fees than that.
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CA: Priča se da naplačujete nešto
više od toga.
više od toga.
15:39
JS: We charged the highest fees
in the world at one time.
in the world at one time.
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JS: U jednom trenutku smo naplačivali
najviše u svijetu.
najviše u svijetu.
15:42
Five and 44, that's what we charge.
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Pet i 44, toliko mi naplačujemo.
15:45
CA: Five and 44.
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CA: Pet i 44.
15:47
So five percent flat,
44 percent of upside.
44 percent of upside.
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935116
3234
Dakle, pet posto uloga,
44 posto više profita.
44 posto više profita.
15:50
You still made your investors
spectacular amounts of money.
spectacular amounts of money.
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938374
2783
I dalje ste svojim investitorima
stvorili spektakularne zarade.
stvorili spektakularne zarade.
15:53
JS: We made good returns, yes.
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1452
JS: Imali smo dobre zarade, da.
15:54
People got very mad:
"How can you charge such high fees?"
"How can you charge such high fees?"
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3000
Ljudi su se jako naljutili: "Kako možete
naplačivati toliko?"
naplačivati toliko?"
15:57
I said, "OK, you can withdraw."
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945681
1627
Ja sam rekao: "OK, možete se povući."
15:59
But "How can I get more?"
was what people were --
was what people were --
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947332
2818
Ali "Kako mogu dobiti više?", to
je zanimalo ljude --
je zanimalo ljude --
16:02
(Laughter)
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1504
(Smijeh)
16:03
But at a certain point,
as I think I told you,
as I think I told you,
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2440
Ali u određenom trenutku, mislim
da sam vam rekao to,
da sam vam rekao to,
16:06
we bought out all the investors
because there's a capacity to the fund.
because there's a capacity to the fund.
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954166
5175
kupili smo sve investitore, jer je
postojao kapacitet fonda.
postojao kapacitet fonda.
16:11
CA: But should we worry
about the hedge fund industry
about the hedge fund industry
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959365
2704
CA: Ali trebamo li se brinuti
da hedge fond industrija
da hedge fond industrija
16:14
attracting too much of the world's
great mathematical and other talent
great mathematical and other talent
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5438
privlači previše svjetskih odličnih
matematičara i drugih talenata
matematičara i drugih talenata
16:19
to work on that, as opposed
to the many other problems in the world?
to the many other problems in the world?
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3238
da rade na tome, umjesto na mnogim
drugim svjetskim problemima?
drugim svjetskim problemima?
16:22
JS: Well, it's not just mathematical.
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1929
JS: Pa, nisu samo matematičari.
16:24
We hire astronomers and physicists
and things like that.
and things like that.
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2679
Zapošljavamo astronome i fizičare
i takve ljude.
i takve ljude.
16:27
I don't think we should worry
about it too much.
about it too much.
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2431
Mislim da ne bismo trebali previše
brinuti o tome.
brinuti o tome.
16:30
It's still a pretty small industry.
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978288
3142
To je još uvijek veoma mala
industrija.
industrija.
16:33
And in fact, bringing science
into the investing world
into the investing world
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981454
5997
I zapravo, dovođenje znanosti
u svijet investicija
u svijet investicija
16:39
has improved that world.
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987475
2159
je poboljšalo svijet.
16:41
It's reduced volatility.
It's increased liquidity.
It's increased liquidity.
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989658
4070
Smanjen volatilitet.
Povećana likvidnost.
Povećana likvidnost.
16:45
Spreads are narrower because
people are trading that kind of stuff.
people are trading that kind of stuff.
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993752
3189
Širenja su uža, jer ljudi
trguju takvim stvarima.
trguju takvim stvarima.
16:48
So I'm not too worried about Einstein
going off and starting a hedge fund.
going off and starting a hedge fund.
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996965
5076
Tako da nisam zabrinut da će Einstein
odustati od svog rada i osnovati
hedge fond.
odustati od svog rada i osnovati
hedge fond.
