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 narednih 16 minuta ću da vas povedem na putovanje koje je verovatno najveći san čovečanstva: razumevanje životnog koda.
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 pre mnogo, mnogo godina kad sam saznao za prvi 3D štampač. Sami koncept je bio fascinantan. 3D štampaču su potrbna tri elementa: delić informacije, nešto sirovine, malo energije i može da proizvede bilo koji objekat koji prethodno nije 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 i shvatio da mi je 3D štampač poznat oduvek. Svima jeste. Majka je 3D štampač.
(Laughter)
(Smeh)
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 majka je uzela tri elementa: delić informacije, koja je u ovom slučaju između mog oca i moje majke, sirovine i energiju u istom medijumu, to jest hrani, i nakon nekoliko meseci, proizvela je mene. A ja pre 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 majke kad je saznala da je 3D štampač, istog trena sam bio očaran tim delom, prvim delom, informacijom. Koliko informacija je potrebno da se sagradi i sastavi čovek? Da li je potrebno mnogo? Malo? Koliko fleš memorija biste mogli da ispunite?
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 pretpostavio sam da je čovek poput džinovske lego slagalice. Zamislite da su kockice sitni atomi i vodonik je ovde, ugljenik ovde, azot ovde. Prema prvoj pretpostavci, ako bih mogao da nabrojim atome od kojih se sastoji ljudsko biće, mogao bih ga sagraditi. Sad, možete da proverite brojke i to je izgleda prilično zapanjujući broj. Pa je broj atoma, dokument koji bih sačuvao na fleš memoriji da bih sastavio bebicu, zapravo bi ispunio čitav Titanik fleš memorijama - pomnoženo 2000 puta. Ovo je čudo života. Od sad, svaki put kad ugledate trudnicu, ona sklapa najveću količinu informacija na koju ćete ikad naići. Zaboravite velike podatke, zaboravite bilo šta što ste čuli. Ovo je najveća količina informacija koja postoji.
(Applause)
(Aplauz)
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?
Ali priroda je srećom daleko pametnija od mladog fizičara i za četiri milijarde godina je uspela da upakuje ove informacije u maleni kristal koji nazivamo DNK. Prvi put smo saznali za njega 1950. kada ga je Rozalind Frenklin, neverovatna naučnica, fotografisala. Ali trebalo nam je preko 40 godina da konačno prodremo unutar ljudske ćelije, izvadimo kristal, odmotamo ga i prvi put ga pročitamo. Ispostavilo se da je kôd prilično jednostavna abeceda, četiri slova: A, T, C i G. A da biste sagradili čoveka, potrebno vam je tri milijarde njih. Tri milijarde. Koliko je tri milijarde? Sami 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.
Pa sam razmišljao kako da bolje objasnim koliko je velik i ogroman ovaj kod. Ali evo ga - mislim, imaću malu pomoć, a najbolja osoba da mi pomogne da predstavim kôd je zapravo prvi čovek koji ga je sekvencirao, dr Kreg Venter. Zato, dobro došao na scenu, dr Kreg Venter.
(Applause)
(Aplauz)
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 čovek glavom i bradom, već prvi put u istoriji, ovo je genom određenog čoveka, odštampan stranicu po stranicu, slovo po slovo: 262.000 stranica informacija, 450 kilograma, isporučen iz SAD-a u Kanadu, zahvaljujući Brunu Boudenu, Lulu.com, startap, su sve odradili. 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 vizuelni doživljaj toga šta je životni kôd. A sada, prvi put, mogu da uradim nešto zabavno. Mogu zapravo da zavirim unutra i da ga čitam. Dozvolite mi da uzmem zanimljivu knjigu... poput ove. Imam zabelešku; prilično je obimna knjiga. Prosto da vidite šta je životni kôd. Hiljade i hiljade i hiljade i milioni slova. I očigledno da imaju smisla. Pođimo do specifičnog dela. Dozvolite da vam pročitam:
(Laughter)
(Smeh)
"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 prazna slova, ali ovaj redosled odaje Kregovu boju očiju. Pokazaću vam još jedan deo iz knjige. Ovo je zapravo nešto komplikovanije.
Chromosome 14, book 132:
Hromozom 14, knjiga 132 -
(Laughter)
(Smeh)
As you might expect.
kao što pretpostavljate -
(Laughter)
(Smeh)
"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.
Ovaj čovek ima sreće jer ako ispustite samo dva slova u ovom redosledu - dva slova od tri milijarde - biće osuđen na užasnu bolest: cističnu fibrozu. Za nju ne postoji lek, ne znamo kako da je izlečimo, a samo dva slova su različita nego kod nas ostalih.
