How to read the genome and build a human being | Riccardo Sabatini

318,374 views

2016-05-24 ・ TED


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How to read the genome and build a human being | Riccardo Sabatini

318,374 views ・ 2016-05-24

TED


Dvaput kliknite na engleske titlove ispod za reprodukciju videozapisa.

Prevoditelj: Sanda L Recezent: Tilen Pigac - EFZG
00:12
For the next 16 minutes, I'm going to take you on a journey
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U sljedećih 16 minuta povest ću vas na putovanje
00:15
that is probably the biggest dream of humanity:
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koje je vjerojatno najveći san čovječanstva:
00:18
to understand the code of life.
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razumijevanje kôda života.
Za mene je sve počelo prije mnogo, mnogo godina,
00:21
So for me, everything started many, many years ago
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00:23
when I met the first 3D printer.
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kada sam saznao za prvi 3D printer.
00:26
The concept was fascinating.
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Sam koncept je bio fascinantan.
00:28
A 3D printer needs three elements:
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3D printeru su potrebna tri elementa:
00:30
a bit of information, some raw material, some energy,
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djelić informacije, nešto sirovine, nešto energije
00:34
and it can produce any object that was not there before.
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i može proizvesti bilo koji predmet koji prethodno nije ni postojao.
00:38
I was doing physics, I was coming back home
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Bavio sam se fizikom, vraćao sam se kući
00:40
and I realized that I actually always knew a 3D printer.
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te shvatio da mi je 3D printer oduvijek bio poznat.
Kao i svima ostalima.
00:44
And everyone does.
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00:45
It was my mom.
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Bila je to moja mama.
00:46
(Laughter)
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(Smijeh)
00:47
My mom takes three elements:
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Moja mama je uzela tri elementa:
djelić informacije, u ovom slučaju između mog oca i moje majke,
00:50
a bit of information, which is between my father and my mom in this case,
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sirovine i energiju u istom mediju, odnosno hrani,
00:54
raw elements and energy in the same media, that is food,
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00:58
and after several months, produces me.
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i nakon nekoliko mjeseci proizvela je mene.
01:00
And I was not existent before.
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A ja prije toga nisam postojao.
01:02
So apart from the shock of my mom discovering that she was a 3D printer,
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Pored zapanjenosti moje mame, saznavši da je se smatra 3D printerom,
01:06
I immediately got mesmerized by that piece,
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istog trena bio sam opčinjen tim dijelom,
01:11
the first one, the information.
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tim prvim dijelom, informacijom.
01:12
What amount of information does it take
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Koliko informacija je potrebno
01:15
to build and assemble a human?
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kako bi se izgradio i sastavio čovjek?
01:17
Is it much? Is it little?
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Mnogo? Malo?
01:18
How many thumb drives can you fill?
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Koliko USB diskova biste mogli ispuniti?
Pa, na početku sam studirao fiziku
01:21
Well, I was studying physics at the beginning
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01:23
and I took this approximation of a human as a gigantic Lego piece.
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i zamislio tu pretpostavku o čovjeku kao golemoj Lego slagalici.
01:29
So, imagine that the building blocks are little atoms
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Dakle, zamislite da su kockice sitni atomi
i vodik je ovdje, ugljik ovdje, a dušik ovdje.
01:33
and there is a hydrogen here, a carbon here, a nitrogen here.
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01:37
So in the first approximation,
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Prema prvoj pretpostavci,
01:39
if I can list the number of atoms that compose a human being,
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ako bih mogao navesti broj atoma od kojih se sastoji ljudsko biće,
01:43
I can build it.
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mogao bih ga sagraditi.
Možete provjeriti brojke
01:45
Now, you can run some numbers
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01:47
and that happens to be quite an astonishing number.
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i to izgleda kao prilično zapanjujući broj.
01:50
So the number of atoms,
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Dakle, broj atoma,
01:53
the file that I will save in my thumb drive to assemble a little baby,
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dokument koji bih sačuvao na USB-u kako bih sastavio jednu bebu,
zapravo bi ispunio prostor veličine Titanika punog USB-ova,
01:58
will actually fill an entire Titanic of thumb drives --
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02:02
multiplied 2,000 times.