16:54
CA: You're at a phase in your life now
where you're actually investing, though,
where you're actually investing, though,
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4164
CA: Sada ste u fazi života kada zapravo
investirate, iako
investirate, iako
16:58
at the other end of the supply chain --
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1006666
3734
s druge strane opskrbnog lanca --
17:02
you're actually boosting
mathematics across America.
mathematics across America.
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1010424
4104
vi zapravo potičete matematiku
u cijeloj Americi.
u cijeloj Americi.
17:06
This is your wife, Marilyn.
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1014552
1865
Ovo je vaša žena, Marilyn.
17:08
You're working on
philanthropic issues together.
philanthropic issues together.
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1016441
4756
Zajedno radite na filantropskim problemima.
17:13
Tell me about that.
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1021221
1163
Recite mi nešto o tome.
17:14
JS: Well, Marilyn started --
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3649
JS: Pa, Marilyn je osnovala --
17:18
there she is up there,
my beautiful wife --
my beautiful wife --
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3447
eno je tamo gore, moja prekrasna
supruga --
supruga --
17:21
she started the foundation
about 20 years ago.
about 20 years ago.
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1029552
2972
ona je osnovala zakladu prije otprilike
20 godina.
20 godina.
17:24
I think '94.
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1151
Mislim '94.
17:25
I claim it was '93, she says it was '94,
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1033723
2095
Ja tvrdim '93., ona kaže da je '94.,
17:27
but it was one of those two years.
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1035842
2571
ali bilo je jedne od te dvije godine.
17:30
(Laughter)
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1038437
2135
(Smijeh)
17:32
We started the foundation,
just as a convenient way to give charity.
just as a convenient way to give charity.
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1040596
6719
Osnovali smo zakladu, samo kao
prikladan način doniranja.
prikladan način doniranja.
17:40
She kept the books, and so on.
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1048346
2507
Ona je bila knjigovođa, itd.
17:42
We did not have a vision at that time,
but gradually a vision emerged --
but gradually a vision emerged --
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1050877
6714
U to vrijeme nismo imali viziju, ali
postupno se ona pojavila --
postupno se ona pojavila --
17:49
which was to focus on math and science,
to focus on basic research.
to focus on basic research.
326
1057615
5504
a to je bilo fokusiranje na matematiku i
znanost, na osnovna istraživanja.
znanost, na osnovna istraživanja.
17:55
And that's what we've done.
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1063569
2772
I to smo napravili.
17:58
Six years ago or so, I left Renaissance
and went to work at the foundation.
and went to work at the foundation.
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1066365
6355
Prije šest godina ili tako nešto, napustio
sam Reneissance i otišao raditi u zakladu.
sam Reneissance i otišao raditi u zakladu.
18:04
So that's what we do.
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1072744
1571
Dakle, to radimo.
18:06
CA: And so Math for America
is basically investing
is basically investing
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1074339
2909
CA: Dakle, Math of America investira
18:09
in math teachers around the country,
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1077272
2638
u profesore matematike iz cijele zemlje,
18:11
giving them some extra income,
giving them support and coaching.
giving them support and coaching.
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1079934
3802
daje im nešto više prihoda, daje im
podršku i usavršavanja.
podršku i usavršavanja.
18:15
And really trying
to make that more effective
to make that more effective
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1083760
3051
I zapravo pokušava to učiniti
učinkovitijim
učinkovitijim
18:18
and make that a calling
to which teachers can aspire.
to which teachers can aspire.
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1086835
2601
i učiniti to nečim čemu profesori teže.
18:21
JS: Yeah -- instead of beating up
the bad teachers,
the bad teachers,
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1089460
4790
JS: Da -- umjesto da prebijamo loše
profesore,
profesore,
18:26
which has created morale problems
all through the educational community,
all through the educational community,
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1094274
4853
što je proizvelo moralne probleme u
cijeloj prosvjetnoj zajednici,
cijeloj prosvjetnoj zajednici,
18:31
in particular in math and science,
337
1099151
2441
posebice u matematici i znanostima,
18:33
we focus on celebrating the good ones
and giving them status.
and giving them status.
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1101616
6130
mi se fokusiramo na slavljenje dobrih
i dajemo im status.
i dajemo im status.
