TED2016
Riccardo Sabatini: How to read the genome and build a human being
Riccardo Sabatini: Kako čitati genom i stvoriti ljudsko biće
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Tajne, bolesti, ljepota - sve je to zapisano u ljudskom genomu, cjelovitom nizu genetskih uputa potrebnih za stvaranje ljudskog bića. Kao znanstvenik i poduzetnik, Riccardo Sabatini nam pokazuje kako posjedujemo moć čitanja tog složenog kôda, predvidjevši stvari poput visine, boje očiju, dobi, čak i oblika lica -- sve to iz samo jedne epruvete krvi. A uskoro, kaže Sabatini, naše novo razumijevanje genoma omogućit će nam osobno prilagođena liječenja bolesti poput raka. Imamo moć mijenjanja života kakvog poznajemo. Kako ćemo je upotrijebiti?
Riccardo Sabatini - Scientist, entrepreneur
Riccardo Sabatini applies his expertise in numerical modeling and data to projects ranging from material science to computational genomics and food market predictions. Full bio
Riccardo Sabatini applies his expertise in numerical modeling and data to projects ranging from material science to computational genomics and food market predictions. Full bio
Double-click the English transcript below to play the video.
00:12
For the next 16 minutes,
I'm going to take you on a journey
I'm going to take you on a journey
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U sljedećih 16 minuta
povest ću vas na putovanje
povest ću vas na putovanje
00:15
that is probably
the biggest dream of humanity:
the biggest dream of humanity:
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koje je vjerojatno
najveći san čovječanstva:
najveći san čovječanstva:
00:18
to understand the code of life.
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razumijevanje kôda života.
00:21
So for me, everything started
many, many years ago
many, many years ago
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Za mene je sve počelo
prije mnogo, mnogo godina,
prije mnogo, mnogo godina,
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,
raw material, some energy,
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djelić informacije,
nešto sirovine, nešto energije
nešto sirovine, nešto energije
00:34
and it can produce any object
that was not there before.
that was not there before.
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i može proizvesti bilo koji predmet
koji prethodno nije ni postojao.
koji prethodno nije ni postojao.
00:38
I was doing physics,
I was coming back home
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.
always knew a 3D printer.
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te shvatio da mi je 3D printer
oduvijek bio poznat.
oduvijek bio poznat.
00:44
And everyone does.
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Kao i svima ostalima.
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:
00:50
a bit of information, which is between
my father and my mom in this case,
my father and my mom in this case,
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djelić informacije, u ovom slučaju
između mog oca i moje majke,
između mog oca i moje majke,
00:54
raw elements and energy
in the same media, that is food,
in the same media, that is food,
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sirovine i energiju
u istom mediju, odnosno hrani,
u istom mediju, odnosno hrani,
00:58
and after several months, produces me.
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i nakon nekoliko mjeseci
proizvela je mene.
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,
discovering that she was a 3D printer,
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Pored zapanjenosti moje mame,
saznavši da je se smatra 3D printerom,
saznavši da je se smatra 3D printerom,
01:06
I immediately got mesmerized
by that piece,
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?
01:21
Well, I was studying physics
at the beginning
at the beginning
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Pa, na početku sam studirao fiziku
01:23
and I took this approximation of a human
as a gigantic Lego piece.
as a gigantic Lego piece.
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i zamislio tu pretpostavku o čovjeku
kao golemoj Lego slagalici.
kao golemoj Lego slagalici.
01:29
So, imagine that the building
blocks are little atoms
blocks are little atoms
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Dakle, zamislite da su kockice sitni atomi
01:33
and there is a hydrogen here,
a carbon here, a nitrogen here.
a carbon here, a nitrogen here.
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i vodik je ovdje,
ugljik ovdje, a dušik ovdje.
ugljik ovdje, a dušik ovdje.
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,
that compose a human being,
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ako bih mogao navesti broj atoma
od kojih se sastoji ljudsko biće,
od kojih se sastoji ljudsko biće,
01:43
I can build it.
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mogao bih ga sagraditi.
01:45
Now, you can run some numbers
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Možete provjeriti brojke
01:47
and that happens to be
quite an astonishing number.
quite an astonishing number.
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i to izgleda kao prilično
zapanjujući broj.