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pomnoženo 2.000 puta.
02:05
This is the miracle of life.
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To je čudo života.
02:09
Every time you see from now on a pregnant lady,
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Od sada, svaki put kada ugledate trudnicu,
ona u sebi sadrži najveću količinu informacija
02:12
she's assembling the biggest amount of information
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02:14
that you will ever encounter.
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koju ćete ikad susresti.
02:16
Forget big data, forget anything you heard of.
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Zaboravite velike količine podataka, ili bilo što što ste čuli.
02:19
This is the biggest amount of information that exists.
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To je najveća količina informacija koja postoji.
02:22
(Applause)
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(Pljesak)
02:26
But nature, fortunately, is much smarter than a young physicist,
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No, srećom, priroda je daleko pametnija od mladog fizičara
02:30
and in four billion years, managed to pack this information
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i u četiri milijarde godina uspjela je složiti ove informacije
02:34
in a small crystal we call DNA.
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u mali kristal koji zovemo DNK.
02:37
We met it for the first time in 1950 when Rosalind Franklin,
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Prvi put smo saznali za njega 1950. kada je Rosalind Franklin,
02:41
an amazing scientist, a woman,
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nevjerojatna znanstvenica,
02:43
took a picture of it.
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napravila sliku kristala.
02:44
But it took us more than 40 years to finally poke inside a human cell,
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No, trebalo nam je više od 40 godina da konačno prodremo u ljudsku stanicu,
izvadimo taj kristal,
02:50
take out this crystal,
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02:51
unroll it, and read it for the first time.
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odmotamo ga i prvi puta pročitamo.
02:55
The code comes out to be a fairly simple alphabet,
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Ispostavilo se da je kôd prilično jednostavna abeceda,
02:58
four letters: A, T, C and G.
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četiri slova: A, T, C i G.
03:02
And to build a human, you need three billion of them.
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A kako biste sagradili čovjeka, potrebno vam je tri milijarde njih.
03:06
Three billion.
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Tri milijarde.
Koliko je tri milijarde?
03:08
How many are three billion?
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03:09
It doesn't really make any sense as a number, right?
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Sam broj zaista nema nikakvog smisla, zar ne?
03:12
So I was thinking how I could explain myself better
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Stoga sam razmišljao kako bih si bolje objasnio
03:16
about how big and enormous this code is.
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koliko je velik i ogroman ovaj kôd.
03:19
But there is -- I mean, I'm going to have some help,
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Ali evo ga, mislim, imat ću malu pomoć,
03:22
and the best person to help me introduce the code
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a najbolja osoba koja bi mi pomogla predstaviti kôd,
zapravo je prvi čovjek koji ga je sekvencirao, dr. Craig Venter.
03:26
is actually the first man to sequence it, Dr. Craig Venter.
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03:29
So welcome onstage, Dr. Craig Venter.
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Stoga, pozdravite dr. Craiga Ventera.
03:32
(Applause)
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(Pljesak)
03:39
Not the man in the flesh,
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Ne čovjek glavom i bradom,
03:43
but for the first time in history,
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već po prvi puta u povijesti,
03:45
this is the genome of a specific human,
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ovo je genom određenog čovjeka,
03:49
printed page-by-page, letter-by-letter:
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otisnut stranicu po stranicu, slovo po slovo:
262.000 stranica informacija,
03:53
262,000 pages of information,
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450 kilograma, isporučenih iz SAD-a u Kanadu,
03:57
450 kilograms, shipped from the United States to Canada
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04:01
thanks to Bruno Bowden, Lulu.com, a start-up, did everything.
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zahvaljujući Bruni Bowdenu, dostupno na Lulu.com, sve je odrađeno.
04:06
It was an amazing feat.
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Bio je to fantastičan podvig.
04:07
But this is the visual perception of what is the code of life.
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Ali ovo je vizualni doživljaj onoga što je kôd života.
A sada, po prvi puta, mogu učiniti nešto zabavno.
04:12
And now, for the first time, I can do something fun.
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04:14
I can actually poke inside it and read.
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Mogu, zapravo, zaviriti unutra i čitati.
04:17
So let me take an interesting book ... like this one.
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Dozvolite mi da uzmem zanimljivu knjigu... poput ove.