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.
Čarobna knjiga, moćna knjiga, moćna knjiga koja mi je pomogla da shvatim i da vam pokažem nešto izvanredno. Svako od vas - ono zbog čega sam ja ja, a vi ste vi - je samo oko pet miliona ovih slova, polovina knjige. Što se tiče ostalog, apsolutno smo identični. Pet stotina stranica je čudo života koje predstavljate vi. Ostalo svi mi delimo. Zato se setite toga kad pomislite kako smo svi različiti. Ovo je suma koju svi delimo.
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.
Pa, sad kad imam vašu pažnju, sledeće pitanje je: kako da to pročitam? Kako da pronađem smisao u tome? Pa, koliko god ste dobri u sastavljanju švedskog nameštaja, ovo uputstvo za upotrebu je nešto što nećete shvatiti dok ste živi.
(Laughter)
(Smeh)
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.
Pa su 2014, dva čuvena Tedovca, Piter Dijamandis i Kreg Venter lično, odlučili da osnuju novu firmu. Rođen je Hjuman Londževiti, sa samo jednom misijom: da pokušamo sve što možemo i da naučimo sve što možemo da naučimo iz ovih knjiga, s jednim ciljem - da ostvarimo san personalizovane medicine, da razumemo šta treba da se uradi 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 naučnika za podatke i još mnogo, mnogo ljudi, s kojima je užitak raditi. Koncept je zapravo veoma jednostavan. Koristićemo tehnologiju koja se zove mašinsko učenje. S jedne strane, imamo genome - hiljade njih. S druge strane, sakupili smo najveću bazu podataka o ljudskim bićima: fenotipe, 3D snimke, nuklearnu magnetnu rezonancu, sve što vam pada na pamet. Unutar toga, na ovim oprečnim stranama, nalazi se tajna prevodilaštva. A u sredini smo sagradili mašinu. Sagradili smo mašinu i obučili smo mašinu - pa, zapravo ne baš jednu mašinu, mnogo, mnogo mašina - da pokašaju da razumeju i prevedu genom u fenotipu. Šta su ta slova i koja je njihova svrha? To je pristup koji može da se koristi svuda, ali njegova upotreba u genetici je naročito komplikovana. Malo po malo smo rasli i želeli smo da napravimo nove izazove. Počeli smo ispočetka, od zajedničkih osobina. Zajedničke osobine su prijatne jer su zajedničke, svako ih ima.
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.
Pa smo počeli da postavljamo naša pitanja: možemo li predvideti visinu? Možemo li čitanjem ovih knjiga predvideti visinu? Pa, zapravo možemo, preciznošću od pet centimetara. Indeks telesne mase je usko povezan s vašim načinom života, ali i dalje možemo, možemo da pogodimo preciznošću od osam kilograma. Možemo li predvideti boju očiju? Da, možemo. Preciznošću od 80 procenata. Možemo li predvideti boju kože? Da, možemo, s 80 procenata tačnosti. Možemo li predvideti starost? Možemo jer se očigledno kôd menja tokom vašeg života. Postaje kraći, gubite delove, dodaju se umeci. Čitamo signale i pravimo 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.
Sad, zanimljiv izazov: možemo li predvideti ljudsko lice? Malo je komplikovano jer je ljudsko lice rasuto među milionima ovih slova. A ljudsko lice nije naročito dobro definisan objekat. Pa smo morali da napravimo čitav niz njih kako bismo naučili i obrazovali mašinu da zna šta je lice, i da ga ugradi i sažme. A ako vam je poznato mašinsko učenje, razumećete o kakvom se izazovu ovde 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.
Sad, nakon 15 godina - 15 godina nakon što smo pročitali prvi isečak - ovog oktobra, počeli smo da zapažamo neke signale. I bio je to izuzetno emotivan trenutak. Ovde vidite subjekta koji je došao u našu laboratoriju. Ovo je lice za nas. Uzimamo pravo lice subjekta, svedemo složenost jer nije sve u vašem licu - mnoge crte i nedostaci i asimetrija potiču iz vašeg života. Ujednačavamo simetriju lica i provlačimo ga kroz naš algoritam. Rezultati koje vam upravo pokazujem, ova predviđanja dobijamo iz krvi.
(Applause)
(Aplauz)
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.