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,
drive to assemble a little baby,
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dokument koji bih sačuvao na USB-u
kako bih sastavio jednu bebu,
kako bih sastavio jednu bebu,
01:58
will actually fill an entire Titanic
of thumb drives --
of thumb drives --
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zapravo bi ispunio prostor
veličine Titanika punog USB-ova,
veličine Titanika punog USB-ova,
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,
a pregnant lady,
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Od sada, svaki put kada ugledate trudnicu,
02:12
she's assembling the biggest
amount of information
amount of information
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ona u sebi sadrži
najveću količinu informacija
najveću količinu informacija
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.
anything you heard of.
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Zaboravite velike količine podataka,
ili bilo što što ste čuli.
ili bilo što što ste čuli.
02:19
This is the biggest amount
of information that exists.
of information that exists.
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To je najveća količina
informacija koja postoji.
informacija koja postoji.
02:22
(Applause)
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(Pljesak)
02:26
But nature, fortunately, is much smarter
than a young physicist,
than a young physicist,
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No, srećom, priroda je
daleko pametnija od mladog fizičara
daleko pametnija od mladog fizičara
02:30
and in four billion years, managed
to pack this information
to pack this information
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i u četiri milijarde godina
uspjela je složiti ove informacije
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,
when Rosalind Franklin,
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Prvi put smo saznali za njega 1950.
kada je Rosalind Franklin,
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,
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,
da konačno prodremo u ljudsku stanicu,
02:50
take out this crystal,
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izvadimo taj kristal,
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,
a fairly simple alphabet,
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Ispostavilo se da je kôd
prilično jednostavna abeceda,
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.
you need three billion of them.
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A kako biste sagradili čovjeka,
potrebno vam je tri milijarde njih.
potrebno vam je tri milijarde njih.
03:06
Three billion.
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Tri milijarde.
03:08
How many are three billion?
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Koliko je tri milijarde?
03:09
It doesn't really make
any sense as a number, right?
any sense as a number, right?
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Sam broj zaista nema
nikakvog smisla, zar ne?
nikakvog smisla, zar ne?
03:12
So I was thinking how
I could explain myself better
I could explain myself better
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Stoga sam razmišljao
kako bih si bolje objasnio
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,
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
introduce the code
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a najbolja osoba koja bi mi
pomogla predstaviti kôd,
pomogla predstaviti kôd,
03:26
is actually the first man
to sequence it, Dr. Craig Venter.
to sequence it, Dr. Craig Venter.
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zapravo je prvi čovjek
koji ga je sekvencirao, dr. Craig Venter.
koji ga je sekvencirao, dr. Craig Venter.
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:
slovo po slovo:
03:53
262,000 pages of information,
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262.000 stranica informacija,
03:57
450 kilograms, shipped
from the United States to Canada
from the United States to Canada
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450 kilograma, isporučenih
iz SAD-a u Kanadu,
iz SAD-a u Kanadu,
04:01
thanks to Bruno Bowden,
Lulu.com, a start-up, did everything.
Lulu.com, a start-up, did everything.
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zahvaljujući Bruni Bowdenu,
dostupno na Lulu.com, sve je odrađeno.
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.
of what is the code of life.
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Ali ovo je vizualni doživljaj
onoga što je kôd života.
onoga što je kôd života.
04:12
And now, for the first time,
I can do something fun.
I can do something fun.
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A sada, po prvi puta,
mogu učiniti nešto zabavno.
mogu učiniti nešto zabavno.
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.
book ... like this one.
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Dozvolite mi da uzmem
zanimljivu knjigu... poput ove.
zanimljivu knjigu... poput ove.
04:25
I have an annotation;
it's a fairly big book.
it's a fairly big book.
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Samo jedna opaska;
knjiga je prilično obimna.
knjiga je prilično obimna.
04:27
So just to let you see
what is the code of life.
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.
04:41
Let's get to a specific part.
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Pogledajmo jedan specifičan dio.
Dozvolite da vam ga pročitam:
04:43
Let me read it to you:
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04:44
(Laughter)
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(Smijeh)
04:46
"AAG, AAT, ATA."
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"AAG, AAT, ATA."
04:50
To you it sounds like mute letters,
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Vama ovo zvuči
kao obična slova bez smisla,
kao obična slova bez smisla,
04:53
but this sequence gives
the color of the eyes to Craig.
the color of the eyes to Craig.
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no, ovaj redoslijed
određuje Craigovu boju očiju.
određuje Craigovu boju očiju.
Pokazat ću vam jedan drugi dio iz knjige.
04:57
I'll show you another part of the book.
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04:59
This is actually a little
more complicated.
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.