Samo jedna opaska; knjiga je prilično obimna.
04:25
I have an annotation; it's a fairly big book.
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04:27
So just to let you see what is the code of life.
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Samo da vidite što je kôd života.
04:32
Thousands and thousands and thousands
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Na tisuće i tisuće i tisuće
04:35
and millions of letters.
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i milijune slova.
04:38
And they apparently make sense.
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I ona očito daju neki smisao.
Pogledajmo jedan specifičan dio.
04:41
Let's get to a specific part.
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04:43
Let me read it to you:
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Dozvolite da vam ga pročitam:
04:44
(Laughter)
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(Smijeh)
"AAG, AAT, ATA."
04:46
"AAG, AAT, ATA."
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04:50
To you it sounds like mute letters,
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Vama ovo zvuči kao obična slova bez smisla,
no, ovaj redoslijed određuje Craigovu boju očiju.
04:53
but this sequence gives the color of the eyes to Craig.
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04:57
I'll show you another part of the book.
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Pokazat ću vam jedan drugi dio iz knjige.
04:59
This is actually a little more complicated.
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Ovaj je, zapravo, nešto složeniji.
05:02
Chromosome 14, book 132:
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Kromosom 14, knjiga 132:
05:05
(Laughter)
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(Smijeh)
05:07
As you might expect.
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Kao što biste i očekivali.
(Smijeh)
05:09
(Laughter)
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05:14
"ATT, CTT, GATT."
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"ATT, CTT, GATT."
05:20
This human is lucky,
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Ova osoba ima sreće,
jer ako izostavite samo dva slova u ovom redoslijedu,
05:22
because if you miss just two letters in this position --
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05:26
two letters of our three billion --
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dva slova od tri milijarde,
05:28
he will be condemned to a terrible disease:
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ova osoba bit će osuđena na užasnu bolest:
05:30
cystic fibrosis.
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cističnu fibrozu.
05:31
We have no cure for it, we don't know how to solve it,
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Za nju još nemamo lijek, ne znamo kako je izliječiti,
05:35
and it's just two letters of difference from what we are.
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a samo su dva slova različita od onih koja mi ostali imamo.
05:39
A wonderful book, a mighty book,
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Predivna knjiga, moćna knjiga,
moćna knjiga koja mi je pomogla razumjeti
05:43
a mighty book that helped me understand
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i pokazati vam nešto zaista izvanredno.
05:45
and show you something quite remarkable.
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05:48
Every one of you -- what makes me, me and you, you --
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Svatko od vas -- ono zbog čega sam ja, ja, a vi ste vi --
05:52
is just about five million of these,
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samo je oko pet milijuna ovih slova,
05:55
half a book.
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polovica knjige.
Što se tiče ostalog,
05:58
For the rest,
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05:59
we are all absolutely identical.
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posve smo identični.
Pet stotina stranica je čudo života koje predstavljate vi.
06:03
Five hundred pages is the miracle of life that you are.
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Ostalo svi mi dijelimo.
06:07
The rest, we all share it.
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06:09
So think about that again when we think that we are different.
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Zato se sjetite toga kada pomislite kako smo svi različiti.
06:12
This is the amount that we share.
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Ovo je količina koju svi dijelimo.
06:15
So now that I have your attention,
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I sada kada imam vašu pažnju,
06:18
the next question is:
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sledeće pitanje je:
06:20
How do I read it?
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Kako da to pročitam?
06:21
How do I make sense out of it?
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Kako da pronađem smisao u tome?
06:23
Well, for however good you can be at assembling Swedish furniture,
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Pa, koliko god ste dobri u sastavljanju švedskog namještaja,
06:27
this instruction manual is nothing you can crack in your life.
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ovaj priručnik za upotrebu je nešto što nećete dešifrirati u svom životu.
06:31
(Laughter)
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(Smijeh)
06:32
And so, in 2014, two famous TEDsters,
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I tako su 2014. godine, dva čuvena TED-ovca,
Peter Diamandis i Craig Venter osobno,
06:36
Peter Diamandis and Craig Venter himself,
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06:38
decided to assemble a new company.
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odlučili osnovati novu tvrtku.