Sačekajte sekund. U ovim trenucima, vaše oči posmatraju levo i desno, levo i desno, a vaš mozak želi da te slike budu identične. Zato tražim od vas drugu vežbu, da budete iskreni. Molim vas da tražite razlike, ima ih mnogo. Najveća količina signala dolazi od roda, potom je tu uzrast, indeks telesne mase, čovekova etnička komponenta. A prenošenje tog signala na veće razmere je daleko komplikovanije. Ali ono što vidite ovde, čak i uz razlike, dozvoljava vam da shvatite da su naše pretpostavke tačne, da smo sve bliži. I već imate neki utisak.
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 subjekat koji se poklopio, a ovo je predviđanje. Nešto sitnije lice, nismo pogodili u potpunosti strukturu lobanje, ali ipak, približno je. Ovo je subjekat koji je došao u našu laboratoriju, a ovo je predviđanje. Dakle, mašina tokom obuke nikad nije videla ove ljude. Ovo je takozvani "izostavljeni" skup. Ali ovo su verovatno za vas neuverljivi ljudi. Sve objavljujemo u naučnim časopisima, možete to da čitate.
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 kako smo na sceni, Kris me je izazvao. Verovatno sam se izložio i pokušao sam da predvidim nekoga koga biste možda prepoznali. Dakle, u ovoj epruveti krvi - i verujte mi, nemate pojma šta smo morali da da uradimo da bismo doneli krv ovde - u ovoj epruveti krvi je količina bioloških informacija koja nam je potrebna da sekvenciramo čitav genom. Svega ovoliko nam je dovoljno. Odradili smo sekvenciranje, i uradiću to s vama. I počeli smo da raslojavamo sve znanje koje imamo. Iz ove epruvete krvi, predvideli smo da je muškarac u pitanju. Subjekat je muškarac. Predvideli smo da je visok metar i 76 cm. Subjekat je visok metar i 77 centimetara. Dakle, predvideli smo da je '76. godište, zapravo je '82. Predvideli smo da ima 38 godina. Subjekat ima 35 godina. Predvideli mo njegovu boju očiju. Suviše je tamna. Predvideli smo boju kože. Skoro da smo pogodili. Ovo je njegovo lice.
Now, the reveal moment: the subject is this person.
A sad, trenutak razotkrivanja: subjekat je ova osoba.
(Laughter)
(Smeh)
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.
Namerno sam to uradio. Ja sam veoma karakterističan i karakterističnog sam porekla. Južnoevropljani, Italijani - nikad se ne uklapaju u kalupe. A karakteristično je - da je narodnost kompleksan izuzetak za naš model. Ali ima tu još nešto. Dakle, nešto što uveliko koristimo da bismo prepoznali ljude nikada neće da bude upisano u genom. To je naša slobodna volja, naš izgled. Ne moja frizura, u ovom slučaju, već moja brada. Pa ću da vam pokažem, u ovom slučaju ću da to prenesem - a ovo nije ništa više od fotošopa, nije modelarstvo - brada na subjektu. I momentalno imamo mnogo, mnogo bolji utisak.
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 da bismo predvideli visinu ili da bismo izradili lepu sliku iz vaše krvi. Radimo to jer ista ova tehnologija i isti pristup, mašinsko učenje ovog koda, pomaže nam da razumemo kako funkcionišemo, kako vaše telo funkcioniše, kako vaše telo stari, kako bolesti nastaju u vašem telu, kako vaš rak raste i razvija se, kako lekovi funkcionišu i da li deluju na vaše telo.
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.
Ovo je ogorman izazov. Ovo je zajednički izazov nas i hiljada drugih istraživača širom sveta. Naziva se personalizovanom medicinom. To je mogućnost da se pomerimo sa statističkog pristupa, u kom ste tačkica u okeanu, do personalizovanog pristupa, gde čitamo sve ove knjige i stičemo saznanje o tome tačno kako ste vi. Ali je naročito složen izazov jer od svih ovih knjiga, do danas, znamo verovatno samo dva procenta: četiri knjige od 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 više. Najveći umovi na svetu se bave ovim pitanjem. Predviđanje će postati bolje, model će biti sve precizniji. A što više naučimo, više ćemo biti suočeni s odlukama s kojima se pre nismo morali suočavati 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 unutrašnju pojedinost kako život funkcioniše. A to je revolucija koja ne može da bude ograničena u domenu nauke ili tehnologije. Ovo mora da bude globalna diskusija. Moramo početi da razmišljamo o budućnosti koju kao čovečanstvo gradimo. Moramo da sarađujemo sa kreativcima, umetnicima, filozofima, političarima. Svi su uključeni jer se radi o budućnosti naše vrste. Bez straha, ali uz razumevanje da će odluke koje donesemo naredne godine zauvek da promene pravac istorije.
Thank you.
Havala vam.
(Applause)
(Aplauz)