18:39
Yeah, we give them extra money,
15,000 dollars a year.
15,000 dollars a year.
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1107770
2931
Da, dajemo im dodatan novac, 15,000
dolara godišnje.
dolara godišnje.
18:42
We have 800 math and science teachers
in New York City in public schools today,
in New York City in public schools today,
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1110725
4467
Imamo 800 profesora matematike i znanosti
u newyorškim javnim školama danas,
u newyorškim javnim školama danas,
18:47
as part of a core.
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1115216
1814
kao dio jezgre.
18:49
There's a great morale among them.
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1117054
3686
Velika je moralnost među njima.
18:52
They're staying in the field.
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1120764
2506
Ostaju u tom polju.
18:55
Next year, it'll be 1,000
and that'll be 10 percent
and that'll be 10 percent
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1123294
2895
Sljedeće godine, bit će ih 1,000
i to će biti 10 posto
i to će biti 10 posto
18:58
of the math and science teachers
in New York [City] public schools.
in New York [City] public schools.
345
1126213
3544
newyorških javnoškolskih profesora
matematike i znanosti.
matematike i znanosti.
19:01
(Applause)
346
1129781
5905
(Pljesak)
19:07
CA: Jim, here's another project
that you've supported philanthropically:
that you've supported philanthropically:
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1135710
3410
CA: Jim, evo još jednog projekta koji
ste filantropski poduprli;
ste filantropski poduprli;
19:11
Research into origins of life, I guess.
348
1139144
2397
Istraživanje postanka života,
pretpostavljam.
pretpostavljam.
19:13
What are we looking at here?
349
1141565
1447
Što to ovdje gledamo?
19:15
JS: Well, I'll save that for a second.
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1143536
1882
JS: Pa, pričekat ću sekundu.
19:17
And then I'll tell you
what you're looking at.
what you're looking at.
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1145442
2162
I onda ću vam reći što gledamo.
19:19
Origins of life is a fascinating question.
352
1147628
3056
Postanak života je fascinantno pitanje.
19:22
How did we get here?
353
1150708
1533
Kako smo dospjeli ovdje?
19:25
Well, there are two questions:
354
1153170
1771
Pa, postoje dva pitanja:
19:26
One is, what is the route
from geology to biology --
from geology to biology --
355
1154965
5868
Jedno je, koji je put od geologije
do biologije --
do biologije --
19:32
how did we get here?
356
1160857
1381
kako smo dospjeli ovdje?
19:34
And the other question is,
what did we start with?
what did we start with?
357
1162262
2364
A drugo je pitanje, čime smo započeli?
19:36
What material, if any,
did we have to work with on this route?
did we have to work with on this route?
358
1164650
3102
S kojim materijalom, ako s ikojim,
smo morali raditi na ovom putu?
smo morali raditi na ovom putu?
19:39
Those are two very,
very interesting questions.
very interesting questions.
359
1167776
3061
To su dva veoma, veoma zanimljiva pitanja.
19:43
The first question is a tortuous path
from geology up to RNA
from geology up to RNA
360
1171773
5834
Prvo je pitanje mučan put
od geologije do RNA
od geologije do RNA
19:49
or something like that --
how did that all work?
how did that all work?
361
1177631
2258
ili nešto tako -- kako je to sve
funkcioniralo?
funkcioniralo?
19:51
And the other,
what do we have to work with?
what do we have to work with?
362
1179913
2388
A drugo, s čim moramo raditi?
19:54
Well, more than we think.
363
1182325
1771
Pa, s više nego što mislimo.
19:56
So what's pictured there
is a star in formation.
is a star in formation.
364
1184120
4843
Dakle, ono što je na ovoj slici je
zvijezda u nastanku.
zvijezda u nastanku.
20:01
Now, every year in our Milky Way,
which has 100 billion stars,
which has 100 billion stars,
365
1189836
3425
Svake godine u našoj Mliječnoj Stazi, koja
ima 100 milijardi zvijezda,
ima 100 milijardi zvijezda,
20:05
about two new stars are created.