06:40
Human Longevity was born,
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Rođen je Human Longevity,
06:41
with one mission:
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sa samo jednom misijom:
06:43
trying everything we can try
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pokušati sve što možemo
06:45
and learning everything we can learn from these books,
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i naučiti sve što možemo naučiti iz ovih knjiga,
s jednim ciljem,
06:48
with one target --
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06:50
making real the dream of personalized medicine,
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ostvariti san o personaliziranoj medicini,
06:53
understanding what things should be done to have better health
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razumjeti što se treba učiniti kako bismo bili zdraviji
06:57
and what are the secrets in these books.
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i koje tajne kriju ove knjige.
07:00
An amazing team, 40 data scientists and many, many more people,
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Fantastična ekipa, 40 znanstvenika za podatke i još mnogo, mnogo ljudi,
07:04
a pleasure to work with.
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s kojima je užitak raditi.
07:05
The concept is actually very simple.
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Koncept je, zapravo, vrlo jednostavan.
07:08
We're going to use a technology called machine learning.
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Koristit ćemo tehnologiju koja se zove strojno učenje.
07:11
On one side, we have genomes -- thousands of them.
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S jedne strane imamo genome -- na tisuće njih.
07:15
On the other side, we collected the biggest database of human beings:
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S druge strane, sakupili smo najveću bazu podataka o ljudskim bićima:
fenotipe, 3D snimke, magnetsku rezonanciju, sve što vam pada na pamet.
07:20
phenotypes, 3D scan, NMR -- everything you can think of.
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07:24
Inside there, on these two opposite sides,
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Unutar toga, na ovim suprotnim stranama,
07:27
there is the secret of translation.
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nalazi se tajna prevođenja.
07:29
And in the middle, we build a machine.
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A u sredini smo izradili stroj.
07:32
We build a machine and we train a machine --
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Izradili smo stroj i obučili ga --
07:35
well, not exactly one machine, many, many machines --
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zapravo, ne baš jedan stroj, već mnogo, mnogo strojeva
07:38
to try to understand and translate the genome in a phenotype.
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kako bi se pokušao razumjeti i prevesti genom u fenotipu.
07:43
What are those letters, and what do they do?
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Što su ta slova i čemu ona služe?
07:46
It's an approach that can be used for everything,
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To je pristup koji se može za sve koristiti,
07:49
but using it in genomics is particularly complicated.
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ali je njegova upotreba u genetici naročito složena.
07:52
Little by little we grew and we wanted to build different challenges.
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Malo po malo smo rasli te smo željeli stvoriti drugačije izazove.
07:55
We started from the beginning, from common traits.
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Počeli smo od početka, od zajedničkih osobina.
07:58
Common traits are comfortable because they are common,
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Zajedničke osobine su prikladne baš zato što su zajedničke,
08:01
everyone has them.
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svi ih imamo.
08:02
So we started to ask our questions:
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Stoga smo počeli postavljati pitanja:
08:04
Can we predict height?
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Možemo li predvidjeti visinu?
08:06
Can we read the books and predict your height?
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Možemo li čitanjem ovih knjiga predvidjeti vašu visinu?
08:09
Well, we actually can,
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Pa, zapravo možemo,
08:10
with five centimeters of precision.
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preciznošću od pet centimetara.
Indeks tjelesne mase usko je povezan s vašim načinom života,
08:12
BMI is fairly connected to your lifestyle,
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08:15
but we still can, we get in the ballpark, eight kilograms of precision.
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ali i dalje možemo pogoditi, preciznošću od osam kilograma.
Možemo li predvidjeti boju očiju?
08:19
Can we predict eye color?
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08:20
Yeah, we can.
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Da, možemo.
08:21
Eighty percent accuracy.
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Preciznošću od 80%.
08:23
Can we predict skin color?
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Možemo li predvidjeti boju kože?
08:25
Yeah we can, 80 percent accuracy.
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Možemo, preciznošću od 80%.
08:27
Can we predict age?
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Možemo li predvidjeti dob?
Možemo, jer izgleda da se kôd mijenja tijekom vašeg života.
08:30
We can, because apparently, the code changes during your life.
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08:33
It gets shorter, you lose pieces, it gets insertions.
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Postaje kraći, gubite dijelove, dodaju se umeci.
