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