05:09
(Laughter)
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(Smijeh)
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,
05:22
because if you miss just
two letters in this position --
two letters in this position --
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jer ako izostavite
samo dva slova u ovom redoslijedu,
samo dva slova u ovom redoslijedu,
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:
to a terrible disease:
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ova osoba bit će osuđena
na užasnu bolest:
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,
we don't know how to solve it,
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Za nju još nemamo lijek,
ne znamo kako je izliječiti,
ne znamo kako je izliječiti,
05:35
and it's just two letters
of difference from what we are.
of difference from what we are.
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a samo su dva slova različita
od onih koja mi ostali imamo.
od onih koja mi ostali imamo.
05:39
A wonderful book, a mighty book,
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Predivna knjiga, moćna knjiga,
05:43
a mighty book that helped me understand
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moćna knjiga koja mi je pomogla razumjeti
05:45
and show you something quite remarkable.
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i pokazati vam nešto zaista izvanredno.
05:48
Every one of you -- what makes
me, me and you, you --
me, me and you, you --
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Svatko od vas -- ono zbog čega
sam ja, ja, a vi ste vi --
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.
05:58
For the rest,
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Što se tiče ostalog,
05:59
we are all absolutely identical.
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posve smo identični.
06:03
Five hundred pages
is the miracle of life that you are.
is the miracle of life that you are.
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Pet stotina stranica je
čudo života koje predstavljate vi.
čudo života koje predstavljate vi.
06:07
The rest, we all share it.
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Ostalo svi mi dijelimo.
06:09
So think about that again
when we think that we are different.
when we think that we are different.
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Zato se sjetite toga
kada pomislite kako smo svi različiti.
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,
at assembling Swedish furniture,
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Pa, koliko god ste dobri
u sastavljanju švedskog namještaja,
u sastavljanju švedskog namještaja,
06:27
this instruction manual
is nothing you can crack in your life.
is nothing you can crack in your life.
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ovaj priručnik za upotrebu je nešto
što nećete dešifrirati u svom životu.
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,
dva čuvena TED-ovca,
06:36
Peter Diamandis and Craig Venter himself,
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Peter Diamandis i Craig Venter osobno,
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,
we can learn from these books,
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i naučiti sve što
možemo naučiti iz ovih knjiga,
možemo naučiti iz ovih knjiga,
06:48
with one target --
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s jednim ciljem,
06:50
making real the dream
of personalized medicine,
of personalized medicine,
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ostvariti san o personaliziranoj medicini,
06:53
understanding what things
should be done to have better health
should be done to have better health
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razumjeti što se treba učiniti
kako bismo bili zdraviji
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,
and many, many more people,
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Fantastična ekipa, 40 znanstvenika
za podatke i još mnogo, mnogo ljudi,
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.
called machine learning.
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Koristit ćemo tehnologiju
koja se zove strojno učenje.
koja se zove strojno učenje.
07:11
On one side, we have genomes --
thousands of them.
thousands of them.
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S jedne strane imamo genome
-- na tisuće njih.
-- na tisuće njih.
07:15
On the other side, we collected
the biggest database of human beings:
the biggest database of human beings:
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S druge strane, sakupili smo
najveću bazu podataka o ljudskim bićima:
najveću bazu podataka o ljudskim bićima:
07:20
phenotypes, 3D scan, NMR --
everything you can think of.
everything you can think of.
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fenotipe, 3D snimke, magnetsku
rezonanciju, sve što vam pada na pamet.
rezonanciju, sve što vam pada na pamet.
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 --
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 --
many, many machines --
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zapravo, ne baš jedan stroj,
već mnogo, mnogo strojeva
već mnogo, mnogo strojeva
07:38
to try to understand and translate
the genome in a phenotype.
the genome in a phenotype.
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kako bi se pokušao razumjeti
i prevesti genom u fenotipu.
i prevesti genom u fenotipu.
07:43
What are those letters,
and what do they do?
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,
be used for everything,
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To je pristup koji se može
za sve koristiti,
za sve koristiti,
07:49
but using it in genomics
is particularly complicated.
is particularly complicated.
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ali je njegova upotreba u genetici
naročito složena.
naročito složena.
07:52
Little by little we grew and we wanted
to build different challenges.
to build different challenges.
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Malo po malo smo rasli
te smo željeli stvoriti drugačije izazove.
te smo željeli stvoriti drugačije izazove.
07:55
We started from the beginning,
from common traits.
from common traits.
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Počeli smo od početka,
od zajedničkih osobina.
od zajedničkih osobina.