08:37
We read the signals, and we make a model.
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Čitamo signale i stvaramo model.
08:40
Now, an interesting challenge:
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A sada, zanimljiv izazov:
08:41
Can we predict a human face?
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Možemo li predvidjeti ljudsko lice?
To je malo složenije,
08:45
It's a little complicated,
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08:46
because a human face is scattered among millions of these letters.
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jer je ljudsko lice razasuto među milijunima ovih slova.
08:49
And a human face is not a very well-defined object.
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A ljudsko lice nije precizno definiran objekt.
08:52
So, we had to build an entire tier of it
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Stoga smo morali napraviti čitav niz njih,
08:54
to learn and teach a machine what a face is,
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kako bismo naučili i uputili stroj da zna što je lice,
08:56
and embed and compress it.
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te ga ugradi i sažme.
A ako vam je poznato strojno učenje,
08:59
And if you're comfortable with machine learning,
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09:01
you understand what the challenge is here.
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razumjet ćete o kakvom se izazovu ovdje radi.
Sada, nakon 15 godina -- 15 godina nakon što smo pročitali prvu sekvencu,
09:04
Now, after 15 years -- 15 years after we read the first sequence --
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ovog listopada, počeli smo primjećivati neke signale.
09:10
this October, we started to see some signals.
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I bio je to izuzetno emotivan trenutak.
09:13
And it was a very emotional moment.
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09:15
What you see here is a subject coming in our lab.
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Ovdje vidite ono što je došlo u naš laboratorij.
09:19
This is a face for us.
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Ovo je za nas lice.
09:21
So we take the real face of a subject, we reduce the complexity,
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Uzimamo pravo lice ovog subjekta, učinimo ga manje složenim,
09:25
because not everything is in your face --
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jer nije sve u vašem licu,
09:27
lots of features and defects and asymmetries come from your life.
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mnoge crte, nedostaci i asimetrija potječu iz vašeg života.
Ujednačavamo simetriju lica i provlačimo ga kroz naš algoritam.
09:31
We symmetrize the face, and we run our algorithm.
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09:35
The results that I show you right now,
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Rezultati koje vam upravo pokazujem,
predviđanja su koja dobivamo iz krvi.
09:37
this is the prediction we have from the blood.
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09:41
(Applause)
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(Pljesak)
Pričekajte na tren.
09:43
Wait a second.
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09:44
In these seconds, your eyes are watching, left and right, left and right,
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U ovim trenucima, vaše oči promatraju lijevo i desno, lijevo i desno,
09:49
and your brain wants those pictures to be identical.
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a vaš mozak želi da te slike budu jednake.
09:53
So I ask you to do another exercise, to be honest.
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Zato tražim od vas drugu vježbu, da budete iskreni.
09:55
Please search for the differences,
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Zamolit ću vas da potražite razlike,
a ima ih mnogo.
09:58
which are many.
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09:59
The biggest amount of signal comes from gender,
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Najveća količina signala dolazi od spola,
zatim je tu dob, indeks tjelesne mase te čovjekovo etničko obilježje.
10:02
then there is age, BMI, the ethnicity component of a human.
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10:07
And scaling up over that signal is much more complicated.
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Sve dalje preko tog signala postaje daleko složenije.
Ali ono što vidite ovdje, čak i uz razlike,
10:11
But what you see here, even in the differences,
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10:14
lets you understand that we are in the right ballpark,
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dozvoljava vam da shvatite kako smo na dobrom putu,
10:17
that we are getting closer.
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i sve smo bliže.
10:19
And it's already giving you some emotions.
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Ovo vam već stvara neke dojmove.
10:21
This is another subject that comes in place,
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Ovo je još jedan primjer koji se posložio,
10:24
and this is a prediction.
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i ovo je predviđanje.
10:25
A little smaller face, we didn't get the complete cranial structure,
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Nešto manje lice, ovdje nismo dobili potpunu strukturu lubanje,
10:30
but still, it's in the ballpark.
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no, ipak, blizu je.
10:33
This is a subject that comes in our lab,
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Ovo je primjer koji je došao u naš laboratorij,
10:35
and this is the prediction.
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a ovo je predviđanje.
Dakle, stroj u svojoj obradi nikada nije imao ove ljude.