366
1193285
2495
nastanu otprilike dvije nove zvijezde.
20:07
Don't ask me how, but they're created.
367
1195804
2470
Nemojte me pitati kako, ali nastanu.
20:10
And it takes them about a million
years to settle out.
years to settle out.
368
1198298
3080
I treba im otprilike miljun godina
da se raspadnu.
da se raspadnu.
20:14
So, in steady state,
369
1202132
2176
Dakle, u stabilnom stanju
20:16
there are about two million stars
in formation at any time.
in formation at any time.
370
1204332
3848
ima oko dva miljuna zvijezda koje nastaju
u bilo koje vrijeme.
u bilo koje vrijeme.
20:20
That one is somewhere
along this settling-down period.
along this settling-down period.
371
1208204
3458
Ova je negdje u razdoblju stabiliziranja.
20:24
And there's all this crap
sort of circling around it,
sort of circling around it,
372
1212067
2936
I tu su sva ova sranja koja kruže oko nje,
20:27
dust and stuff.
373
1215027
1498
prašina i tome slično.
20:29
And it'll form probably a solar system,
or whatever it forms.
or whatever it forms.
374
1217479
3023
I vjerojatno će formirati solarni sustav,
ili što god već formira.
ili što god već formira.
20:32
But here's the thing --
375
1220526
2176
Ali, evo u čemu je stvar --
20:34
in this dust that surrounds a forming star
376
1222726
6348
u ovoj prašini koja okružuje zvijezde
20:41
have been found, now,
significant organic molecules.
significant organic molecules.
377
1229098
6035
nađene su značajne organske molekule.
20:47
Molecules not just like methane,
but formaldehyde and cyanide --
but formaldehyde and cyanide --
378
1235958
6139
Molekule, ne samo poput metana,
nego poput formaldehida i cijanida --
nego poput formaldehida i cijanida --
20:54
things that are the building blocks --
the seeds, if you will -- of life.
the seeds, if you will -- of life.
379
1242121
6517
stvari koje izgrađuju -- sjeme,
može i tako -- života.
može i tako -- života.
21:01
So, that may be typical.
380
1249136
2692
To može biti tipično.
21:04
And it may be typical
that planets around the universe
that planets around the universe
381
1252395
6934
I može biti tipično da planeti
u svemiru
u svemiru
21:11
start off with some of these
basic building blocks.
basic building blocks.
382
1259353
3612
nastaju od nekih osnovnih
građevnih tvari poput tih.
građevnih tvari poput tih.
21:15
Now does that mean
there's going to be life all around?
there's going to be life all around?
383
1263830
2715
Znači li to da će život
zauvijek postojati?
zauvijek postojati?
21:18
Maybe.
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1266569
1364
Možda.
21:19
But it's a question
of how tortuous this path is
of how tortuous this path is
385
1267957
4127
Ali pitanje je koliko je mučan taj put
21:24
from those frail beginnings,
those seeds, all the way to life.
those seeds, all the way to life.
386
1272108
4394
od tih nepouzdanih početaka,
tog sjemena, sve do života.
tog sjemena, sve do života.
21:28
And most of those seeds
will fall on fallow planets.
will fall on fallow planets.
387
1276526
5192
I većina tog sjemena će pasti na
neobrađene planete.
neobrađene planete.
21:33
CA: So for you, personally,
388
1281742
1409
CA: Dakle, za vas osobno,
21:35
finding an answer to this question
of where we came from,
of where we came from,
389
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pronaći odgovor na pitanje odakle
dolazimo,
dolazimo,
21:37
of how did this thing happen,
that is something you would love to see.
that is something you would love to see.
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pitanje kako se ovo dogodilo, to je nešto
što biste voljeli vidjeti.
što biste voljeli vidjeti.
21:41
JS: Would love to see.
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JS: Volio bih to vidjeti,
21:43
And like to know --
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I volio bih znati --
21:44
if that path is tortuous enough,
and so improbable,
and so improbable,
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ako je taj put dovoljno težak i toliko
nevjerojatan,
nevjerojatan,
21:50
that no matter what you start with,
we could be a singularity.
we could be a singularity.