07:58
Common traits are comfortable
because they are common,
because they are common,
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Zajedničke osobine su prikladne
baš zato što su zajedničke,
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?
Možemo li čitanjem ovih knjiga
predvidjeti vašu visinu?
predvidjeti vašu visinu?
08:06
Can we read the books
and predict your height?
and predict your height?
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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.
08:12
BMI is fairly connected to your lifestyle,
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Indeks tjelesne mase usko je povezan
s vašim načinom života,
s vašim načinom života,
08:15
but we still can, we get in the ballpark,
eight kilograms of precision.
eight kilograms of precision.
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ali i dalje možemo pogoditi,
preciznošću od osam kilograma.
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?
08:30
We can, because apparently,
the code changes during your life.
the code changes during your life.
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Možemo, jer izgleda da se kôd
mijenja tijekom vašeg života.
mijenja tijekom vašeg života.
08:33
It gets shorter, you lose pieces,
it gets insertions.
it gets insertions.
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Postaje kraći, gubite dijelove,
dodaju se umeci.
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?
08:45
It's a little complicated,
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To je malo složenije,
08:46
because a human face is scattered
among millions of these letters.
among millions of these letters.
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jer je ljudsko lice razasuto
među milijunima ovih slova.
među milijunima ovih slova.
08:49
And a human face is not
a very well-defined object.
a very well-defined object.
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A ljudsko lice nije
precizno definiran objekt.
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,
a machine what a face is,
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kako bismo naučili i uputili stroj
da zna što je lice,
da zna što je lice,
08:56
and embed and compress it.
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te ga ugradi i sažme.
08:59
And if you're comfortable
with machine learning,
with machine learning,
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A ako vam je poznato strojno učenje,
09:01
you understand what the challenge is here.
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razumjet ćete o kakvom
se izazovu ovdje radi.
se izazovu ovdje radi.
09:04
Now, after 15 years -- 15 years after
we read the first sequence --
we read the first sequence --
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Sada, nakon 15 godina -- 15 godina nakon
što smo pročitali prvu sekvencu,
što smo pročitali prvu sekvencu,
09:10
this October, we started
to see some signals.
to see some signals.
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ovog listopada, počeli smo
primjećivati neke signale.
primjećivati neke signale.
09:13
And it was a very emotional moment.
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I bio je to izuzetno emotivan trenutak.
09:15
What you see here is a subject
coming in our lab.
coming in our lab.
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Ovdje vidite ono
što je došlo u naš laboratorij.
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,
we reduce the complexity,
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Uzimamo pravo lice ovog subjekta,
učinimo ga manje složenim,
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.
and asymmetries come from your life.
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mnoge crte, nedostaci i asimetrija
potječu iz vašeg života.
potječu iz vašeg života.
09:31
We symmetrize the face,
and we run our algorithm.
and we run our algorithm.
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Ujednačavamo simetriju lica
i provlačimo ga kroz naš algoritam.
i provlačimo ga kroz naš algoritam.
09:35
The results that I show you right now,
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Rezultati koje vam upravo pokazujem,
09:37
this is the prediction we have
from the blood.
from the blood.
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predviđanja su koja dobivamo iz krvi.
09:41
(Applause)
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(Pljesak)
09:43
Wait a second.
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Pričekajte na tren.
09:44
In these seconds, your eyes are watching,
left and right, left and right,
left and right, left and right,
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U ovim trenucima, vaše oči promatraju
lijevo i desno, lijevo i desno,
lijevo i desno, lijevo i desno,
09:49
and your brain wants
those pictures to be identical.
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.
another exercise, to be honest.
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Zato tražim od vas drugu vježbu,
da budete iskreni.
da budete iskreni.
09:55
Please search for the differences,
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Zamolit ću vas da potražite razlike,
09:58
which are many.
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a ima ih mnogo.
09:59
The biggest amount of signal
comes from gender,
comes from gender,
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Najveća količina signala dolazi od spola,
10:02
then there is age, BMI,
the ethnicity component of a human.
the ethnicity component of a human.
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zatim je tu dob, indeks tjelesne mase
te čovjekovo etničko obilježje.
te čovjekovo etničko obilježje.
10:07
And scaling up over that signal
is much more complicated.
is much more complicated.
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Sve dalje preko tog signala
postaje daleko složenije.
postaje daleko složenije.