10:38
So these people have never been seen in the training of the machine.
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10:42
These are the so-called "held-out" set.
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Ovo je tzv. "izostavljeni" skup.
10:45
But these are people that you will probably never believe.
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Ovi ljudi vam vjerojatno nikada neće djelovati uvjerljivo.
10:49
We're publishing everything in a scientific publication,
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Sve objavljujemo u znanstvenim časopisima
i možete pročitati.
10:52
you can read it.
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10:53
But since we are onstage, Chris challenged me.
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Ali budući smo na sceni, Chris me je izazvao.
10:55
I probably exposed myself and tried to predict
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Vjerojatno sam se otkrio i pokušao predvidjeti
10:59
someone that you might recognize.
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nekoga koga biste mogli prepoznati.
11:02
So, in this vial of blood -- and believe me, you have no idea
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Dakle, u ovoj epruveti krvi -- i vjerujte mi, nemate pojma
11:06
what we had to do to have this blood now, here --
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što smo sve morali učiniti da bismo donijeli krv danas ovdje,
11:09
in this vial of blood is the amount of biological information
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u ovoj epruveti krvi je količina bioloških informacija
11:13
that we need to do a full genome sequence.
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koja nam je potrebna za sekvenciranje čitavog genoma.
11:16
We just need this amount.
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Samo nam je ovoliko potrebno.
11:18
We ran this sequence, and I'm going to do it with you.
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Izvršili smo sekvenciranje i učinit ću to s vama.
11:21
And we start to layer up all the understanding we have.
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Počinjemo raslojavati svo znanje koje imamo.
11:25
In the vial of blood, we predicted he's a male.
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Iz ove epruvete krvi, predvidjeli smo da se radi o muškarcu.
Subjekt i jest muškarac.
11:29
And the subject is a male.
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11:30
We predict that he's a meter and 76 cm.
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Predvidjeli smo da je visok 176 cm.
11:33
The subject is a meter and 77 cm.
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Subjekt je visok 177 cm.
11:35
So, we predicted that he's 76; the subject is 82.
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Nadalje, predvidjeli smo da ima 76 kg, zapravo ima 82 kg.
11:40
We predict his age, 38.
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Predvidjeli smo da ima 38 godina.
11:43
The subject is 35.
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Subjekt ima 35 godina.
11:45
We predict his eye color.
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Predvidjeli smo njegovu boju očiju.
11:48
Too dark.
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Pretamna je.
Predvideli smo boju kože.
11:50
We predict his skin color.
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Skoro da smo pogodili.
11:52
We are almost there.
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11:53
That's his face.
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Ovo je njegovo lice.
A sada, trenutak razotkrivanja:
11:57
Now, the reveal moment:
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12:00
the subject is this person.
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subjekt je ova osoba.
12:02
(Laughter)
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(Smijeh)
12:04
And I did it intentionally.
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Učinio sam to namjerno.
12:06
I am a very particular and peculiar ethnicity.
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Ja sam vrlo specifičnog, osebujnog porijekla.
Južni Europljani, Talijani -- nikada se ne uklapaju u kalupe.
12:10
Southern European, Italians -- they never fit in models.
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12:12
And it's particular -- that ethnicity is a complex corner case for our model.
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A specifično je -- etničko porijeklo je složeni izuzetak za naš model.
Ali, ovdje je još nešto ključno.
12:18
But there is another point.
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12:19
So, one of the things that we use a lot to recognize people
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Dakle, nešto što mnogo koristimo kako bismo prepoznali ljude,
nikada neće biti zapisano u genomu.
12:23
will never be written in the genome.
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12:24
It's our free will, it's how I look.
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To je naša slobodna volja, naš izgled.
12:27
Not my haircut in this case, but my beard cut.
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Ne moja frizura, u ovom slučaju, već moja brada.
12:30
So I'm going to show you, I'm going to, in this case, transfer it --
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Stoga ću vam pokazati, u ovom slučaju ću to prenijeti,
a ovo nije ništa više od Photoshopa, nije modeliranje,
12:34
and this is nothing more than Photoshop, no modeling --
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12:36
the beard on the subject.
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brada ovog subjekta.
12:38
And immediately, we get much, much better in the feeling.