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da bez obzira s čim počnemo, mogli
bismo biti jedinstveni.
bismo biti jedinstveni.
21:55
But on the other hand,
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Ali s druge strane,
21:56
given all this organic dust
that's floating around,
that's floating around,
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kada vidimo svu tu organsku prašinu
koja lebdi uokolo,
koja lebdi uokolo,
22:00
we could have lots of friends out there.
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mogli bismo imati puno prijatelja tamo.
22:04
It'd be great to know.
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Bilo bi super znati.
22:06
CA: Jim, a couple of years ago,
I got the chance to speak with Elon Musk,
I got the chance to speak with Elon Musk,
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CA: Jim, prije nekoliko godina sam imao
priliku razgovarati s Elonom Muskom
priliku razgovarati s Elonom Muskom
22:09
and I asked him the secret of his success,
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i pitao sam ga za tajnu njegova uspjeha,
22:12
and he said taking
physics seriously was it.
physics seriously was it.
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a on je odgovorio da je tajna njegova
uspjeha što je ozbiljno shvaćao fiziku.
uspjeha što je ozbiljno shvaćao fiziku.
22:16
Listening to you, what I hear you saying
is taking math seriously,
is taking math seriously,
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Slušajući vas, ono što čujem da vi
govorite je ozbiljno shvaćanje matematike,
govorite je ozbiljno shvaćanje matematike,
22:20
that has infused your whole life.
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koja je obuzela vaš cijeli život.
22:24
It's made you an absolute fortune,
and now it's allowing you to invest
and now it's allowing you to invest
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Obogatila vas je i sad vam dopušta
da investirate
da investirate
22:28
in the futures of thousands and thousands
of kids across America and elsewhere.
of kids across America and elsewhere.
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u budućnost tisuće i tisuće djece
u Americi i drugdje.
u Americi i drugdje.
22:33
Could it be that science actually works?
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Može li biti da znanost
zapravo funkcionira?
zapravo funkcionira?
22:36
That math actually works?
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Da matematika zapravo funkcionira?
22:39
JS: Well, math certainly works.
Math certainly works.
Math certainly works.
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JS: Pa, matematika zasigurno funkcionira.
Matematika sigurno funkcionira.
Matematika sigurno funkcionira.
22:43
But this has been fun.
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Ali ovo je bilo zabavno.
22:44
Working with Marilyn and giving it away
has been very enjoyable.
has been very enjoyable.
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Raditi s Marilyin i donirati je bilo
veoma ugodno.
veoma ugodno.
22:49
CA: I just find it --
it's an inspirational thought to me,
it's an inspirational thought to me,
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CA: Ja smatram -- za mene je to
inspirativna misao,
inspirativna misao,
22:52
that by taking knowledge seriously,
so much more can come from it.
so much more can come from it.
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da kada znanje shvaćamo ozbiljno,
toliko više može proizaći iz toga.
toliko više može proizaći iz toga.
22:56
So thank you for your amazing life,
and for coming here to TED.
and for coming here to TED.
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Pa vam zahvaljujem na nevjerojatnom životu
i na dolasku na TED.
i na dolasku na TED.
22:59
Thank you.
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Hvala Vam.
23:00
Jim Simons!
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Jim Simons!
23:01
(Applause)
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(Pljesak)
ABOUT THE SPEAKER
Jim Simons - Philanthropist, mathematicianAfter astonishing success as a mathematician, code breaker and billionaire hedge fund manager, Jim Simons is mastering yet another field: philanthropy.
Why you should listen
As a mathematician who cracked codes for the National Security Agency on the side, Jim Simons had already revolutionized geometry -- and incidentally laid the foundation for string theory -- when he began to get restless. Along with a few hand-picked colleagues he started the investment firm that went on to become Renaissance, a hedge fund working with hitherto untapped algorithms, and became a billionaire in the process.
Now retired as Renaissance’s CEO, Simons devotes his time to mathematics and philanthropy. The Simons Foundation has committed more than a billion dollars to math and science education and to autism research.
Jim Simons | Speaker | TED.com