10:11
But what you see here,
even in the differences,
even in the differences,
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Ali ono što vidite ovdje,
čak i uz razlike,
čak i uz razlike,
10:14
lets you understand
that we are in the right ballpark,
that we are in the right ballpark,
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dozvoljava vam da shvatite
kako smo na dobrom putu,
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,
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,
the complete cranial structure,
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Nešto manje lice, ovdje nismo dobili
potpunu strukturu lubanje,
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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2224
Ovo je primjer koji je
došao u naš laboratorij,
došao u naš laboratorij,
10:35
and this is the prediction.
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a ovo je predviđanje.
10:38
So these people have never been seen
in the training of the machine.
in the training of the machine.
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Dakle, stroj u svojoj obradi
nikada nije imao ove ljude.
nikada nije imao ove ljude.
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.
probably never believe.
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Ovi ljudi vam vjerojatno nikada
neće djelovati uvjerljivo.
neće djelovati uvjerljivo.
10:49
We're publishing everything
in a scientific publication,
in a scientific publication,
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Sve objavljujemo u znanstvenim časopisima
10:52
you can read it.
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i možete pročitati.
10:53
But since we are onstage,
Chris challenged me.
Chris challenged me.
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2344
Ali budući smo na sceni,
Chris me je izazvao.
Chris me je izazvao.
10:55
I probably exposed myself
and tried to predict
and tried to predict
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Vjerojatno sam se otkrio
i pokušao predvidjeti
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
and believe me, you have no idea
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Dakle, u ovoj epruveti krvi --
i vjerujte mi, nemate pojma
i vjerujte mi, nemate pojma
11:06
what we had to do to have
this blood now, here --
this blood now, here --
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što smo sve morali učiniti
da bismo donijeli krv danas ovdje,
da bismo donijeli krv danas ovdje,
11:09
in this vial of blood is the amount
of biological information
of biological information
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u ovoj epruveti krvi je
količina bioloških informacija
količina bioloških informacija
koja nam je potrebna
za sekvenciranje čitavog genoma.
za sekvenciranje čitavog genoma.
11:13
that we need to do a full genome sequence.
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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.
and I'm going to do it with you.
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Izvršili smo sekvenciranje
i učinit ću to s vama.
i učinit ću to s vama.
11:21
And we start to layer up
all the understanding we have.
all the understanding we have.
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Počinjemo raslojavati
svo znanje koje imamo.
svo znanje koje imamo.
11:25
In the vial of blood,
we predicted he's a male.
we predicted he's a male.
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Iz ove epruvete krvi,
predvidjeli smo da se radi o muškarcu.
predvidjeli smo da se radi o muškarcu.
11:29
And the subject is a male.
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Subjekt i jest muškarac.
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.
the subject is 82.
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Nadalje, predvidjeli smo
da ima 76 kg, zapravo ima 82 kg.
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.
11:50
We predict his skin color.
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Predvideli smo boju kože.
11:52
We are almost there.
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Skoro da smo pogodili.
11:53
That's his face.
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Ovo je njegovo lice.
11:57
Now, the reveal moment:
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A sada, trenutak razotkrivanja:
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.
and peculiar ethnicity.
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Ja sam vrlo specifičnog,
osebujnog porijekla.
osebujnog porijekla.
12:10
Southern European, Italians --
they never fit in models.
they never fit in models.
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Južni Europljani, Talijani --
nikada se ne uklapaju u kalupe.
nikada se ne uklapaju u kalupe.
12:12
And it's particular -- that ethnicity
is a complex corner case for our model.
is a complex corner case for our model.
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A specifično je -- etničko porijeklo je
složeni izuzetak za naš model.
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
a lot to recognize people
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Dakle, nešto što mnogo koristimo
kako bismo prepoznali ljude,
kako bismo prepoznali ljude,
12:23
will never be written in the genome.
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nikada neće biti zapisano u genomu.
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.
but my beard cut.
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Ne moja frizura,
u ovom slučaju, već moja brada.
u ovom slučaju, već moja brada.
12:30
So I'm going to show you, I'm going to,
in this case, transfer it --
in this case, transfer it --
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Stoga ću vam pokazati,
u ovom slučaju ću to prenijeti,
u ovom slučaju ću to prenijeti,
12:34
and this is nothing more
than Photoshop, no modeling --
than Photoshop, no modeling --
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a ovo nije ništa više
od Photoshopa, nije modeliranje,
od Photoshopa, nije modeliranje,
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.
much, much better in the feeling.
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3472
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
for predicting height
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5140
Sigurno to ne radimo
kako bismo predvidjeli visinu,
kako bismo predvidjeli visinu,
12:53
or taking a beautiful picture
out of your blood.
out of your blood.