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I odmah imamo mnogo, mnogo bolji dojam.
12:42
So, why do we do this?
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Dakle, zašto ovo radimo?
12:47
We certainly don't do it for predicting height
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Sigurno to ne radimo kako bismo predvidjeli visinu,
ili da bismo izradili predivnu sliku iz vaše krvi.
12:53
or taking a beautiful picture out of your blood.
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12:56
We do it because the same technology and the same approach,
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Radimo to jer ista ova tehnologija i isti pristup,
13:00
the machine learning of this code,
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strojno učenje ovog kôda,
13:02
is helping us to understand how we work,
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pomaže nam razumjeti kako funkcioniramo,
kako vaše tijelo funkcionira,
13:06
how your body works,
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13:07
how your body ages,
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kako vaše tijelo stari,
13:09
how disease generates in your body,
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kako nastaje bolest u vašem tijelu,
kako u vama raste i razvija se rak,
13:12
how your cancer grows and develops,
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kako djeluju lijekovi
13:15
how drugs work
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13:16
and if they work on your body.
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i djeluju li na vaše tijelo.
13:19
This is a huge challenge.
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To je ogroman izazov.
13:21
This is a challenge that we share
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To je zajednički izazov nas
13:23
with thousands of other researchers around the world.
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i tisuće drugih istraživača diljem svijeta.
Zove se personalizirana medicina.
13:26
It's called personalized medicine.
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To je mogućnost da se odmaknemo od statističkog pristupa,
13:29
It's the ability to move from a statistical approach
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13:32
where you're a dot in the ocean,
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u kojem ste samo točkica u oceanu,
13:34
to a personalized approach,
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prema osobno prilagođenom pristupu,
13:36
where we read all these books
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gdje čitamo sve ove knjige
13:38
and we get an understanding of exactly how you are.
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i dobivamo saznanje o tome kako ste baš vi.
13:42
But it is a particularly complicated challenge,
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Ali ovo je izrazito složen izazov,
13:45
because of all these books, as of today,
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jer od svih ovih knjiga koje ste danas vidjeli,
13:49
we just know probably two percent:
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znamo vjerojatno samo 2%.
Četiri knjige od njih preko 175.
13:53
four books of more than 175.
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A ovo nije tema mog govora,
13:58
And this is not the topic of my talk,
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jer ćemo saznati i više.
14:02
because we will learn more.
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14:05
There are the best minds in the world on this topic.
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Najveći umovi na svijetu bave se ovim pitanjem.
Predviđanje će postati bolje,
14:09
The prediction will get better,
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14:10
the model will get more precise.
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model će biti sve precizniji.
14:13
And the more we learn,
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I što više naučimo,
više ćemo biti suočeni s odlukama,
14:15
the more we will be confronted with decisions
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14:19
that we never had to face before
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s kojima se prije nismo susretali,
14:22
about life,
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o životu,
14:24
about death,
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o smrti,
o roditeljstvu.
14:26
about parenting.
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Dakle, dodirujemo samu unutarnju pojedinost onoga kako život funkcionira.
14:32
So, we are touching the very inner detail on how life works.
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A to je revolucija koja ne može biti ograničena
14:38
And it's a revolution that cannot be confined
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14:41
in the domain of science or technology.
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na područje znanosti ili tehnologije.
14:44
This must be a global conversation.
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To mora biti globalna rasprava.
14:47
We must start to think of the future we're building as a humanity.
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Moramo početi razmišljati o budućnosti koju gradimo kao o čovječanstvu.
Moramo surađivati s kreativcima, umjetnicima, filozofima,
14:53
We need to interact with creatives, with artists, with philosophers,
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s političarima.
14:57
with politicians.
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14:58
Everyone is involved,
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Svi su uključeni,
14:59
because it's the future of our species.
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jer se radi o budućnosti naše vrste.
15:03
Without fear, but with the understanding
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Bez straha, ali uz razumijevanje
15:07
that the decisions that we make in the next year
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da će odluke koje donesemo u sljedećoj godini
zauvijek promijeniti tijek povijesti.
15:11
will change the course of history forever.
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15:15
Thank you.
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Hvala.
15:16
(Applause)
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(Pljesak)
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