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761102
2372
ili da bismo izradili
predivnu sliku iz vaše krvi.
predivnu sliku iz vaše krvi.
12:56
We do it because the same technology
and the same approach,
and the same approach,
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Radimo to jer ista ova
tehnologija i isti pristup,
tehnologija i isti pristup,
13:00
the machine learning of this code,
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2520
strojno učenje ovog kôda,
13:02
is helping us to understand how we work,
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pomaže nam razumjeti
kako funkcioniramo,
kako funkcioniramo,
13:06
how your body works,
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kako vaše tijelo funkcionira,
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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2769
kako nastaje bolest u vašem tijelu,
13:12
how your cancer grows and develops,
264
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kako u vama raste i razvija se rak,
13:15
how drugs work
265
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kako djeluju lijekovi
13:16
and if they work on your body.
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2314
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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1638
To je zajednički izazov nas
13:23
with thousands of other
researchers around the world.
researchers around the world.
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i tisuće drugih istraživača
diljem svijeta.
diljem svijeta.
13:26
It's called personalized medicine.
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2222
Zove se personalizirana medicina.
13:29
It's the ability to move
from a statistical approach
from a statistical approach
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3460
To je mogućnost da se odmaknemo
od statističkog pristupa,
od statističkog pristupa,
13:32
where you're a dot in the ocean,
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2032
u kojem ste samo točkica u oceanu,
13:34
to a personalized approach,
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1813
prema osobno prilagođenom pristupu,
13:36
where we read all these books
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2185
gdje čitamo sve ove knjige
13:38
and we get an understanding
of exactly how you are.
of exactly how you are.
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2864
i dobivamo saznanje o tome
kako ste baš vi.
kako ste baš vi.
13:42
But it is a particularly
complicated challenge,
complicated challenge,
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3362
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,
koje ste danas vidjeli,
13:49
we just know probably two percent:
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znamo vjerojatno samo 2%.
13:53
four books of more than 175.
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Četiri knjige od njih preko 175.
13:58
And this is not the topic of my talk,
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A ovo nije tema mog govora,
14:02
because we will learn more.
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jer ćemo saznati i više.
14:05
There are the best minds
in the world on this topic.
in the world on this topic.
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Najveći umovi na svijetu
bave se ovim pitanjem.
bave se ovim pitanjem.
14:09
The prediction will get better,
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Predviđanje će postati bolje,
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,
14:15
the more we will
be confronted with decisions
be confronted with decisions
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više ćemo biti suočeni s odlukama,
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,
14:26
about parenting.
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o roditeljstvu.
Dakle, dodirujemo samu unutarnju
pojedinost onoga kako život funkcionira.
pojedinost onoga kako život funkcionira.
14:32
So, we are touching the very
inner detail on how life works.
inner detail on how life works.
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14:38
And it's a revolution
that cannot be confined
that cannot be confined
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A to je revolucija
koja ne može biti ograničena
koja ne može biti ograničena
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.
we're building as a humanity.
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Moramo početi razmišljati o budućnosti
koju gradimo kao o čovječanstvu.
koju gradimo kao o čovječanstvu.
14:53
We need to interact with creatives,
with artists, with philosophers,
with artists, with philosophers,
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Moramo surađivati s kreativcima,
umjetnicima, filozofima,
umjetnicima, filozofima,
14:57
with politicians.
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s političarima.
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
that we make in the next year
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da će odluke koje donesemo
u sljedećoj godini
u sljedećoj godini
15:11
will change the course of history forever.
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zauvijek promijeniti tijek povijesti.
15:15
Thank you.
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Hvala.
15:16
(Applause)
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(Pljesak)
ABOUT THE SPEAKER
Riccardo Sabatini - Scientist, entrepreneurRiccardo Sabatini applies his expertise in numerical modeling and data to projects ranging from material science to computational genomics and food market predictions.
Why you should listen
Data scientist Riccardo Sabatini harnesses numerical methods for a surprising variety of fields, from material science research to the study of food commodities (as a past director of the EU research project FoodCAST). His most recent research centers on computational genomics and how to crack the code of life.
In addition to his data research, Sabatini is deeply involved in education for entrepreneurs. He is the founder and co-director of the Quantum ESPRESSO Foundation, an advisor in several data-driven startups, and funder of The HUB Trieste, a social impact accelerator.
Riccardo Sabatini | Speaker | TED.com